Search Results (762)

This paper presents a probabilistic sensitivity analysis of a nonlinear electrochemical model for lithium-ion batteries. The model is treated as a reduced virtual replica for uncertainty-aware analysis rather than as a full digital twin. A reduced electrochemical formulation is combined with constrained inverse parameter identification using experimental current–voltage data to relate observable battery behavior to effective model parameters. Predictive variability is assessed through Monte Carlo uncertainty propagation and global sensitivity analysis under both charging and discharging conditions. The results indicate that the particle radius of the positive active material and the effective electrodes area are the dominant contributors to terminal-voltage uncertainty, whereas the electrode thickness parameter and negative electrode active material particle radius have a moderate influence within the studied ranges. Rank-based and variance-based sensitivity measures are more informative than linear indices for this reduced nonlinear system. From a mathematical perspective, the work integrates reduced-order modeling, inverse problem formulation, numerical simulation, and uncertainty quantification in one computational framework for battery analysis. The results support uncertainty-aware parameter prioritization, calibration of reduced electrochemical models, and provide a basis for future work on battery design, control, and digital-twin-oriented extensions under uncertainty. Full article

Five modifications of polytetrafluoroethylene (PTFE) are considered as a modern alternative to PTFE as sliding layers of bridge bearing parts. Radiation-modified PTFE without additives and with nano-additives as well as composites based on PTFE with bronze inclusions and nanomodified carbon fiber fillers were investigated. Ultra-high-molecular-weight polyethylene (UHMWPE) and classic pure PTFE were considered as control samples. The thermomechanical properties of the materials were studied within the framework of dynamic mechanical analysis in the operating temperature range of bridge structures [−40; +80] °C. The exit zones from the linear theory of viscoelasticity were established for all the materials considered. Temperature dependencies of the storage modulus and the loss modulus were determined. Thermoviscoelastic models of material behavior were constructed using a numerical identification procedure, experimental data, and simulation models. The thermomechanics of materials during the deformation of the spherical support part of the bridge were analyzed. Temperature dependencies of the parameters of the contact stress-strain state were determined with an average coefficient of determination R² = 0.97 and an average error size RMSE = 0.092. Full article

This study presents a simulation-assisted engineering framework intended to support comparative machining parameter selection for quartz sintered materials. The approach integrates CAD/CAM-based analysis, an illustrative Design of Experiments (DOE) framework, and preliminary experimental validation to improve process planning and machining quality. The analysis focuses on key technological parameters, including cutting speed (vc), feed rate (f), and depth of cut (ap), evaluated across cutting, milling, and finishing stages. The results indicate that feed rate is the dominant parameter influencing process stability, surface quality, and edge integrity. A practical transition region of approximately 1200 mm/min was identified, above which increased vibration, defect formation, and surface degradation occur. The complementary DOE analysis confirms the relative importance of process parameters and reveals interaction effects, particularly between feed rate and depth of cut, which significantly influence defect formation under high-load conditions. Preliminary industrial observations provide trend-oriented support for the simulation-predicted process behavior. Based on the integrated analysis, a preliminary technological operating region was identified (vc = 1080–1320 m/min, f = 800–1200 mm/min, ap = 0.5–1.0 mm), suggesting a practical compromise between machining efficiency and surface integrity. The proposed methodology provides preliminary engineering support for comparative process planning and defect-reduction-oriented parameter selection in the machining of brittle materials. The novelty of this work lies in the integration of CAD/CAM simulation, DOE-based interaction analysis, and experimental validation for supporting the identification of a practical technological operating region for machining brittle materials. The presented results should therefore be interpreted as engineering-oriented comparative process-planning guidelines rather than statistically generalized machining laws. The presented study should be interpreted as an exploratory simulation-assisted engineering investigation intended to support comparative process planning rather than as a fully experimentally validated machining model. Full article

