Computers & Fluids最新文献

{"title":"High-fidelity 2D interface reconstruction from high-order moments","authors":"Roxana Bujack,&nbsp;Mikhail Shashkov","doi":"10.1016/j.compfluid.2025.106681","DOIUrl":"10.1016/j.compfluid.2025.106681","url":null,"abstract":"<div><div>Multi-material flows involve boundaries between two non-mixing fluids, seen in scenarios such as underwater detonations, oil–water separation in petroleum extraction, lava–water interactions during volcanic eruptions, and gas–liquid interfaces in bubble column reactors. Accurate algorithms for these dynamic interfaces are crucial in computational fluid dynamics. While various methods exist, moment-of-fluid interface reconstruction stands out because of its efficiency and accuracy. This technique uses non-linear optimization to derive a shape that matches not only the volume, but also the first and even the second and third order moments of the fluid. However, the main limitation of the moment-of-fluid methods is their dependence on, and lack of, a good initial guess, which is critical for their robust convergence. We derive a highly accurate initial guess using the iso-contour of the polynomial that exactly matches the higher-order moments. This allows a vertex-wise optimization and with it the reconstruction of many interface shapes that have not been possible in the past, such as polygons with multiple connected components and holes.</div></div>","PeriodicalId":287,"journal":{"name":"Computers & Fluids","volume":"298 ","pages":"Article 106681"},"PeriodicalIF":2.5,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144131093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A difference-free conservative phase-field Lattice Boltzmann method","authors":"Chunheng Zhao ,&nbsp;Saumil Patel ,&nbsp;Taehun Lee","doi":"10.1016/j.compfluid.2025.106674","DOIUrl":"10.1016/j.compfluid.2025.106674","url":null,"abstract":"<div><div>We propose an innovative difference-free scheme that combines the one-fluid Lattice Boltzmann method (LBM) with the conservative phase-field (CPF) LBM to effectively solve large-scale two-phase fluid flow problems. The difference-free scheme enables the derivation of the derivative of the order parameter and the normal vector through the moments of the particle distribution function (PDF). We further incorporate the surface tension force in a continuous surface stress form into the momentum equations by modifying the equilibrium PDF to eliminate the divergence operator. Consequently, the entire computation process, executed without any inter-grid finite difference formulation, demonstrates improved efficiency, making it an ideal choice for high-performance computing applications. We conduct simulations of a single static droplet to evaluate the intensity of spurious currents and assess the accuracy of the scheme. We then introduce the density or viscosity ratio and apply an external body force to model the Rayleigh–Taylor instability and the behavior of a single rising bubble, respectively. Finally, we employ our method to study the phenomenon of a single bubble breaking up in a Taylor–Green vortex. The comparison between the difference-free scheme and the finite difference method demonstrates the scheme’s capability to yield accurate results. Furthermore, based on the performance evaluation, the current scheme exhibits an impressive 47% increase in efficiency compared to the previous method.</div></div>","PeriodicalId":287,"journal":{"name":"Computers & Fluids","volume":"298 ","pages":"Article 106674"},"PeriodicalIF":2.5,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144107506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Structure and dynamics of the blue whirl","authors":"E. Tarik Balci ,&nbsp;Paul Anderson ,&nbsp;Elaine S. Oran","doi":"10.1016/j.compfluid.2025.106676","DOIUrl":"10.1016/j.compfluid.2025.106676","url":null,"abstract":"<div><div>The blue whirl is an unusual type of flame discovered accidentally in laboratory experiments and partly explained by previous experimental and numerical studies. Here, three-dimensional simulations are used to investigate the dynamics and structure of the blue whirl. Proper Orthogonal Decomposition (POD) is applied to identify the dominant flow structures, while Fast Fourier Transform (FFT) is used to analyze their frequency content. The findings reveal large- and medium-scale dynamics, including motion inside the domain, flapping, and puffing, offering a quantitative framework to understand the physical mechanisms behind the blue whirl’s stability and structure. The outcomes provide a robust foundation for future studies, including investigation of changes in background conditions, flow perturbations, and effects of tilting on structural stability and dynamics.