Search Results (977)

Laser processing of heterogeneous composites requires a clear understanding of coupled thermal and mechanical responses to ensure structural integrity and patterning precision. In this study, a thermal–mechanical coupling model based on the finite element method was developed to investigate laser–material interactions in Al–Tedlar–Kevlar composite films. The effects of key parameters—including pulse energy, spot size, pulse duration, and repetition frequency—on the evolution of temperature and stress fields were systematically examined. The simulations reveal that pulse energy leads to a linear rise in peak temperature, while pulse duration exerts a nonlinear influence on energy density and thermal uniformity. Increasing repetition frequency promotes thermal accumulation, enlarging the heat-affected zone. Coupled analyses further indicate significant stress concentrations at material interfaces, which may trigger delamination and compromise film reliability. Through comprehensive parameter evaluation, the optimal processing conditions were identified as 0.5 mJ pulse energy, 20 kHz repetition rate, 45 μm spot diameter, and 120 ns pulse duration. These findings clarify the governing mechanisms of thermal–mechanical interactions in multilayer composites and provide theoretical guidance for optimizing laser micropatterning processes while enhancing interfacial stability and manufacturing quality. Full article

Molecular dynamics (MD) can dynamically reveal the structural evolution and mechanical response of Zirconium (Zr) at the atomic scale under complex service conditions such as high temperature, stress, and irradiation. However, traditional empirical potentials are limited by their fixed function forms and parameters, making it difficult to accurately describe the multi-body interactions of Zr under conditions such as multi-phase structures and strong nonlinear deformation, thereby limiting the accuracy and generalization ability of simulation results. This paper combines high-throughput first-principles calculations (DFT) with the machine learning method to develop the Deep Potential (DP) for Zr. The developed DP of Zr was verified by performing molecular dynamic simulations on lattice constants, surface energies, grain boundary energies, melting point, elastic constants, and tensile responses. The results show that the DP model achieves high consistency with DFT in predicting multiple key physical properties, such as lattice constants and melting point. Also, it can accurately capture atomic migration, local structural evolution, and crystal structural transformations of Zr under thermal excitation. In addition, the DP model can accurately capture plastic deformation and stress softening behavior in Zr under large strains, reproducing the characteristics of yielding and structural rearrangement during tensile loading, as well as the stress-induced phase transition of Zr from HCP to FCC, demonstrating its strong physical fidelity and numerical stability. Full article

To investigate the contact performances and meshing characteristics of gears systematically, an improved comprehensive meshing stiffness model of spur gears with consideration of the tooth surface morphology, lubrication, friction, and thermal effects is presented based on the thermal elastohydrodynamic lubrication (TEHL) theory. The fractal feature of the tooth surface morphology is verified experimentally and characterized by the Weierstrass–Mandelbrot fractal function. Based on this, the rough contact stiffness, oil film stiffness, and thermal stiffness are introduced into the stiffness model. Comparisons between smooth and rough models are carried out, and the maximum temperature rise is increased by 24.7%. Subsequently, the influences of the torque, rotational speed, and fractal parameters on the oil film pressure and thickness, friction and temperature rise, and contact stiffness and comprehensive meshing stiffness are investigated. The results show that the oil film pressure and the maximum temperature rise increase by 125.18% and 69.08%, respectively, with an increasing torque from 20 N·m to 300 N·m. As the rotational speed is increased, the oil film thickness sharply increases, the rough peak contact area and friction reduce, and the stiffness fluctuation weakens. For fractal parameters, the oil film pressure and film thickness, friction, and temperature rise are nonlinear with changes in the fractal dimension D and fractal scale characteristic G. The results reveal that this work provides a more reasonable analysis for understanding the meshing characteristics and the design and processing of gears. Full article

