Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta4020017

Authors: Misbah Aslam Michal Pawlak Sidra Aslam

This study demonstrates the application of Laser-Induced Breakdown Spectroscopy (LIBS) for the elemental analysis of agricultural soils in South Punjab, Pakistan. Soil degradation due to intensive farming, imbalanced fertilizer use, and declining organic matter has reduced crop productivity in the region. To address this, rapid and accurate soil diagnostics are essential. LIBS, coupled with Calibration-Free analysis (CF-LIBS), was employed to quantitatively determine the concentrations of major and trace elements—including calcium, silicon, iron, aluminum, magnesium, titanium, potassium, sodium, lithium, and barium—without requiring chemical standards. Plasma characterization was performed using the Boltzmann plot method, yielding temperatures between 7750 and 9000 K, and electron number densities were derived from Stark-broadened spectral profiles. The results reveal significant spatial variability in elemental composition, reflecting differences in land use and irrigation sources. This work confirms LIBS as a versatile, efficient, and reliable tool for soil health assessment, offering a practical solution for monitoring soil nutrients and supporting sustainable agricultural management in resource-limited settings.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta4020016

Authors: Paolo Massimo Buscema Marco Breda Riccardo Petritoli Giulia Massini Guido Ferilli

The TWC Sigma model, part of the Topological Weighted Centroid (TWC) family, is introduced as a spatial framework for source localization in systems where network information is incomplete or unavailable. Its architecture relies on two alternative approaches: one based on nonlinear correlation, capable of capturing complex spatial dependencies among observed signals, and another based on supervised neural networks, which use adaptive learning on a discretized spatial grid to estimate the probability of hidden source localization. In both cases, TWC Sigma provides a robust and consistent mechanism to estimate the probable positions of hidden sources using only spatial coordinates and signal intensity. Applications on both synthetic and real-world datasets—such as those collected by Minna-no Data Site on post-Fukushima radiocesium contamination—confirm the model’s ability to identify both primary and secondary emission zones with strong spatial coherence. These results highlight TWC Sigma as an efficient and interpretable model that can be used both independently and as a complementary tool to more complex network-based frameworks, offering rapid and reliable localization even in the presence of sparse, noisy, or heterogeneous data.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta4020015

Authors: Kyle Vernyi Matthew Stanko K. Merve Dogan

Robust control, adaptive control, and adaptive control allocation methods can create resilient systems that are able to handle uncertainties as well as unknown deficiencies in actuator effectiveness. The capabilities of these methods can further enable advanced missions for autonomous space systems. Thus, in this paper, a resilient control with an adaptive control allocation method is proposed and implemented on a vehicle with 3 degrees of freedom (DoF) that operates with eight thrusters to reduce the impact of external uncertainties as well as unknown effects of the actuator. Specifically, the method includes a combination of sliding mode and novel adaptive control design elements to ensure trajectory tracking in the presence of uncertainties. Moreover, an adaptive control allocation method is also introduced to obtain the desired forces and moments in the presence of unknown effects of the actuator. The boundedness of the closed-loop system is proven with Lyapunov stability analysis. The proposed controller results are compared to a baseline sliding mode controller without adaptive control and adaptive control allocation enhancement, where different uncertainties and unknown actuator degradation, as well as failure cases, are considered within several experimental cases under external fan-induced disturbances. The experimental metrics, including integral squared tracking error, maximum tracking error, actuator effort, actuator impulse, and settling time, are provided. Across all cases, the proposed method reduces the integral squared tracking error, improves settling time, and significantly improves yaw regulation compared to a baseline sliding mode controller. This, in turn, yields a slightly increased control effort for the proposed method.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta4020014

Authors: David Velez André Lourenço Miguel Pereira David P. Coutinho Carlos Carreiras

Electrocardiogram (ECG)-based biometrics have emerged as a promising solution for continuous and intrinsic human identification; nevertheless, the robustness of these systems under realistic noise conditions remains a critical challenge for practical deployment. This work presents a theoretical and experimental analysis of how different noise types and levels affect ECG biometric recognition by comparing three methodological families: fiducial-based approaches using morphological features with traditional classifiers such as SVM and k-NN, non-fiducial methods based on signal compression and global descriptors, and Deep Learning models. Controlled distortions and additive noise injection into public ECG databases enable systematic quantification of feature degradation. Experimental validation is performed using the CardioWheel system, a real-world in-vehicle ECG acquisition platform, to evaluate performance under realistic motion and noise conditions. The methodological framework proposed for robustness evaluation and noise-aware training is inherently generic and can be extended to other biometric tasks subject to noise. Results show that different algorithmic families exhibit distinct resilience profiles under noise contamination and reveal a practical signal quality boundary for reliable ECG biometric recognition, with performance deteriorating under severe noise conditions. Noise-aware training improves robustness, particularly for Deep Learning and SVM-based classifiers, highlighting the trade-off between interpretability and robustness. By bridging theoretical analysis and applied experimentation, this work provides practical signal quality guidelines for real-world ECG biometric systems.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta4010013

Authors: Renjithkumar Surendran Pillai Patrick Denny Eoin O'Connell Adam Dooley Mihai Penica

This paper proposes a new Unified Namespace (UNS)-based architecture to improve predictive and prescriptive maintenance of industrial equipment and overcome challenges such as incomplete data, poor interoperability, and disconnected IT/OT environments. The framework combines interoperable data formats in real-time sensor data, predictive modeling, prescriptive analytics, and simulations of digital twins, using UNS as a centralized, protocol-agnostic data layer that is scalable and complies with Industry 4.0 and Pharma 4.0 standards. The suggested methodology increases data accessibility, reduces integration complexity, and allows low-latency analytics and automated decision-making. Machine learning predictive models achieved more than 94% accuracy in predicting equipment failures. Prescriptive analytics provides maintenance recommendations to reduce downtime and risks. The feedback loops of digital twins can enhance the accuracy of predictions and allow decision optimization through what-if analysis. A test-bench deployment showed a higher performance compared to traditional point-to-point integration, with lower latency (approximately 18 ms vs. approximately 31 ms), decreasing packet loss (0.40% vs. 3.11%), and higher model accuracy (94.20% vs. 87.51%). The structure avoided more than 4000 simulated breakdowns in the test-bench environment, indicating dependability. The study connects the theoretical applications of the UNS with the actual maintenance processes and provides a sound approach to the industrial analytics and optimization of the equipment.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta4010012

Authors: Pranav Avinash Khadkotkar André Martin Ingo Schaarschmidt

Electrochemical machining (ECM) offers precise shaping by material dissolution with negligible mechanical or thermal impact on the workpiece. Metal parts with three-dimensional shapes, such as freeform surfaces or additively manufactured parts, can be addressed by robots with up to six degrees of freedom without significant mechanical impacts on the end-effectors and robots. This study summarizes the state-of-the-art of the use of robots in ECM by assessing the relevant literature. Several investigations were found that implemented or conceptualized the use of robotic arms in ECM sinking, jet-ECM or wire ECM, mainly for effective utilization of the processes. This study includes results of pure ECM, as well as hybrid ECM processes and the use of robots considering their accuracy, degrees of freedom and their application potential. Special emphasis is given to the role of robots in improving machining accessibility and their usability for valuable components in the aerospace, biomedical, and tooling industries. Furthermore, the review provides insights into electrolyte delivery mechanisms and pump configurations that facilitate efficient process performance. Overall, the utilization of robots in ECM not only enhances the process flexibility and surface quality but also aligns well with the aim of intelligent, automated, and high-precision manufacturing.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta4010011

Authors: Efosa Osagie Rebecca Balasundaram

Deep learning models have become central to automated Printed Circuit Board (PCB) defect detection. However, recent work has raised concerns about how reliably these models express confidence in their predictions, particularly when deployed in safety-critical inspection systems. This study conducts an empirical investigation of epistemic uncertainty across representative architectures used in PCB inspection: the two-stage Faster R-CNN detector, the one-stage YOLOv8 detector, and their corresponding classification counterparts, ResNet-50 and YOLOv8-Cls. Monte Carlo Dropout (MCD) was applied during inference to compute predictive entropy, mutual information, softmax variance, and bounding-box variability across multiple stochastic forward passes on both multiclass and binary inspection datasets. On the multiclass SolDef_AI dataset, Faster R-CNN achieved substantially stronger detection performance (mAP = 0.7607, F1 = 0.9304) and lower predictive entropy, with more stable localisation. In contrast, YOLOv8 produced markedly weaker performance (mAP = 0.2369, F1 = 0.3130) alongside higher entropy and greater bounding-box variability. On the binary Jiafuwen datasets, the YOLOv8-Cls model achieved higher overall performance (F1 = 0.6493) compared with the ResNet-50 classifier (F1 = 0.4904), reflecting its strength in simpler binary inspection tasks. Across uncertainty metrics, predictive entropy and mutual information were more sensitive to dataset size, showing higher and more variable values in the smaller multiclass dataset, whereas softmax variance and bounding-box variability appeared more architecture-dependent. These findings demonstrate that architectural choice, dataset structure, and task formulation jointly influence both performance and uncertainty behaviour. By integrating conventional metrics with uncertainty estimates, this study provides a transparent benchmark for assessing model confidence in automated optical inspection of PCBs.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta4010010

Authors: Daniel Gruner Tim Gestrich Mathias Herrmann Anne Günther Jan Mahling Chao Liu Christoph Broeckmann Alexander Michaelis

In this work, the sintering behavior of tapes prepared via tape casting from stainless-steel and zirconia powders is investigated by optical—as well as push-rod—dilatometry. Both methods are compared in terms of sample preparation, measurement conditions, and advantages and disadvantages. The experimental work shows the advantages of optical dilatometry in the characterization of the sintering behavior of load-free sintering tapes and the possibility of simultaneously observing sample warpage and deformation. Push-rod dilatometry requires a constant load on the sample, which influences measurement in the case of tapes with lower mechanical stability due to their sensitivity to deformation, but it has advantages because of its higher accuracy in measuring dimensional changes. In the case of warpage, shrinkage due to the sintering of the sample is superimposed by an irregular deformation process that can be separated by analytical methods. No in-plane shrinkage anisotropy of the tapes is observed for either type of tape. In the case of the push-rod dilatometer, an additional peak in the shrinkage rate is observed in the early stage of compaction, along with a slight shift and an increased maximum in the compaction rate. This is most likely due to the effects of the contact pressure of the push-rod.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta4010009

Authors: István Sztankovics

Tangential turning produces an asymmetric cutting-force system that may cause tool and workpiece deflection, leading to cylindricity, coaxiality, and roundness deviations in practice. This study investigates the relationships between three cutting force components and form errors during tangential turning of 42CrMo4 steel. Tangential, axial, and radial forces were measured under systematically varied cutting speed, feed, and depth of cut, and the resulting cylindricity, coaxiality, and roundness parameters were obtained through precision form measurements. The depth of cut showed the strongest influence on cutting forces, with high correlations to all components (r = 0.709–0.870). Feed was most closely associated with coaxiality error (r = 0.730), while cutting speed was primarily related to cylindricity deviation (r = 0.766). The novelty of this work lies in the combined and quantitative analysis of full cutting-force components and multiple form–accuracy descriptors within a single experimental framework for tangential turning. The results directly link process load to geometric accuracy and provide guidance for selecting cutting parameters to improve dimensional precision in tangential turning of alloy steels.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta4010008

Authors: Marco Rossi

The open access Journal of Experimental and Theoretical Analyses (JETA) [...]

