Pub Date : 2025-12-05DOI: 10.1016/j.compfluid.2025.106941
Yixiang Xu , Gang Yang , Yulin Xing , Dean Hu
The motion of bubbles in conductive fluid under the external magnetic field is affected by multiple physical fields, so that the flow characteristics of bubbles are usually difficult to predict, which has gradually become a hot topic in magnetohydrodynamics research. In this paper, the dynamic behavior of bubble rising in conductive liquids under magnetic field is simulated by three-dimensional (3D) ISPH-FVM coupling method. The bubble motion is captured by the ISPH particles, while the FVM mesh is used to calculate the flow field. For the combination of magnetohydrodynamics model and ISPH-FVM coupling method, FVM grid is used to solve Maxwell equations combining electric field and magnetic field, and the calculated Lorentz force is introduced into the momentum equation. To verify the accuracy and stability of the present ISPH-FVM coupling model, the Shercliff case, bubble rising under horizontal magnetic field and two horizontal bubbles coalescence are tested. Subsequently, the single bubble rising under different magnetic field directions and intensities is simulated, and the dynamic mechanism behind the morphological changes and rising velocity of the bubble is deeply analyzed. Finally, the influence of different magnetic field directions and intensities on the terminal velocity and bubble shape of rising bubble with different sizes are discussed.
{"title":"Development of ISPH-FVM coupling method by embedded MHD model to simulate bubble rising in conductive liquids under magnetic field","authors":"Yixiang Xu , Gang Yang , Yulin Xing , Dean Hu","doi":"10.1016/j.compfluid.2025.106941","DOIUrl":"10.1016/j.compfluid.2025.106941","url":null,"abstract":"<div><div>The motion of bubbles in conductive fluid under the external magnetic field is affected by multiple physical fields, so that the flow characteristics of bubbles are usually difficult to predict, which has gradually become a hot topic in magnetohydrodynamics research. In this paper, the dynamic behavior of bubble rising in conductive liquids under magnetic field is simulated by three-dimensional (3D) ISPH-FVM coupling method. The bubble motion is captured by the ISPH particles, while the FVM mesh is used to calculate the flow field. For the combination of magnetohydrodynamics model and ISPH-FVM coupling method, FVM grid is used to solve Maxwell equations combining electric field and magnetic field, and the calculated Lorentz force is introduced into the momentum equation. To verify the accuracy and stability of the present ISPH-FVM coupling model, the Shercliff case, bubble rising under horizontal magnetic field and two horizontal bubbles coalescence are tested. Subsequently, the single bubble rising under different magnetic field directions and intensities is simulated, and the dynamic mechanism behind the morphological changes and rising velocity of the bubble is deeply analyzed. Finally, the influence of different magnetic field directions and intensities on the terminal velocity and bubble shape of rising bubble with different sizes are discussed.</div></div>","PeriodicalId":287,"journal":{"name":"Computers & Fluids","volume":"306 ","pages":"Article 106941"},"PeriodicalIF":3.0,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145787872","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}
Pub Date : 2025-12-05DOI: 10.1016/j.compfluid.2025.106933
Manira E. Narvaez-Saucedo, Carlos A. López-Villalobos, Manuel A. Ramírez-Cabrera, Eduardo Ramos, Patricio Javier Valades-Pelayo
This work introduces a novel Markov Matrix Iterative Splitting Algorithm (MISA), a probabilistic operator splitting Monte Carlo approach designed for simulating natural convection in incompressible flows within the continuum regime. This approach effectively captures the nonlinear convective transport and stochastic diffusion while enforcing global incompressibility, without requiring mesh generation or matrix inversions common to deterministic solvers. Variance reduction techniques and a Multi-Level Monte Carlo framework improve convergence and stability. MISA naturally handles complex geometries with internal obstacles, incorporated effortlessly by excluding walkers inside them and imposing boundary conditions on their surfaces. Benchmark natural convection simulations in square cavities demonstrate MISA’s accuracy and computational efficiency compared to traditional finite-volume solvers. The method reliably resolves convective flow structures and temperature fields, highlighting its promising potential for complex fluid-thermal transport problems in irregular domains
{"title":"A Markov Matrix iterative splitting algorithm for natural convection","authors":"Manira E. Narvaez-Saucedo, Carlos A. López-Villalobos, Manuel A. Ramírez-Cabrera, Eduardo Ramos, Patricio Javier Valades-Pelayo","doi":"10.1016/j.compfluid.2025.106933","DOIUrl":"10.1016/j.compfluid.2025.106933","url":null,"abstract":"<div><div>This work introduces a novel Markov Matrix Iterative Splitting Algorithm (MISA), a probabilistic operator splitting Monte Carlo approach designed for simulating natural convection in incompressible flows within the continuum regime. This approach effectively captures the nonlinear convective transport and stochastic diffusion while enforcing global incompressibility, without requiring mesh generation or matrix inversions common to deterministic solvers. Variance reduction techniques and a Multi-Level Monte Carlo framework improve convergence and stability. MISA naturally handles complex geometries with internal obstacles, incorporated effortlessly by excluding walkers inside them and imposing boundary conditions on their surfaces. Benchmark natural convection simulations in square cavities demonstrate MISA’s accuracy and computational efficiency compared to traditional finite-volume solvers. The method reliably resolves convective flow structures and temperature fields, highlighting its promising potential for complex fluid-thermal transport problems in irregular domains</div></div>","PeriodicalId":287,"journal":{"name":"Computers & Fluids","volume":"306 ","pages":"Article 106933"},"PeriodicalIF":3.0,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145920697","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}
Pub Date : 2025-12-04DOI: 10.1016/j.compfluid.2025.106940
Manuel A. Taborda, Martin Sommerfeld
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.