During the Fused Deposition Modeling (FDM) molding process, temperature changes are nonlinear and instantaneous, which is a key parameter affecting FDM printing efficiency, molding accuracy, warpage deformation, and other factors. This study presents a finite element simulation framework that integrates ANSYS Parametric Design Language (APDL) with birth–death element technology to investigate the temperature evolution and thermomechanical behavior during the FDM process. The framework enables dynamic simulation of the complete printing and cooling cycle, capturing the layer-by-layer material deposition and subsequent thermal history. Results indicate that temperature distribution follows a gradient pattern along the printing path, with rapid heat dissipation at the periphery and heat accumulation in the central regions. Thermomechanical coupling analysis reveals significant stress concentration at the part bottom (310 MPa) and progressive strain increase from bottom (3.68 × 10⁻⁵ m) to top (2.95 × 10⁻⁴ m). Experimental validation demonstrates strong agreement with numerical predictions, showing maximum temperature deviations below 8% and strain distribution errors within 5%. This integrated approach provides an effective tool for predicting thermal-induced deformations and optimizing FDM process parameters to enhance part quality. Full article

Understanding and controlling thermal rectification is pivotal for designing phononic devices that guide heat flow in a preferential direction. This study investigates one-dimensional atomic chains with binary mass asymmetry and nonlinear interatomic potentials, focusing on how energy propagates under thermal and wave excitation. Two potential models—the β-FPU and Morse potentials—were employed to examine the role of nonlinearity and bond softness in energy transport. Simulations reveal strong directional energy transport governed by the interplay of mass distribution, nonlinearity, and excitation type. In FPU chains, pronounced rectification occurs: under “cold-heavy” conditions, energy in the left segment increases from ~1% to over 63%, while reverse (“hot-heavy”) cases show less than 4% net transfer. For wave-driven excitation, the rectification coefficient reaches ~0.58 at 100:1. In contrast, Morse-based systems exhibit weaker rectification (∆E < 1%) and structural instabilities at high asymmetry due to bond breaking. A comprehensive summary and heatmap visualization highlight how system parameters govern rectification efficiency. These findings provide mechanistic insights into nonreciprocal energy transport in nonlinear lattices and offer design principles for nanoscale thermal management strategies based on controlled asymmetry and potential engineering. Full article

Calculating the thermal moment is of paramount importance in the design of nuclear power plants. In order to optimize the calculation method of the thermal moment acting on reinforced concrete (RC) columns in nuclear power plants, a theoretical calculation model for the thermal moment of RC columns during accidental thermal loading is proposed using theoretical analyses. In order to verify the validity of the theoretical calculation model, the bearing capacity of the RC columns under accidental thermal loading was tested, and the sample comprised 10 specimens with different parameters. Furthermore, nonlinear finite element modeling of the specimens was conducted and subsequently verified through a series of tests. The thermal moments of the specimens were also calculated using the method stipulated within ACI 307. Finally, a comparison and analysis of the results obtained from the finite elements, from the specification, and from the theoretical calculation model was undertaken. The findings of this paper indicate that the theoretical calculation model of the thermal moment acting on RC columns, developed in this study, is reliable. Full article

The study presents a robust methodology for simultaneous state and parameter estimation in nonlinear thermal systems, demonstrated on a counter-current heat exchanger model operating with nitrogen under industrial conditions. To address challenges of ill-conditioning and parameter correlation, local sensitivity analysis is combined with regularization through optimal parameter subset selection using orthogonalization and D-optimal experimental design. The Unscented Kalman Filter (UKF) is employed to jointly estimate the augmented state vector in real time, leveraging high-fidelity dynamic simulations generated in Unisim Design with the Peng–Robinson equation of state. The proposed framework achieves high estimation accuracy and numerical stability, even under limited sensor availability and measurement noise. Monte Carlo simulations confirm robustness to ±2.5% uncertainty in initial conditions, while residual autocorrelation analysis validates estimator optimality. The approach provides a practical solution for real-time monitoring and model-based control in industrial heat exchangers and offers a generalizable strategy for building identifiable, noise-resilient models of complex nonlinear systems. Full article