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta4010007

Authors: Axel Wellendorf Leonard Klemenz Sebastian Trampnau Anton Güthenke Jan Madalinski Nils Landefeld Joachim Uhl

A vibration energy harvester (VEH) based on the principle of variable magnetic reluctance has been developed to enable wireless and maintenance-free power supply for condition monitoring sensors in vibrating machinery. Conventional battery or wired solutions are often impractical due to limited lifetime and high installation costs, motivating the use of vibration-based energy harvesting. The proposed VEH converts mechanical vibrations into electrical energy through the relative motion of a movable ferromagnetic core within a magnetic circuit. Unlike conventional VEH designs, where the magnet is the moving element, this concept utilizes a movable ferromagnetic core in combination with a stationary pole piece for voltage induction. This configuration enables a compact and easily adjustable proof mass, as neither the coil nor the magnet needs to be moved. The VEH is designed to operate effectively under excitation frequencies between 16 Hz and 50 Hz and acceleration levels from 9.81 ms2 (equivalent to 1 g) up to 98.1 ms2 (equivalent to 10 g). To ensure a reliable power supply, the VEH must deliver a minimum electrical output of 0.1 mW at the lowest excitation (1 g) while maintaining structural integrity. Additionally, the maximum permissible displacement amplitude of the movable core is limited to 1.15 mm to avoid mechanical damage and ensure durability over long-term operation. Coupled magnetic-transient and mechanical finite element method (FEM) simulations were conducted to analyze the system’s dynamic behavior and electrical power output across varying excitation frequencies and accelerations. A laboratory prototype was developed and tested under controlled vibration conditions to validate the simulation results. The experimental measurements confirm that the VEH achieves an electrical output of 0.166 mW at 9.81 ms2 and 16 Hz, while maintaining the maximum allowable displacement amplitude of 1.15 mm, even at 98.1 ms2 (10 g) and 50 Hz. The strong agreement between simulation and experimental data demonstrates the reliability of the coupled FEM approach. Overall, the proposed VEH design meets the defined performance targets and provides a robust solution for powering wireless sensor systems under a wide range of vibration conditions.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta4010006

Authors: Riddhiman Raut Amrita Basak

Multi-laser powder bed fusion is an emerging additive manufacturing technology that enables the production of high-performance components with intricate geometries and large aspect ratios. These tall, slender structures are highly susceptible to steep thermal gradients and residual stress, leading to deformation that compromises dimensional accuracy and structural integrity. This study investigates how geometric compensation, support structure design, and part scaling influence thermal deformation in Inconel 718 components fabricated via multi-laser powder bed fusion. Using pre-compensation, iterative support refinements, and scaled experimental builds, the deformation response across multiple geometries and print strategies is evaluated. Both compensated and original designs are printed on a commercial system equipped with three simultaneously operating lasers. Results show that printing high-angle surfaces without support structures is infeasible, as thermally induced warping and delamination lead to catastrophic failures. Conical support structures spanning critical regions reduce deformation by more than 50% compared to unsupported builds. Reduced-scale parts, however, do not reliably replicate full-scale deformation behavior due to altered boundary conditions and thermal pathways. These findings highlight the need for integrated design-for-AM workflows where compensation, support design, and scale effects are addressed jointly. The study demonstrates that deformation mechanisms do not scale linearly, emphasizing the limitations of small-scale proxies and the necessity of full-scale validation when developing reliable, deformation-aware design strategies for multi-laser powder bed fusion.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta4010005

Authors: Greesh Nanda Vaidya Arya Ebrahimpour Bruce Savage

This study explores the viability of ultra-high-performance concrete (UHPC) as a structural material for compressed air storage (CAES) systems, combining comprehensive experimental testing and numerical simulations. Scaled (1:20) CAES tanks were designed and tested experimentally under controlled pressure conditions up to 4 MPa (580 psi), employing strain gauges to measure strains in steel cylinders both with and without UHPC confinement. Finite element models (FEMs) developed using ANSYS Workbench 2024 simulated experimental conditions, enabling detailed analysis of strain distribution and structural behavior. Experimental and numerical results agreed closely, with hoop strain relative errors between 0.9% (UHPC-confined) and 1.9% (unconfined), confirming the numerical model’s accuracy. Additionally, the study investigated the role of a rubber interface layer integrated between the steel and UHPC, revealing its effectiveness in mitigating localized stress concentrations and enhancing strain distribution. Failure analyses conducted using the von Mises criterion for steel and the Drucker–Prager criterion for UHPC confirmed adequate safety factors, validating the structural integrity under anticipated operational pressures. Principal stresses from numerical analyses were scaled to real-world operational pressures. These thorough results highlight that incorporating rubber enhances the system’s structural performance.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta4010004

Authors: Francisco Castro Francisco Queirós de Melo David Faria Job Silva João Nunes Pedro José Sousa Mário Augusto Pires Vaz Pedro M. G. P. Moreira

This paper presents a practical and cost-effective method for in-motion estimation of a vehicle’s CoG position in all three directions by measuring accelerations during two types of maneuvers: braking (longitudinal and vertical CoG estimation) and cornering (lateral and vertical CoG estimation). The proposed method’s main advantage is that it does not require knowledge of vehicle characteristics, such as mass distribution, suspension geometry, or inertia parameters. It relies solely on the known distances between the sensors and their positions relative to a defined reference point on the vehicle. To validate the developed method, experimental tests were conducted on a prototype vehicle, varying the load conditions for the proposed driving scenarios. The CoG position obtained from dynamic maneuvers was compared with reference values derived from static measurements. The results showed that the proposed method could estimate the CoG position with an average error of 3% in the longitudinal direction, a maximum error of 12% in the lateral direction, and a maximum error of 14% in the vertical direction.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta4010003

Authors: Lillian Chang Diya Devendiran Julian Gard Tiffany Gu Annie Guan Akira Yamamoto Tapash Jay Sarkar Edward Njoo Joseph Pazzi

Simplified and scalable models of physical systems are extremely valuable in a variety of different engineering fields to test and diagnose particular modes of failure and optimize build conditions. In this work, we develop a practical method to prepare and analyze giant unilamellar vesicles (GUVs) for detailed biophysical interrogations. The method is rapid, scalable, and versatile, where characterization of lipid membrane conformational changes can be performed on multiplexed samples using tissue culture plates and a convenient, high-throughput fluorescence microscopy setup. The simplicity of the setup is enabled by an AI image recognition model that, when trained on the appearance of GUVs in the images, outperforms other image segmentation methods such as the watershed algorithm or the Hough transform. The method allows for the rapid quantification of entire 96-well plates containing in total O (1,000,000) GUVs and provides a potential testbed for the development of drugs. We highlight the power of our system by including large-scale data on the screening of lipophilic analogs of the small molecule antimetabolite carmofur.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta4010002

Authors: Rui Zhang Chi Yang Youwei Song

This study investigates the fatigue strength of a motor hanger used in high-speed electric multiple units (EMUs). Finite element analysis and field measurements revealed that reduced weld penetration significantly increases stresses in welded regions. Line tests demonstrated that a 100 Hz torque ripple induces elastic vibration of the hanger, serving as the primary driver of stress propagation, with stress and acceleration levels increasing proportionally with the torque ripple amplitude. This 100 Hz excitation lies close to the hanger’s constrained modal frequency of about 109 Hz, creating a near-resonance condition that amplifies dynamic deformation at the welded joints and accelerates fatigue crack initiation. Hangers with lower in situ modal frequencies exhibited higher equivalent stresses. Joint dynamic simulation further showed that increasing motor mass reduces the longitudinal acceleration of the hanger, while enhancing the radial stiffness of rubber nodes markedly decreases both longitudinal and vertical vibration accelerations as well as stress responses. Based on these insights, a structural improvement scheme was developed. Strength analysis and on-track tests confirmed substantial reductions in overall and weld stresses after modification. Fatigue bench tests indicated that the critical welds of the improved hanger achieved a service life of 15 million km, more than twice that of the original structure (7.08 million km), thereby satisfying operational safety requirements.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta4010001

Authors: Ren Kai Tan Zi Jie Choong Michael Lau

Optimising the learning rate is essential for efficient neural network training, but static methods can cause overshooting or undershooting, while adaptive techniques like ADAM often struggle to balance exploration and exploitation. We introduce the Polling Method, an ensemble-based optimisation approach that dynamically selects the most effective learning rate at each step, improving convergence and mitigating issues inherent in traditional optimisation strategies. By evaluating base models with varying learning rates at each epoch, the method adaptively balances exploration and exploitation without being constrained by predefined functions or gradient noise. This study details the theoretical foundation, implementation, and integration of the Polling Method with the ADAM optimiser, demonstrating its effectiveness in Artificial Neural Networks and Bayesian variational inference. The results demonstrate that Polling Method-ADAM reduces absolute error by 50% compared to ADAM alone, while also accelerating convergence. In Bayesian optimisation, it reduces the mean gradient shift from 0.85 to 0.7835 over 500 iterations, indicating improved stability in high-dimensional problems. By introducing adaptive learning rate selection within training, the Polling Method enhances optimisation efficiency while mitigating noise accumulation. This framework provides a computationally efficient, flexible alternative for deep learning applications, offering significant improvements over traditional optimisers and a potential breakthrough in neural network training strategies.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta3040042

Authors: Jason Knight Mojtaba Ghodsi Bradley Horne Edward John Taylor Niah Laurel Virhuez Montaño Daniel George Chattock James Buick Ethan Krauss Andrew Lewis

Ionic propulsion, where charged particles, ions, are produced between electrodes and accelerate towards the negative electrode, has practical applications as a propulsion system in the space industry; however, its adoption to in-atmosphere ionic propulsion is relatively new and faces different challenges. A high potential difference is required to achieve a corona discharge between a positive and negative electrode. In this work, we will explore the feasibility of ionic propulsion using CFD modelling to replicate the effect of the ions, with a future aim of improving efficiency. The ionization region is modelled for a 15 kV potential difference, which is replicated with a velocity inlet, based on experimental data. The output velocity from the numerical simulation shows the same trend as theoretical predictions but significantly underestimates the magnitude of the ionic wind when compared with theoretical estimates. Further modelling is highlighted to improve predictions and assess if the theoretical model overestimates the ionic wind.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta3040041

Authors: Koon Wan Wong Vanissorn Vimonsatit

This paper presents three different approaches for generating points along the interaction diagram corresponding to design load effects—shear, bending moment, and axial force—to achieve optimal shear strength adequacy with the Australian bridge design standard AS 5100.5. The methodology targets the optimal shear condition by matching the design shear V* with the capacity ϕVu, which represents achieving a load rating factor of unity within the specified tolerance limits. The first typical approach for generating points for two load effects is by increasing the moment–shear ratio ηm in small increments from zero to a large value (theoretically infinity), and for each increment, to goal-seek the condition. The other approaches investigated are the use of increasing factored moment M* and the use of Monte Carlo simulation. A pretensioned bridge I-girder section reported in the literature was used in the study. The Monte Carlo simulation method was found to be the simplest to program. It allows an interaction surface for the influence of three load effects for optimal shear adequacy to be obtained with minimal program coding and outperforms the goal–seeking approaches for multi-variable interactions. It can create 2-D interaction lines for various levels of shear adequacy for the interaction of M* and V*, and 3-D interaction surfaces for M*, V*, and N*. The potential use of interaction diagrams was explored, and the advantages and limitations of using each method are presented. The interaction curves of two typical pretensioned concrete sections of a plank girder, one next to an end support and the other close to mid-span, were created to show the distinguishing features resulting from their reinforcement.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta3040040

