Computers & Fluids最新文献

{"title":"Central-differencing quasi-Newton method for solving the Eikonal equation with application to wall distance computation","authors":"Yair Mor-Yossef","doi":"10.1016/j.compfluid.2025.106897","DOIUrl":"10.1016/j.compfluid.2025.106897","url":null,"abstract":"<div><div>A new implicit method for solving the Eikonal equation on unstructured grids is proposed. The implicit Jacobian is obtained by modifying a direct linearization of the residual. The modified Jacobian is designed to form an M-matrix without the artificial time derivative commonly used. Namely, no artificial time step is involved in solving the Eikonal equation. Moreover, the algorithm guarantees the solution’s positivity and the linearized problem’s convergence. Usually, upwinding is introduced into the algorithm to enhance its stability. However, a second-order, central-differencing method is proposed in the present work for solving the Eikonal equation. It relies on a newly developed weighted least-squares scheme. Common weighting depends on the inverse of the distance between the sought cell and its neighboring cells. The new scheme adds directional weighting. This scheme was found to outperform the common weighted least-squares in terms of solution accuracy. An artificial diffusion term is introduced to smooth the solution. A dynamic smoothing coefficient is developed to control spurious oscillations. It distinguishes between freely propagating and colliding solution fronts. Moreover, it allows the artificial diffusion to be minimized, thereby increasing solution accuracy, while maintaining the stability of the algorithm. The numerical simulations demonstrated the algorithm’s robustness. It exhibits consistent and rapid residual convergence across various cases involving high aspect-ratio grid elements.</div></div>","PeriodicalId":287,"journal":{"name":"Computers & Fluids","volume":"306 ","pages":"Article 106897"},"PeriodicalIF":3.0,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145734354","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 Markov matrix iterative splitting algorithm for incompressible flow","authors":"Manuel A. Ramirez-Cabrera ,&nbsp;Eduardo Ramos ,&nbsp;Manira E. Narvaez-Saucedo ,&nbsp;Patricio J. Valades-Pelayo","doi":"10.1016/j.compfluid.2025.106943","DOIUrl":"10.1016/j.compfluid.2025.106943","url":null,"abstract":"<div><div>We present a Monte Carlo method for the incompressible Navier-Stokes equations, enforcing divergence-free solutions through a probabilistic projection framework. Using short random walks embedded in Markov matrices, the method sequentially solves diffusion, convection, and pressure projection steps at each timestep. The method achieves near-linear CPU scaling (<em>O</em>(<em>N</em>^1.18)) for transient simulations through pre-computed transition probability matrices for linear operators, with Multi-Level Monte Carlo acceleration improving steady-state convergence to (<em>O</em>(<em>N</em>^1.58)). Validation on lid-driven cavity flows (Re=100, 1000) shows differences below 3 % versus benchmarks. Additionally, the mesh-free nature of the Monte Carlo approach handles complex geometries simply by tagging random walkers within non-conforming obstacles, bypassing traditional meshing requirements. The method combines accuracy, unconditional stability, and inherent parallelizability, offering a compelling alternative to deterministic approaches.</div></div>","PeriodicalId":287,"journal":{"name":"Computers & Fluids","volume":"306 ","pages":"Article 106943"},"PeriodicalIF":3.0,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145836956","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":"Tackling compressible turbulent multi-component flows with dynamic hp-adaptation","authors":"Pascal Mossier ,&nbsp;Philipp Oestringer ,&nbsp;Steven Jöns ,&nbsp;Jens Keim ,&nbsp;Catherine Mavriplis ,&nbsp;Andrea D. Beck ,&nbsp;Claus-Dieter Munz","doi":"10.1016/j.compfluid.2025.106928","DOIUrl":"10.1016/j.compfluid.2025.106928","url":null,"abstract":"<div><div>In this paper, we present an hp-adaptive hybrid Discontinuous Galerkin/Finite Volume method for simulating compressible, turbulent multi-component flows. Building on a previously established hp-adaptive strategy for hyperbolic gas- and droplet-dynamics problems, this study extends the hybrid DG/FV approach to viscous flows with multiple species and incorporates non-conforming interfaces, enabling enhanced flexibility in grid generation. A central contribution of this work lies in the computation of both convective and dissipative fluxes across non-conforming element interfaces of mixed discretizations. To achieve accurate shock localization and scale-resolving representation of turbulent structures, the operator dynamically switches between an h-refined FV sub-cell scheme and a p-adaptive DG method, based on an a priori modal solution analysis. The method is implemented in the high-order open-source framework FLEXI and validated against benchmark problems, including the supersonic Taylor-Green vortex and a triplepoint shock interaction, demonstrating its robustness and accuracy for under-resolved shock-turbulence interactions and compressible multi-species scenarios. Finally, the method’s capabilities are showcased through an implicit large eddy simulation of an under-expanded hydrogen jet mixing with air, highlighting its potential for tackling challenging compressible multi-species flows in engineering.