Journal of Computational Physics最新文献

{"title":"A reinterpreted discrete fracture model for wormhole propagation in fractured porous media","authors":"Xinyu Wu ,&nbsp;Hui Guo ,&nbsp;Ziyao Xu ,&nbsp;Lulu Tian ,&nbsp;Yang Yang","doi":"10.1016/j.jcp.2025.113953","DOIUrl":"10.1016/j.jcp.2025.113953","url":null,"abstract":"<div><div>Wormholes are high-permeability, deep-penetrating, narrow channels formed during the acidizing process, which serves as a popular stimulation treatment. For the study of wormhole formation in naturally fractured porous media, we develop a novel hybrid-dimensional two-scale continuum wormhole model, with fractures represented as Dirac-<em>δ</em> functions. As an extension of the reinterpreted discrete fracture model (RDFM) <span><span>[50]</span></span>, the model is applicable to nonconforming meshes and adaptive to intersecting fractures in reservoirs without introducing additional computational complexity. A numerical scheme based on the local discontinuous Galerkin (LDG) method is constructed for the corresponding dimensionless model to accommodate the presence of Dirac-<em>δ</em> functions and the property of flux discontinuity. Moreover, a bound-preserving technique is introduced to theoretically ensure the boundedness of acid concentration and porosity between 0 and 1, as well as the monotone increase in porosity during simulation. The performance of the model and algorithms is validated, and the effects of various parameters on wormhole propagation are analyzed through several numerical experiments, contributing to the acidizing design in fractured reservoirs.</div></div>","PeriodicalId":352,"journal":{"name":"Journal of Computational Physics","volume":"532 ","pages":"Article 113953"},"PeriodicalIF":3.8,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725289","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}

{"title":"CGKN: A deep learning framework for modeling complex dynamical systems and efficient data assimilation","authors":"Chuanqi Chen ,&nbsp;Nan Chen ,&nbsp;Yinling Zhang ,&nbsp;Jin-Long Wu","doi":"10.1016/j.jcp.2025.113950","DOIUrl":"10.1016/j.jcp.2025.113950","url":null,"abstract":"<div><div>Deep learning is widely used to predict complex dynamical systems in many scientific and engineering areas. However, the black-box nature of these deep learning models presents significant challenges for carrying out simultaneous data assimilation (DA), which is a crucial technique for state estimation, model identification, and reconstructing missing data. Integrating ensemble-based DA methods with nonlinear deep learning models is computationally expensive and may suffer from large sampling errors. To address these challenges, we introduce a deep learning framework designed to simultaneously provide accurate forecasts and efficient DA. It is named Conditional Gaussian Koopman Network (CGKN), which transforms general nonlinear systems into nonlinear neural differential equations with conditional Gaussian structures. CGKN aims to retain essential nonlinear components while applying systematic and minimal simplifications to facilitate the development of analytic formulae for nonlinear DA. This allows for seamless integration of DA performance into the deep learning training process, eliminating the need for empirical tuning as required in ensemble methods. CGKN compensates for structural simplifications by lifting the dimension of the system, which is motivated by Koopman theory. Nevertheless, CGKN exploits special nonlinear dynamics within the lifted space. This enables the model to capture extreme events and strong non-Gaussian features in joint and marginal distributions with appropriate uncertainty quantification. We demonstrate the effectiveness of CGKN for both prediction and DA on three strongly nonlinear and non-Gaussian turbulent systems: the projected stochastic Burgers–Sivashinsky equation, the Lorenz 96 system, and the El Niño-Southern Oscillation. The results justify the robustness and computational efficiency of CGKN.</div></div>","PeriodicalId":352,"journal":{"name":"Journal of Computational Physics","volume":"532 ","pages":"Article 113950"},"PeriodicalIF":3.8,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738003","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}

