Journal of Computational Physics最新文献_第10页

The KAN-MHA model: A novel physical knowledge based multi-source data-driven adaptive method for airfoil flow field prediction

IF 3.8 2区物理与天体物理

Journal of Computational Physics Pub Date : 2025-02-11 DOI: 10.1016/j.jcp.2025.113846

Siyao Yang , Kun Lin , Annan Zhou

{"title":"The KAN-MHA model: A novel physical knowledge based multi-source data-driven adaptive method for airfoil flow field prediction","authors":"Siyao Yang , Kun Lin , Annan Zhou","doi":"10.1016/j.jcp.2025.113846","DOIUrl":"10.1016/j.jcp.2025.113846","url":null,"abstract":"<div><div>The power generation efficiency and operational safety of wind turbines depend heavily on the aerodynamic performance of their airfoils, which is primarily controlled by the flow field. Traditional methods for predicting this flow field rely on solving the Navier-Stokes (NS) equations, which are computationally expensive and inefficient. To address these challenges, this paper proposes a novel neural network model, the KAN-MHA, which integrates a Kolmogorov-Arnold Network (KAN) with a Multi-Head Attention (MHA) mechanism, leveraging multi-source datasets that include wind tunnel experiments, XFoil results, and CFD simulations. By incorporating physical constraints, the KAN-MHA model achieves precise predictions of flow fields with a simpler architecture and reduced computational cost. Experimental results indicate that, compared to traditional multilayer perceptron (MLP) networks, the KAN-MHA model achieves a substantial reduction in testing loss, while maintaining a significantly simpler architecture with fewer neurons. Through an analysis of the attention weights in the MHA mechanism, we found that MHA effectively guides the network to focus on regions with more intricate flow variations, thereby enhancing the model's ability to capture subtle flow field features with higher accuracy. As a result, the model demonstrates excellent prediction performance for aerodynamic coefficients and effectively identifies stall behavior in airfoils at high angles of attack. This work provides a novel approach for the design and optimization of wind turbine airfoils, offering valuable insights for enhancing aerodynamic performance under complex flow conditions.</div></div>","PeriodicalId":352,"journal":{"name":"Journal of Computational Physics","volume":"528 ","pages":"Article 113846"},"PeriodicalIF":3.8,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Entropy conservative discretization of compressible Euler equations with an arbitrary equation of state

IF 3.8 2区物理与天体物理

Journal of Computational Physics Pub Date : 2025-02-11 DOI: 10.1016/j.jcp.2025.113836

Alessandro Aiello , Carlo De Michele , Gennaro Coppola

{"title":"Entropy conservative discretization of compressible Euler equations with an arbitrary equation of state","authors":"Alessandro Aiello , Carlo De Michele , Gennaro Coppola","doi":"10.1016/j.jcp.2025.113836","DOIUrl":"10.1016/j.jcp.2025.113836","url":null,"abstract":"<div><div>This study proposes a novel spatial discretization procedure for the compressible Euler equations which guarantees entropy conservation at a discrete level when an arbitrary equation of state is assumed. The proposed method, based on a locally-conservative discretization, guarantees also the spatial conservation of mass, momentum, and total energy and is kinetic-energy-preserving. In order to achieve the entropy-conservation property for an arbitrary non-ideal gas, a general strategy is adopted based on the manipulation of discrete balance equations through the imposition of global entropy conservation and the use of a summation-by-parts rule. The procedure, which is extended to an arbitrary order of accuracy, conducts to a general form of the internal-energy numerical flux which results in a nonlinear function of thermodynamic and dynamic variables and still admits the mass flux as a residual degree of freedom. The effectiveness of the novel entropy-conservative formulation is demonstrated through numerical tests making use of some of the most popular cubic equations of state.</div></div>","PeriodicalId":352,"journal":{"name":"Journal of Computational Physics","volume":"528 ","pages":"Article 113836"},"PeriodicalIF":3.8,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Entropy-stable model reduction of one-dimensional hyperbolic systems using rational quadratic manifolds