We develop an analytical framework to describe the impact of in-plane strain on the electronic and transport properties of graphene. Starting from a strain-modified nearest-neighbor tight-binding model, we derive the energy spectrum and group velocities, explicitly incorporating bond-dependent hopping renormalization. A dimensionless anisotropy parameter, derived from velocity fluctuations, is introduced to quantify directional transport imbalance. We show that this parameter admits a closed-form expression entirely determined by the strain tensor, linking lattice deformation directly to measurable transport quantities. In the small-strain regime, a compact expression is obtained, $\eta \approx ϵ\left(1+\nu \right)\cos \left(2\theta \right)$, revealing an angular dependence controlled solely by the orientation of the applied deformation. This establishes that strain acts as a purely geometric control parameter, separating magnitude and orientation effects. Within the semiclassical Boltzmann framework, the same parameter fully determines the conductivity tensor, leading to simple expressions for the longitudinal components ${\sigma }_{x,y}={\sigma }_0\left(1\mp \eta \right)$ and a clear identification of the preferred transport direction. Importantly, the total conductivity remains constant, while strain redistributes transport between orthogonal directions. These results provide a transparent and predictive description of strain-induced transport anisotropy, demonstrating that the directional electronic response can be tuned without modifying the material composition, offering a practical route to control electronic response in graphene through purely mechanical means. Full article

Auxetic metamaterials exhibit unique mechanical behavior due to their negative Poisson’s ratio, but reliable determination of their effective elastic properties remains challenging. In this study, an experimental–numerical approach is proposed to characterize additively manufactured polylactic acid (PLA)-based auxetic sandwich structures. Material properties were first assessed using tensile testing, melt flow rate/volume rate (MFR/MVR) measurements, Fourier-transform infrared (FTIR) spectroscopy, differential scanning calorimetry (DSC), dilatometry, and nanoindentation, revealing stable mechanical behavior, good processability, and slight increases in crystallinity induced by the printing process. Impulse excitation technique (IET) measurements provided highly reproducible resonant frequencies, demonstrating a strong dependence on core geometry and orientation. However, classical ASTM-based evaluation yielded non-physical elastic properties, highlighting its limitations for architected metamaterials. Finite element modal analyses, combined with inverse parameter identification, enabled the determination of effective elastic properties using a transversely isotropic homogenized model. This approach significantly improved the agreement between experimental and numerical results. The findings revealed pronounced anisotropy and orientation-dependent auxetic behavior, including a negative Poisson’s ratio for specific configurations. The proposed methodology provides a suitable framework for the reliable characterization and design of complex metamaterials. Full article

Laser powder bed fusion (L-PBF) has emerged as a core metal additive manufacturing technology for high-end sectors, including aerospace and medical device manufacturing. However, melting anomalies that occur during fabrication accumulate layer by layer, leading to degraded surface quality and impaired mechanical performance of as-built components—a critical bottleneck limiting their large-scale industrial adoption. Accurate and robust layer-wise melting quality recognition remains a challenge due to the complex surface morphologies induced by such melting anomalies. This study presents a machine learning-enabled in situ monitoring approach for layer-wise melting quality identification in L-PBF. By systematically varying laser power and scanning speed, 24 parameter combinations were designed to fabricate specimens with three distinct melting states: over-melting (OM), lack of fusion (LOF), and normal melting. A high-resolution complementary meta–oxide–semiconductor (CMOS) camera was used to capture layer-wise surface images of the specimens, and following abnormal layer filtering and manual validation, a high-quality dataset comprising 5110 layer-wise images was constructed. Two mainstream machine learning approaches were systematically evaluated and optimized for melting quality classification: a support vector machine (SVM) model leveraging handcrafted gray-level co-occurrence matrix (GLCM) texture features achieved a classification accuracy of 96.77%, while a convolutional neural network (CNN) model with end-to-end feature learning directly from raw images attained a superior accuracy of 98.14%. In terms of computational efficiency, the CNN model exhibited a faster inference speed with a per-layer inference time of just 0.036 s, nearly half that of the SVM model (0.068 s per layer). Most critically, the CNN model completely eliminated fatal cross-class misclassification between OM and LOF—an error mode common in the SVM model that would trigger erroneous process corrective actions in practical industrial applications. The findings demonstrate that image-based machine learning provides a reliable technical foundation for intelligent in situ monitoring of the L-PBF process. With its high accuracy, strong robustness, and superior computational efficiency, the CNN model can effectively support on-site operational decision-making, reduce material and time losses, and enhance process stability in industrial settings, thus exhibiting significant potential for practical engineering deployment. Full article