{"title":"Higher-order MPS models and higher-order Explicit Incompressible MPS (EI-MPS) method to simulate free-surface flows","authors":"Tibing Xu ,&nbsp;Seiichi Koshizuka ,&nbsp;Tsuyoshi Koyama ,&nbsp;Toshihide Saka ,&nbsp;Osamu Imazeki","doi":"10.1016/j.jcp.2025.113951","DOIUrl":"10.1016/j.jcp.2025.113951","url":null,"abstract":"<div><div>In this study, higher-order spatial models including the gradient model and Laplacian model based on Taylor's series and using their coordinates as coefficients are evaluated by calculating some simple functions and a diffusion problem. The numerical convergence is achieved by the models as the smaller particle distance can calculate more accurate results. By using the models, when the particle distribution is significantly irregular, increasing the search radius can involve more neighboring particles which consequently improves the accuracy. Based on the proposed higher-order models, the higher-order Explicit Incompressible version of the Moving Particle Semi-implicit method (EI-MPS) is developed. The numerical scheme is validated by simulating various free surface flows including the rotation of a fluid square patch, the impact of two identical rectangular fluid patches, oscillating drop under a central force field, a hydrostatic problem, and dam-break flow. The parameters of the particle distance, search radius, and repeated time in the pressure calculation are all examined in the free surface flows. The proposed method can reproduce the free surface variations, kinetic energy, and total energy variation in the violent flows. It can also obtain the hydrostatic pressure achieving numerical convergence. Increasing the search radius can result in larger errors in simulating the hydrostatic pressure. The impacting pressure caused by the dam-break flow is reflected by the method in good agreement with the experimental measurements.</div></div>","PeriodicalId":352,"journal":{"name":"Journal of Computational Physics","volume":"532 ","pages":"Article 113951"},"PeriodicalIF":3.8,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706232","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}

{"title":"A thermodynamically consistent and conservative diffuse-interface model for gas/liquid-liquid-solid flows","authors":"Chengjie Zhan ,&nbsp;Xi Liu ,&nbsp;Zhenhua Chai ,&nbsp;Baochang Shi","doi":"10.1016/j.jcp.2025.113949","DOIUrl":"10.1016/j.jcp.2025.113949","url":null,"abstract":"<div><div>In this work, a thermodynamically consistent and conservative diffuse-interface model for gas/liquid-liquid-solid flows is proposed. In this model, a novel free energy for the gas/liquid-liquid-solid system is established according to a ternary phase-field model, and it not only contains the standard bulk and interface free energies for two-phase flows, but also includes some additional terms to reflect the penalty in the solid phase and the wettability on the solid surface. Furthermore, a smooth indicator function of the solid phase is also introduced in the consistent Navier-Stokes equations to achieve a high viscosity in the solid phase, and to preserve the velocity boundary conditions on the solid surface by the force caused by fluid-structure interaction. Based on the proposed diffuse-interface model, the fluid interface dynamics, the fluid-structure interaction, and the wetting property of the solid surface can be described simply and efficiently. Additionally, the total energy is also proved to be dissipative for the two-phase flows in the stationary geometries. To test the present diffuse-interface model, we develop a consistent and conservative lattice Boltzmann method and conduct some simulations. The numerical results also confirm the energy dissipation and good capability of the proposed diffuse-interface model in the study of two-phase flows in complex geometries and two-phase flows with moving particles.</div></div>","PeriodicalId":352,"journal":{"name":"Journal of Computational Physics","volume":"532 ","pages":"Article 113949"},"PeriodicalIF":3.8,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143696117","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}

{"title":"An efficient particle locating method on unstructured meshes in two and three dimensions based on patch searching","authors":"Shuang Chen ,&nbsp;Fanyi Yang","doi":"10.1016/j.jcp.2025.113948","DOIUrl":"10.1016/j.jcp.2025.113948","url":null,"abstract":"<div><div>We present a particle locating method for unstructured meshes in two and three dimensions. Our algorithm is based on a patch searching process, and includes two steps. We first locate the given point to a patch near a vertex, and then the host element is determined within the patch domain. Here, the patch near a vertex is the domain of elements around this vertex. We prove that in the first step the patch can be rapidly identified by constructing an auxiliary Cartesian grid with a prescribed resolution. Then, the second step can be converted into a searching problem, which can be easily solved by searching algorithms. Only coordinates to particles are required in our method. We conduct a series of numerical tests in two and three dimensions to illustrate the robustness and efficiency of our method.</div></div>","PeriodicalId":352,"journal":{"name":"Journal of Computational Physics","volume":"531 ","pages":"Article 113948"},"PeriodicalIF":3.8,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143686400","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}