</div></div>","PeriodicalId":287,"journal":{"name":"Computers & Fluids","volume":"306 ","pages":"Article 106928"},"PeriodicalIF":3.0,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145651756","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":"Data-driven regression of thermodynamic models in entropic form using physics-informed machine learning","authors":"Evert Bunschoten,&nbsp;Alessandro Cappiello,&nbsp;Matteo Pini","doi":"10.1016/j.compfluid.2025.106932","DOIUrl":"10.1016/j.compfluid.2025.106932","url":null,"abstract":"<div><div>This article presents a data-driven method to evaluate thermodynamic properties of pure fluids and mixtures of fixed composition in the ideal- and nonideal thermodynamic states. Thermodynamic consistency is ensured by computing the fluid properties on the basis of the entropy potential and its first- and second- order derivatives, calculated with a physics-informed neural network. The computational performance of the method was investigated by implementing the resulting data-driven model in the open-source SU2 CFD software and by performing RANS simulations of the nonideal compressible flows through an organic Rankine cycle turbine cascade. Compared to using a multiparameter equation of state through a thermodynamic library coupled with SU2, the method was found to be 60 % more computationally efficient while maintaining high accuracy.</div></div>","PeriodicalId":287,"journal":{"name":"Computers & Fluids","volume":"306 ","pages":"Article 106932"},"PeriodicalIF":3.0,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145683873","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":"Intermittency-based transition models for different flow conditions in a high-order framework","authors":"A. Crivellini ,&nbsp;A. Ghidoni ,&nbsp;E. Mantecca ,&nbsp;G. Noventa","doi":"10.1016/j.compfluid.2025.106918","DOIUrl":"10.1016/j.compfluid.2025.106918","url":null,"abstract":"<div><div>This work proposes a modified formulation of the Spalart-Allmaras and <span><math><mrow><mi>k</mi><mo>−</mo><mover><mi>ω</mi><mo>˜</mo></mover></mrow></math></span> turbulence models for predicting transition in subsonic, supersonic, and hypersonic flows. Both models are algebraic and correlation-based, where the intermittency function includes corrections for pressure gradients and compressibility effects, using only local and free-stream flow conditions. Both models are implemented in a high-order discontinuous Galerkin solver, with particular attention to compressibility corrections to overcome the limitations of turbulence models in high-supersonic and hypersonic flows and/or with cold-wall conditions. The accuracy of the models is proved for turbulent and transitional flows on flat plates with different free-stream flow conditions, transition modes, and pressure gradients. Results are in agreement with experiments and high-fidelity simulations in terms of transition onset location and skin friction and/or heat transfer distribution on the plate. Both models are characterized by ease of implementation and robustness, and are suitable for high-order solvers.</div></div>","PeriodicalId":287,"journal":{"name":"Computers & Fluids","volume":"306 ","pages":"Article 106918"},"PeriodicalIF":3.0,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145651837","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":"Effect of wall-collision models on the transport of rigid, elongated non-spherical particles in a turbulent channel flow using an Euler/Lagrange approach","authors":"Manuel A. Taborda,&nbsp;Martin Sommerfeld","doi":"10.1016/j.compfluid.2025.106940","DOIUrl":"10.1016/j.compfluid.2025.106940","url":null,"abstract":"<div><div>The present contribution focuses on an extended modelling of elongated, non-spherical particle transport in wall-bounded, particle-laden flows. A turbulent channel flow is considered using a point-particle Euler/Lagrange framework, with the fluid phase computed by a numerically filtered, scale-resolving approach. The developed method for inertial fibres was implemented in OpenFOAM^Ⓡ, neglecting two-way coupling. Particle tracking with respect to translation and rotation is conducted in different frames of reference which are transformed through the use of quaternions, so that the fibre centroid position and orientation are known along their trajectory. Aerodynamic resistance coefficients for drag, lift, and torque are taken from correlations dependent on fibre orientation at an aspect ratio of five. Wall collisions of fibres are treated with an extended hard-body collision