Authors: Cameron Rusnak Aya Al-hamami Craig Menzemer

Aluminum light poles are essential components of modern infrastructure, providing illumination for highways, urban areas, and pedestrian pathways. Despite their importance, structural vulnerabilities in handholes—necessary for electrical access—can reduce fatigue life due to the structure’s response to wind. This study addresses a critical gap in translating laboratory-derived S–N data into field-applicable methods for assessing cumulative damage in these structures. A probabilistic cumulative damage analysis framework was developed to quantify the structural degradation of handholes due to variable wind velocities. Using a series of controlled cyclic fatigue tests and Miner’s Rule, the study establishes a methodology to convert stress ranges into equivalent wind velocities and correlate laboratory cycle counts with real-world loading conditions. The findings reveal a logarithmic progression of damage accumulation and highlight the utility of integrating standardized factors from ASCE-7 for scalable and geographically adaptable assessments. A proof-of-concept application demonstrates the model’s potential to predict failure risks during extreme wind events, such as hurricanes and tornadoes. This research provides a practical and predictive tool for engineers and contractors to evaluate the structural integrity of aluminum light poles, enabling proactive maintenance and reducing unplanned failures.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta3040039

Authors: Shuyi Jia Maryam Horri Rezaei Barmak Honarvar Shakibaei Asli

This study addresses the critical challenge of variable image quality in deep learning-based automated pest identification. We propose a holistic pipeline that integrates systematic Image Quality Assessment (IQA) with tailored preprocessing to enhance the performance of a YOLOv5 object detection model. The methodology begins with a No-Reference IQA using BRISQUE, PIQE, and NIQE metrics to quantitatively diagnose image clarity, noise, and distortion. Based on this assessment, a tailored preprocessing stage employing six different filters (Wiener, Lucy–Richardson, etc.) is applied to rectify degradations. Enhanced images are then used to train a YOLOv5 model for detecting four common pest species. Experimental results demonstrate that our IQA-anchored pipeline significantly improves image quality, with average BRISQUE and PIQE scores reducing from 40.78 to 25.42 and 34.94 to 30.38, respectively. Consequently, the detection confidence for challenging pests increased, for instance, from 0.27 to 0.44 for Peach Borer after dataset enhancement. This work concludes that a methodical approach to image quality management is not an optional step but a critical prerequisite that directly dictates the performance ceiling of automated deep learning systems in agriculture, offering a reusable blueprint for robust visual recognition tasks.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta3040038

Authors: Ahmed Fawzy Henri Fledderus Jie Shen Wiel H. Manders Emile Verstegen Hylke B. Akkerman

Roll-to-roll production of thin organic and large-area electronic (TOLAE) devices often involves a two-step process per functional layer: a continuous, un-pattered deposition of the film and subsequent structuring process, such as laser ablation. Thin-film organic devices should be protected using ultra-barrier films. To perform laser ablation of functional layers on top of such barrier films, in particular that of transparent electrodes, highly selective laser ablation is required to completely remove the layers without damaging the thin-film barrier layers underneath. When targeting highly selective laser ablation of indium tin oxide (ITO) on top of silicon nitride (SiN) barrier layers with a 1064 nm picosecond or 1030 nm femtosecond laser, we observed the emergence of visible large-scale patterns due to local variations in ablation quality. Our investigations using a very sensitive Raman spectroscopy setup show that the observed ablation variations stem from subtle differences in optical path length within the heat-stabilized plastic substrates. These variations are likely caused by minute, localized changes in the refractive index, introduced during the bi-axial stretching process used in film fabrication. Depending on the optical path length, these variations lead to either constructive or destructive interference between the incoming laser beam and the light reflected from the back surface of the substrate. By performing laser ablation under an angle such that the reflected and incoming laser beam do not spatially overlap, highly selective uniform laser ablation can be performed, even for two stacked optically transparent layers.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta3040037

Authors: Walid Chaaibi Abderrazak El Ouafi Narges Omidi

In this paper, an integrated geometric and thermal-induced errors prediction approach for active error compensation in machine tools is proposed and evaluated. The proposed approach is based on a hybrid of physical and neural network predictive modeling to drive an adaptive position controller for real-time error compensation including geometric and thermal-induced errors. Error components are formulated as a three-dimensional error field in the time-space domain. This approach involves four key steps for its development and implementation: (i) simplified experimental procedure combining a multicomponent laser interferometer measurement system and sixteen thermal sensors for error components measurement, (ii) artificial neural network-based predictive modeling of both position-dependent and position-independent error components, (iii) tridimensional volumetric error mapping using rigid body kinematics, and finally (iv) implementation of the real-time error compensation. Assessed on a turning center, the proposed approach conducts a significant improvement of the machine accuracy. The maximum error is reduced from 30 µm to less than 3 µm under thermally varying conditions.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta3040036

Authors: Jakiya Sultana Md Mazedur Rahman Gyula Varga Szabolcs Szávai Saiaf Bin Rayhan

Natural fiber-reinforced unidirectional composites are increasingly adopted in modern industries due to their superior mechanical performance and desirable properties from both material and engineering perspectives. Among various approaches, representative volume element (RVE) generation and analysis is considered one of the most suitable and convenient methods for predicting the elastic moduli of composites. The main aim of this study is to investigate and compare the elastic moduli of natural fiber–reinforced unidirectional composite RVEs using theoretical, numerical, and machine learning models. The numerical predictions in this study were generated using the ANSYS Material Designer tool (version ANSYS 19). A comparison was made between experimental results reported in the literature and different theoretical models, showing high accuracy in validating these numerical outcomes. A dataset comprising 1600 samples was generated from numerical models in combination with the well-known theory of RVE, namely rule of mixture (ROM), to train and test two machine learning algorithms: Random Forest and Linear Regression, with the goal of predicting three major elastic moduli—longitudinal Young’s modulus (E11), in-plane shear modulus (G12), and major Poisson’s ratio (V12). To evaluate model performance, mean squared error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination (R2) were calculated and compared against datasets with and without the theoretical values as input variables. The performance metrics revealed that with the theoretical values, both Linear Regression and Random Forest predict E11, G12, and V12 well, with a maximum MSE of 0.033 for G12 and an R2 score of 0.99 for all cases, suggesting they can predict the mechanical properties with excellent accuracy. However, the Linear Regression model performs poorly when theoretical values are not included in the dataset, while Random Forest is consistent in accuracy with and without theoretical values.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta3040035

Authors: Irini Spyrolari Alexandra Alexandropoulou Eleni Didaskalou Dimitrios Georgakellos

This research presents a detailed Life Cycle Assessment (LCA) of 26 mm Crown cork metal closures used in glass bottle packaging, with the objective of quantifying and comparing their environmental impacts across all life cycle stages. This study adheres to ISO 14040 and ISO 14044 standards and utilizes Microsoft Excel for structuring and documenting input–output data across each phase. The LCA encompasses three primary stages: raw material production (covering iron ore extraction and steel manufacturing), manufacturing processes (including metal sheet printing, forming, and packaging of closures), and the transport phase (distribution to bottling facilities). During the Life Cycle Inventory (LCI), steel production emerged as the most environmentally burdensome phase. It accounted for the highest emissions of carbon dioxide (CO2), carbon monoxide (CO), nitrogen oxides (NOx), and sulphur oxides (SOx), while emissions of heavy metals and volatile organic compounds were found to be negligible. The Life Cycle Impact Assessment (LCIA) was carried out using the Eco-Indicator 99 methodology, which organizes emissions into impact categories related to human health, ecosystem quality, and resource depletion. Final weighting revealed that steel production is the dominant contributor to overall environmental impact, followed by the manufacturing stage. In contrast, transportation exhibited the lowest relative impact. The interpretation phase confirmed these findings and emphasized steel production as the critical stage for environmental optimization. This study highlights the potential for substantial environmental improvements through the adoption of low-emission steel production technologies, particularly Electric Arc Furnace (EAF) processes that incorporate high percentages of recycled steel. Implementing such technologies could reduce CO2 emissions by up to 68%, positioning steel production as a strategic focus for sustainability initiatives within the packaging sector.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta3040034

Authors: Toru Takahashi Taiki Kanbayashi Ryota Aoki Yuta Ochi Akira Lee Masato Nakayama

We describe the problem of estimating the speaker’s head orientation from the asynchronous multi-channel waveforms observed by microphones distributed in a room. In particular, we address a novel problem of estimating head orientation from sound captured by fewer microphones than the number of distinct head orientations to be distinguished. This is because the head orientation is an important clue indicating the speaker’s intended conversational partners. Head orientation estimation technology is an essential technology within environmental intelligence technology, which uses sensors embedded in rooms to monitor and support people’s activities. We propose a head orientation estimation method that aims to achieve high angular resolution using a small number of microphones. The proposed method achieves high estimation accuracy by using the spatial radiation pattern of the sound source as clues and by integrating information from multiple frequency bands. We conducted an experiment to estimate head orientation with an angular resolution of 15degrees under observation conditions using six microphones. Experimental results showed that higher estimation accuracy was obtained than the conventional method using distributed microphone arrays (Oriented Global Coherence Field method) and the conventional method using distributed microphones (Radiation Pattern Matching method). The proposed method utilizing multiple frequency bands achieved the best performance with a mean absolute error of 10.58degrees in the task of classifying 24 distinct head orientations.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta3040033

Authors: Florent Clavier Lionel Desgranges Christophe Goupil

Conventional thermomechanical models recently failed to reproduce the temperature profile measured during rapid annular laser heating of a disk, with discrepancies of up to 150 K. One might have thought that these discrepancies resulted from neglecting the so-called “strong” thermomechanical coupling. However, the discrepancies seemed too large to be explained in this way, suggesting that another more significant phenomenon was involved. In this paper, we first present the laser heating experiment that highlights the failure of conventional models. We then demonstrate that the established strong coupling thermomechanical theory cannot account for the observed divergences, as its impact on temperature does not exceed about 1 K. To address this limitation, we propose a new, more comprehensive thermomechanical coupling formalism based on the thermodynamics of irreversible processes (TIP). Its originality lies in the explicit consideration of spatial strain transport, introduced through the notion of strain flux. This approach reveals a previously unrecognized coupling term representing mechanical work production by heat-to-work conversion. Finally, we provide a quantitative estimate of the influence of this new term by reconsidering the heating experiment. The calculation shows that it could explain the discrepancies between theory and measurement. Although applied here to a specific case, this result supports the validity of our approach. It demonstrates that such coupling must be considered whenever a system is subjected to rapid thermal and mechanical transients.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta3040032

