Search Results (72)

The underwater explosion bubble is one of the primary loads generated by underwater explosive detonations, and the presence of complex detonation products results in its unique physical evolution characteristics. Based on classical bubble dynamics theory, this paper introduces the JWL equation of state for explosives and the instantaneous detonation assumption to determine the initial boundary conditions of the explosion bubble, establishing a second-order analytical model. Addressing the mass loss during bubble pulsation, the physical mechanisms of convective mass transfer in the boundary layer and the inertial scattering of insoluble elements are analyzed. Accordingly, a modified dynamic model incorporating mass loss is established. The accuracy and reliability of the proposed model are verified through comparison with experimental data from underwater explosions. The results indicate that the inertial scattering of insoluble elements is the dominant mechanism governing bubble mass loss, while the macroscopic effects of the mass loss of detonation products primarily manifest during the secondary pressure pulsation and subsequent evolution stages. This study provides reliable theoretical predictions within the primary pulsation cycles of explosion bubble pulsation characteristics, providing theoretical support for further elucidating the underlying mechanisms of underwater explosion bubble dynamics. Full article

Inductors are critical components in power electronic systems, yet their thermal behavior is often approximated using simplified lumped models that neglect internal gradients and transient spatial effects. This paper presents a benchmarking study of analytical thermal modeling approaches for cylindrical inductors, including 0D lumped, 1D radial, and 2D radial–axial transient formulations. Starting from the general heat conduction equation in cylindrical coordinates, closed-form or semi-analytical solutions are discussed under uniform internal heat generation and convective boundary conditions. The proposed framework provides a benchmark-oriented analytical reference for selecting the appropriate thermal model complexity in reliability-oriented design of inductive components in power electronic systems. The models are applied to a representative two-layer cylindrical inductor composed of a ferrite core and a copper winding, under identical loss and cooling assumptions, considering two axial lengths in order to assess geometric influence. Steady-state temperature levels, transient responses, modal time constants, and axial gradient indicators are extracted to quantify the differences among modeling levels. The results show that the dominant thermal behavior is governed by a single slow mode with a time constant on the order of one hour. The spatially averaged temperature predicted by the 0D model deviates by less than 2.5% from the 2D solution in steady-state conditions, with the 1D model providing accurate predictions when axial gradients remain weak. Full article

Efficient thermal management is critical for maintaining the safety, durability, and performance of lithium-ion batteries used in electric vehicles (EVs). In this study, a comprehensive numerical investigation is conducted to evaluate the heat transfer characteristics of mono- and hybrid-nanofluids in a microchannel-cooled lithium-ion battery module. A three-dimensional computational model of a 5S7P battery module composed of cylindrical 21700 cells is developed. Battery heat generation during 3C high discharge rate operation is predicted using the Newman-Tiedemann-Gu-Kim (NTGK) electrochemical model, while coolant flow and heat transfer are simulated using the governing conservation equations for mass, momentum, and energy. The cooling system consists of six liquid-cooling plates with circular microchannels. The performance of water-glycol (50/50) coolant is compared with several mono nanofluids of Al₂O₃ and Cu, and hybrid nanofluids of Al₂O₃-Cu, Al₂O₃-MWCNT, Al₂O₃-Graphene, Cu-MWCNT, and Cu-Graphene across multiple coolant flow rates from 1–5 LPM. The results demonstrate that nanofluids significantly enhance convective heat transfer and reduce battery temperature compared with the conventional water-glycol coolant. Among the investigated coolants, the Al₂O₃-Cu hybrid nanofluid (0.45–0.45%) operating at 1 LPM achieves the best overall thermo-hydraulic performance with a performance evaluation criterion (PEC) of 1.065. Further analysis of nanoparticle composition ratios shows that a Cu-dominant hybrid mixture (Al₂O₃-Cu: 0.27–0.63%) slightly improves the PEC to 1.0657, indicating marginally superior cooling performance. The findings highlight the potential of hybrid nanofluids as advanced coolants for microchannel-based battery thermal management systems in EVs, particularly under moderate coolant flow conditions. Full article