</div></div>","PeriodicalId":287,"journal":{"name":"Computers & Fluids","volume":"298 ","pages":"Article 106676"},"PeriodicalIF":2.5,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144107505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A BDF2 characteristic-Galerkin isogeometric analysis for the miscible displacement of incompressible fluids in porous media","authors":"Ilham Asmouh ,&nbsp;Abdelouahed Ouardghi","doi":"10.1016/j.compfluid.2025.106675","DOIUrl":"10.1016/j.compfluid.2025.106675","url":null,"abstract":"<div><div>Incompressible-miscible problems arise in many fields of application where the main objective is to describe the change of the pressure and the velocity during displacement. These problems are usually subject to some complicated features related to the dominance of convection. Therefore, the multiphysical scales in these problems represent a challenging endeavor. In this study, we propose a NURBS-based isogeometric analysis (IgA) combined with an <span><math><msup><mrow><mi>L</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>-projection characteristic Galerkin method to deal with this class of equations. The advection part is treated in a characteristic Galerkin framework where high-order nonuniform rational B-spline functions are used to interpolate the solution. The resulting semi-discrete equation is solved using an efficient backward differentiation time-stepping algorithm. The accuracy of the method is analyzed through several Darcy’s flow problems with analytical solutions on differently shaped computational domains, including a miscible displacement of an incompressible fluid, and a real problem with a viscous fingering in porous media. The numerical results presented in this study demonstrate the potential of the proposed IgA characteristic Galerkin method to allow for large time steps in the computations without deteriorating the accuracy of the obtained solutions, and to accurately maintain the shape of the solution in the presence of complex patterns on complex geometries.</div></div>","PeriodicalId":287,"journal":{"name":"Computers & Fluids","volume":"298 ","pages":"Article 106675"},"PeriodicalIF":2.5,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144098920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive fully implicit and thermodynamically consistent modeling of multiphase flow in porous media on three dimensional grids","authors":"Qiuyu Sheng ,&nbsp;Haijian Yang ,&nbsp;Huangxin Chen ,&nbsp;Tianpei Cheng ,&nbsp;Shuyu Sun","doi":"10.1016/j.compfluid.2025.106673","DOIUrl":"10.1016/j.compfluid.2025.106673","url":null,"abstract":"<div><div>The development of innovative mathematical models and state-of-the-art simulators for multiphase flow in porous media is a key focus in hydrogeology. However, many traditional models for multiphase flow in porous media lack complete thermodynamic consistency, as the energy reconstructed from the capillary pressure fails to yield a time-dependent system with a dissipated free energy. In this paper, we utilize a thermodynamically consistent model for multiphase flow in porous media, which satisfies the second law of thermodynamics. For large-scale numerical simulation of the resultant flow model, we introduce and investigate a robust and scalable fully implicit simulator with a suitable time adaptivity strategy designed for distributed memory parallel computers. In particular, our approach enhances the numerical formulation by proposing a family of inexact Newton–Krylov methods for efficient computation and several types of field-split algorithms for large-scale preconditioning. The numerical experiments indicate that the proposed fully implicit simulator accurately predicts the highly complex physical processes of thermodynamically consistent problems, particularly those characterized by high heterogeneity with complex reservoir topography on 3D structured or unstructured grids.</div></div>","PeriodicalId":287,"journal":{"name":"Computers & Fluids","volume":"298 ","pages":"Article 106673"},"PeriodicalIF":2.5,"publicationDate":"2025-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144090253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mean mesh adaptation for efficient CFD simulations with operating conditions variability","authors":"Hugo Dornier ,&nbsp;Olivier P. Le Maître ,&nbsp;Pietro M. Congedo ,&nbsp;Itham Salah el Din ,&nbsp;Julien Marty ,&nbsp;Sébastien Bourasseau","doi":"10.1016/j.compfluid.2025.106666","DOIUrl":"10.1016/j.compfluid.2025.106666","url":null,"abstract":"<div><div>When numerically solving partial differential equations, for a given problem and operating condition producing a steady-state, mesh adaptation has proven its efficiency to automatically build a discretization achieving a prescribed error level at low cost. However, with continuously varying operating conditions, such as those encountered in uncertainty quantification, adapting a mesh for each condition and controlling the error level becomes complex and computationally expensive. To enable more effective error and cost control, this work proposes a novel approach to mesh adaptation. The method consists in building a single adapted mesh that aims to minimize the average error for a continuous set of operating conditions. In