Search Results (398)

The interface between high-performance thermoelectric materials and electrodes critically governs the conversion efficiency and long-term reliability of thermoelectric generators under high-temperature operation. Here, we propose Al_xCoCrFeNi high-entropy alloys (HEA) as barrier layers to bond Cu-W electrodes with p-type skutterudite (p-SKD) thermoelectric materials. The HEA/p-SKD interface exhibited excellent chemical bonding with a stable and controllable reaction layer, forming a dense, defect-free (Fe,Ni,Co,Cr)Sb phase (thickness of ~2.5 μm) at the skutterudites side. The interfacial resistivity achieved a low value of 0.26 μΩ·cm² and remained at 7.15 μΩ·cm² after aging at 773 K for 16 days. Moreover, the interface demonstrated remarkable mechanical stability, with an initial shear strength of 88 MPa. After long-term aging for 16 days at 773 K, the shear strength retained 74 MPa (only 16% degradation), ranking among the highest reported for thermoelectric materials/metal joints. Remarkably, the joint maintained a shear strength of 29 MPa even after 100 continuous thermal cycles (623–773 K), highlighting its outstanding thermo-mechanical stability. These results validate the Al_xCoCrFeNi high-entropy alloys as an ideal interfacial material for thermoelectric generators, enabling simultaneous optimization of electrical and mechanical performance in harsh environments. Full article

To address the freezing damage to tunnel lining caused by frost heaving of the surrounding rock in water-rich tunnels in cold regions, a numerical thermo-mechanical coupling model for tunnel-surrounding rock that considers the anisotropy of frost heave deformation was established by examining overall frost heaves in a freeze–thaw cycle. Using a COMSOL Multiphysics 6.0 platform and the sequential coupling method, the temperature field evolution of tunnel-surrounding rock, freezing cycle development, and distribution characteristics of the frost heaving force of a tunnel lining under different minimum temperatures, numbers of negative temperature days, frost heave ratios, and anisotropy coefficients of frost heave deformation were systematically simulated. The results revealed that the response of the temperature field of tunnel-surrounding rock to the external temperature varies spatially with time lags, the shallow surface temperatures and the area around the lining fluctuate with the climate, and the temperature of the deep surrounding rock is dominated by the geothermal gradient. The extent of the freezing cycle and the frost heaving force increase significantly when lowering the minimum temperature. The maximum frost heaving force usually occurs in the region of the side wall and the spring line, and tensile stress is prone to be generated at the spring line; the influence of slight fluctuations in the minimum temperature or the short shift in the coldest day on the frost heaving force is limited. A substantial increase in frost heaving force is observed with higher frost heave ratios; for example, an increase from 0.25% to 2.0% results in a 116% rise at the sidewall. Although the increase in the anisotropy coefficient of frost heave deformation does not change the overall distribution pattern of frost heaving force, it can exacerbate the directional concentration of frost heave strain, which can increase the frost heaving force at the periphery of the top arch of the lining. This study revealed the distribution pattern and key influencing factors of the freezing cycle and frost heaving force for tunnels, providing a theoretical basis and data reference for the frost resistance design of tunnels in cold regions. Full article

The commercialization of proton-conducting fuel cells (PCFCs) is hindered by the limited electroactivity and durability of cathodes at intermediate temperatures ranging from 400 to 700 °C, a challenge exacerbated by an insufficient understanding of high-entropy perovskite (HEP) materials for oxygen reduction reaction (ORR) optimization. This study introduces an yttrium-doped HEP to address these limitations. A comparative analysis of Ce_0.2−xY_xBa_0.2Sr_0.2La_0.2Ca_0.2CoO_3−δ (x = 0, 0.2; designated as CBSLCC and YBSLCC) revealed that yttrium doping enhanced the ORR activity, reduced the thermal expansion coefficient (19.9 × 10⁻⁶ K⁻¹, 30–900 °C), and improved the thermomechanical compatibility with the BaZr_0.1Ce_0.7Y_0.1Yb_0.1O_3−δ electrolytes. Electrochemical testing demonstrated a peak power density equal to 586 mW cm⁻² at 700 °C, with a polarization resistance equaling 0.3 Ω cm². Yttrium-induced lattice distortion promotes proton adsorption while suppressing detrimental Co spin-state transitions. These findings advance the development of durable, high-efficiency PCFC cathodes, offering immediate applications in clean energy systems, particularly for distributed power generation. Full article