IF 3.8 2区物理与天体物理

Journal of Computational Physics Pub Date : 2025-02-10 DOI: 10.1016/j.jcp.2025.113817

R.B. Klein , B. Sanderse , P. Costa , R. Pecnik , R.A.W.M. Henkes

{"title":"Entropy-stable model reduction of one-dimensional hyperbolic systems using rational quadratic manifolds","authors":"R.B. Klein , B. Sanderse , P. Costa , R. Pecnik , R.A.W.M. Henkes","doi":"10.1016/j.jcp.2025.113817","DOIUrl":"10.1016/j.jcp.2025.113817","url":null,"abstract":"<div><div>In this work we propose a novel method to ensure important entropy inequalities are satisfied semi-discretely when constructing reduced order models (ROMs) on nonlinear reduced manifolds. We are in particular interested in ROMs of systems of nonlinear hyperbolic conservation laws. The so-called entropy stability property endows the semi-discrete ROMs with physically admissible behaviour. The method generalizes earlier results on entropy-stable ROMs constructed on linear spaces. The ROM works by evaluating the projected system on a well-chosen approximation of the state that ensures entropy stability. To ensure accuracy of the ROM after this approximation we locally enrich the tangent space of the reduced manifold with important quantities. Using numerical experiments on some well-known equations (the inviscid Burgers equation, shallow water equations and compressible Euler equations) we show the improved structure-preserving properties of our ROM compared to standard approaches and that our approximations have minimal impact on the accuracy of the ROM. We additionally generalize the recently proposed polynomial reduced manifolds to rational polynomial manifolds and show that this leads to an increase in accuracy for our experiments.</div></div>","PeriodicalId":352,"journal":{"name":"Journal of Computational Physics","volume":"528 ","pages":"Article 113817"},"PeriodicalIF":3.8,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Structure-preserving dimensionality reduction for learning Hamiltonian dynamics

IF 3.8 2区物理与天体物理

Journal of Computational Physics Pub Date : 2025-02-10 DOI: 10.1016/j.jcp.2025.113832

Jānis Bajārs, Dāvis Kalvāns

{"title":"Structure-preserving dimensionality reduction for learning Hamiltonian dynamics","authors":"Jānis Bajārs, Dāvis Kalvāns","doi":"10.1016/j.jcp.2025.113832","DOIUrl":"10.1016/j.jcp.2025.113832","url":null,"abstract":"<div><div>Structure-preserving data-driven learning algorithms have recently received high attention, e.g., the development of the symplecticity-preserving neural networks SympNets for learning the flow of a Hamiltonian system. The preservation of structural properties by neural networks has been shown to produce qualitatively better long-time predictions. Learning the flow of high-dimensional Hamiltonian dynamics still poses a great challenge due to the increase in neural network model complexity and, thus, the significant increase in training time. In this work, we investigate dimensionality reduction techniques of training datasets of solutions to Hamiltonian dynamics, which can be well modeled in a lower-dimensional subspace. For learning the flow of such Hamiltonian dynamics with symplecticity-preserving neural networks SympNets, we propose dimensionality reduction with the proper symplectic decomposition (PSD). PSD was originally proposed to obtain symplectic reduced-order models of Hamiltonian systems. We demonstrate the proposed purely data-driven approach by learning the nonlinear localized discrete breather solutions in a one-dimensional crystal lattice model. Considering three near-optimal PSD solutions, i.e., cotangent lift, complex SVD, and dimension-reduced nonlinear programming solutions, we find that learning the SPD-reduced Hamiltonian dynamics is not only more computationally efficient compared to learning the whole high-dimensional model, but we can also obtain comparably qualitatively good long-time predictions. Specifically, the cotangent lift and nonlinear programming PSD solutions demonstrate significantly enhanced long-term prediction capabilities, outperforming the approach of learning Hamiltonian dynamics with non-symplectic proper orthogonal decomposition (POD) dimensionality reduction.</div></div>","PeriodicalId":352,"journal":{"name":"Journal of Computational Physics","volume":"528 ","pages":"Article 113832"},"PeriodicalIF":3.8,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Regularized lattice Boltzmann method based maximum principle and energy stability preserving finite-difference scheme for the Allen-Cahn equation