model that includes fibre orientation and the actual contact point. By solving the impulse equations with the parameters for restitution ratio and Coulomb friction coefficient the momentum loss was modelled. The flow validation was carried out against DNS data for a turbulent channel. Particular consideration was focused on the fibre-wall interactions, comparing the extended model with reduced approaches, such as centre-of-gravity specular reflection and spherical particle wall collision for the same equivalent diameter. The results highlight the important role of realistic wall-collision modelling. Accounting for the actual fibre-wall contact point leads to significantly different predictions of near-wall mean concentration, particle flux, and orientation profiles. In particular, fibre tilting during wall interactions enhances wall contact, increasing collision rates and modifying rebound angles compared to simplified models.</div></div>","PeriodicalId":287,"journal":{"name":"Computers & Fluids","volume":"306 ","pages":"Article 106940"},"PeriodicalIF":3.0,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145734352","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":"Leading-edge vortex monitoring in dynamically stalled flows via persistent homology","authors":"Quentin Martinez ,&nbsp;Chetan Jagadeesh ,&nbsp;Marinos Manolesos ,&nbsp;Mohammad Omidyeganeh","doi":"10.1016/j.compfluid.2025.106931","DOIUrl":"10.1016/j.compfluid.2025.106931","url":null,"abstract":"<div><div>A novel vortex core identification pipeline is developed based on template matching. Using persistent homology, a template similarity field is constructed from a sliding window template-target feature space distance. This scalar field is then used to accentuate localised regions of spanwise vorticity via nonlinear weighting. This method is successfully applied to track the leading-edge vortex trajectory in a stall flutter starting cycle for a pitching NACA 63(3)418 aerofoil. Trajectory results are compared with several user-based vortex core identifiers like local vorticity minimum, local Q-criterion maximum, local swirling strength maximum, and manual tracking. The results of this comparison are quite satisfactory as the developed method is capable of automatically monitoring the leading-edge vortex core through several critical stages of its lifecycle. The effects of template size and down sampling are also investigated with respect to the vortex core identification. It is found that a template radius of <span><math><mrow><mi>r</mi><mo>=</mo><mn>0.04</mn><mi>c</mi></mrow></math></span> and down sampling factor <span><math><mrow><mi>M</mi><mo>=</mo><mn>10</mn></mrow></math></span> are sufficient for accurate vortex core monitoring in dynamically stalled flows. In general, this method acts primarily as a field-based filter that can be useful for isolating highly vortical regions like the leading-edge vortex core in stall flutter or dynamic stall scenarios.</div></div>","PeriodicalId":287,"journal":{"name":"Computers & Fluids","volume":"306 ","pages":"Article 106931"},"PeriodicalIF":3.0,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145734355","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":"Accelerating CFD-driven training of transition and turbulence models for turbine flows by one-shot and real-time transformer integration","authors":"Yuan Fang ,&nbsp;Maximilian Reissmann ,&nbsp;Roberto Pacciani ,&nbsp;Yaomin Zhao ,&nbsp;Andrew S.H. Ooi ,&nbsp;Michele Marconcini ,&nbsp;Harshal D. Akolekar ,&nbsp;Richard D. Sandberg","doi":"10.1016/j.compfluid.2025.106927","DOIUrl":"10.1016/j.compfluid.2025.106927","url":null,"abstract":"<div><div>Recent studies have demonstrated the effectiveness of applying the computational fluid dynamics (CFD)-driven symbolic machine learning (ML) frameworks to assist in the development of explicit physical models within Reynolds-averaged Navier-Stokes (RANS), particularly for modeling transition, turbulence, and heat flux. These approaches can yield improved flow predictions with marginal increase in computational cost compared to baseline models. Nevertheless, a key limitation lies in the substantial computational expense during the training phase, which often requires thousands of RANS evaluations. This challenge becomes severe in training models for complex industrial applications, where each RANS run is computationally intensive, and is further exacerbated when attempting to develop more generalizable and coupled multiple models across multiple product designs. Take the development of general transition and turbulence model corrections for both low- and high-pressure turbines as the study case, this work introduces two transformer-assisted strategies to accelerate model training. In the first, previously trained models are stored and used as inputs to the transformer, which generates new models informed by prior knowledge to partially replace randomly initialized models at the first training iteration. Results show that leveraging prior knowledge trained from different turbine configurations all effectively guide the search toward more promising regions of the solution space, thereby accelerating the training process. In the second scenario, when no prior knowledge is available, the transformer is integrated into the training loop to dynamically generate candidate models based on the small error models from the last training iteration and discarding high-error models. Results indicate that more frequent transformer updates, such as after every training iteration, further enhance the acceleration effect.