This study investigates the fire dynamics and structural response of steel-framed warehouse racking systems under various fire scenarios, emphasizing the critical importance of fire safety measures in mitigating structural damage. Through advanced computational simulations (Fire Dynamics Simulator) and thermomechanical analysis, this research reveals that fire intensity and progression are highly influenced by the ignition point and the stored material types, with maximum recorded temperatures reaching 720 °C and 970 °C in different scenarios. The results highlight the localization of significant strain and drift ratios in structural elements near the ignition zone, underscoring their vulnerability. This study demonstrates the rapid loss of load-bearing capacity in steel elements at elevated temperatures, leading to severe deformations and increased collapse risks. Key findings emphasize the necessity of strategically positioned sprinkler systems and the integration of passive fire protection measures, such as fire-resistant coatings, to enhance structural resilience. Performance-based fire design approaches, aligning with FEMA-356 criteria, offer realistic frameworks for improving the fire safety of warehouse structures. Full article

Enhancing damage tolerance and wear resistance in Al–Si-based alloys under thermomechanical stress remains a key challenge in lightweight structural applications. This study investigates the microstructural and tribomechanical behavior of hypoeutectic Al–Si–(Cu)–Mg alloys with varying Cu:Mg ratios (3:1 vs. 1:3) under a T6 heat treatment. Alloys A and B, with identical Si contents but differing Cu and Mg levels, were subjected to multiscale microstructural characterization and mechanical and wear testing at 25 °C, 150 °C, and 250 °C. Alloy A (Cu-rich) exhibited refined α-Al(FeMn)Si phases and homogeneously dissolved Cu in the Al matrix, promoting lattice contraction and dislocation pinning. In contrast, Alloy B (Mg-rich) retained coarse Mg₂Si and residual β-AlFeSi phases, which induced local stress concentrations and thermal instability. Under tribological testing, Alloy A showed slightly higher friction coefficients (0.38–0.43) but up to 26.4% lower wear rates across all temperatures. At 250 °C, Alloy B exhibited a 25.2% increase in the wear rate, accompanied by surface degradation such as delamination and spalling due to β-AlFeSi fragmentation and matrix softening. These results confirm that the Cu:Mg ratio critically influences the dominant hardening mechanism—the solid solution vs. precipitation—and determines the high-temperature performance. Alloy A maintained up to 14.1% higher tensile strength and 22.3% higher hardness, exhibiting greater shear resistance and interfacial stability. This work provides a compositionally guided framework for designing thermally durable Al–Si-based alloys with improved wear resistance under elevated temperature conditions. Full article

Gallium nitride high-electron-mobility transistors (GaN HEMTs) are critical for high-power applications like AI power supplies and robotics but face reliability challenges due to increased dynamic ON-resistance (R_{DS_ON}) from electrical and thermomechanical stresses. This paper presents a novel self-calibrating temperature-sensitive electrical parameter (TSEP) model that uses gate leakage current (I_G) to estimate junction temperature with high accuracy, uniquely addressing aging effects overlooked in prior studies. By integrating I_G, aging-induced degradation, and failure-in-time (FIT) models, the approach achieves a junction temperature estimation error of less than 1%. Long-term hard-switching tests confirm its effectiveness, with calibrated R_{DS_ON} measurements enabling precise remaining useful life (RUL) predictions. This methodology significantly improves GaN HEMT reliability assessment, enhancing their performance in resilient power electronics systems. Full article