the proposed implementation, this single mesh is built iteratively, informed by an estimate of the local average interpolation error. The proposed method leverages the iterative nature of mesh adaptation by re-sampling Monte Carlo quadratures to obtain accurate average error estimates over a reduced set of sample conditions, ensuring a low computational cost. This approach is especially effective for localized flow features whose positions change only slightly with operating conditions, such as moving shocks in supersonic flows, as the refinement is confined to smaller areas of the computational domain. The study focuses on evaluating the method’s average error convergence, robustness, and computational cost in comparison to state-of-the-art adaptation techniques. Additionally, the sensitivity of the approach to the choice and size of the quadrature, as well as to the error estimation method, is assessed. For this purpose, the methodology is applied to a one-dimensional variable-step solution of the Burgers equation and a two-dimensional Euler scramjet flow with a variable inlet Mach number. The results show that Mean Mesh Adaptation (MMA) achieves error convergence comparable to specific mesh adaptation while reducing the evaluation cost by up to a factor of five (in the scramjet case). This efficiency gain stems from the reduced dependence on the number of sampled conditions, thanks to robust Monte Carlo re-sampling, as well as the shared computational expense of mesh construction across multiple evaluations. Therefore, the proposed method enables computational efficiency while maintaining error control across varying operating conditions within a prescribed parameter variation range.</div></div>","PeriodicalId":287,"journal":{"name":"Computers & Fluids","volume":"298 ","pages":"Article 106666"},"PeriodicalIF":2.5,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144098244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring denoising diffusion models for compressible fluid field prediction","authors":"R. Abaidi ,&nbsp;N.A. Adams","doi":"10.1016/j.compfluid.2025.106665","DOIUrl":"10.1016/j.compfluid.2025.106665","url":null,"abstract":"<div><div>Building upon our prior success with Pix2Pix generative adversarial networks (GANs), this work explores the potential of denoising diffusion probabilistic models (DDPMs) for supersonic flow prediction. DDPMs, renowned for their stable training and superior mode coverage, are constructed to predict key flow field quantities for compressible flows over generic aerodynamic geometries. We employ fully-conditioned DDPMs to generate high-resolution predictions of density, temperature, and Mach number fields for supersonic flows over ramps. For flows around supersonic airfoils, DDPMs are used to generate high-resolution synthetic Schlieren images, enabling detailed analysis of complex shock wave phenomena in analogy to classical experimental approaches. For ramp flows, where the training dataset is relatively small, we address residual noise in the DDPM outputs by training a U-Net to remove the noise. This approach significantly improves the accuracy of the predicted flow fields. Comparative analysis against Pix2Pix GANs reveals that DDPMs achieve superior performance, particularly in capturing shock-waves and secondary-shock details around airfoils. Furthermore, we explore the generative capabilities of DDPMs by introducing degrees of freedom into the flow problems. This is achieved, for instance, by removing ramp geometry constraints, allowing the model to generate new flow field configurations not present in the training data. To address the challenge of evaluating semi-conditioned models in scenarios lacking ground truth data, we introduce a novel proxy evaluator method. This method leverages the superior quality of fully-conditioned DDPMs to assess the outputs of semi-conditioned models. We validate this approach by comparing generated outputs to a limited set of actual ground truth samples obtained from high-fidelity numerical simulations. This work highlights the significant potential of DDPMs not only as surrogate models for predicting flow field data but also for rapidly generating synthetic data and augmenting datasets, paving the way for advancements in supersonic flow analysis and design.