This study aims to develop an artificial neural network (ANN) model to predict the predicted mean vote (PMV) index, a key Indicator of thermal comfort. Based on the ASHRAE II dataset, our approach uses the six PMV variables: air temperature, relative humidity, air velocity, radiative mean temperature, clothing insulation, and metabolic rate. However, accurately calculating PMV to determine the thermal comfort of a space can be complex due to the non-linear relationships between these different parameters. Sensitivity analysis of these parameters, performed by the Spearman rank method, identifies the most influential parameters on thermal comfort. The ANN model is trained and tested on 26,805 datasets. The results demonstrate a strong predictive capacity of the ANN, attested by a coefficient of determination R² of 0.99 and a low root mean square error RMSE. Full article

The seepage characteristics and heat transfer efficiency in rough fractures are indispensable for assessing the lifetime and production performance of geothermal reservoirs. In this study, a two-dimensional rough rock fracture model with different secondary roughness is developed using the wavelet analysis method to simulate the coupled flow and heat transfer process under multiscale roughness based on two theories: local thermal equilibrium (LTE) and local thermal nonequilibrium (LTNE). The simulation results show that the primary roughness controls the flow behavior in the main flow zone in the fracture, which determines the overall temperature distribution and large-scale heat transfer trend. Meanwhile, the nonlinear flow behaviors induced by the secondary roughness significantly influence heat transfer performance: the secondary roughness usually leads to the formation of more small-scale eddies near the fracture walls, increasing flow instability, and these changes profoundly affect the local water temperature distribution and heat transfer coefficient in the fracture–matrix system. The eddy aperture and eddy area fraction are proposed for analyzing the effect of nonlinear flow behavior on heat transfer. The eddy area fraction significantly and positively correlates with the overall heat transfer coefficient. Meanwhile, the overall heat transfer coefficient increases by about 3% to 10% for eddy area fractions of 0.3% to 3%. As the eddy aperture increases, fluid mixing is enhanced, leading to a rise in the magnitude of the local heat transfer coefficient. Finally, the roughness characterization was decomposed into primary roughness root mean square and secondary roughness standard deviation, and for the first time, an empirical correlation was established between multiscale roughness, flow velocity, and the overall heat transfer coefficient. Full article

High energy consumption in Chinese rural residential buildings, caused by rudimentary construction methods and the poor thermal performance of building envelopes, poses a significant challenge to national sustainability and “dual carbon” goals. To address this, this study proposes a comprehensive modeling and analysis framework integrating an improved Bio-inspired Black-winged Kite Optimization Algorithm (IBKA) with Support Vector Regression (SVR). Firstly, to address the limitations of the original B-inspired BKA, such as premature convergence and low efficiency, the proposed IBKA incorporates diversification strategies, global information exchange, stochastic behavior selection, and an NGO-based random operator to enhance exploration and convergence. The improved algorithm is benchmarked against BKA and six other optimization methods. An orthogonal experimental design was employed to generate a dataset by systematically sampling combinations of influencing factors. Subsequently, the IBKA-SVR model was developed for energy consumption prediction and analysis. The model’s predictive accuracy and stability were validated by benchmarking it against six competing models, including GA-SVR, PSO-SVR, and the baseline SVR and so forth. Finally, to elucidate the model’s internal decision-making mechanism, the SHAP (SHapley Additive exPlanations) interpretability framework was employed to quantify the independent and interactive effects of each influencing factor on energy consumption. The results indicate that: (1) The IBKA demonstrates superior convergence accuracy and global search performance compared with BKA and other algorithms. (2) The proposed IBKA-SVR model exhibits exceptional predictive accuracy. Relative to the baseline SVR, the model reduces key error metrics by 37–40% and improves the R² to 0.9792. Furthermore, in a comparative analysis against models tuned by other metaheuristic algorithms such as GA and PSO, the IBKA-SVR consistently maintained optimal performance. (3) The SHAP analysis reveals a clear hierarchy in the impact of the design features. The Insulation Thickness in Outer Wall and Insulation Thickness in Roof Covering are the dominant factors, followed by the Window-wall Ratios of various orientations and the Sun space Depth. Key features predominantly exhibit a negative impact, and a significant non-linear relationship exists between the dominant factors (e.g., insulation layers) and the predicted values. (4) Interaction analysis reveals a distinct hierarchy of interaction strengths among the building design variables. Strong synergistic effects are observed among the Sun space Depth, Insulation Thickness in Roof Covering, and the Window-wall Ratios in the East, West, and North. In contrast, the interaction effects between the Window-wall Ratio in the South and other variables are generally weak, indicating that its influence is approximately independent and linear. Therefore, the proposed bio-inspired framework, integrating the improved IBKA with SVR, effectively predicts and analyzes residential building energy consumption, thereby providing a robust decision-support tool for the data-driven optimization of building design and retrofitting strategies to advance energy efficiency and sustainability in rural housing. Full article