Mass transfer is central to food processing but remains difficult to quantify because food materials are heterogeneous, multiphase, porous, biologically structured, and dynamically changing. Under these conditions, experiments alone cannot fully capture the spatiotemporal complexity of transport behavior, making modeling and simulation essential for mechanism interpretation, process prediction, and engineering optimization. Existing reviews mainly address specific operations or numerical methods, with limited synthesis of governing equations, simulation workflows, application implementation, and practical applicability. This review examines food mass transfer by linking coupled momentum, heat, and mass transfer laws with governing equation selection, simulation workflow, and representative food processing applications. Governing formulations for Fickian diffusion, conservation-based transport, heat–mass coupling, multicomponent transfer, Darcy-type porous-medium flow, and related model extensions are summarized, together with their assumptions, geometric applicability, and dimensionless criteria. A unified simulation workflow is then organized, covering transport type identification, governing equation and physical model selection, geometric representation, parameter determination, initial and boundary condition specifications, numerical method and simulation tool selection, numerical implementation, validation, and transferability assessment. Representative applications are discussed for drying, heat–mass coupled processes, multicomponent transfer, transport in porous foods, and redistribution in multi-ingredient or multilayer foods. Overall, future progress requires more integrated, structure-aware, experimentally validated, transferable, and application-oriented simulation frameworks. Full article

To improve the efficiency, accuracy, and operational stability of postharvest potato tuber sorting in the presence of mineral impurities, mainly soil clods and stones, an intelligent sorting system for potato tubers and mineral impurities was designed and developed. The system employed YOLOv10n as the baseline network and incorporated a PSA module together with a dynamic blur augmentation strategy to establish a task-adapted detection model, termed YOLOv10n-PB. Rather than treating detection accuracy alone as the optimization objective, the proposed system jointly considered detection performance, inference-latency stability, temporal–spatial coordination, and pneumatic rejection reliability. In addition, a programmable logic controller and pneumatic actuators were integrated to enable online target identification and dynamic removal. Comparative experiments involving lightweight YOLO models and $L_{25}\left(5^3\right)$ orthogonal tests were conducted to evaluate the effects of conveyor belt speed, material spacing, and classification threshold on sorting performance. The results showed that YOLOv10n-PB achieved a mAP@0.5 of 98.9% on the test set. Among the investigated factors, conveyor belt speed had the greatest effect on overall sorting accuracy, followed by material spacing and classification threshold. Range analysis identified the optimal parameter combination as a conveyor belt speed of 0.2 m/s, a material spacing of 9 cm, and a classification threshold of 0.4. Validation experiments under these conditions yielded an overall sorting accuracy of 98.3%, a combined mineral-impurity removal accuracy of 98.3%, and a potato tuber false rejection rate of 1.7%. These results demonstrate the feasibility of the proposed system for accurate and stable automatic sorting of potato tubers and mineral impurities under postharvest operating conditions. Full article