</div></div>","PeriodicalId":287,"journal":{"name":"Computers & Fluids","volume":"298 ","pages":"Article 106665"},"PeriodicalIF":2.5,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144125253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Isobaric steady-state preserving adaptive surface reconstruction schemes for the two-dimensional Ripa system on triangles","authors":"Jian Dong,&nbsp;Xu Qian,&nbsp;Zige Wei","doi":"10.1016/j.compfluid.2025.106664","DOIUrl":"10.1016/j.compfluid.2025.106664","url":null,"abstract":"<div><div>This work aims to introduce adaptive surface reconstruction schemes to solve the Ripa system on moving triangular meshes. We use surface reconstructions to define approximate Riemann states to preserve stationary steady states, including the still-water steady state and the highly nontrivial isobaric steady state. To prevent spurious pressure oscillations near contact waves, we introduce a <em>provable positivity-preserving parameter</em>. Importantly, the scheme equipped with the parameter is provably positivity-preserving. To enhance numerical accuracy, we reconstruct piecewise linear polynomials that satisfy the <em>local maximum principle</em>, which ensures the positivity-preserving property and eliminates spurious oscillations near solutions with large gradients. In particular, the adaptive surface reconstruction scheme preserves stationary steady states, including the still-water steady state and the isobaric steady state. Finally, we validate these properties by presenting several computed results for the Ripa system.</div></div>","PeriodicalId":287,"journal":{"name":"Computers & Fluids","volume":"297 ","pages":"Article 106664"},"PeriodicalIF":2.5,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large airfoil models","authors":"Howon Lee,&nbsp;Aanchal Save,&nbsp;Pranay Seshadri,&nbsp;Juergen Rauleder","doi":"10.1016/j.compfluid.2025.106662","DOIUrl":"10.1016/j.compfluid.2025.106662","url":null,"abstract":"<div><div>The development of a Large Airfoil Model (LAM), a transformative approach for answering technical questions on airfoil aerodynamics, requires a vast dataset and a model to leverage it. To build this foundation, a novel probabilistic machine learning approach, A Deep Airfoil Prediction Tool (ADAPT), has been developed. ADAPT makes uncertainty-aware predictions of airfoil pressure coefficient (<span><math><msub><mrow><mi>C</mi></mrow><mrow><mi>p</mi></mrow></msub></math></span>) distributions by harnessing experimental data and incorporating measurement uncertainties. By employing deep kernel learning, performing Gaussian Process Regression in a ten-dimensional latent space learned by a neural network, ADAPT effectively handles unstructured experimental datasets. In tandem, Airfoil Surface Pressure Information Repository of Experiments (ASPIRE), the first large-scale, open-source repository of airfoil experimental data, has been developed. ASPIRE integrates century-old historical data with modern reports, forming an unparalleled resource of real-world pressure measurements. This addresses a critical gap left by prior repositories, which relied primarily on numerical simulations. Demonstrative results for three airfoils show that ADAPT accurately predicts <span><math><msub><mrow><mi>C</mi></mrow><mrow><mi>p</mi></mrow></msub></math></span> distributions and aerodynamic coefficients across varied flow conditions, achieving a mean absolute error in enclosed area (<span><math><msub><mrow><mtext>MAE</mtext></mrow><mrow><mtext>enclosed</mtext></mrow></msub></math></span>) of 0.029. ASPIRE and ADAPT lay the foundation for an interactive airfoil analysis tool driven by a large language model, enabling users to perform design tasks based on natural language questions rather than explicit technical input.</div></div>","PeriodicalId":287,"journal":{"name":"Computers & Fluids","volume":"298 ","pages":"Article 106662"},"PeriodicalIF":2.5,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144069420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neural operators learn the local physics of magnetohydrodynamics","authors":"Taeyoung Kim ,&nbsp;Youngsoo Ha ,&nbsp;Myungjoo Kang","doi":"10.1016/j.compfluid.2025.106661","DOIUrl":"10.1016/j.compfluid.2025.106661","url":null,"abstract":"<div><div>Magnetohydrodynamics (MHD) plays a pivotal role in describing the dynamics of plasma and conductive fluids, essential for understanding phenomena such as the structure and evolution of stars and galaxies, and in nuclear fusion for plasma motion through ideal MHD equations. Solving these hyperbolic PDEs requires sophisticated numerical methods, presenting computational challenges due to complex structures and high costs. Recent advances introduce neural operators like the Fourier Neural Operator (FNO) as surrogate models for traditional numerical analysis. This study proposes a modified Flux Neural Operator (Flux NO) model to approximate the numerical flux of ideal MHD, offering a novel approach with enhanced generalization capabilities and significant computational efficiency. Our methodology adapts the Flux NO to process each physical quantity individually and incorporates loss functions ensuring total variation diminishing (TVD) property and divergence freeness for numerical stability. The proposed method achieves superior generalization beyond sampled distributions compared to existing neural operators and demonstrates computation speeds 25 times faster than the reference numerical scheme.</div></div>","PeriodicalId":287,"journal":{"name":"Computers & Fluids","volume":"297 ","pages":"Article 106661"},"PeriodicalIF":2.5,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143936340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}