IF 3.8 2区物理与天体物理

Journal of Computational Physics Pub Date : 2025-02-07 DOI: 10.1016/j.jcp.2025.113831

Ying Chen , Xi Liu , Zhenhua Chai , Baochang Shi

{"title":"Regularized lattice Boltzmann method based maximum principle and energy stability preserving finite-difference scheme for the Allen-Cahn equation","authors":"Ying Chen , Xi Liu , Zhenhua Chai , Baochang Shi","doi":"10.1016/j.jcp.2025.113831","DOIUrl":"10.1016/j.jcp.2025.113831","url":null,"abstract":"<div><div>The Allen-Cahn equation (ACE) inherently possesses two crucial properties: the maximum principle and the energy dissipation law. How to preserve these two properties at the discrete level is of significant importance in the numerical methods for the ACE. In this paper, unlike the traditional macroscopic numerical schemes that directly discretize the ACE, we first propose a novel mesoscopic regularized lattice Boltzmann method based macroscopic numerical scheme for the <em>d</em> (=1, 2, 3)-dimensional ACE, where the D<em>d</em>Q<span><math><mo>(</mo><mn>2</mn><mi>d</mi><mo>+</mo><mn>1</mn><mo>)</mo></math></span> [(<span><math><mn>2</mn><mi>d</mi><mo>+</mo><mn>1</mn></math></span>) discrete velocities in <em>d</em>-dimensional space] lattice structure is employed. In particular, the proposed numerical scheme has a second-order accuracy in space, and can also be regarded as an implicit-explicit finite-difference scheme for the ACE. In this scheme, the nonlinear term is discretized semi-implicitly, the temporal derivative term and the dissipation term are discretized via the explicit Euler and second-order central difference methods, respectively. Compared to the implicit schemes for the ACE, the present scheme proves to be more efficient as it is actually explicit and avoids the use of iterative methods when dealing with the nonlinear term. In addition, we also demonstrate that under some certain conditions, the proposed scheme can preserve the maximum bound principle and the original energy dissipation law at the discrete level. Finally, we conduct numerical simulations of several benchmark problems, and find that the numerical results are not only in agreement with our theoretical analysis, but also show that in comparison with the mesoscopic lattice Boltzmann method, the proposed macroscopic scheme has a great advantage in reducing the memory usage.</div></div>","PeriodicalId":352,"journal":{"name":"Journal of Computational Physics","volume":"528 ","pages":"Article 113831"},"PeriodicalIF":3.8,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A high order ensemble algorithm for dual-porosity-Navier-Stokes flows

IF 3.8 2区物理与天体物理

Journal of Computational Physics Pub Date : 2025-02-07 DOI: 10.1016/j.jcp.2025.113833