The integration of wood and concrete in building structures is a well-established practice typically realized through mechanical connectors. However, the thermomechanical behavior of wood–concrete composite façades assembled via adhesive bonding remains underexplored. This study introduces a novel concept—the adhesive-bonded wood–concrete façade, termed “Hybrimur”—and evaluates the response of these façade panels under thermal gradients, with a focus on thermal bowing phenomena. Four full-scale façade prototypes (3 m high × 6 m wide), consisting of 7 cm thick concrete and 16 cm thick laminated timber (GL24h), were fabricated and tested both with and without insulation. Two reinforcement types were considered: fiberglass-reinforced concrete and welded mesh reinforcement. The study combines thermal analysis of temperature gradients at the adhesive interface with analytical and numerical methods to investigate thermal expansion effects. The experimental and numerical results revealed thermal strains concentrated at the wood–concrete interface without inducing panel failure. Thermal bowing (out-of-plane deflection) exhibited a nonlinear behavior influenced by the adhesive bond and the anisotropic nature of the wood. These findings highlight the importance of accounting for both interface behavior and wood anisotropy in the design of hybrid façades subjected to thermal loading. A tentative finite element model is proposed that utilizes isotropic wood with properties that limit the accuracy of the results obtained by the model. Full article

This study examines the contrast in the nonlinear dynamics of Thrinax radiata Lodd. ex Schult. & Schult. f. Seed germplasm explored by optical and electrical signals. By integrating chaotic attractors for the modulation of the optical and electrical measurements, the research ensures high sensitivity monitoring of seed germplasm dynamics. Reflectance measurements and electrical responses were analyzed across different laser pulse energies using Newton–Leipnik and Rössler chaotic attractors for signal characterization. The optical attractor captured laser-induced changes in reflectance, highlighting nonlinear thermal effects, while the electrical attractor, through a custom-designed circuit, revealed electromagnetic interactions within the seed. Results showed that increasing laser energy amplified voltage magnitudes in both systems, demonstrating their sensitivity to energy inputs and distinct energy-dependent chaotic patterns. Fractional calculus, specifically the Caputo fractional derivative, was applied for modeling temperature distribution within the seeds during irradiation. Simulations revealed heat transfer about 1 °C in central regions, closely correlating with observed changes in chaotic attractor morphology. This interdisciplinary approach emphasizes the unique strengths of each method: optical attractors effectively analyze photoinduced thermal effects, while electrical attractors offer complementary insights into bioelectrical properties. Together, these techniques provide a realistic framework for studying seed germplasm dynamics, advancing knowledge of their responses to external perturbations. The findings pave the way for future applications and highlight the potential of chaos theory for early detection of structural and bioelectrical changes induced by external energy inputs, thereby contributing to sample protection. Our results provide quantitative dynamical descriptors of laser-evoked seed responses that establish a tractable framework for future studies linking these metrics to physiological outcomes. Full article