{"title":"Multi-head physics-informed neural networks for learning functional priors and uncertainty quantification","authors":"Zongren Zou,&nbsp;George Em Karniadakis","doi":"10.1016/j.jcp.2025.113947","DOIUrl":"10.1016/j.jcp.2025.113947","url":null,"abstract":"<div><div>In numerous applications, the integration of prior knowledge and historical information is essential, particularly for tasks requiring the solution of ordinary or partial differential equations (ODEs/PDEs) in data-sparse or noisy environments. For instance, achieving accurate solutions to time-dependent PDEs with limited initial condition measurements necessitates an effective strategy for embedding prior knowledge. Hard-parameter sharing architectures in neural networks (NNs) have demonstrated success in both traditional and scientific machine learning domains, facilitating the learning of informative representations.</div><div>In this study, we introduce a novel, yet efficient, method to enhance physics-informed neural networks (PINNs) by incorporating a multi-head structure that enables the learning of functional priors from both empirical data and governing physical laws. This prior information can then be used to address data sparsity and high-level noise in solving ODE/PDE problems with uncertainty quantification (UQ). The approach, termed Multi-Head PINN (MH-PINN), consists of a shared <em>body</em> NN and multiple <em>head</em> NNs, each corresponding to an individual PINN instance. Our framework for functional prior learning is carried out in two stages: (1) training the MH-PINNs to develop a shared body NN alongside multiple head NNs, and (2) employing these trained head NNs to estimate a prior distribution through a normalizing flow-based density estimator.</div><div>The learned functional prior can then be applied as a regularization mechanism in deterministic contexts or as an informative prior within a Bayesian inference framework, aiding in the resolution of subsequent ODE/PDE tasks. We evaluate the efficacy of MH-PINNs across five benchmark problems, including a high-dimensional parametric PDE, all characterized by data sparsity or substantial noise levels. Our findings reveal that MH-PINNs deliver accurate solutions and robust UQ, demonstrating adaptability across a range of complex and challenging scenarios.</div></div>","PeriodicalId":352,"journal":{"name":"Journal of Computational Physics","volume":"531 ","pages":"Article 113947"},"PeriodicalIF":3.8,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143686399","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}

{"title":"A Bayesian extension to FEMU for identification of spatially varying stochastic elastic properties from digital image and volume correlation measurements","authors":"Armand Touminet ,&nbsp;Sabine Cantournet ,&nbsp;Victor Fabre ,&nbsp;Pierre Kerfriden","doi":"10.1016/j.jcp.2025.113946","DOIUrl":"10.1016/j.jcp.2025.113946","url":null,"abstract":"<div><div>We present a Bayesian framework for the identification of stochastic and spatially varying elastic parameters using noisy displacement observations obtained with DIC or DVC trials. Our method is a generalization of identification procedures such as FEMU or I-DIC to materials with spatially varying properties and stochastic mesostructures, where the elasticity tensor is modelled as a parametric non-Gaussian random field. Both the elastic parameters and the parameters of the random field model are identified jointly from the displacement measurement. We formulate the approach as a hierarchical Bayesian PDE-constrained inverse problem and MAP estimates are obtained through gradient based optimization. We resort to an adjoint based formulation and leverage automatic differentiation to derive the parameter sensitivities. We show how modelling unknown parameters with Gaussian Random Fields leads to a natural Bayesian regularization and leverage the use of Whittle-Matérn priors. Covariance parameter estimation is discussed, and we propose an empirical Bayes approach to avoid numerical shortcomings related to a standard hierarchical model. A set of numerical examples is presented to assess the performance of the proposed method, based on synthetic data generated through Matérn Random fields. In particular, we show how data noise is naturally modelled by the Bayesian formulation and impacts spatial covariance of identified parameters.</div></div>","PeriodicalId":352,"journal":{"name":"Journal of Computational Physics","volume":"531 ","pages":"Article 113946"},"PeriodicalIF":3.8,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143686403","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}