Two-phase displacement between cement slurry and drilling fluid in wellbore systems is inherently nonlinear, interface-dominated, and strongly affected by geometric confinement, posing substantial challenges to efficient and stable numerical simulation. Conventional CFD approaches rely on mesh discretization and explicit interface tracking, which become computationally demanding and sensitive to grid quality in complex geometries and convection-dominated regimes. To address these limitations, this study develops a unified physics-informed neural network (PINN) framework for directly solving the coupled incompressible Navier–Stokes and Volume of Fluid (VOF) equations governing pressure-driven displacement. The framework is first validated against canonical transient flows and then applied to two-phase displacement in parallel-plate channels, semicircular bends, and a casing–annulus geometry representative of well cementing operations. The predicted velocity, pressure, and volume fraction fields exhibit strong agreement with ANSYS Fluent (2024R1) results, with relative errors generally around 5%, thereby demonstrating physical consistency and numerical stability without mesh generation or pressure–velocity splitting, while also showing favorable computational efficiency for the cases considered. Sensitivity analyses demonstrate that a smoother casing-shoe geometry significantly enhances PINN convergence, while higher Péclet numbers deteriorate training stability by increasing convection-dominated stiffness and optimization difficulty. The results demonstrate that the proposed PINN framework, with its mesh-free and geometrically flexible characteristics, is a promising approach for modeling multiphase displacement in cementing applications. Full article

We use deep Physics-Informed Neural Networks (PINNs) to simulate stratified forced convection in plane Couette flow. This process is critical for atmospheric boundary layers (ABLs) and oceanic thermoclines under global warming. The buoyancy-augmented energy equation is solved under two boundary conditions: Isolated-Flux (single-wall heating) and Flux–Flux (symmetric dual-wall heating). Stratification is parameterized by the Richardson number $(R_i\in [-1,1\left]\right),$ representing $\pm 2 °\mathrm{C}$ thermal perturbations. We employ a decoupled model (linear velocity profile) valid for low-Re, shear-dominated flow. Consequently, this approach does not capture the full coupled dynamics where buoyancy modifies the velocity field, limiting the results to the laminar regime. Novel contribution: This is the first deep PINN to robustly converge in stiff, buoyancy-coupled flows ($\mid R_i\mid \le 1$) using residual connections, adaptive collocation, and curriculum learning—overcoming standard PINN divergence (errors $>28\%$). The model is validated against analytical ($R_i=0$) and RK4 numerical ($R_i\ne 0$) solutions, achieving $L_2$ errors $\le 0.009\%$ and $L_{\mathrm{\infty }}$ errors $\le 0.023\%$. Results show that stable stratification $(R_i>0)$ suppresses convective transport, significantly reduces local Nusselt number ($Nu$) by up to $100\%$ (driving $Nu$ towards zero at both boundaries), and induces sign reversals and gradient inversions in thermally developing regions. Conversely, destabilizing buoyancy $(R_i<0)$ enhances vertical mixing, resulting in an asymmetric response: $Nu$ increases markedly (by up to $140\%$) at the lower wall but decreases at the upper wall compared to neutral forced convection. At $5–10\times$ lower computational cost than DNS or RK4, this mesh-free PINN framework offers a scalable and energy-efficient tool for subgrid-scale parameterization in general circulation models (GCMs), supporting SDG 13 (Climate Action). Full article

Accurate determination of opacity is critical for understanding radiation transport in both astrophysical and laboratory plasmas. We employ atomic data from R-Matrix calculations to investigate radiative properties in high-energy-density (HED) plasma sources, focusing on opacity variations under extreme plasma conditions. Specifically, we analyze environments such as the base of the convective zone (BCZ) of the Sun ($2\times {10}^6$ K, $N_e={10}^{23}$/cc), and radiative opacity data collected using the inertial confinement fusion (ICF) devices at the Sandia Z facility ($2.11\times {10}^6$ K, $N_e=3.16\times {10}^{22}$/cc) and the Lawrence Livermore National Laboratory National Ignition Facility. We calculate Rosseland Mean Opacities (RMO) within a range of temperatures and densities and analyze how they vary under different plasma conditions. A significant factor influencing opacity in these environments is line and resonance broadening due to plasma effects. Both radiative and collisional broadening modify line shapes, impacting the absorption and emission profiles that determine the RMO. In this study, we specifically focus on electron collisional and Stark ion microfield broadening effects, which play a dominant role in HED plasmas. We assume a Lorentzian profile factor to model combined broadening and investigate its impact on spectral line shapes, resonance behavior, and overall opacity values. Our results are relevant to astrophysical models, particularly in the context of the solar opacity problem, and provide insights into discrepancies between theoretical calculations and experimental measurements. In addition, we investigate the equation-of-state (EOS) and its impact on opacities. In particular, we examine the “chemical picture” Mihalas–Hummer–Däppen EOS with respect to level populations of excited levels included in the extensive R-matrix calculations. This study should contribute to improving opacity models of HED sources such as stellar interiors and laboratory plasma experiments. Full article