The Chebei integral Immersed Tunnel introduced an innovative full-section interval casting process, where post-casting belts impose restraint effects on the full-section casting segments. To mitigate concrete cracking, this study investigates the influence of the bottom steel plate and steel bars in the post-casting belts on the mechanical behavior of full-section casting segments through comparative analysis of field tests and numerical simulations. Requirements for post-casting belt length are proposed. Key findings include: under post-casting belt restraint, the full-section casting segment’s shrinkage strain reached 348 με, with hydration heat-induced cooling and drying shrinkage contributing 60% and 40%, respectively. A temperature-dependent thermal expansion coefficient model was developed to characterize the nonlinear relationship between concrete strain and hydration heat temperature. Restraint effects diminished with increasing post-casting belt length, and the post-casting belt length should be control. At 1.6 m (Chebei design), restraint-induced tensile stress was 1.4 MPa (restraint coefficient β = 0.12), with the bottom steel plate and steel bars contributing about 70% and 30%, respectively. Relationships between post-casting belt length, stress, and restraint coefficient are established for engineering reference. These research findings have been successfully applied in the Chebei Immersed Tunnel, enabling high-quality prefabrication of full-section interval casting immersed tubes. Full article

Background: The study of non-Newtonian fluids in thin channels is crucial for advancing technologies in microfluidic systems and targeted industrial coating processes. Nanofluids, which exhibit enhanced thermal properties, are of particular interest. This paper investigates the complex flow and heat transfer characteristics of a Sutterby nanofluid (SNF) within a thin channel, considering the combined effects of magnetohydrodynamics (MHD), Brownian motion, and bioconvection of microorganisms. Analyzing such systems is essential for optimizing design and performance in relevant engineering applications. Method: The governing non-linear partial differential equations (PDEs) for the flow, heat, concentration, and bioconvection are derived. Using lubrication theory and appropriate dimensionless variables, this system of PDEs is simplified into a more simplified system of ordinary differential equations (ODEs). The resulting nonlinear ODEs are solved numerically using the boundary value problem (BVP) Midrich method in Maple software to ensure accuracy. Furthermore, data for the Nusselt number, extracted from the numerical solutions, are used to train an artificial neural network (ANN) model based on the Levenberg–Marquardt algorithm. The performance and predictive capability of this ANN model are rigorously evaluated to confirm its robustness for capturing the system’s non-linear behavior. Results: The numerical solutions are analyzed to understand the variations in velocity, temperature, concentration, and microorganism profiles under the influence of various physical parameters. The results demonstrate that the non-Newtonian rheology of the Sutterby nanofluid is significantly influenced by Brownian motion, thermophoresis, bioconvection parameters, and magnetic field effects. The developed ANN model demonstrates strong predictive capability for the Nusselt number, validating its use for this complex system. These findings provide valuable insights for the design and optimization of microfluidic devices and specialized coating applications in industrial engineering. Full article

In deepwater oil and gas transportation, Pipe-in-Pipe (PIP) systems are an effective solution for mitigating external loads while preserving internal thermal integrity. A finite element model with ITT elements and nonlinear spring contacts was developed in ABAQUS to simulate thermal expansion and contraction under extreme conditions. The coupled mechanical response of double-layer pipelines under non-uniform temperature fields and internal pressure was analyzed, focusing on stress distribution and deformation coordination between the inner and outer pipes. The inner pipe primarily sustains compressive or tensile stress depending on the thermal load direction, while the outer pipe experiences opposing stresses due to mechanical coupling. Distinct stress transfer zones are present near the pipe ends, governed by pipe-soil interaction and internal bending moments. The proposed model for double-layer pipelines under coupled thermal and internal pressure loads demonstrates a prediction accuracy within 5% as compared with benchmark numerical solutions. The simulations capture axial stress variations of up to 68% between extreme thermal expansion and contraction scenarios, with radial deformation ranging from 0.9 mm to 3.4 mm. These findings provide valuable insights into the safe and efficient design of subsea PIP systems, particularly for optimizing material selection and structural configuration in high-temperature, high-pressure environments. Full article