International Journal for Numerical Methods in Engineering最新文献

We propose a new reduced-order modeling (ROM) strategy for tackling parametrized Darcy-flow systems in which the constraint is given by mass conservation. Our approach employs classical neural-network architectures and supervised learning, but it is constructed in such a way that the resulting ROM is guaranteed to satisfy the linear constraints exactly. The procedure is based on a splitting of the solution into a particular solution satisfying the constraint and a homogenous solution. The homogeneous solution is approximated by mapping a suitable potential function, generated by a neural-network model, onto the kernel of the constraint operator. For the particular solution, instead, we propose an efficient spanning-tree algorithm. Starting from this paradigm, we present three approaches that follow this methodology, obtained by exploring different choices of the potential spaces: derived either via proper orthogonal decomposition (POD) or using the properties of a differential complex. To demonstrate the effectiveness of the proposed strategies and to emphasize their advantages over neural-network regression approaches, we present a series of numerical experiments, ranging from mixed-dimensional problems to nonlinear systems.

This paper generalizes recent advances on quadratic manifold (QM) dimensionality reduction by developing kernel methods-based nonlinear-augmentation dimensionality reduction. QMs, and more generally feature map-based nonlinear corrections, augment linear dimensionality reduction with a nonlinear correction term in the reconstruction map to overcome approximation accuracy limitations of purely linear approaches. While feature map-based approaches typically learn a least squares optimal polynomial correction term, we generalize this approach by learning an optimal nonlinear correction from a user-defined reproducing kernel Hilbert space. Our approach allows one to impose arbitrary nonlinear structure on the correction term, including polynomial structure, and includes feature map and radial basis function-based corrections as special cases. Furthermore, our method has relatively low training cost and has monotonically decreasing error as the latent space dimension increases. We compare our approach to proper orthogonal decomposition and several recent QM approaches on data from several example problems.

The explicit moving particle simulation (EMPS) method, due to its pressure calculation through the equation of state, has high computational efficiency and broad prospects for engineering applications. However, the conventional EMPS method has issues of numerical instability such as high-frequency oscillations and discontinuous distributions of pressure. In this paper, to improve the accuracy and stability of numerical simulations, an enhanced δ_MPS-EMPS method was proposed, in which a new enhanced equation of state and a diffusion term of particle number density were developed. The new enhanced equation of state includes the terms of the velocity divergence and the particle number density. It describes the relationship between pressure and fluid density through the changes in the velocity field and the particle number density field, resulting in a smooth pressure. It is worth noting that the artificial bulk modulus K in this equation is accurately calibrated using the specified pressure field calculated by the MPS method at the first time step. Additionally, the particle number density is corrected by introducing a diffusion term into the mass conservation equation. This approach can reduce high-frequency pressure oscillations by filtering out non-physical density fluctuations. The proposed method was evaluated and validated through simulations of four benchmark cases, including a hydrostatic pressure tank, the deformation of an elliptical droplet, the Taylor-Green vortex flow, the 2D water dam-break flow, and the evolution of a square patch of fluid. The simulation results proved the effectiveness and robustness of the enhanced δ_MPS-EMPS method.

Engineers can accomplish customised thermal properties that maximise fluid performance in various operating scenarios by hybridising alloy nanoparticles. The present study provides new insights into the heat and mass transfer of the squeezing flow of hybrid nanofluid between the parallel Riga plates under the impacts of shape effects, non-uniform heat generation/absorption and activation energy. A suitable similarity approach is implemented to transform governing equations into non-linear systems of ordinary differential equations and is solved numerically using the bvp-5c technique. The results show that the velocity profile increases near the lower plate and decreases near the upper plate. In contrast, both the temperature and concentration profiles rise for growing values of the squeezing number. It is found that both temperature and concentration profile rise for augmented values of the Biot number parameters. Also, engineering factors are discussed graphically with specific parameters. Further, properly aligned residual plots and less variance in fitted values reveal the response surface methodology's great accuracy and validity. And from the sensitivity analysis, it is observed that the Skin friction has a positive influence for Hartman and porosity parameters and negative influence for squeezing parameter also the Nusselt number has a negative sensitivity for both squeezing and Biot number and less positive sensitive to the Hartman number.

This study introduces a microstructure-temperature coupled framework to analyze the collapse behavior of non-API high-toughness casings (BG110V/BG140V), addressing a 16%–18% prediction error in conventional API standards through experimental validation, 3D modeling, and a novel multifactor correction formula. Full-scale collapse tests at 50°C and 85°C revealed temperature-dependent elastoplastic behavior. BG140V showed higher-pressure resistance (75.89 MPa at 50°C vs. 59.47 MPa for BG110V), attributed to its finer grain structure (ASTM 10.0 vs. BG110V's 6.5). Thermal softening reduced yield strength by 4.3% (BG110V) and 2.4% (BG140V) at 85°C, correlating with accelerated dislocation mobility (1.5 × 10⁸/cm² vs. 10⁸/cm² at 50°C). Finite element simulations (mesh size: 5 mm global/2 mm local) replicated collapse pressures with ≤ 3% error, validated against experimental data. Microstructural analysis identified dual failure mechanisms: axial collapse initiated at stress-concentrated zones (100–150 mm from ends, 30% above average stress), while radial failure originated from surface defects (0.1 mm scratches amplifying strain by 40%). A novel multifactor correction formula integrating API standards with microstructure-aware terms (grain size: n = 0.5 Hall–Petch exponent; defect tolerance: ovality ≤ 2%) reduced collapse pressure prediction errors from 16% to 18% (API) to ≤ 6.7%. The model quantifies temperature effects via Arrhenius-type decay (α_T = 0.0032 for BG110V) and inclusion-driven microcrack thresholds (C_inclusion ≤ 1.2 vol%). These findings establish a microstructure-property-performance framework for optimizing casing design in ultra-deep reservoirs, balancing computational efficiency with industrial reliability. Recommendations include expanding validation to cyclic fatigue scenarios and hydrogen embrittlement environments to advance next-generation well integrity protocols.