Authors: Edgar Santiago Galicia Andres Hernandez-Matamoros Akio Miyara

Traditional empirical correlations for predicting saturated flow boiling heat transfer coefficients (HTC) often struggle with accuracy and generalizability, particularly across different refrigerants, heat exchanger geometries, and operating conditions. To address these limitations, this study investigates the application of machine learning for more robust HTC prediction. A comprehensive dataset was compiled, consisting of 22,608 data points from over 140 published studies, covering 18 pure refrigerants under diverse experimental setups. The primary goal was to evaluate the performance of different machine learning approaches—Wide Neural Network (WNN), Linear Regression (LR), and Support Vector Machine (SVM)—in predicting HTCs across varying tube types and heat exchanger configurations. The results indicate that the WNN model achieved the highest predictive accuracy, with a Root Mean Square Error (RMSE) of 1.97 and a coefficient of determination (R2) of 0.91, corresponding to less than 5% prediction error for all refrigerants. These outcomes confirm that machine learning models can effectively capture the complex thermofluid interactions involved in boiling heat transfer. This work demonstrates that data-driven methods provide a reliable and generalizable alternative to empirical correlations.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta3040031

Authors: Thomas Kletschkowski

To demonstrate the difference between non-ideal experiments and idealized analytical models, bending vibrations of a frame structure have been analyzed in the context of hands-on teaching in structural dynamics. Both experimental modal analysis and model-based evaluation of system dynamics have been performed. The investigations have been limited to mechanical vibrations in the low-frequency range. It has been found that even simple mechanical models are very useful to explain, understand, and validate experimental results. The latter have been derived from one key principle of analytical dynamics—the Lagrange formalism. The article is written for students in mechanical engineering and related fields as well as for the academic community. The latter could use the results as a benchmark problem in academic teaching as well as in applied research.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta3040030

Authors: Chandan Roy Landon Yarbrough Hammad Quddus Megan Batchelor

The research presents the thermal performance comparison of silicone and non-silicone thermal pads using a steady-state thermal interface material (TIM) testing apparatus. The TIM tester follows standard guidelines for testing thermal properties. TIMs are applied between two solid surfaces to improve heat transfer by eliminating air gaps that naturally occur due to surface roughness and non-flatness. Since TIMs possess significantly higher thermal conductivity than air, they effectively reduce contact resistance at the interface, thereby minimizing the risk of overheating in electronic systems. In this work, the thermal resistances of silicone and non-silicone thermal pads were compared over a pressure range of 10–50 psi. Results indicate that non-silicone pads consistently exhibit lower thermal resistance than their silicone counterparts under identical testing conditions.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta3040029

Authors: M. R. Riazi

In this review paper, we briefly discuss the occurrence of oil spills and their behavior under natural sea conditions and clean-up methods, as well as their environmental and economic impacts. We discuss methodologies for oil spill modeling used to predict the fate of a spill under dynamic physical and chemical processes. Weathering processes such as evaporation, emulsification, spreading, dissolution, dispersion, biodegradation, and sedimentation are considered within easy-to-use modeling frameworks. We present simple models based on the principles of thermodynamics, mass transfer, and kinetics that under certain conditions can predict oil thickness, volume, area, composition, and the distribution of toxic compounds in water and air over time for various types of oil and their products. Modeling approaches for underwater oil jets, including applications related to the 2010 BP oil spill in the Gulf of Mexico, are reviewed. The influence of sea surface velocity and wind speed on oil spill mapping, spill location, oil spill trajectory over time, areas affected by light, medium, and heavy oil, and comparisons between satellite images and model predictions are demonstrated. Finally, we introduce several recently published articles on more recent oil spill incidents and the application of predictive models in different regions. We also discuss the challenges, advantages, and disadvantages of various models and offer recommendations at the end of the paper.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta3030028

Authors: Konstantina-Roxani Chatzipanagiotou Athanasios Pappas Foteini Petrakli George Antonaropoulos Elias P. Koumoulos

According to the International Diabetes Federation, approximately 537 million adults suffered from diabetes in 2021, a number that is projected to rise to 783 million by 2045. Insulin is a hormone produced by the pancreas that regulates blood glucose levels; for people suffering from diabetes, insulin activity may be reduced or absent, and therefore, administration of insulin may be necessary to maintain healthy blood glucose levels. Recombinant human insulin is commercially produced using a variety of host microorganisms, such as bacteria and yeast. Nevertheless, few studies have assessed the environmental impacts associated with different upstream medium formulations and their contribution to the overall environmental footprint of recombinant insulin production. Here, Life Cycle Assessment (LCA) is conducted on various upstream media used in insulin production—including pre-cultivation, growth, feed, and induction media—capturing the impacts associated with both their supply chains and their on-site preparation. Hotspots of environmental impacts are identified, and different alternatives for input materials and process conditions are compared in terms of impacts. The findings reported here can serve to guide process and sustainability optimization of the upstream production process from an operational process perspective. Finally, the identification of hotspots enables the implementation of impact reduction measures in bioprocess design, which have the potential to significantly improve the sustainability of insulin production.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta3030027

Authors: Vasilissa Nikonova Veronica Bortolotto Costanza Bebber Irene Presti Gabriele Angelo Valtorta Serena Biella Claudia Letizia Bianchi

This study evaluates the environmental impact of swimming goggles through a Life Cycle Assessment (LCA), comparing virgin and recycled polycarbonate models. It identifies key hotspots, assesses circular economy benefits, and examines barriers to sustainable disposal, aligning with European Union’s (EU) 2050 sustainability objectives. The LCA was modeled using SimaPro, with the Environmental Footprint (EF) 3.1 method to analyze 16 impact categories (e.g., climate change, human toxicity, resource depletion). Two scenarios were assessed: (1) virgin polycarbonate production and (2) a closed-loop system (80% recycled content, 30% reintegration). Primary data from a survey of 150 competitive swimmers quantified disposal behaviors. The lens production phase (bisphenol A processing) dominated impacts, contributing to 62% of climate change and 75% of human toxicity. The recycling scenario reduced total impact by 23.1% (119 → 91.5 mPt), with significant declines in freshwater ecotoxicity (−28.6%) and marine eutrophication (−25.1%). Survey data highlighted critical gaps: low consumer participation in recycling due to lack of awareness and inadequate disposal infrastructure. Recycled polycarbonate can substantially mitigate environmental impacts, but systemic barriers (consumer behavior, collection gaps) limit progress. Future work should explore bio-based polymers and policy incentives to accelerate circularity.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta3030026

Authors: Sriharsha Rowduru Mohit Bhola Niranjan Kumar Ajit Kumar

The present article compares the Proportional Directional Control Valve (PDCV) and the Stepper Motor-Driven Orbitrol Valve (SMDOV) coupled to the steering system of the articulated steered vehicles. Simulation models of both valve coupled steering systems are developed in a MATLAB (r2019b) environment, and results are well validated with the experimental data. Comparison analysis is performed between the PDCV and SMDOV steering systems by controlling the desired position demand using a conventional PID controller. From the comparative study, it is observed that the SMDOV provides almost 50% energy reduction, but the valve response is low compared to PDCV. However, the steering response provided by the SMDOV is quite enough for performing steering operations in mining conditions. Overall, the orbitrol valve-assisted steering system offers more efficient and smooth steering than the PDCV valve. The future work of the present study extends to the development of autonomous steering operation using an orbitrol valve-operated articulated steering system.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta3030025

Authors: Roberto Doglione Francesco Tanucci

The Portevin-Le Chatelier (PLC) effect has been studied for many decades, yet the influence of testing modes has received limited attention. In the past 20 years, it has become increasingly recognized that the stiffness of the testing machine can significantly affect the occurrence of jerky flow, particularly the serrations observed during tensile tests. This study addresses this issue by conducting tests on the Al-Mg alloy AA5083H111, which contains a substantial amount of diffusible magnesium in solid solution and exhibits dynamic strain aging, resulting in a pronounced PLC effect. Both electromechanical and servohydraulic testing machines were used in the tests; these machines differ in stiffness and control technology for applied strain rates. The study also explored different control modes, including stroke control for both machines and true strain control for the servohydraulic machine. The findings indicate that machine stiffness has a moderate effect on material behavior, and no single machine or testing mode can precisely control the strain rate in the sample during the PLC effect. However, it was noted that true strain rate control using a servohydraulic machine comes closest to accurately reflecting the material’s behavior during jerky flow.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta3030024

Authors: Manar Alenezi Amrit Prasad Kafle Meznh Alsubaie Ian L. Pegg Najwa Albalawi Biprodas Dutta

This study presents the electrical and optical properties of 35P2O5–xV2O5–(65–x) Sb2O3 glasses for 0 ≤ x ≤ 65 mol%. The direct current (DC) resistivity was measured by the Van der Pauw method and optical absorption spectra were taken in the Ultraviolet–Visible-Near-Infrared (UV–VIS–NIR) range. Electrical transport is attributed to simultaneous hopping of small polarons (SPs) between V4+ and V5+ (vanadium ion) sites and small bipolarons (SBPs) between the Sb3+ and Sb5+ (antimony ion) sites. The resistivity exhibits a non-linear dependence on the ionic fraction of vanadium (nv), whereas the resistivity exhibits a minimum in the composition range 0 ≤ nV ≤ 0.3, and a resistivity maximum was observed in the range 0.3 ≤ nV ≤ 0.5. On further increasing nv, the resistivity exhibits a monotonic decline. In the composition range 0 ≤ nV ≤ 0.3, where the hopping distance between V ions decreases, while that between the Sb ions increases, the resistivity minimum has been shown to be the consequence of decreasing tunneling distance of SPs between the V4+ and V5+ ion sites. In the composition range 0.3 ≤ nV ≤ 0.5, the resistivity, activation energy for DC conduction, glass transition temperature, and density exhibit their respective maxima even though the separation between the V4+ and V5+ sites continues to decrease. This feature is explained by enhanced localization of electrons on account of increased disorder (entropy) among the SPs and SBPs, like that of Anderson localization. This argument is further supported by a shift in the polaronic optical absorption bands associated with the SPs and SBPs toward higher energies. The transport behavior of all the glasses except the x = 0 composition has been explained by adiabatic transport, principally, by the SPs on V ions while the Sb ions contribute little to the total transport process. The results provide a clear relation between composition, polaron/bipolaron contributions, and conduction in these glasses.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta3030023

Authors: Wesley Chorney Sing Hui Ling

Background: Different treatments may be required for paroxysmal versus non-paroxysmal atrial fibrillation. However, they may be difficult to distinguish on an electrocardiogram (ECG). Machine learning methods may aid in differentiating these conditions, yet current approaches either do not preserve patient privacy or tend to make the unrealistic assumption of uniform data. Methods: We create a non-independently and identically distributed dataset for paroxysmal versus non-paroxysmal atrial fibrillation detection. Two baselines (a centralized classifier and a federated classifier) are trained, and the performances of classifiers pretrained on shared data before federated training are compared. Results: The centralized classifier outperforms all other models (p<0.001), while the federated model is the worst-performing model (p<0.0001). Classifiers that are pretrained on 10%, 30%, and 50% of shared data (CNN-10, CNN-30, CNN-50, respectively) perform better than the purely federated model (p<0.0001 for all models). Furthermore, no performance difference is observed between any of the models trained on shared data (the null hypothesis of a one-way analysis of variance test between the shared data models is not rejected, p=0.954). Conclusions: The partial sharing of data in creating federated machine learning models may significantly improve performance. Furthermore, the volume of data required to be shared may be relatively small.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta3030022

