Search Results (16,220)

The transient pressure rise in occupants’ ears during vehicle door closure remains a key challenge for cabin comfort, but existing simulation methods often lack accuracy or efficiency. This study develops an integrated experimental–CFD–theoretical framework. A high-fidelity vehicle model was constructed from point cloud data and validated against airtightness and door-closure tests. A theoretical model was derived and calibrated using flow hysteresis and fluctuation attenuation coefficients from CFD results. Uncontrolled leakage was represented by distributed circular holes, and the one-way flow through the pressure relief valve was implemented numerically. The refined CFD model reduced the peak-pressure and amplitude errors from 6.33% and 17.62% to 1.75% and 2.66%, respectively. The calibrated theoretical model achieved 93.94% accuracy in pressure amplitude relative to the CFD results, with much lower computational cost. An optimization strategy combining early valve opening with an auxiliary fan at the relief valve reduced peak pressure, amplitude, and pressure change rate by 25.88%, 22.83%, and 41.23%, respectively. By deeply integrating experiments, simulation, and theory with refined modeling of key physical features, this research overcomes the accuracy and efficiency limitations of traditional methods, offering a systematic solution for cabin comfort optimization and advancing forward-development capabilities in vehicle NVH. Full article

This paper develops and rigorously analyzes a fully discrete upwind interior penalty discontinuous Galerkin (IPDG) scheme for simulating solute transport in two-dimensional variable-order fractional mobile–immobile media. The temporal variable-order Caputo derivative is discretized via a Grünwald–Letnikov approximation in conjunction with a first-order backward difference, while the spatial discretization employs an IPDG method featuring an upwind numerical flux for the convection term and a penalty formulation for the diffusion operator. Under the physically relevant assumption of a divergence-free velocity field, we establish the unconditional stability of the proposed scheme. A comprehensive error analysis in the $L^2$ norm yields a convergence rate of $O(\Delta t+h^{min(k+1,s)-\chi -1/2})$, explicitly linking the polynomial degree k, solution regularity s, and the penalty variant $\chi$. Numerical experiments in two dimensions are conducted to verify the accuracy and robustness of the proposed scheme in simulating anomalous transport phenomena in subsurface environments. Full article

To address the limitations of individual battery thermal management technologies, this study proposes a hybrid battery thermal management system coupling static immersion cooling with refrigerant-based direct cooling. The system employs parallel upper and lower direct cooling plates, with the refrigerant flow split regulated to enhance buoyancy-driven convection within the sealed immersion chamber. Numerical simulations are conducted to compare an R134a direct cooling system with a 50% ethylene glycol solution indirect cooling system over total flow rates of 6–18 L⋅min⁻¹ and upper plate flow ratios of 10–90%. The effects of the total flow rate and flow distribution on the pressure drop, battery temperature, temperature uniformity, flow characteristics, and pumping power are systematically evaluated. The R134a direct cooling system reduces the average battery temperature by approximately 0.5–1.0 °C compared with the indirect cooling system. Increasing the upper plate flow ratio strengthens the natural convection within the immersion chamber and alleviates vertical temperature non-uniformity, whereas excessive flow redistribution weakens the cooling capacity of the lower plate. A Kriging surrogate model coupled with a multi-objective genetic algorithm identifies the optimal condition at a total flow rate of 8.82 L⋅min⁻¹ and an upper plate flow ratio of 56.45%. Relative to the baseline condition of 9 L⋅min⁻¹ and an upper plate flow ratio of 10%, the optimized condition reduces the average battery temperature, maximum temperature difference, and pumping power by 10.1%, 7.2%, and 52.1%, respectively, while maintaining a low cell temperature standard deviation of 0.032 °C. Full article

In this paper, we develop modified inertial hybrid projection methods to obtain common solutions of generalized mixed equilibrium problems involving monotone and Lipschitz continuous operators. The use of alternative half-space constructions reduces both projection steps and operator evaluations. Strong convergence is established under standard conditions. The proposed approach is further extended to common solutions of variational inequality problems, and numerical results are provided for illustration. Full article

Aircraft electrification requires high-performance thermal management systems able to cool down power-plants with increasing power densities in electric aircraft motors. The demanding mission profiles and the request for compact electric components, in fact, induce high temperatures in power-plant systems that must be cooled by proper thermal management systems, to assure efficiency and reliability. This paper investigates and compares two promising approaches for the cooling of megawatt-order electric motors for aviation applications: nanofluid-based liquid cooling and radial-tube systems. Nanofluids are an innovative approach to system cooling leveraging the physical properties of the coolant; radial tubes, conversely, represent a structural solution aimed at improving the heat removal. In particular, nanofluids are composed of colloidal suspensions of nanoparticles in a base fluid, enabling enhanced thermal conductivity and convective heat transfer coefficients compared to conventional coolants. Radial tubes improve heat removal through optimized conduction paths and increased surface-to-volume ratios without altering the working fluid. Through numerical analysis carried out by using commercial Computational Fluid Dynamics (CFDs) tools, results highlight the main advantages of the two systems: nanofluids provide a significant average heat transfer enhancement on the tooth, while the radial tubes involve a strong increase in the global heat exchange despite a larger oil flow rate. Full article