Nowadays, Fused Filament Fabrication (FFF) 3D printing offers promising opportunities for the customized manufacturing of ankle–foot orthoses (AFOs) targeted towards rehabilitation purposes. Polypropylene (PP) represents an ideal candidate in orthotic applications due to its light weight and superior mechanical properties, offering an excellent balance between flexibility, chemical resistance, biocompatibility, and long-term durability. However, Additive Manufacturing (AM) of AFOs based on PP remains a major challenge due to its limited bed adhesion and high shrinkage, especially for making large parts such as AFOs. The primary innovation of the present study lies in the optimization of FFF 3D printing parameters for the fabrication of functional, patient-specific orthoses using PP, a material still underutilized in the AM of medical devices. Firstly, a thorough thermomechanical characterization was conducted, allowing the implementation of a (thermo-)elastic material model for the used PP filament. Thereafter, a Taguchi design of experiments (DOE) was established to study the influence of several printing parameters (extrusion temperature, printing speed, layer thickness, infill density, infill pattern, and part orientation) on the mechanical properties of 3D-printed specimens. Three-point bending tests were conducted to evaluate the strength and stiffness of the samples, while additional tensile tests were performed on the 3D-printed orthoses using a home-made innovative device to validate the optimal configurations. The results showed that the maximum flexural modulus of 3D-printed specimens was achieved when the printing speed was around 50 mm/s. The most significant parameter for mechanical performance and reduction in printing time was shown to be infill density, contributing 73.2% to maximum stress and 75.2% to Interlaminar Shear Strength (ILSS). Finally, the applicability of the finite element method (FEM) to simulate the FFF process-induced deflections, part distortion (warpage), and residual stresses in 3D-printed orthoses was investigated using a numerical simulation tool (Digimat-AM^®). The combination of Taguchi DOE with Digimat-AM for polypropylene AFOs highlighted that the 90° orientation appeared to be the most suitable configuration, as it minimizes deformation and von Mises stress, ensuring improved quality and robustness of the printed orthoses. The findings from this study contribute by providing a reliable method for printing PP parts with improved mechanical performance, thereby opening new opportunities for its use in medical-grade additive manufacturing. Full article

With the rapid development of wearable electronics, traditional rigid thermal management materials face limitations in flexibility, conformability, and multi-physics adaptability. Low-dimensional carbon materials such as graphene and carbon nanotubes combine ultrahigh thermal conductivity with outstanding mechanical compliance, making them promising building blocks for flexible thermal regulation. This review summarizes recent advances in integrating these materials into textile architectures, mapping the evolution of this emerging field. Key topics include phonon-dominated heat transfer mechanisms, strategies for modulating interfacial thermal resistance, and dimensional effects across scales; beyond these intrinsic factors, hierarchical textile configurations further tailor macroscopic performance. We highlight how one-dimensional fiber bundles, two-dimensional woven fabrics, and three-dimensional porous networks construct multi-directional thermal pathways while enhancing porosity and stress tolerance. As for practical applications, the performance of carbon-based textiles in wearable systems, flexible electronic packaging, and thermal coatings is also critically assessed. Current obstacles—namely limited manufacturing scalability, interfacial mismatches, and thermal performance degradation under repeated deformation—are analyzed. To overcome these challenges, future studies should prioritize the co-design of structural and thermo-mechanical properties, the integration of multiple functionalities, and optimization guided by data-driven approaches. This review thus lays a solid foundation for advancing carbon-based smart textiles toward next-generation flexible thermal management technologies. Full article

Underground production and injection operations result in mechanical compaction and mineral chemical reactions that alter porosity and permeability. These changes impact the flow and, eventually, the long-term sustainability of reservoirs utilized for CO₂ sequestration and geothermal energy. Even though mechanical and chemical deformations in rocks take place at the pore scale, it is important to investigate their impact at the continuum scale. Rock deformation can be examined using intergranular pressure solution (IPS) models, primarily for uniaxial compaction. Because the reaction rate parameters are estimated using empirical methods and the assumption of constant mineral saturation indices, these models frequently overestimate the rates of compaction and strain by several orders of magnitude. This study presents a new THMC algorithm by combining thermo-mechanical computation with a fractal approach and hydrochemical computations using PHREEQC to evaluate the pressure solution. Thermal stress and strain under axisymmetric conditions are calculated analytically by combining a derived hollow circle mechanical structure with a thermal resistance model. Based on the pore scale, porosity and its impact on the overall excessive stress and strain rate in a domain are estimated by applying the fractal scaling law. Relevant datasets from CO₂ core flooding experiments are used to validate the proposed approach. The comparison is consistent with experimental findings, and the novel analytical method allows for faster inspection compared to numerical simulations. Full article