Authors: Saubhagya Subhadarsini Sahoo Dwipak Prasad Sahu Rajendra Kumar Behera

Seed priming is an innovative pre-planting technique to improve germination and accelerate early seedling growth, offering a sustainable and eco-friendly alternative to chemical treatments. In this study, silver nanoparticles (AgNPs) were synthesized using flower extracts of neem plants for the first time, alongside the conventional neem leaf extract-based AgNPs, and their comparative efficacy was evaluated in wheat seed priming. The biosynthesized AgNPs were characterized through UV–Vis spectroscopy, Fourier Transform Infrared Spectroscopy (FTIR), X-ray Diffraction (XRD), Field Emission Scanning Electron Microscopy (FESEM), Energy-Dispersive Spectroscopy (EDS), Dynamic Light Scattering (DLS), and zeta potential analysis to confirm their formation, stability, and surface functionality. Wheat seeds were primed with varying concentrations (25, 50, 75, 100 mg/L) of flower-mediated nanoparticles (F-AgNPs) and leaf-mediated nanoparticles (L-AgNPs). Effects on seed germination, seedling growth, plant pigments, secondary metabolites, and antioxidant enzyme activities were systematically investigated. The results indicated that F-AgNP priming treatment significantly enhanced wheat seedlings’ performances in comparison to L-AgNPs, which could be attributed to the difference in phytochemical profiles in the extracts. This study contributes a comparative experimental analysis highlighting the potential of biogenic AgNPs—particularly those derived from neem flower extract—offering a promising strategy for enhancing seedling establishment in wheat through seed priming.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta3030021

Authors: Alexander Isiani Kelly Crittenden Leland Weiss Okeke Odirachukwu Ramanshu Jha Okoye Johnson Osinachi Abika

Material extrusion additive manufacturing (MEAM) has emerged as a versatile and widely adopted 3D printing technology due to its cost-effectiveness and ability to process a diverse range of materials. However, achieving consistent part quality and repeatability remains a challenge, mainly due to variations in process parameters and material behavior during fabrication. In-situ monitoring and advanced process control systems have been increasingly integrated into MEAM to address these issues, enabling real-time detection of defects, optimization of printing conditions, reliability of fabricated parts, and enhanced control over mechanical properties. This review examines the state-of-the-art in-situ monitoring techniques, including thermal imaging, vibrational sensing, rheological monitoring, printhead positioning, acoustic sensing, image recognition, and optical scanning, and their integration with process control strategies, such as closed-loop feedback systems and machine learning algorithms. Key challenges, including sensor accuracy, data processing complexity, and scalability, are discussed alongside recent advancements and their implications for industrial applications. By synthesizing current research, this work highlights the critical role of in-situ monitoring and process control in advancing the reliability and precision of MEAM, paving the way for its broader adoption in high-performance manufacturing.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta3030020

Authors: Collin G. White Ayuba Fasasi Chanda Swalley Barry K. Lavine

Renewable fuels produced from animal- and plant-based edible oils have emerged as an alternative to oil and natural gas. Burgeoning interest in renewables can be attributed to the rapid depletion of fossil fuels caused by the global energy demand and the environmental advantages of renewables, specifically reduced emissions of greenhouse gases. An important property of the feedstock that is crucial for the conversion of edible oils to renewable fuels is the total acid number (TAN), as even a small increase in TAN for the feedstock can lead to corrosion of the catalyst in the refining process. Currently, the TAN is determined by potentiometric titration, which is time-consuming, expensive, and requires the preparation of reagents. As part of an effort to promote the use of renewable fuels, a partial least squares regression method with orthogonal signal correction to remove spectral information related to the sample background was developed to determine the TAN from the mid-infrared (IR) spectra of the feedstock. Digitally blended mid-IR spectral data were generated to fill in regions of the PLS calibration where there were very few samples. By combining experimental and digitally blended mid-IR spectral data to ensure adequate sample representation in all regions of the spectra–property calibration and better understand the spectra–property relationship through the identification of sample outliers in the original data that can be difficult to detect because of swamping, a PLS regression model for TAN (R2 = 0.992, cross-validated root mean square error = 0.468, and bias = 0.0036) has been developed from 118 experimental and digitally blended mid-IR spectra of commercial feedstock. Thus, feedstock whose TAN value is too high for refining can be flagged using the proposed mid-IR method, which is faster and easier to use than the current titrimetric method.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta3030019

Authors: Nuha S. Mashaan Sammy Kibutu Chathurika Dassanayake Ali Ghodrati

Mining by-products present both an environmental challenge and a resource opportunity. This review investigates their potential application in road pavement construction, focusing on materials such as fly ash, slag, sulphur, red mud, tailings, and silica fume. Drawing from laboratory and field studies, the review examines their roles across pavement layers—subgrade, base, subbase, asphalt mixtures, and rigid pavements—emphasising mechanical properties, durability, moisture resistance, and ageing performance. When properly processed or stabilised, many of these wastes meet or exceed conventional performance standards, contributing to reduced use of virgin materials and greenhouse gas emissions. However, issues such as variability in composition, leaching risks, and a lack of standardised design protocols remain barriers to adoption. This review aims to consolidate current research, evaluate practical feasibility, and identify directions for future studies that would enable the responsible and effective reuse of mining waste in transportation infrastructure.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta3020018

Authors: José M. Campos-Salazar Jorge Gonzalez-Salazar

While extensive research has addressed electromechanical systems interacting with power electronic converters, most studies lack a unified modeling framework that simultaneously captures converter switching behavior, nonlinear dynamics, and linearized control-oriented representations. In particular, the dynamic interaction between two-level full-bridge converters and angular-motion electromechanical switching devices (EMDs) is often simplified or abstracted, thereby limiting control system design and frequency-domain analysis. This work presents a comprehensive dynamic modeling methodology for an angular-motion EMD driven by a full-bridge dc-dc converter. The modeling framework includes (i) a detailed nonlinear switching model, (ii) an averaged nonlinear model suitable for control design, and (iii) a small-signal linearized model for deriving transfer functions and evaluating system stability. The proposed models are rigorously validated through time-domain simulations and Bode frequency analysis, confirming both theoretical equilibrium points and dynamic characteristics such as resonant frequencies and phase margins. The results demonstrate strong consistency across the modeling hierarchy and reveal critical features—such as ripple-induced resonance and low-frequency coupling—that are essential for robust controller design. This framework established a foundational tool for advancing the control and optimization of electromechanical switching systems in high-performance applications.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta3020017

Authors: Athanasios Koulianos Antonios Litke Nikolaos K. Papadakis

In this paper, we introduce the Levy Flight-enhanced Whale Optimization Algorithm with Tabu Search elements (LWOATS), an innovative hybrid optimization approach that enhances the standard Whale Optimization Algorithm (WOA) with advanced local search techniques and elite solution management to improve performance on global optimization problems. Techniques from the Tabu Search algorithm are adopted to balance the exploration and exploitation phases, while an elite reintroduction strategy is implemented to retain and refine the best solutions. The efficient optimization of LWOATS is further aided by the utilization of Levy flights and local search based on the Nelder–Mead simplex method. An Orthogonal Experimental Design (OED) analysis was employed to fine-tune the algorithm’s parameters. LWOATS was tested against three different algorithm sets: fundamental algorithms, advanced Differential Evolution (DE) variants, and improved WOA variants. Wilcoxon tests demonstrate the promising performance of LWOATS, showing improvements in convergence speed, accuracy, and robustness compared to traditional WOA and other metaheuristic algorithms. After extensive testing against a challenging set of benchmark functions and engineering optimization problems, we conclude that our proposed method is well suited for tackling high-dimensional optimization tasks and constrained optimization problems, providing substantial computational efficiency gains and improved overall solution quality.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta3020016

Authors: Mohammad Reza Vaziri Sereshk Hamed Mohamadi Bidhendi

Springback represents the deflection of a workpiece after releasing the forming tools or dies, which influences the quality and precision of the final products. It is basically governed by the elastic strain recovery of the material after unloading. Most approaches only implement reverse bending to determine the final shape of the formed product. However, stretch plays significant role whe the blank is held by a blank holder. In this paper, an algorithm is presented to calculate the contributions of both stretch loads and bending moments to elastic deformation during springback for each element, and to combine them mathematically and geometrically to achieve the final shape of the product. Comparing the results of this algorithm for different sheet metal forming processes with experimental measurements demonstrates that this technique successfully predicts a wide range of springback with reasonable accuracy. The advantage of this approach is its accuracy, which is not sensitive to hardening and softening mechanisms, the magnitude of plastic deformation during the forming process, or the size of the object. The application of the proposed formulation is limited to long profiles (plane-strain cases). However, it can be extended to more general applications by adding the effect of torsion and developing equations in 3D space. Due to the explicit nature of the calculations, data-processing time would be reduced significantly compared to the sophisticated algorithms used in commercial software.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta3020015

Authors: Karim Asami Mehar Prakash Reddy Medapati Titus Rakow Tim Röver Claus Emmelmann

This study introduced a systematic framework to develop practical design guidelines specifically for filament-based material extrusion of metals (MEX/M), an additive manufacturing (AM) process defined by ISO/ASTM 52900. MEX/M provides a cost-efficient alternative to conventional manufacturing methods, which is particularly valuable for rapid prototyping. Although AM offers significant design flexibility, the MEX/M process imposes distinct geometric and process constraints requiring targeted optimization. The research formulates and validates design guidelines tailored for the MEX/M using an austenitic steel 316L (1.4404) alloy filament. The feedstock consists of a uniform blend of 316L stainless steel powder and polymeric binder embedded within a thermoplastic matrix, extruded and deposited layer by layer. Benchmark parts were fabricated to examine geometric feasibility, such as minimum printable wall thickness, feature inclination angles, borehole precision, overhang stability, and achievable resolution of horizontal and vertical gaps. After fabrication, the as-built (green-state) components undergo a two-step thermal post-processing treatment involving binder removal (debinding), followed by sintering at elevated temperatures to reach densification. Geometric accuracy was quantitatively assessed through a 3D scan by comparing the manufactured parts to their original CAD models, allowing the identification of deformation patterns and shrinkage rates. Finally, the practical utility of the developed guidelines was demonstrated by successfully manufacturing an impeller designed according to the established geometric constraints. These design guidelines apply specifically to the machine and filament type utilized in this study.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta3020014

Authors: Santosh Kr. Mishra Gyan Wrat Prabhat Ranjan Joseph T. Jose Jayanta Das

Hydrostatic transmissions are essential in applications demanding variable torque and speed, such as mining and agricultural machinery, due to their compact design, high power-to-weight ratio, and efficient variable speed control. Despite these advantages, their inherent nonlinearities and susceptibility to parametric uncertainties pose significant challenges for precise motion control. This study presents a comparative analysis of classical PID and robust H-infinity controllers for regulating the speed of hydraulic motors under varying torsional loads. A linearized uncertain system model is developed using upper Linear Fractional Transformations (LFTs) to capture key parametric uncertainties. A simplified H-infinity controller is designed to robustly manage system dynamics, particularly addressing phase lags induced by uncertain loads. Simulation results demonstrate that the H-infinity controller offers superior performance over the PID controller in terms of stability, disturbance rejection, and robustness to load fluctuations. This work contributes a practically viable robust control solution for improving the reliability and precision of electro-hydraulic systems, particularly in demanding, real-world environments.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta3020013