Changxin Qiu , Jiangyong Hou , Yinhua Xia , Li Shan

{"title":"A high order ensemble algorithm for dual-porosity-Navier-Stokes flows","authors":"Changxin Qiu , Jiangyong Hou , Yinhua Xia , Li Shan","doi":"10.1016/j.jcp.2025.113833","DOIUrl":"10.1016/j.jcp.2025.113833","url":null,"abstract":"<div><div>In this paper, we introduce and analyze an efficient high-order ensemble algorithm incorporating the semi-implicit spectral deferred correction (SDC-Ensemble algorithm) tailored to simulate flows exhibiting multiple realizations within the stochastic dual-porosity-Navier-Stokes system. This framework accommodates uncertainties stemming from initial conditions, forcing terms, interface boundary conditions and the hydraulic conductivity tensor. By consolidating all realizations into a unified coefficient matrix at each time step, the SDC-Ensemble algorithm significantly diminishes computational overhead compared to traditional methods that treat each realization independently, while preserving high-order accuracy. Furthermore, it disentangles the dual-porosity-Navier-Stokes system into three manageable sub-physics problems, thereby reducing the dimensionality of linear systems and facilitating parallel computation. Employing some novel techniques, this paper also provides a comprehensive stability analysis for the proposed method without relying on the theoretical stability results of lower-order ensemble algorithms. Numerical experiments corroborate the theoretical findings and illustrate the applicability of the algorithm and its features to flow problems in multistage fractured horizontal wellbore with appropriate boundary/interface conditions.</div></div>","PeriodicalId":352,"journal":{"name":"Journal of Computational Physics","volume":"529 ","pages":"Article 113833"},"PeriodicalIF":3.8,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Corrigendum to “A TVD neural network closure and application to turbulent combustion” [Journal of Computational Physics 523 (2025)/113638]

IF 3.8 2区物理与天体物理

Journal of Computational Physics Pub Date : 2025-02-07 DOI: 10.1016/j.jcp.2025.113800

Seung Won Suh , Jonathan F. MacArt , Luke N. Olson , Jonathan B. Freund

引用次数: 0

An efficient stochastic particle method for moderately high-dimensional nonlinear PDEs

IF 3.8 2区物理与天体物理

Journal of Computational Physics Pub Date : 2025-02-06 DOI: 10.1016/j.jcp.2025.113818

Zhengyang Lei , Sihong Shao , Yunfeng Xiong

{"title":"An efficient stochastic particle method for moderately high-dimensional nonlinear PDEs","authors":"Zhengyang Lei , Sihong Shao , Yunfeng Xiong","doi":"10.1016/j.jcp.2025.113818","DOIUrl":"10.1016/j.jcp.2025.113818","url":null,"abstract":"<div><div>Numerical resolution of moderately high-dimensional nonlinear PDEs remains a huge challenge due to the curse of dimensionality for the classical numerical methods including finite difference, finite element and spectral methods. Starting from the weak formulation of the Lawson-Euler scheme, this paper proposes a stochastic particle method (SPM) by tracking the deterministic motion, random jump, resampling and reweighting of particles. Real-valued weighted particles are adopted by SPM to approximate the high-dimensional solution, which automatically adjusts the point distribution to intimate the relevant feature of the solution. A piecewise constant reconstruction with virtual uniform grid is employed to evaluate the nonlinear terms, which fully exploits the intrinsic adaptive characteristic of SPM. Combining both, SPM can achieve the goal of adaptive sampling in time. Numerical experiments on the 6-D Allen-Cahn equation and the 7-D Hamiltonian-Jacobi-Bellman equation demonstrate the potential of SPM in solving moderately high-dimensional nonlinear PDEs efficiently while maintaining an acceptable accuracy.</div></div>","PeriodicalId":352,"journal":{"name":"Journal of Computational Physics","volume":"527 ","pages":"Article 113818"},"PeriodicalIF":3.8,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143350966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A moment-of-fluid interface reconstruction using polygon inscribed in ellipse in 2D

IF 3.8 2区物理与天体物理

Journal of Computational Physics Pub Date : 2025-02-06 DOI: 10.1016/j.jcp.2025.113814