Nanocrystalline (NC) alloys hold significant promise as structural alloys due to their superior mechanical properties over the traditional coarser grained microcrystalline alloys. Often, there is an optimal range of mean grain size for most metals about which maximum material strength can be realized. In the context of NC alloys, stabilization of the grain size in this optimal range is one of the primary synthesis challenges. A large volume fraction of NC alloy microstructure is occupied by grain boundaries (GBs). Since GBs increase the internal surface energy of the system, during solidification and grain growth phases, there is a tendency to minimize GBs through grain coarsening. However, in NC alloys, phenomena like GB–solute segregation and solute precipitation are active and mitigate grain growth and thus stabilize the desired small grains. Numerically modeling these phenomena of GB–solute interactions, and the evolution of these stabilized GBs under mechanical load, is of immense interest to the NC alloy community. To enrich the numerical modeling formulations available in this space, we present here a phase-field-method-based numerical framework to model GB segregation, solute precipitation and effect of external loading on NC alloys. While some of these effects have been modeled in isolation, a unified treatment of the solute–GB segregation-related effects and its coupling with mechanics has not be considered in the literature. We present a three-dimensional, finite element method (FEM)-based, finite-strain phase-field formulation for modeling grain evolution and microstructure stabilization in NC alloys. Beyond the formulation and its computational implementation, various case studies demonstrate the applicability of this framework. Further, thermodynamic and kinetic arguments are provided based on the evolution of GB energy to explain the effects of solute drag, GB pinning and mechanical deformation. Full article

Bidirectional DC–DC converters are key interfaces in battery energy storage systems, electric vehicles, fuel cell vehicles, and DC microgrids, where transparent mathematical models are required for simulation, controller evaluation, and energy-flow analysis. This paper presents a hybrid index matrix framework for the Python-based modeling of a bidirectional buck–boost converter coupled to a first-order Thevenin battery model. In contrast to a classical state-space formulation, the index matrix is used as a label-aware model-assembly layer: component equations are aligned by explicit row and column identifiers and subsequently projected into ordered numerical matrices for solution. Charging, idle, and discharging equations are solved using a fixed-step backward-Euler procedure, and a PI current controller with duty-cycle saturation and anti-windup regulates the power-flow direction. A conventional switched ODE implementation is retained only as a software-level numerical-consistency check between two implementations of the same assumptions; it is not presented as experimental validation or as an independent physical benchmark. For the reported 60 s current profile, the model gives a current RMSE of 0.0863 A and a peak current of 4.3684 A, corresponding to 9.2094% overshoot at the idle-to-discharge transition. The power-integration balance is 1.6091 Wh input, 1.5868 Wh output, and 0.0223 Wh estimated loss under the adopted conduction-oriented loss model. The conditional one-step sensitivity matrices have a spectral radius of 1.00000 in all three modes; the unit eigenvalue is consistent with the slowly varying SOC state, while the remaining electrical eigenvalues lie inside the unit circle. These eigenvalue results are interpreted as local non-divergence diagnostics rather than proof of asymptotic closed-loop or switched-system stability. The framework provides a transparent and reproducible numerical workflow, while experimental validation, detailed switching-level loss modeling, step-size convergence, and formal closed-loop/switched-system stability analysis remain necessary future work. Full article

In this paper, we study the existence of positive solutions for a nonlinear fifth-order boundary value problem arising from fully developed couple-stress fluid flow through a porous channel. Under a natural restriction relating the couple-stress and permeability parameters, the associated linear operator factorizes into two positive second-order operators; we construct the corresponding Green function, show that it is strictly positive, and obtain it in an explicit closed form. By reformulating the problem as a completely continuous operator on a cone in $C[0,1]$ and applying Krasnosel’skiĭ’s fixed point theorem of cone compression–expansion type, sufficient conditions for the existence of positive solutions are established in terms of the load parameter. The positivity of the velocity further yields a strictly positive cumulative-flow solution on $(0,1]$. Several examples and a numerically calibrated application are given to illustrate the main results. Full article