Authors: Julian De Los Rios Sinniah Ilanko Yusuke Mochida David Kennedy

The Dynamic Stiffness Matrix (DSM) of a structure is a frequency-dependent stiffness matrix relating the actions (forces and moments) and displacements (translations and rotations) when the structure vibrates at a given frequency. The DSM may be used to find the natural frequencies, modes, and structural response. For many structures, including skeletal frames of prismatic members, exact transcendental expressions for the DSM are readily available. This paper presents a mathematical proof of a linear determinantal relationship between the DSM of a skeletal frame when it is undamaged, cracked, and hinged at the crack location. The rotational stiffness or flexibility of the crack also appears as a linear term. This relationship gives, for the first time, an explicit equation to directly calculate the stiffness of the rotational spring representing a crack from measured natural frequencies for any potential crack location. Numerical examples demonstrate that computing the DSM of the intact and hinged structures gives an efficient solution method for the inverse problem of identifying crack location and severity. This paper also shows that an approximate DSM based on a finite element model can be used in the same way, making this procedure more versatile. Furthermore, new approximate expressions for the natural frequencies of structures with very small or very severe cracks are derived. An interesting relationship between the square of the bending moment in an undamaged beam and the determinant of the DSM of a hinged beam is also derived. This relationship, which can also be inferred from previous work, leads to a better understanding of the effect of crack location in specific vibration modes.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta3020012

Authors: Karim Asami Sebastian Roth Jan Hünting Tim Röver Claus Emmelmann

Additive manufacturing (AM) technologies have witnessed remarkable advancements, offering opportunities to produce complex components across various industries. This paper explores the potential of AM for fabricating bipolar plates (BPPs) in fuel cell or electrolysis cell applications. BPPs play a critical role in the performance and efficiency of such cells, and conventional manufacturing methods often face limitations, particularly concerning the complexity and customization of geometries. The focus here lies in two specific AM methods: the laser powder bed fusion of metals (PBF-LB/M) and material extrusion of metals (MEX/M). PBF-LB/M, tailored for high-performance applications, enables the creation of highly complex geometries, albeit at increased costs. On the other hand, MEX/M excels in rapid prototyping, facilitating the swift production of diverse geometries for real-world testing. This approach can facilitate the evaluation of geometries suitable for mass production via sinter-based manufacturing processes. The geometric deviations of different BPPs were identified by evaluating 3D scans. The PBF-LB/M method is more suitable for small features, while the MEX/M method has lower deviations for geometrically less complex BPPs. Through this investigation, the limits of the capabilities of these AM methods became clear, knowledge that can potentially enhance the design and production of BPPs, revolutionizing the energy conversion and storage landscape and contributing to the design of additive manufacturing technologies.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta3020011

Authors: Gustavo Koury Costa

This paper presents a new computational library for pneumatic circuits, written in the specialized circuit-oriented language “Modelica”, and executed within an open-source IDE, “OpenModelica”, freely available for downloading on the Internet. The library focuses on the problem of energy efficiency and energy savings (two different concepts, that we intend to clarify in the text). The idea is to use the Modelica scripts to simulate typical circuits, known by their energy-efficient designs. We reason that air throttling within valves is one of the great challenges when it comes to energy losses. Also, we argue that compressed air reuse can be seen as a means of increasing efficiency, basically through replacing air throttling with counter-pressure velocity control. A simplified version of the developed Modelica library is made available to the reader in the Appendix A, to be used with new scripts and adapted to different realities. In our view, in many situations, open-code Modelica programs may constitute an alternative to proprietary software, where the mathematical models of components are mostly hidden from the end user. Theoretical experiments are carried out, focusing on energy management. The results show that the Modelica library hereby presented is solid, with great prospects of future development. They also show that energy efficiency in pneumatic circuits, at times, comes with the cost of poorly controlled velocity and pressure at the actuator, which requires a careful analysis by the designer, before an actual implementation.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta3010010

Authors: Patrick Brag Yvonne Holzapfel Marcel Daumüller Ralf Grimme Uwe Mai Tobias Iseringhausen

Pillar-to-pillar dashboards have become common in modern electric vehicles. These dashboards are made of liquid crystal displays (LCDs), of which backlight units (BLUs) are an integral part. Particulate contamination inside BLUs can lead to either an aesthetic or functional failure and is in consequence a part of quality control. Automatic optical inspection (AOI) was used to detect particulate matter to enable a process chain analysis to be carried out. The investigation showed that a high percentage of all contaminants originated from the assembly of the edge/side lightguide. The implementation of an additional cleaning process was the favored countermeasure to reduce the contaminants. The objective (cleanliness requirement) was to remove all contaminants larger than 100 µm from the lightguide with contactless (non-destructive) cleaning methods. The preferred cleaning methods of choice were compressed air and CO2 snow jet cleaning. This work investigates the cleaning efficacy of both cleaning methods under consideration of the following impact factors: distance, orientation (inclination) and speed. The central question of this paper was as follows: would cleaning with compressed air be sufficient to meet the cleanliness requirements? In order to answer this question, a cleaning validation was carried out, based on a Box–Behnken design of experiments (DoE). To do so, representative test contaminants had to be selected in step one, followed by the selection of an appropriate measurement technology to be able to count the contaminants on the lightguide. In the third step, a test rig had to be designed and built to finally carry out the experiments. The data revealed that CO2 was able to achieve a cleaning efficacy of 100% in five of the experiments, while the best cleaning efficacy of compressed air was 89.87%. The cleaning efficacy of compressed air could be improved by a parameter optimization to 94.19%. In contrast, a 100% cleaning efficacy is achievable with CO2 after parameter optimization, which is what is needed to meet the cleanliness requirements.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta3010009

Authors: Amir Javidinejad

The vibration response of a component, particularly the frequency response of the component, can be used in the determination of the loss factor damping, η, due to energy dissipation and the elastic modulus (E). The ASTM E756-04 standard provides the methodology and the guidance for the determination of the loss factor damping and elastic modulus experimentally. This standard specifically calls for the use of a beam with a rectangular cross-section. Also, the theoretical formulation developed there is based on such a beam cross-section. Here, in this paper, the theoretical formulation and numerical simulation for determining the loss factor damping and elastic modulus are a derivation of the methodology used in the ASTM standard and other R&D work, but for a circular plate configuration. The delta change derivation, both theoretically and numerically, is proven to be accurate and validated here. This method is useful in the characterization of materials that have applications in structural vibration, aerospace subcomponents, micro and mini sensory devices, medical devices, and many other areas. Similar to the ASTM standard, the materials could include metals, ceramics, rubbers, plastics, reinforced epoxy matrices, composites, and woods. This paper mainly formulates the technique via numerical and computational methods. It is the intention of the author to also, as a future research agenda, experimentally produce data that can be correlated with this theoretical and numerical methodology.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta3010008

Authors: Faras Brumand-Poor Tim Kotte Abdulaziz Hanifa Christian Reese Marius Hofmeister Katharina Schmitz

Precise flow measurement is crucial in fluid power systems. Especially in combination with pressure, hydraulic power can be particularly beneficial for predictive maintenance and control applications. However, conventional flow sensors in fluid power systems are often invasive, thus disrupting the flow and yielding unreliable measurements, especially under transient conditions. A common alternative is to estimate the flow rate using pressure differentials along a pipe and the Hagen–Poiseuille law, which is limited to steady, laminar, and incompressible flows. This study advances a previously introduced analytical soft sensor, demonstrating its ability to accurately determine the transient pipe flow beyond laminar conditions, without requiring a dedicated flow rate sensor. This method provides a robust and computationally efficient solution for real-world hydraulic systems by applying two pressure transducers. A key contribution of this work is the investigation of signal filtering, revealing that even a simple first-order low-pass filter with a 100 Hz cutoff frequency significantly improves accuracy, which is demonstrated for pulsation frequencies of 5, 10, and 15 Hz, where the filtered results closely match experimental data from a test rig. These findings underscore the soft sensor’s potential as a reliable alternative to traditional flow sensors, offering high accuracy with minimal computational overhead for a wide range of flow conditions.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta3010007

Authors: Marco Rossi

The landscape of experimental and theoretical analysis is evolving rapidly, driven by advancements in computational methods, data analytics, and artificial intelligence (AI) [...]

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta3010006

Authors: Rodrigo Mateo-Reyes Irving A. Cruz-Albarran Luis A. Morales-Hernandez

Stress is a natural response of the organism to challenging situations, but its accurate detection is challenging due to its subjective nature. This study proposes a model based on depth-separable convolutional neural networks (DSCNN) to analyze heart rate variability (HRV) and detect stress. Electrocardiogram (ECG) signals are pre-processed to remove noise and ensure data quality. The signals are then transformed into two-dimensional images using the continuous wavelet transform (CWT) to identify pattern recognition in the time–frequency domain. These representations are classified using the DSCNN model to determine the presence of stress. The methodology has been validated using the SWELL-KW dataset, achieving an accuracy of 99.9% by analyzing the variability in three states (neutral, time pressure, and interruptions) of the 25 samples in the experiment, scanning the acquired signal every 5 s for 45 min per state. The proposed approach is characterized by its ability to transform ECG signals into time–frequency representations by means of short duration sampling, achieving an accurate classification of stress states without the need for complex feature extraction processes. This model is an efficient and accurate tool for stress analysis from biomedical signals.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta3010005

Authors: Faras Brumand-Poor Tim Kotte Marwin Schüpfer Felix Figge Katharina Schmitz

Accurate flow measurement is critical for hydraulic systems because it represents a crucial parameter in the control of fluid power systems and enables the calculation of hydraulic power when combined with pressure data, which is valuable for applications such as predictive maintenance. Existing flow sensors in fluid power systems typically operate invasively, disturbing the flow and providing inaccurate results, especially under transient conditions. A conventional method involves calculating the flow rate using the pressure difference along a pipe via the Hagen–Poiseuille law, which is limited to steady, laminar, incompressible flow. This paper presents a novel soft sensor with an analytical model for transient pipe flow based on two pressure signals, thus eliminating the need for an actual volumetric flow sensor. The soft sensor was derived in previous research and validated with a distributed parameter simulation. This work uses a constructed test rig to validate the soft sensor with real-world experiments. The results highlight the potential of the soft sensor to accurately and computationally efficiently measure transient pipe volumetric flow based on two pressure signals.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta3010004

Authors: Thibaud Schaller Jun Li Karl W. Jenkins

Regular aircraft engine inspections play a crucial role in aviation safety. However, traditional inspections are often performed manually, relying heavily on the judgment and experience of operators. This paper presents a data-driven deep learning framework capable of automatically detecting defects on reactor blades. Specifically, this study develops Deep Neural Network models to detect defects in borescope images using various datasets, based on Computer Vision and YOLOv8n object detection techniques. Firstly, reactor blade images are collected from public resources and then annotated and preprocessed into different groups based on Computer Vision techniques. In addition, synthetic images are generated using Deep Convolutional Generative Adversarial Networks and a manual data augmentation approach by randomly pasting defects onto reactor blade images. YOLOv8n-based deep learning models are subsequently fine-tuned and trained on these dataset groups. The results indicate that the model trained on wide-shot blade images performs better overall at detecting defects on blades compared to the model trained on zoomed-in images. The comparison of multiple models’ results reveals inherent uncertainties in model performance that while some models trained on data enhanced by Computer Vision techniques may appear more reliable in some types of defect detection, the relationship between these techniques and subsequent results cannot be generalized. The impact of epochs and optimizers on the model’s performance indicates that incorporating rotated images and selecting an appropriate optimizer are key factors for effective model training. Furthermore, models trained solely on artificially generated images from collages perform poorly at detecting defects in real images. A potential solution is to train the model on both synthetic and real images. Future work will focus on improving the framework’s performance and conducting a more comprehensive uncertainty analysis by utilizing larger and more diverse datasets, supported by enhanced computational power.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta3010003