Inverse analysis from indentation experiments has been a challenging problem due to the nonlinear relationship between indentation response and material parameters. In this work, a data-driven method is proposed that integrates an artificial neural network (ANN) and evolutionary optimization for the reliable and efficient inverse parameter identification. A large dataset is generated by simulating the indentation process based on different combinations of material parameters in a systematic way. Then, by using the simulated data, a set of ANN models is trained that can efficiently predict the indentation responses, i.e., the displacement–time curve, the indentation force, and the surface profile, as a function of material parameters. These trained models exhibit the potential to replace the computationally expensive numerical simulations for the identification of material parameters by inverse analysis. In this way, the surrogate models make the numerical evaluation of the loss function, which is minimized during the inverse analysis, orders of magnitude faster. This enables the use of the powerful genetic algorithm for the minimization of the loss function, which would be impossible without numerically efficient surrogate models, as this algorithm requires many iterations to produce robust results. In this work, we systematically investigate which mathematical loss function leads to robust and unique results in determining the material parameters through inverse analysis of indentation results. The results show that such an inverse analysis can be successfully performed for simulation data. In forthcoming work, this method will be generalized to experimental indentation data, which will allow the characterization of the mechanical behaviour of materials by micro- or nano-indentation tests. Full article

With the rapid development of clean and low-carbon energy systems, non-metallic pipelines have become increasingly important in urban gas distribution, water supply, and emerging energy-transport applications, including hydrogen service. As a critical joining technology that governs system integrity and long-term operational safety, electrofusion welding requires a comprehensive and mechanism-oriented understanding beyond empirical process control. In this study, a review is conducted on research published over the past decade in the field of electrofusion welding of non-metallic pipelines, with emphasis on fundamental technical issues including the formation and evolution of temperature fields, characteristics of the molten fusion zone and defect development, and thermo-mechanical coupling with residual stress generation. Based on a synthesis of the literature, the review clarifies the global research landscape, core research communities, and underlying knowledge structure. The results indicate a clear transition of the field from empirically driven parameter optimization toward a mechanism-based and process-controllable paradigm centered on temperature field evolution, fusion zone development, and thermo-mechanical behavior. Current research hotspots converge on HDPE material adaptability, welding process regulation, and the long-term reliability of welded joints. Building on these insights, future research directions are discussed, including mechanism-driven process design, intelligent defect identification based on multi-source data, and full-life reliability assessment under service conditions. This review provides a theoretical framework to support process optimization and engineering application of electrofusion welding in non-metallic pipeline systems. Full article

The growing demand for sustainable and energy-efficient materials has positioned aerogels as promising candidates for advanced insulation applications. Among them, polyurethane (PU) aerogels are attracting increasing interest due to their thermal insulation properties and mechanical versatility. However, their development commonly relies on trial-and-error experimentation, which is time-consuming and resource-intensive. This study presents a Digital Twin (DT) framework to support PU aerogel design and reduce the experimental workload. A pilot-scale DT was developed using data from 21 synthesis experiments, including process configuration, parameter mapping, model development, and process analysis. Two predictive models were evaluated, with the Support Vector Regression (SVR) model showing good agreement with the experimental data (R² = 0.964) and being selected to estimate aerogel density within the parameter range studied. The DT framework enabled the identification of synthesis conditions associated with lower density, which may contribute to improved thermal insulation performance. These results illustrate the potential of DT-assisted modelling to support material development, improve process understanding, and guide more efficient experimentation in PU aerogel synthesis. Overall, this work highlights a data-driven approach for advancing sustainable and scalable aerogel manufacturing. Full article

As pressures from climate change and global trade increase, developing cost-effective tools for origin tracking becomes essential to ensure the traceability and adaptability of forest reproductive material (FRM). Our objectives were (a) to test the efficiency of a set of nuclear microsatellite loci (nSSR) for revealing the genetic structures identified by high-input sequencing studies and (b) to verify this set of nSSR loci for genetic assignment of commercial seed lots into reference regions. We used 12 nSSR markers to genotype 220 trees from 11 populations representing the eastern Baltic, Scandinavian and southern European ranges of Norway spruce. The results showed that the populations from the eastern Baltic range had relatively higher allelic diversity parameters. The Bayesian clustering revealed a geographically consistent genetic structuring of Norway spruce populations by distinguishing the eastern Baltic from southern European and Scandinavian populations. GENECLASS analysis correctly assigned Lithuanian commercial seed lots into the Lithuanian reference region with markedly higher probability than to any other reference regions. Our study demonstrates promising results for origin identification of Norway spruce, particularly in contexts where high-resolution genomic approaches remain financially or logistically inaccessible. Full article