</div></div>","PeriodicalId":287,"journal":{"name":"Computers & Fluids","volume":"306 ","pages":"Article 106927"},"PeriodicalIF":3.0,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145683871","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":"An improved immersed boundary method for investigating flows over multiple irregular geometries with fractal interpolation","authors":"Dongheng Lai ,&nbsp;Xingyu Zhu","doi":"10.1016/j.compfluid.2025.106942","DOIUrl":"10.1016/j.compfluid.2025.106942","url":null,"abstract":"<div><div>In this study, we propose an improved immersed boundary method with dual-layer local triangulation. A novel code was developed for high-order numerical simulations of supersonic flows over multiple complex irregular geometries. A fifth-order weighted essentially non-oscillatory scheme was implemented to capture any steep gradients in the flow created by the geometries. The simulations were carried out on Cartesian grids and the Delaunay triangulation method was implemented twice near the boundary to refine the object boundary discretization and improve the numerical simulation robustness for complex irregular geometries. The proposed method could successfully evaluate various two- and three-dimensional compressible flows with immersed boundaries. Moreover, we studied the flow mechanism over irregularly shaped debris generated by multiple disintegrations during spacecraft re-entry in near-space, with a particular focus on spherical debris objects. We also propose a self-affine fractal interpolation surface method for spherical surfaces to effectively characterize the near-space debris. The improved immersed boundary method with the dual-layer local triangulation was used to simulate the supersonic flow over multiple side-by-side fractal spherical objects. Numerical examples conclusively verified the effectiveness, generality, and robustness of the proposed method.</div></div>","PeriodicalId":287,"journal":{"name":"Computers & Fluids","volume":"306 ","pages":"Article 106942"},"PeriodicalIF":3.0,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145734353","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":"RANS-CNN: A physics-informed convolutional neural network for solving reynolds-averaged Navier-Stokes equations in duct flows","authors":"Gaurav Bokil,&nbsp;Sebastian Merbold,&nbsp;Stefanie De Graaf","doi":"10.1016/j.compfluid.2025.106946","DOIUrl":"10.1016/j.compfluid.2025.106946","url":null,"abstract":"<div><div>Classical Computational Fluid Dynamics (CFD) simulations of turbulent flows in aerospace applications are computationally demanding and limit rapid design exploration. Convolutional Neural Networks (CNN) are being employed as surrogate models to overcome this challenge. Physics-informed approaches have also been applied to CNNs albeit only for simple flow fields such as laminar flow and heat conduction. This study advances Physics-Informed Convolutional Neural Networks (PICNN) to solve the steady incompressible Reynolds-Averaged Navier-Stokes (RANS) equations in wall-bounded geometries. The proposed method employs a higher-order finite difference scheme for computing spatial gradients, thus enhancing numerical accuracy. Additionally, the Dirichlet boundary conditions are strongly enforced in the network architecture using custom output layers and boundary masks. Numerical stabilisation is incorporated to enable the CNN to simulate high Reynolds number flows without losing stability. To assess the capabilities of this approach on aerospace use cases, it is tested on three data-free cases: S-shaped duct, a ducted body force heat exchanger, and flow over a forward facing step along with a backward facing step geometry with sparse labelled data. Moreover, a comparison between zero-equation and one-equation turbulence models is presented when employed in this framework. The RANS-CNN models performed with over 95 % accuracy on geometries with attached flow and 80 % on separated flow cases. The results obtained from the case studies confirm the capability of the RANS-CNN method in developing a robust and computationally efficient surrogate model with sparse data for smooth ducts.</div></div>","PeriodicalId":287,"journal":{"name":"Computers & Fluids","volume":"306 ","pages":"Article 106946"},"PeriodicalIF":3.0,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145787800","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}