Authors: Vitor Eduardo Motta Gabrielle Ücker Thum Rafael Adriano Alves Camargo Gonçalves Luiz Alberto Oliveira Rocha Elizaldo Domingues dos Santos Bianca Neves Machado Liércio André Isoldi

The climate crisis represents one of the greatest contemporary global challenges, requiring actions to mitigate its impacts and sustainable solutions to meet the growing demands for clean energy and coastal protection. Therefore, the study of devices such as the submerged plate (SP), which simultaneously acts as a breakwater (BW) and wave energy converter (WEC), is especially relevant. In this context, the present numerical study compares the efficiency of an SP device under regular waves across different geometric configurations considering inclination angles. To achieve this, a horizontal SP was adopted as a reference. Its thickness and total material volume were kept constant while ten alternative geometries, each with a different inclination for the SP, were proposed and investigated. The computational domain was modeled as a full-scale regular wave channel with each SP positioned below the free surface. The volume of fluid (VOF) multiphase model was employed to represent the interaction between water and air. The finite volume method (FVM) was applied to solve the transport equations for volume fraction, momentum, and mass. The SP’s efficiency as a BW was evaluated by assessing the free surface elevation upstream and downstream of the SP, while its efficiency as a WEC was measured by evaluating the axial velocity below the SP. Results indicated that the efficiency of the SP can vary significantly depending on its inclination, with the optimal case at θ = 15° showing improvements of 11.95% and 16.59%, respectively, as BW and WEC.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta3010002

Authors: Majid Gholipour Samad Narimani Seyed Morteza Davarpanah Balázs Vásárhelyi

Tunneling in loose soil and urban areas presents numerous challenges. One effective solution is the use of Earth Pressure Balance Shields (EPBSs). Maintaining the correct balance of pressure at the tunnel face is critical, as applying too little pressure can cause a collapse, while excessive pressure may result in a blow-out. Therefore, a key aspect of using EPBSs in urban environments is determining the optimal pressure required to stabilize the tunnel face, taking into account the existing soil in the excavation chamber and controlling the screw conveyor’s rotation rate. This study focuses on a section of the second line of the Tabriz subway to evaluate the minimum pressure needed for tunnel face stability using empirical, analytical, and numerical approaches. The analytical methods involve evaluating the limit equilibrium of forces and considering soil buckling due to overburden, while the numerical methods employ 3D finite element analysis. Additionally, a sensitivity analysis of the parameters affecting the required pressure was conducted and compared across the three approaches. The results revealed that the formation of a pressure arch mitigates the full impact of overburden pressure on the tunnel face. For soil cohesion values below 20 kPa, the numerical results aligned well with the empirical and analytical findings. For a tunnel depth of 22.5 m and a water table 2 m below the surface, the estimated minimum pressure ranged from 150 to 180 kPa. Moreover, the analytical methods were deemed more suitable for determining the required support pressure at the tunnel face. These methods considered wedge and semi-circular mechanisms as the most probable failure modes. Also, for cohesive ground, the pressure from the finite element analysis was found to be almost always equal to or greater than the values obtained with the analytical solutions.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta3010001

Authors: Rajmund Kuti Petr Tánczos Zoltán Tánczos Tamás Stadler Csenge Papp

Nowadays, electromobility has a significant role in transportation; different electrically driven vehicles are spreading continuously. Due to this form of drivetrain, fire safety hazards have also changed when compared to those of conventional vehicles. Lately, electric vehicle fires have become more common; thus, we have chosen to investigate the negative impacts of these fires on humans and the environment, in addition to the toxic properties of the resulting combustion products. In our research work, we conducted a full-scale fire experiment on an electric passenger car. Fire extinguishing was executed with fire-fighting foam, and its efficiency was examined. After extinguishing the fire, we took samples from the combustion gases and soil. Samples were subjected to laboratory investigations. Our results and experiences are presented in this article.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta2040012

Authors: Felix Müller Patrick Wingertszahn Oliver Koch Bernd Sauer

The accurate design of tribological contacts, such as those in bearings and gearboxes, makes them highly efficient and helps reduce emission in all driven systems. Traditionally, this process requires more lubricant data than data sheets typically provide, mainly kinematic viscosity at 40 °C and 100 °C and density, which limits the design process. This study introduces a simplified methodology for determining lubricant film thickness, one of the main design critical parameters, using minimal viscosity measurements obtained with a high-pressure viscometer. The researchers demonstrate that essential lubricant parameters can be derived effectively from a few measurements. By combining state-of-the-art models for film thickness with practical measurements from an EHL tribometer, this study confirms that reliable film thickness predictions can be made from basic viscosity data. This approach streamlines the design process, making tribological simulations more accessible and cost-effective, and enhances the design of tribological contacts under extreme conditions.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta2040011

Authors: Mirza M. Shah

Many applications involve condensation in annuli; therefore, accurate prediction of heat transfer is important. While there have been a large number of experimental studies on condensation in tubes and several well-verified correlations are available for them, there have been very few experimental studies on annuli, and no well-verified correlation is available for prediction of heat transfer during condensation in annuli. This research was done to identify reliable correlations for this purpose and to develop a new one if needed. Literature was surveyed to identify experimental studies, test data, and predictive methods. Test data was compared to general correlations which have had considerable verification with data for condensation in channels. None of them was found fully satisfactory. A new correlation was developed by modifying the present author’s published correlation for condensation in tubes. It gives a MAD of 19.2% with available data from eight sources. Deviations of other correlations were much higher. The occurrence of surface tension effects and mini/macro channel boundary are investigated. The results of this research are presented and discussed.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta2040010

Authors: Hussein Kokash Catherine Mavriplis Gbemeho Gilou Agbaglah

The flow characteristics of a 30P30N three-element high-lift airfoil at low Reynolds numbers O104 are examined through three-dimensional simulations using a high-order spectral element method. This study primarily investigates the flow structures of the slat cove and Görtler vortices formed on the upper surface of the main airfoil. Spanwise instability grows exponentially in the slat cove with a constant wavelength, corresponding to that of the subsequently formed Görtler vortices. Görtler number calculations show that curvature-induced centrifugal instability at the slat cusp leads to the subsequent formation of Görtler vortices. Proper orthogonal decomposition (POD) is used to analyze the development of flow structures in the slat cove in different time ranges. At early time, the flow in the slat cove is dominated by shear layers that evolve into spanwise perturbations. These perturbations further evolve into distinct bell-shaped structures close to the slat cusp and are advected to the upper surface of the main airfoil, leading to the formation of Görtler vortices.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta2040009

Authors: István Sztankovics

The present challenges in the automotive industry require the development and practical implication of novel machining procedures, which will provide appropriate solutions. These procedures should still meet the requirements of productivity, surface quality and energy efficiency. The further development of novel machining procedures introduces new problems that did not occur (or occurred to a lesser extent) with traditionally applied procedures. Rotational turning has come to the attention of production engineers in the previous decade since it can be used to machine ground-like surfaces in an ecologically friendly and highly productive manner. However, the chip removal characteristic is slightly different from traditional turning due to the applied special kinematic relation and complex tool edge geometry. The run-in phase will take longer, which is the time period between the first contact of the tool and the formation of a constant chip cross-sectional area. The clarification of the chip formation is important in any machining procedure. To achieve this goal, the geometric parameters of the chip must be determined. Since the start of the chip removal is a crucial stage in rotational turning due to its length, the chip height, chip width and the cross-sectional area of the chip should be separately defined in the initial stage. Therefore, in this paper, the initial phase of chip removal in rotational turning is studied. The increasing cross-sectional area of the chip is determined analytically by the application of the previously elaborated equation of the cut surface. Calculating formulas are defined for the different stages of the start of the chip removal, which could be used in the forthcoming studies to analyze the chip formation. The effects of different determining parameters are analyzed theoretically by the deduced formulas of the run-in phase and practical experiments are also carried out. The analytical and experimental analyses showed that increasing feed also increases the dynamic load on the cutting edge, while the depth of cut lowers the growth of the characteristic parameters of the chip, which results in a lower dynamic load on the tool.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta2040008

Authors: Qingyuan Dong Haiyin Gang Jinxiao Xu Zuxiang Li Zhongxiang Wang

With the rapid development of new energy vehicles and the digital electronics industry, the demand for lithium has surged, necessitating advanced lithium extraction technologies. Electrochemical methods, noted for their high selectivity and efficiency in extracting target ions from liquid sources in an environmentally friendly manner, have become increasingly vital. These methods are versatile, applicable in scenarios such as lithium extraction from saline lakes, mother liquor separation, and lithium enrichment. They include electrochemical deintercalation, electrochemical ion pumps, and electrodialysis, each offering unique benefits and challenges depending on the application context. This review provides a detailed exploration of the research progress in lithium extraction using electrochemical methods and discusses future prospects for these technologies, emphasizing their potential to meet the growing demand for lithium.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta2040007

Authors: Viktoria Ferencsik

Characterization of surface integrity is possible with three critical metrics: microstructure, surface roughness, and residual stress. The latter two are discussed in this paper for low-alloyed aluminum material quality. Ball burnishing is a regularly used finishing procedure to improve surface roughness, shape accuracy, and fatigue life, taking advantage of the fact that it can favorably influence the variation in stress conditions in the material. The effect of burnishing is investigated using finite element simulation with DEFORM 2D software using the real surface roughness of the workpiece. The FEM model of the process is validated with experimental tests, the surface roughness is measured using an AltiSurf520 measuring device, and the residual stress is analyzed with a Stresstech Xstress 3000 G3R X-ray diffraction system (Stresstech, Vaajakoski, Finland). The results indicate that the burnishing process improves the surface roughness and stress conditions of AlCu6BiPb low-alloyed aluminum, and the study shows that there is good agreement between the FE and experimental results, further revealing the effect of the process parameters on the distribution of the compressive residual stress.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta2030006

Authors: Mostafa Kandil Tamer A. El-Sayed Ahmed M. Kamal

Water hammer (WH) is a critical phenomenon in fluid-filled piping systems that can lead to severe pressure surges and structural damage. The characteristics of the pipe material, geometry, and support conditions play a crucial role in the fluid–structure interaction (FSI) during WH events. This study investigates the impact of various pipe parameters, including material, length, thickness, and diameter, on the WH behavior using an FSI-based numerical approach. A comprehensive computational model was developed based on the algorithm presented in Delft Hydraulics Benchmark Problem (A) to simulate the WH phenomenon in pipes made of different materials, such as steel, copper, ductile iron, PPR (polypropylene random copolymer), and GRP (glass-reinforced plastic). This study examines the influence of pipe parameters on WH performance in pipelines, utilizing FSI to analyze the phenomenon. The results show that the pipe material has a significant influence on the pressure wave speed, stress wave propagation, and the overall system response during WH. Pipes with lower modulus of elasticity, such as PPR and GRP, exhibit lower pressure wave speeds but higher stress wave speeds compared with steel pipes. Increasing the elastic modulus, pipe wall thickness, length, and diameter enhances the pipe’s stiffness and impacts the timing, magnitude of pressure surges, and the likelihood of cavitation. The findings of this study provide valuable insights into the design and mitigation of WH in piping systems.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta2020005

Authors: Barry K. Lavine Karl S. Booksh Sharon L. Neal

For most of the twentieth century, chemistry has been a data-poor discipline relying on well-thought-out hypotheses and carefully planned experiments to develop solutions to real-world problems [...]

