J. Exp. Theor. Anal., Volume 3, Issue 4 (December 2025) – 14 articles

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. Full article

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 $\varphi V_u$, 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 ${\eta }_m$ 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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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 (NO_x), and sulphur oxides (SO_x), 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. Full article

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 $15\mathrm{degrees}$ 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 $10.58\mathrm{degrees}$ in the task of classifying 24 distinct head orientations. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article