Natural convection of liquid metals under magnetic fields is a phenomenon of interest in various industrial and scientific applications, including fusion reactor blankets and magnetohydrodynamic (MHD) power systems. While the application of a magnetic field generally suppresses convection and reduces the heat transfer rate, recent studies have reported cases where the Nusselt number increases under certain magnetic field conditions. In this study, we conduct numerical simulations of natural convection in an annular container filled with a liquid metal, subject to a circumferential static magnetic field. The governing equations, incorporating both temperature and electromagnetic fields, are solved using a high-order finite difference scheme. The results show that, within a specific range of parameters, the Nusselt number increases at moderate Hartmann numbers, even under low Rayleigh number conditions. Notably, this enhancement in heat transfer occurs alongside a reduction in kinetic energy, indicating that convective strength is not necessarily the dominant factor. Further analysis confirms that this phenomenon weakens and eventually vanishes as the Rayleigh number approaches 10⁶. These findings provide evidence that magnetic field-induced heat transfer enhancement can occur without a corresponding increase in convective motion, thereby challenging conventional assumptions in magnetoconvection theory. Full article

The convection-diffusion-reaction (CDR) equation is a fundamental mathematical model for simulating the transport of pollutants. It is a crucial tool for addressing global environmental challenges. However, most existing computational solutions are proprietary and inaccessible, making the development of open-source educational platforms with advanced stabilization capabilities necessary. This study developed and validated a computational framework that solves CDR equations using algebraic sub-grid scale (ASGS) stabilization. The research addressed the fundamental challenge of spurious oscillations that emerge in standard Galerkin formulations when convective transport significantly exceeds diffusive processes. This is a prevalent issue in transport phenomena modeling. A novel, Python-based educational software platform called CDR-Solv was developed to demonstrate the effectiveness of ASGS stabilization across polynomial degrees ranging from linear to cubic approximations. Numerical experiments with minimal diffusion coefficients showed that numerical instabilities were successfully eliminated while maintaining solution accuracy across various source term configurations. The stabilization parameter, ${\tau }_K$, was instrumental in achieving computational stability without compromising mathematical rigor. Comparative analysis revealed the superior performance of higher-order approximations in capturing boundary layer phenomena and sharp gradient regions. The primary contribution of this study is the development of an open-source educational platform that provides access to advanced stabilization techniques and algorithmic transparency. The CDR-Solv framework also allows for the systematic exploration of the effects of selecting different polynomial degrees on solution quality in transport-dominated regimes. Full article

This study presents a three-dimensional numerical analysis of natural convection during the postharvest storage of corn and wheat in a galvanized steel silo with a conical roof and floor, measuring 3 m in radius and 18.7 m in height, located in the Bajío region of Mexico. Simulations were carried out specifically for December, a period characterized by cold ambient temperatures (10–20 °C) and comparatively lower solar radiation than in warmer months, yet still sufficient to induce significant heating of the silo’s metallic surfaces. The governing conservation equations of mass, momentum, energy, and species were solved using the finite volume method under the Boussinesq approximation. The model included grain–air sorption equilibrium via sorption isotherms, as well as metabolic heat generation: for wheat, a constant respiration rate was assumed due to limited biochemical data, whereas for corn, respiration heat was modeled as a function of grain temperature and moisture, thereby more realistically representing metabolic activity. The results, obtained for December storage conditions, reveal distinct thermal and hygroscopic responses between the two grains. Corn, with higher thermal diffusivity, developed a central thermal core reaching 32 °C, whereas wheat, with lower diffusivity, retained heat in the upper region, peaking at 29 °C. Radial temperature profiles showed progressive transitions: the silo core exhibited a delayed response relative to ambient temperature fluctuations, reflecting the insulating effect of grain. In contrast, grain at 1 m from the wall displayed intermediate amplitudes. In contrast, zones adjacent to the wall reached 40–41 °C during solar exposure. In comparison, shaded regions exhibited minimum temperatures close to 15 °C, confirming that wall heating is governed primarily by solar radiation and metal conductivity. Axial gradients further emphasized critical zones, as roof-adjacent grain heated rapidly to 38–40 °C during midday before cooling sharply at night. Relative humidity levels exceeded 70% along roof and wall surfaces, leading to condensation risks, while core moisture remained stable (~14.0% for corn and ~13.9% for wheat). Despite the cold ambient temperatures typical of December, neither temperature nor relative humidity remained within recommended safe storage ranges (10–15 °C; 65–75%). These findings demonstrate that external climatic conditions and solar radiation, even at reduced levels in December, dominate the thermal and hygroscopic behavior of the silo, independent of grain type. The identification of unstable zones near the roof and walls underscores the need for passive conservation strategies, such as grain redistribution and selective ventilation, to mitigate fungal proliferation and storage losses under non-aerated conditions. Full article