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta2020004

Authors: Ahmad Kouta Tomáš Vít Petra Dančová

In this paper, 2D simulations were carried out to prove the potential of thermoacoustic technology in separating a binary gas mixture. A 2D model of a gas mixture separator was developed, including a loudspeaker responsible for producing acoustic waves in the separation pipe. As a result of the imposed sound waves propagating inside the separator, main parameters including pressure, temperature, and density undergo oscillations, which in turn drive the light and heavy gas components in opposite directions. Through time, one end of the separator is enriched with the light component while the other end is enriched with the heavy one. Simulations were all performed using ANSYS Fluent. The aim was to separate an ideal gas mixture of Helium–Argon and study the impact of different parameters on the separation process.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta2010003

Authors: Gargi Shankar Nayak Heinz Palkowski Adele Carradò

The demand for innovative materials has been a significant driving force in material development in a variety of industries, including automotive, structural, and biomedical. Even though a tremendous amount of research has already been conducted on metallic, polymeric, and ceramic materials, they all have distinct drawbacks when used as mono-materials. This gave rise to the development of nature-inspired sandwich-structured composite materials. The combination of strong metallic skins with soft polymeric cores provides several advantages over mono-materials in terms of weight, damping, and mechanical property tuning. With this in mind, this review focuses on the various aspects of MPM SMs (Metal/polymer/metal Sandwich Materials). The reasons for the improved qualities of MPM SMs have been discussed, as well as the numerous approaches to producing such SMs. This review shows the various possibilities of achieving such SMs in complicated forms via different shaping techniques and intends to highlight the properties of MPM SMs’ remarkable qualities, the current trend in this field, and their potential to meet the demands of many industries.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta2010002

Authors: Marco Rossi

Six months ago (September 2023), we began the journey of publishing a new and unique Open Access journal dedicated to publishing papers on the methods and applications of analysis science in both experimental and theoretical aspects in the more relevant fields of engineering, with a focus on its hottest specialized areas [...]

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta2010001

Authors: Jian Feng Marion Herrmann Anne-Maria Reinecke Antonio Hurtado

The pursuit of reliable energy devices sealing solutions stands as a paramount engineering challenge for ensuring energy safety and dependability. This review focuses on an examination of recent scientific publications, primarily within the last decade, with a central aim to grasp and apply critical concepts relevant to the efficient design and specification of brazements for ceramic–metal active-brazed assemblies, emphasizing the sealing of energy devices. The goal is to establish robust and enduring joints capable of withstanding water-vapor and hydrogen environments. The review commences with a concise recapitulation of the fundamental principles of active brazing, followed by an in-depth exploration of material selection, illustrated using water-vapor-resistant sensors as illustrative examples. Furthermore, the review presents practical solutions for the sealing of energy devices while also scrutinizing the factors that exert significant influence on the deterioration of these active-brazed connections. Ultimately, the review culminates in a comprehensive discussion of emerging trends and developments in active brazing techniques for energy-related applications.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta1020007

Authors: Matthew J. Sydor Monica A. Serban

Time-resolved fluorescence anisotropy has been extensively used to detect changes in bimolecular rotation associated with viscosity levels within cells and other solutions. Physiological alterations of the viscosity of biological fluids have been associated with numerous pathological causes. This current work serves as proof of concept for a method to measure viscosity changes in small analyte volumes representative of biological fluids. The fluorophores used in this study were fluorescein disodium salt and Enhanced Green Fluorescent Protein (EGFP). To assess the ability of the method to accurately detect viscosity values in small volume samples, we conducted measurements with 12 µL and 100 µL samples. No statistically significant changes in determined viscosities were recorded as a function of sample volume for either fluorescent probe. The anisotropy of both fluorescence probes was measured in low viscosity standards ranging from 1.02 to 1.31 cP, representative of physiological fluid values, and showed increasing rotational correlation times in response to increasing viscosity. We also showed that smaller fluid volumes can be diluted to accommodate available cuvette volume requirements without a loss in the accuracy of detecting discrete viscosity variations. Moreover, the ability of this technique to detect subtle viscosity changes in complex fluids similar to physiological ones was assessed by using fetal bovine serum (FBS) containing samples. The presence of FBS in the analytes did not alter the viscosity specific rotational correlation time of EGFP, indicating that this probe does not interact with the tested analyte components and is able to accurately reflect sample viscosity. We also showed that freeze–thaw cycles, reflective of the temperature-dependent processes that biological samples of interest could undergo from the time of collection to analyses, did not impact the viscosity measurements’ accuracy. Overall, our data highlight the feasibility of using time-resolved fluorescence anisotropy for precise viscosity measurements in biological samples. This finding is relevant as it could potentially expand the use of this technique for in vitro diagnostic systems.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta1020006

Authors: Flavio Cognigni Lucia Miraglia Silvia Contessi Francesco Biancardi Marco Rossi

Correlative light and electron microscopy (CLEM) is an advanced imaging approach that faces critical challenges in the analysis of both materials and biological specimens. CLEM integrates the strengths of both light and electron microscopy, in a hardware and software correlative environment, to produce a composite image that combines the high resolution of the electron microscope with the large field of view of the light microscope. It enables a more comprehensive understanding of a sample’s microstructure, texture, morphology, and elemental distribution, thereby facilitating the interpretation of its properties and characteristics. CLEM has diverse applications in the geoscience field, including mineralogy, petrography, and geochemistry. Despite its many advantages, CLEM has some limitations that need to be considered. One of its major limitations is the complexity of the imaging process. CLEM requires specialized equipment and expertise, and it can be challenging to obtain high-quality images that are suitable for analysis. In this study, we present a CLEM workflow based on an innovative sample holder design specially dedicated to the examination of thin sections and three-dimensional samples, with a particular emphasis on geosciences.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta1020005

Authors: Emre Firat Özel Dennis Pede Claas Müller Yi Thomann Ralf Thomann Hadi Mozaffari-Jovein

In this study, the microstructural properties of selective laser melted 316L stainless steel were investigated using optical, scanning and transmission electron microscopy as well as X-ray diffraction (XRD) and energy dispersive X-ray spectroscopy. The results show a very fine microstructure with visible melt pool boundaries and austenite as the predominant phase. Extremely fine sub-grain structures can be found within the grains, consisting of colonies of round or elongated cellular structures depending on orientations. Due to the prevailing cooling and solidification conditions, micro-segregations occur, leading to enrichment of the sub-grain boundaries with alloying elements such as silicon, chromium, manganese and molybdenum. The presence of ferrite could be detected in this area using TEM analysis.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta1010004

Authors: Mou Adhikari Rola Houhou Julian Hniopek Thomas Bocklitz

Fluorescence lifetime imaging microscopy (FLIM) has emerged as a promising tool for all scientific studies in recent years. However, the utilization of FLIM data requires complex data modeling techniques, such as curve-fitting procedures. These conventional curve-fitting procedures are not only computationally intensive but also time-consuming. To address this limitation, machine learning (ML), particularly deep learning (DL), can be employed. This review aims to focus on the ML and DL methods for FLIM data analysis. Subsequently, ML and DL strategies for evaluating FLIM data are discussed, consisting of preprocessing, data modeling, and inverse modeling. Additionally, the advantages of the reviewed methods are deliberated alongside future implications. Furthermore, several freely available software packages for analyzing the FLIM data are highlighted.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta1010003

Authors: Ana Paula Giussani Mocellin Rafael Pereira Maciel Phelype Haron Oleinik Elizaldo Domingues dos Santos Luiz Alberto Oliveira Rocha Juliana Sartori Ziebell Liércio André Isoldi Bianca Neves Machado

Given the increasing global energy demand, the present study aimed to analyze the influence of bathymetry on the generation and propagation of realistic irregular waves and to geometrically optimize a wave energy converter (WEC) device of the oscillating water column (OWC) type. In essence, the OWC WEC can be defined as a partially submerged structure that is open to the sea below the free water surface (hydropneumatic chamber) and connected to a duct that is open to the atmosphere (in which the turbine is installed); its operational principle is based on the compression and decompression of air inside the hydropneumatic chamber due to incident waves, which causes an alternating air flow that drives the turbine and enables electricity generation. The computational fluid dynamics software package Fluent was used to numerically reproduce the OWC WEC according to its operational principles, with a simplification that allowed its available power to be determined, i.e., without considering the turbine. The volume of fluid (VOF) multiphase model was employed to treat the interface between the phases. The WaveMIMO methodology was used to generate realistic irregular waves mimicking those that occur on the coast of Tramandaí, Rio Grande do Sul, Brazil. The constructal design method, along with an exhaustive search technique, was employed. The degree of freedom H1/L (the ratio between the height and length of the hydropneumatic chamber of the OWC) was varied to maximize the available power in the device. The results showed that realistic irregular waves were adequately generated within both wave channels, with and without bathymetry, and that wave propagation in both computational domains was not significantly influenced by the wave channel bathymetry. Regarding the geometric evaluation, the optimal geometry found, H1/Lo = 0.1985, which maximized the available hydropneumatic power, i.e., the one that yielded a power of 25.44 W, was 2.28 times more efficient than the worst case found, which had H1/L = 2.2789.

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta1010002

Authors: Marco Rossi

As Editor-in-Chief, I am pleased to introduce Journal of Experimental and Theoretical Analyses—JETA [...]

Journal of Experimental and Theoretical Analyses doi: 10.3390/jeta1010001

Authors: Rania Mehdi Saadia Zrira Rossella Vadalà Vincenzo Nava Concetta Condurso Nicola Cicero Rosaria Costa

Background: This work aimed to perform a comprehensive investigation of organic Moroccan honeys obtained from plants of euphorbia, arbutus, and carob, based on the determination of physico-chemical profiles and volatile fingerprints. Methods: The selected analytical approach involved different techniques, including physico-chemical procedures for determination of humidity, acidity, diastase activity; solid-phase microextraction (SPME) coupled to GC-MS for aromatic fraction exploration; and ICP-MS for multi-element analysis. Results: The results obtained from the physico-chemical analyses were highly comparable to those of other commercial honeys. In 50% of samples investigated, the diastase number was just above the legal limit fixed by Honey Quality Standards. The analysis of the volatile fraction highlighted the presence of numerous compounds from the terpenoid group along with characteristic molecules such as furfural, isophorone, and derivatives. In most cases, VOCs were distinct markers of origin; in others, it was not possible to assess an exclusive source for bees to produce honey. Conclusion: The results contributed to place the three varieties of honey investigated among the commercial products available in the market. Many variables determined returned positive indications about quality and safety of these special honeys.