Shape Memory Alloy (SMA) actuators are pivotal in modern engineering due to their unique thermomechanical properties, but their inherent non-linearities, hysteresis, and temperature sensitivity pose significant control challenges. This systematic review evaluates artificial intelligence (AI)-based control methodologies to address these limitations, analyzing their efficacy in enhancing precision, adaptability, and reliability for SMA and Magnetic SMA (MSMA) systems. A PRISMA-guided literature review (2003–2025) identified 24 studies, which were categorized by control architectures (hybrid AI-linear, pure AI, adaptive, and model predictive control) and evaluated through quantitative metrics, including Root Mean Square Error (RMSE%) and a weighted scoring system for experimental rigor. Results revealed hybrid AI-linear controllers as the dominant approach (36%), with online-trained neural networks achieving superior accuracy (+2.4%) over offline methods. Feedforward neural networks outperformed recurrent architectures (+3.1%), while Model Predictive Control (MPC) excelled for SMA actuators (+5.8% accuracy) but underperformed for MSMAs (−7.7%). Sensorless strategies proved advantageous for MSMAs (+5.0%), leveraging intrinsic material properties like electrical resistance for state estimation. The analysis underscores AI’s capacity to mitigate hysteresis and non-linear dynamics, though material-specific optimization is critical: SMA systems favor dynamic control and MPC, whereas MSMAs benefit from sensorless AI and pure neural networks. Challenges persist in computational demands for online training and reinforcement learning’s exploration–exploitation trade-offs. Future research should prioritize adaptive algorithms for fatigue compensation, lightweight AI models for embedded deployment, and standardized benchmarking to bridge material-specific performance gaps. This synthesis establishes AI as a transformative paradigm for SMA actuation, enabling precise control in aerospace, biomedical, and soft robotics applications. Full article

This paper focuses on the use of advanced optimization design strategies to improve the performance and service life of engine pistons, with emphasis on enhancing their stiffness, strength, and dynamic characteristics. As a core component of the engine, the structural design and optimization of the piston are of great significance to its efficiency and reliability. First, a three-dimensional (3D) model of the piston was constructed and imported into ANSYS Workbench for finite element modeling and high-quality meshing. Based on the empirical formula, the actual working environment temperature and heat transfer coefficient of the piston were accurately determined and used as boundary conditions for thermomechanical coupling analysis to accurately simulate the thermal and deformation state under complex working conditions. Dynamic characteristic analysis was used to obtain the displacement–frequency curve, providing key data support for predicting resonance behavior, evaluating structural strength, and optimizing the design. In the optimization stage, five geometric dimensions are selected as design variables. The deformation, mass, temperature, and the first to third natural frequencies are considered as optimization goals. The response surface model is constructed by means of the design of the experiments method, and the fitted model is evaluated in detail. The results show that the models are all significant. The adequacy of the model fitting is verified by the “Residuals vs. Run” plot, and potential data problems are identified. The “Predicted vs. Actual” plot is used to evaluate the fitting accuracy and prediction ability of the model for the experimental data, avoiding over-fitting or under-fitting problems, and guiding the optimization direction. Subsequently, the sensitivity analysis was carried out to reveal the variables that have a significant impact on the objective function, and in-depth analysis was conducted in combination with the response surface. The multi-objective genetic algorithm (MOGA), screening, and response surface methodology (RSM) were, respectively, used to comprehensively optimize the objective function. Through experiments and analysis, the optimal solution of the MOGA algorithm was selected for implementation. After optimization, the piston mass and deformation remained relatively stable, and the working temperature dropped from 312.75 °C to 308.07 °C, which is conducive to extending the component life and improving the thermal efficiency. The first to third natural frequencies increased from 1651.60 Hz to 1671.80 Hz, 1656.70 Hz to 1665.70 Hz, and 1752.90 Hz to 1776.50 Hz, respectively, significantly enhancing the dynamic stability and vibration resistance. This study integrates sensitivity analysis, response surface models, and genetic algorithms to solve multi-objective optimization problems, successfully improving piston performance. Full article