Urban reservoirs are increasingly exposed to multiple interacting pressures associated with eutrophication, pollutant inflow, ageing sewerage and stormwater infrastructure, and climate-related hydrological instability. This issue is of growing concern because municipalities often possess fragmented monitoring and planning evidence without an operational framework for translating it into remediation action. This study develops an integrated decision-support framework for the Ternopil Reservoir based primarily on recent hydrochemical monitoring data, complemented by historical targeted sampling and local environmental and planning materials. The analysis focuses on the most informative indicators of ecological deterioration in an urban reservoir, including oxygen regime, organic pollution, nutrient-related parameters, suspended solids, and selected pollution markers. The available evidence indicates that the Ternopil Reservoir is among the most environmentally stressed water bodies within the local reservoir system, with recurrent eutrophication symptoms, seasonal water blooming, and spatially differentiated exceedances of selected water-quality indicators. The results further indicate persistent nutrient-related and organic pressure, pronounced hydrochemical tension in 2022, and hotspot vulnerability in hydraulically weak sectors of the reservoir. To address these pressures, the study proposes a four-stage monitoring-to-remediation framework that links environmental diagnosis with the identification of vulnerable zones, the prioritisation of hydraulic and hydrobiological measures, and post-remediation control. The proposed framework is intended as an operational planning tool for translating fragmented local evidence into a coherent remediation pathway for urban reservoirs under multiple environmental stressors. Full article

In this work, we applied Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Artificial Neural Networks (ANN) to predict key performance characteristics of quantum cascade lasers (QCLs), including material gain, current density, and emission frequency. By developing a machine learning-based surrogate modeling framework that replaces computationally expensive simulations of QCLs, we enable orders-of-magnitude-faster evaluation and optimization of a high-dimensional configuration space. The training dataset was generated using a numerical simulator based on the density-matrix transport model. By combining physics simulations with machine learning, we achieved reliable predictions of device characteristics, with standardized RMSE values ranging from 0.21 to 0.55 for RF, 0.16 to 0.51 for XGBoost, and 0.04 to 0.22 for the ANN model, demonstrating the superior predictive performance of the ANN across all investigated performance characteristics. The ANN was subsequently used to analyze the full configuration space defined by possible layer thicknesses and electric fields. Approximately 44 million configurations were evaluated in about five minutes, achieving a speedup of approximately 90,000 times over the numerical simulator for a single configuration. This approach allowed the identification of designs with improved material gain and facilitated the efficient optimization of key parameters while maintaining high prediction reliability. Full article

Wear in microscale material removal is difficult to predict because material loss can proceed through several distinct pathways, including plastic deformation, adhesion, atom-by-atom attrition, tribochemical reactions, oxidation-assisted removal, and fracture. Since these mechanisms operate under different contact and environmental conditions, no single wear law is reliable across all cases. This review examines the main quantitative wear models used in microscale material removal, from classical Archard-type and Reye-type relations to atomistic Arrhenius-type descriptions and models developed for adhesive, tribochemical, oxidation-related, and fracture-dominated wear. Attention is given to the assumptions behind these models, the regimes in which they remain useful, and the conditions under which their predictions begin to fail. The discussion also considers how material properties, tool characteristics, operating conditions, and environmental factors act alone and in combination to influence wear behavior and the reliability of different models. Across the literature, a consistent conclusion is that model selection is most reliable when it is based on the active wear mechanism and the evolving contact state. On this basis, practical guidelines are outlined for different classes of contacts, and current limitations are discussed, including poor treatment of regime transitions, difficulty in parameter identification, and the gap between atomistic models and engineering use. Future progress will depend on multi-regime modeling, better treatment of coupled effects, and improved in situ characterization under realistic operating conditions. Full article