The numerical simulation of unsteady, high-Reynolds-number incompressible flows governed by the Navier–Stokes (NS) equations presents significant challenges in computational fluid dynamics, primarily concerning numerical stability and computational efficiency. Standard Galerkin finite element methods often suffer from non-physical oscillations in convection-dominated regimes, while the multiscale nature of these flows demands prohibitively high computational resources for uniformly refined meshes. This paper proposes an improved Galerkin framework that synergistically integrates a Variational Multiscale Stabilization (VMS) method with an adaptive mesh refinement (AMR) strategy to overcome these dual challenges. Based on the Ritz–Galerkin formulation with the stable Taylor–Hood ($P_2-P_1$) element, a VMS term is introduced, derived from a generalized $\theta$-scheme. This explicitly constructs a subgrid-scale model to effectively suppress numerical oscillations without introducing excessive artificial diffusion. To enhance computational efficiency, a novel a posteriori error estimator is developed based on dual residuals. This estimator provides the robust and accurate localization of numerical errors by dynamically weighting the momentum and continuity residuals within each element, as well as the flux jumps across element boundaries. This error indicator guides an AMR algorithm that combines longest-edge bisection with local Delaunay re-triangulation, ensuring optimal mesh adaptation to complex flow features such as boundary layers and vortices. Furthermore, the stability of the Taylor–Hood element, essential for stable velocity–pressure coupling, is preserved within this integrated framework. Numerical experiments are presented to verify the effectiveness of the proposed method, demonstrating its ability to achieve stable, high-fidelity solutions on adaptively refined grids with a substantial reduction in computational cost. Full article

To address the challenge of heat transfer enhancement in the condensation of steam with non-condensable gases on a vertical wall under natural convection conditions, an improved boundary layer model with coupled multi-physics field was proposed in this paper, and traditional theoretical limitations were broken through by innovations. The particle swarm optimization algorithm was first introduced into the solution of the condensation boundary layer, and the convergence difficulty in the laminar–turbulent transition region under infinite boundary conditions was overcome. A coupled momentum–energy–mass equation system that simultaneously considered temperature–concentration dual-driven gravity terms and liquid film drag–suction dual effects was established, and higher computational efficiency and accuracy were achieved. A new mechanism where the concentration boundary layer dominated heat transfer resistance under the coupled action of the Prandtl number (Pr) and Schmidt number (Sc) was revealed. Experimental validation demonstrated that a prediction error of less than 5% was exhibited by the model under typical operating conditions of passive containment cooling systems (pressures of 1.5–4.5 atm and subcooling temperatures of 14–36 °C), and a theoretical tool for high-precision condensation heat transfer design was provided. Full article