Robert Chiodi , Mikhail Shashkov

{"title":"A moment-of-fluid interface reconstruction using polygon inscribed in ellipse in 2D","authors":"Robert Chiodi , Mikhail Shashkov","doi":"10.1016/j.jcp.2025.113814","DOIUrl":"10.1016/j.jcp.2025.113814","url":null,"abstract":"<div><div>In this paper we consider interface reconstruction as a problem of approximating the intersection of a material body with a particular computational cell. This intersection results in a fragment of material, or “FOM”, whose boundary is formed by parts of the interface and the cell boundary. The most general characteristics of any shape, and a FOM in particular, are its moments, such as the zeroth moment (the area) and the normalized first order moment (the centroid). In this paper, we assume that the moments of the FOM are known a priori, which we will refer to as reference moments. In this context, we develop a new class of moment-of-fluid (MOF) method <span><span>[3]</span></span>, <span><span>[1]</span></span>, <span><span>[4]</span></span>, which approximates a FOM by trying to match the moments through an optimization procedure. Our new addition to the MOF family, MOF-PIE, centers on constructing approximate FOM polygons from discretizing ellipses using varying numbers of vertices. As in other MOF methods, this reconstruction procedure is cell-local and involves a non-linear optimization procedure to minimize a cost function constructed from area moments while conserving area. The optimization is performed using the Levenberg-Marquardt algorithm with a cost function involving up to third-order area moments. Due to the reconstruction being based on an ellipse, this reconstruction also has the unique ability to recover shapes laying entirely inside a computational cell and can be tuned to better resolve cell-local curvature. The new method has been implemented in the open-source Interface Reconstruction Library (IRL) <span><span>[7]</span></span>, <span><span>[8]</span></span>, making it publicly available. IRL is a collection of computational geometry and interface reconstruction methods designed to be both easy to use and computationally performant. Using this implementation, we perform and present numerous single-cell and whole-mesh reconstruction cases, comparing results to existing MOF methods.</div></div>","PeriodicalId":352,"journal":{"name":"Journal of Computational Physics","volume":"528 ","pages":"Article 113814"},"PeriodicalIF":3.8,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A physical-constraints-preserving arbitrary Lagrangian-Eulerian discontinuous Galerkin scheme on adaptive quadrilateral meshes for compressible multi-material flows

IF 3.8 2区物理与天体物理

Journal of Computational Physics Pub Date : 2025-02-06 DOI: 10.1016/j.jcp.2025.113816

Xiaolong Zhao , Xijun Yu , Fang Qing , Shijun Zou

{"title":"A physical-constraints-preserving arbitrary Lagrangian-Eulerian discontinuous Galerkin scheme on adaptive quadrilateral meshes for compressible multi-material flows","authors":"Xiaolong Zhao , Xijun Yu , Fang Qing , Shijun Zou","doi":"10.1016/j.jcp.2025.113816","DOIUrl":"10.1016/j.jcp.2025.113816","url":null,"abstract":"<div><div>In this paper, a high-order physical-constraints-preserving arbitrary Lagrangian-Eulerian (ALE) discontinuous Galerkin (DG) scheme on adaptive quadrilateral meshes is proposed for compressible multi-material flows. Our scheme couples a conservative equation related to the volume fraction model with Euler equations for describing dynamics of fluid mixture. The mesh velocity in the ALE framework is obtained by using an adaptive mesh method which can automatically concentrate the mesh nodes near the regions involving large gradient values, and it can help the scheme greatly reduce the numerical dissipation near material interfaces. Using this adaptive mesh, the resolution of solution near some special regions such as material interfaces can be improved effectively by our scheme. With the appropriate time step condition and using a physical-constraints-preserving limiter, our scheme can ensure the positivity of density and pressure and the boundness of volume fraction, which further ensures the computational robustness and degree of confidence of simulations under large density or pressure ratios and so on. In general, our scheme can be applied into the simulations of compressible multi-material flows efficiently with the essentially non-oscillatory property and physical-constraints-preserving (bound-preserving and positivity-preserving) property, and its steps are more concise compared to some other methods such as the indirect ALE methods. Some examples are tested to demonstrate the accuracy, essentially non-oscillatory property and physical-constraints-preserving property of our scheme.</div></div>","PeriodicalId":352,"journal":{"name":"Journal of Computational Physics","volume":"528 ","pages":"Article 113816"},"PeriodicalIF":3.8,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0