The textile industry is among the world’s largest, producing an estimated 124 million tonnes of fibres in 2023, with more than half of these being made from virgin polyester. Less than 0.1% of polyester fibres are recycled into new textiles at the end of their lives. Mechanical, thermo-mechanical, and chemical textile-to-textile polyester recycling are all technically possible, but thermo-mechanical recycling is reported to provide the most promising compromise between cost and quality. Myriad chemicals are used in polyester production, and this paper is the first to review the related academic literature to better understand their impact on recyclability. It has been demonstrated that chemicals used during the production and processing of polyester textiles can either provide resistance to, or catalyse, the degradation of polyester during thermo-mechanical recycling processes. However, the effect of combinations of these chemicals on recycling is largely unknown. Limiting, standardising, and transparently reporting the chemicals used during textile production would simplify research and could lead to better quality products after recycling. Full article

Single-lip deep-hole drilling is a key technology for the precision machining of high-temperature nickel-based alloy pore structures in aero engines. However, the intense thermo-mechanical coupling effects during machining can easily lead to surface integrity deterioration, and the correlation mechanism between microstructure and properties remains unclear. By adjusting the spindle speed and feed rate, a series of orthogonal experiments were carried out to study the integrity characteristics of the machined surface, including surface morphology, roughness, work hardening, and subsurface microstructure. The results reveal gradient structural features along radial depth: a dynamic recrystallized layer (RL) at the surface and a plastically deformed layer (PDL) containing high-density subgrains/distorted grains in the subsurface. With the increase in the spindle speed, the recrystallization phenomenon is intensified, the RL ratio of the machined-affected zone (MAZ) is increased, and the surface roughness is reduced to ~0.5 μm. However, excessive heat input will reduce the nanohardness. Low feed rates (<0.012 mm/rev) effectively suppress pit defects, whereas high feed rates (≥0.014 mm/rev) trigger pit density resurgence through shear instability. Progressive material removal rate (MRR) elevation drives concurrent PDL thickness reduction and RL proportion growth. Optimal medium MRR range (280–380 mm³/min) achieves synergistic RL/PDL optimization, reducing machining-affected zone thickness (MAZ < 35 μm) while maintaining fatigue resistance. These findings establish theoretical foundations for balancing efficiency and precision in aerospace high-temperature component manufacturing. Full article

This study presents the results of investigations into the properties of a composite made from the commercial biopolymer with varying concentrations of ground coffee husks (CH) at 10, 30, and 50 wt%. Thermal, thermomechanical, physical, and mechanical properties were determined for the composites. Results indicate that the inclusion of CH into the polymer matrix significantly enhances the thermomechanical properties of the obtained composites, particularly in terms of storage modulus at 30 °C. The addition of coffee filler did not alter the characteristic thermal curves. Still, it reduced the thermal resistance of the composites, lowering the degradation temperature by approximately 40 °C compared to the pure polymer. Furthermore, the incorporation of CH introduced an additional stage of mass loss on the thermogravimetric curves, associated with the thermal decomposition of CH. The physico-mechanical properties of the composite depend on the content of the filler. Increasing the coffee content increases the dynamics of water absorption by the composite. It also increases the composite’s stiffness while reducing its tensile and flexural strength. The obtained results suggest that biopolymer-based composites filled with ground CH can be effectively used for manufacturing biodegradable products, with the moisture diffusion behavior indicating susceptibility to degradation that is modulated by the CH content. Full article

In this study, we have explored the thermomechanical processing on 0.1C3Mn steel to produce an ultrafine-grained (UFG) dual-phase (DP) microstructure. The composition was designed to allow a decrease in temperature for the warm deformation of austenite. It was found that the warm deformation of austenite induced a dramatic ferrite transformation, in contrast to the absence of the formation of ferrite in the well-annealed state. Compression by 60% at 650 °C resulted in the generation of a UFG-DP microstructure with a ferrite grain size of 1.4 μm and a ferrite volume fraction of 62%. The UFG-DP 0.1C3Mn steel presents a good combination of strength, ductility and fracture resistance, and the fracture strain of the UFG-DP is higher than the as-quenched low-carbon martensite. The high fracture strain of the UFG-DP could be attributed to delayed void nucleation and constrained void growth, as revealed by the quantitative X-ray tomography. Full article