{"title":"A mesh-constrained discrete point method for incompressible flows with moving boundaries","authors":"Takeharu Matsuda ,&nbsp;Satoshi Ii","doi":"10.1016/j.jcp.2025.113945","DOIUrl":"10.1016/j.jcp.2025.113945","url":null,"abstract":"<div><div>Particle-based methods are a practical tool in computational fluid dynamics, and novel types of methods have been proposed. However, widely developed Lagrangian-type formulations suffer from the nonuniform distribution of particles, which is enhanced over time and result in problems in computational efficiency and parallel computations. To mitigate these problems, a mesh-constrained discrete point (MCD) method was developed for stationary boundary problems (Matsuda et al., 2022). Although the MCD method is a meshless method that uses moving least-squares approximation, the arrangement of particles (or discrete points (DPs)) is specialized so that their positions are constrained in a background mesh to obtain a closely uniform distribution. This achieves a reasonable approximation for spatial derivatives with compact stencils without encountering any ill-posed condition and leads to good performance in terms of computational efficiency. In this study, a novel meshless method based on the MCD method for incompressible flows with moving boundaries is proposed. To ensure the mesh constraint of each DP in moving boundary problems, a novel updating algorithm for the DP arrangement is developed so that the position of DPs is not only rearranged but the DPs are also reassigned the role of being on the boundary or not. The proposed method achieved reasonable results in numerical experiments for well-known moving boundary problems.</div></div>","PeriodicalId":352,"journal":{"name":"Journal of Computational Physics","volume":"532 ","pages":"Article 113945"},"PeriodicalIF":3.8,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143696115","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}

{"title":"Moment-preserving Monte-Carlo Coulomb collision method for particle codes","authors":"Justin Ray Angus,&nbsp;Yichen Fu,&nbsp;Vasily Geyko,&nbsp;Dave Grote,&nbsp;David Larson","doi":"10.1016/j.jcp.2025.113927","DOIUrl":"10.1016/j.jcp.2025.113927","url":null,"abstract":"<div><div>Binary-pairing Monte-Carlo methods are widely used in particle-in-cell codes to capture effects of small angle Coulomb collisions. These methods preserve momentum and energy exactly when the simulation particles have equal weights. However, when the interacting particles are of varying weight, these physical conservation laws are only preserved on average. Here, we 1) extend these methods to weighted particles such that the scattering physics is correct on average, and 2) describe a new method for adjusting the particle velocities post scatter to restore exact conservation of momentum and energy. The efficacy of the model is illustrated with various test problems.</div></div>","PeriodicalId":352,"journal":{"name":"Journal of Computational Physics","volume":"531 ","pages":"Article 113927"},"PeriodicalIF":3.8,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143686396","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}

{"title":"Structure-preserving parametric finite element methods for simulating axisymmetric solid-state dewetting problems with anisotropic surface energies","authors":"Meng Li,&nbsp;Chunjie Zhou","doi":"10.1016/j.jcp.2025.113944","DOIUrl":"10.1016/j.jcp.2025.113944","url":null,"abstract":"<div><div>Solid-state dewetting (SSD), a widespread phenomenon in solid-solid-vapor system, could be used to describe the accumulation of solid thin films on the substrate. In this work, we consider the sharp-interface model for axisymmetric SSD with anisotropic surface energy. By introducing two types of surface energy matrices from the anisotropy functions, we aim to design two structure-preserving algorithms for the axisymmetric SSD. The newly designed schemes are applicable to a broader range of anisotropy functions, and we can theoretically prove their volume conservation and energy stability. In addition, based on a novel weak formulation for the axisymmetric SSD, we further build another two numerical schemes that have good mesh properties. Finally, numerous numerical tests are reported to showcase the accuracy and efficiency of the numerical methods.</div></div>","PeriodicalId":352,"journal":{"name":"Journal of Computational Physics","volume":"531 ","pages":"Article 113944"},"PeriodicalIF":3.8,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143686402","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}