Longitudinal dispersion coefficient is a key parameter governing solute transport in porous media, with significant implications for various industrial processes. However, the impact of microfractures on the longitudinal dispersion coefficient remains insufficiently understood. In this study, pore-scale direct numerical simulations are performed to analyze solute transport in microfractured porous media during unstable miscible displacement. Spatiotemporal concentration profiles were fitted to the analytical solution of the convection–dispersion equation to quantify the longitudinal dispersion coefficient across different microfracture configurations. The results indicate that the longitudinal dispersion coefficient is highly sensitive to microfracture characteristics. Specifically, an increased projection length of microfractures in the flow direction and a reduced lateral projection length enhance longitudinal dispersion at the outlet. When Peclet number ≥1, the longitudinal dispersion coefficient follows a three-stage variation pattern along the flow direction, with microfracture connectivity and orientation dominating its scale sensitivity. Furthermore, both diffusion-dominated and mixed advective-diffusion regimes are observed. In diffusion-dominated regimes, significant channeling alters the applicability of traditional scaling laws, with the relationship between longitudinal dispersion coefficient and porosity holding only when the Peclet number is below 0.07. These results provide a comprehensive scale-up framework for CO₂ miscible flooding in unconventional reservoirs and CO₂ storage in saline aquifers, offering valuable insights for the numerical modeling of heterogeneous reservoir development. Full article

A kind of finite difference Hermite WENO (HWENO) method is presented in this paper to deal with convection-dominated convection-diffusion equations in uniform grids. The benefit of the HWENO method is its compactness, allowing great accuracy to be attained in the solution’s smooth regions and maintaining the essential nonoscillation in the solution’s discontinuities. We discretize the convection term using the HWENO method and the diffusion term using the Hermite central interpolation schemes. However, it is difficult to deal with mixed derivative terms when solving two-dimensional problems using the HWENO method mentioned. To address this problem, we also employ the Hermite interpolation approach, which can keep the compactness. Lastly, we apply this method to two-dimensional Navier-Stokes problems that are incompressible. The efficiency and stability of the presented method are illustrated through numerous numerical experiments. Full article

The efficient mixing of fluids at microscale dimensions presents challenges due to the dominant laminar flow regime which restricts convective mixing. This study introduces a numerical analysis of a novel microrobotic mixing system with a levitated propeller robot, driven by magnetic fields, within a Y-shaped microchannel with a square cross-section (500 × 500 μm). Our research investigates the fluid mixing effectiveness facilitated by the microrobot through various levitation heights and orientations to enhance the mixing index (MI). This index is tested under different conditions by leveraging the dynamics of the propeller robot, characterized by adjustable roll and pitch angles and varying levitation heights. The numerical simulations, conducted using COMSOL^® (Finite Element Method, FEM) software, integrate Maxwell’s equations for magnetic field interaction with momentum and transport-diffusion equations to analyze fluid dynamics within the microchannel. Results indicate that the propeller robot can achieve an MI of up to 98.94% at a 150 μm levitation height and 1500 rpm propeller speed within 3 s. Additionally, the study examines the impact of propeller speed, Reynolds number, and robot length on mixing performance, providing comprehensive guidance for optimizing microscale fluid mixing in lab-on-a-chip applications. Full article

The natural convection heat transfer of a trihybrid nanofluid comprising Fe₂O₃, MoS₂, and CuO nanoparticles dispersed in water (Fe₂O₃ + MoS₂ + CuO/H₂O) has been investigated within a cavity exposed to a uniform magnetic field. Three cold fins were strategically positioned on the top, right, and left walls of the enclosure. The study employs numerical simulations conducted using a custom-developed FORTRAN code. The computational approach integrates the finite volume method and full multigrid acceleration to solve the coupled governing equations for continuity, momentum, energy, and entropy generation, along with the associated boundary conditions. Prior to obtaining the results, a meticulous parameterization process was undertaken to accurately capture the fluid dynamics and thermal behavior characteristic of this geometric configuration. The findings underscored the key parameters’ significant impact on the flow structure and thermal performance. The results revealed that natural convection is more dominant at high Rayleigh and low Hartmann numbers, leading to higher Nusselt numbers and stronger dependence on the tilt angle α. Moreover, the optimal heat transfer conditions were obtained for the following parameters: Ha = 25, α = 45°, ϕ = 6%, and Ra = 10⁶ with a rate of 4.985. This study offers valuable insights into achieving a balance between these competing factors by determining the optimal conditions for maximizing heat transfer while minimizing entropy generation. The findings contribute to enhancing the design of thermal systems that utilize magnetic nanofluids for efficient heat dissipation, making the research particularly relevant to advanced cooling technologies and compact thermal management solutions. Full article