End-of-life (EoL) electric vehicle (EV) batteries comprise numerous interconnected components with complex topological and precedence relationships. These constraints significantly increase the difficulty of disassembly sequence planning (DSP), as feasible sequences must satisfy multiple dependency requirements. Moreover, the large number of possible disassembly alternatives creates a vast search space, making sequence optimisation susceptible to combinatorial explosion and convergence to local optima. Therefore, effective DSP requires both robust constraint-handling mechanisms to ensure sequence feasibility and efficient optimisation strategies to identify high-quality solutions. To address these challenges, this paper proposes a disassembly sequence optimisation method that integrates a hard-constraint rule base, the linear upper confidence bound (LinUCB) algorithm, and the Bees Algorithm (BA). First, a disassembly-oriented hard-constraint rule base is developed to standardise the identification of component topological relationships and precedence constraints, thereby ensuring the generation of feasible disassembly sequences. A LinUCB-based contextual adaptive operator-selection mechanism is subsequently introduced to dynamically select neighbourhood operators according to the current search state. A weighted multi-criteria evaluation function incorporating disassembly time, payment cost, and human–robot utility is integrated into the BA. Two representative EoL-EV battery case studies with different levels of structural complexity are used for validation. Across 50 independent runs, LinUCB-BA reduced the mean normalised weighted objective value by 39.01% and 28.12% relative to simplified swarm optimisation (SSO) and teaching–learning-based optimisation (TLBO), respectively, in the 27-component case, and by 7.19% and 2.33% in the 16-component case. Compared with the enhanced discrete Bees Algorithm (EDBA) ablation baseline, further reductions of 1.98% and 0.43% were achieved, together with lower run-to-run variability. These results indicate that the proposed framework is effective for the two investigated battery disassembly scenarios, while broader validation across additional battery architectures and operating conditions remains necessary. Full article

Stable motion of differential-drive mobile robots on uneven surfaces remains a challenging control problem because local variations in rolling resistance and wheel–terrain interaction generate asymmetric traction forces, trajectory deviation, and heading instability. This study investigates a dual-motor mobile robot in which the left and right driving wheels are actuated independently and coordinated through a coupled control structure that simultaneously regulates linear speed and suppresses yaw motion. A control-oriented nonlinear dynamic model is developed by combining the longitudinal and yaw dynamics of the platform with first-order actuator models and resistance terms that represent uneven-terrain effects through side-dependent rolling losses. Based on this model, a coordinated control law is formulated to redistribute the control effort between the two drives in response to both speed and angular-velocity errors. Numerical experiments are carried out for nominal motion, localized asymmetric resistance, prolonged uneven-surface excitation, robustness-boundary analysis, and actuator-dynamics sensitivity. The results show that the proposed strategy improves directional stability under uneven-surface disturbances, limits the degradation of forward motion, and enlarges the admissible operating region compared with decoupled drive action. The study demonstrates that coordinated inter-wheel control provides a physically interpretable and computationally efficient solution for enhancing the stability of wheeled mobile robots operating on irregular terrain. Full article

Matrix differential equations with time delay are crucial to the modeling of complex multivariate systems. However, the existing semi-analytic Laplace residual power series method (LRPSM) literature mainly focuses on scalar or no-delay problems, and lacks rigorous theoretical guarantees for matrix-valued time delay systems. This study systematically generalizes LRPSM to the linear time-delay matrix differential equation $X^{\prime }\left(t\right)=AX\left(t\right)+BX(t-\tau )+F\left(t\right)$, where $X\left(t\right)\in {\mathbb{R}}^{n\times n}$, A and B are constant matrices, $\tau >0$ is a constant delay, and the forcing term $F\left(t\right)$ and the history function $\mathrm{\Phi }\left(t\right)$ are analytic. The method of steps is employed to construct the solution piecewise: on each local interval, the solution is expanded asymptotically in the Laplace domain, and the coefficients are determined recursively via the Laplace residual function. We establish local error bounds on the initial interval and derive a global error propagation bound across successive delay interfaces using a variation-of-constants framework. Numerical experiments, including non-diagonal matrices, non-zero history functions, and multi-interval tests, illustrate the effectiveness of the approach. The proposed method reduces exactly to the standard LRPSM for scalar cases, demonstrating its validity as a natural and rigorous generalization of the existing semi-analytical framework. Full article

In this study, the double-neutral (3P + 2N) configuration, considered a traditional solution for busbar systems subjected to overcurrent and thermal stresses under harmonic loads, and alternative cross-sectional expansion (from 6 mm × 55 mm to 6 mm × 65 mm) scenarios are investigated using a multidisciplinary approach. In this context, the electrical, electromagnetic, current density distributions, and magnetic flux densities (B_max) of the systems are modeled in the COMSOL Multiphysics® (AC/DC Module 6.2 version) environment; the obtained q″ (W/m³) loss maps were transferred to FLOEFD convective airflow (CFD) simulations as volumetric heat sources, and steady-state electro-thermal analyses were performed. Convergence tests were conducted with the BiCGStab solver to ensure numerical stability, solver convergence, and spatial grid independence, and high accuracy was obtained at a margin of error of 3.5222 × 10⁻⁴. The findings showed that the proximity effect, due to the close placement of the pair of neutral conductors at the 150 Hz harmonic frequency, increased the current density to 4.68 A/mm² and turned the neutral line into an additional heat source. In contrast, in the 4-conductor scheme where all conductor cross-sections were increased by 18.18%, the magnetic flux density at 150 Hz was suppressed from 27.21 mT to 23.50 mT, and the current density was distributed more homogeneously, optimizing the temperature rise limits (ΔT). Furthermore, the techno-economic cost analysis conducted revealed that, under premium scenarios for LME raw copper and processed bar copper, the application of increased cross-sectional area offered an optimization that was approximately 5.77% more economical per meter (approximately $36 USD for a standard 3 m length) compared to the double-neutral configuration. Consequently, it has been proven that in BTS designs, not only total harmonic distortion (THD) but also the triplen harmonic ratio, frequency-dependent AC resistance (R_ac) variations, and material mass–cost balance should be considered together. Full article

Solar thermal energy storage systems play a vital role in bridging the gap between intermittent solar availability and continuous energy demand; however, their efficiency is severely constrained by the inherently low thermal conductivity of phase change materials. Integrating physical heat transfer enhancements, such as radial fins, offers a practical solution, but evaluating these non-linear thermal dynamics across diverse design configurations typically incurs heavy computational costs. To address this challenge, this research investigates an artificial intelligence-based predictive framework capable of accurately modeling complex phase change dynamics in fin-configured storage tanks. Utilizing high-fidelity 2D Computational Fluid Dynamics simulation data of a stainless-steel double-tube storage tank filled with RT-50 paraffin wax across 10, 20, and 29 fin configurations, a Multi-Layer Perceptron Artificial Neural Network trained with the Bayesian Regularization algorithm was developed. The model predicts liquid fraction, latent heat distribution, and buoyancy-driven natural convection (Reynolds number) based on fin count and time. The optimal architecture, featuring 30 hidden neurons, achieved exceptional predictive precision, yielding a coefficient of determination of 0.99999, along with individual Mean Squared Error values of 1.29 × 10⁻³ for liquid fraction, 5.73 × 10⁻¹ for latent heat distribution, and 2.64 × 10⁻⁵ for Reynolds number, with average prediction deviation rates consistently below 0.5%. These results demonstrate that high-precision surrogate modeling can effectively replace computationally intensive numerical simulations, offering significant practical implications for the real-time thermal monitoring, rapid design optimization, and intelligent control of advanced solar energy storage technologies. Full article

This study investigates the stability of three-dimensional (3D) tunnel roofs with varied burial depth in saturated rock strata following the Hoek–Brown (HB) failure criterion. Within the framework of limit analysis, a 3D kinematic collapse mechanism for tunnel roofs, incorporating the existence of pore water pressure, is developed, and corresponding stability indices are formulated. Numerical methods are utilized to determine the optimal solutions of these indices. A comprehensive parametric study evaluates the influence of 3D geometric characteristics, HB parameters, and burial depth on tunnel stability. Results demonstrate the evolution of tunnel-roof stability as the critical depth-to-span ratio C/R increases from shallow- to deep-buried conditions; for the parameter combinations examined in this study, the critical C/R separating the two mechanisms ranges approximately from 0.15 to 2.0, depending on the rock-mass properties, pore-pressure condition, and tunnel geometry. Furthermore, stability charts correlating supporting pressure and factor of safety (FoS) in saturated strata are proposed to offer practical design guidance. Findings indicate that neglecting pore water pressure may significantly underestimate the structural stability, thereby emphasizing the necessity of incorporating hydrogeological effects in tunnel design. The proposed stability assessment framework provides theoretical support for the safe development of deep underground spaces under complex geological conditions, including deep energy exploitation, underground storage facilities, and related geotechnical engineering applications. Full article

In this work, we introduce an efficient spectral Galerkin method for solving one-dimensional time-fractional reaction–subdiffusion equations. Traditional fractional operators often pose severe computational challenges and exhibit weak singularities near the initial time. To address these issues, we analytically transform the governing differential equation into a weakly singular Volterra integral equation. The numerical solution is then constructed in a two-dimensional tensor-product space using orthogonal Legendre cardinal functions on both Gauss and Gauss–Lobatto grids. A major computational advantage of this cardinal basis is that it eliminates the need for expensive numerical quadratures when assembling the operational matrices. From a theoretical view, we establish a rigorous a priori error bound and a fully computable a posteriori error estimator based on the residual. The analysis confirms that the convergence rate is governed algebraically by the Sobolev regularity of the exact solution, naturally accelerating to exponential (spectral) convergence for sufficiently smooth profiles. Extensive numerical experiments validate these theoretical claims, demonstrating that the proposed framework offers significant improvements in accuracy and efficiency. Full article