{"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, 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<sup>Ⓡ</sup>, 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":"2025-12-04","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}
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.
{"title":"Data-driven regression of thermodynamic models in entropic form using physics-informed machine learning","authors":"Evert Bunschoten, Alessandro Cappiello, 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":"2025-11-30","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}
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 and down sampling factor 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.
{"title":"Leading-edge vortex monitoring in dynamically stalled flows via persistent homology","authors":"Quentin Martinez , Chetan Jagadeesh , Marinos Manolesos , 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":"2025-11-29","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}
Pub Date : 2025-11-29DOI: 10.1016/j.compfluid.2025.106927
Yuan Fang , Maximilian Reissmann , Roberto Pacciani , Yaomin Zhao , Andrew S.H. Ooi , Michele Marconcini , Harshal D. Akolekar , Richard D. Sandberg
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.
{"title":"Accelerating CFD-driven training of transition and turbulence models for turbine flows by one-shot and real-time transformer integration","authors":"Yuan Fang , Maximilian Reissmann , Roberto Pacciani , Yaomin Zhao , Andrew S.H. Ooi , Michele Marconcini , Harshal D. Akolekar , 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":"2025-11-29","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}
Pub Date : 2025-11-27DOI: 10.1016/j.compfluid.2025.106929
Giovanni Catalani , Jean Fesquet , Xavier Bertrand , Frédéric Tost , Michael Bauerheim , Joseph Morlier
This paper introduces a novel surrogate modeling framework for aerodynamic applications based on Neural Fields. The proposed approach, MARIO (Modulated Aerodynamic Resolution Invariant Operator), addresses non parametric geometric variability through an efficient shape encoding mechanism and exploits the discretization-invariant nature of Neural Fields. It enables training on significantly downsampled meshes, while maintaining consistent accuracy during full-resolution inference. These properties allow for efficient modeling of diverse flow conditions, while reducing computational cost and memory requirements compared to traditional CFD solvers and existing surrogate methods. The framework is validated on two complementary datasets that reflect industrial constraints. First, the AirfRANS dataset consists of a two-dimensional airfoil benchmark with non-parametric shape variations. Performance evaluation of MARIO on this case demonstrates an order of magnitude improvement in prediction accuracy over existing methods across velocity, pressure, and turbulent viscosity fields, while accurately capturing boundary layer phenomena and aerodynamic coefficients. Second, the NASA Common Research Model features three-dimensional pressure distributions on a full aircraft surface mesh, with parametric control surface deflections. This configuration confirms MARIO’s accuracy and scalability. Benchmarking against state-of-the-art methods demonstrates that Neural Field surrogates can provide rapid and accurate aerodynamic predictions under the computational and data limitations characteristic of industrial applications.
{"title":"Towards scalable surrogate models based on neural fields for large scale aerodynamic simulations","authors":"Giovanni Catalani , Jean Fesquet , Xavier Bertrand , Frédéric Tost , Michael Bauerheim , Joseph Morlier","doi":"10.1016/j.compfluid.2025.106929","DOIUrl":"10.1016/j.compfluid.2025.106929","url":null,"abstract":"<div><div>This paper introduces a novel surrogate modeling framework for aerodynamic applications based on Neural Fields. The proposed approach, MARIO (Modulated Aerodynamic Resolution Invariant Operator), addresses non parametric geometric variability through an efficient shape encoding mechanism and exploits the discretization-invariant nature of Neural Fields. It enables training on significantly downsampled meshes, while maintaining consistent accuracy during full-resolution inference. These properties allow for efficient modeling of diverse flow conditions, while reducing computational cost and memory requirements compared to traditional CFD solvers and existing surrogate methods. The framework is validated on two complementary datasets that reflect industrial constraints. First, the AirfRANS dataset consists of a two-dimensional airfoil benchmark with non-parametric shape variations. Performance evaluation of MARIO on this case demonstrates an order of magnitude improvement in prediction accuracy over existing methods across velocity, pressure, and turbulent viscosity fields, while accurately capturing boundary layer phenomena and aerodynamic coefficients. Second, the NASA Common Research Model features three-dimensional pressure distributions on a full aircraft surface mesh, with parametric control surface deflections. This configuration confirms MARIO’s accuracy and scalability. Benchmarking against state-of-the-art methods demonstrates that Neural Field surrogates can provide rapid and accurate aerodynamic predictions under the computational and data limitations characteristic of industrial applications.</div></div>","PeriodicalId":287,"journal":{"name":"Computers & Fluids","volume":"306 ","pages":"Article 106929"},"PeriodicalIF":3.0,"publicationDate":"2025-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145683872","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}
Pub Date : 2025-11-26DOI: 10.1016/j.compfluid.2025.106928
Pascal Mossier , Philipp Oestringer , Steven Jöns , Jens Keim , Catherine Mavriplis , Andrea D. Beck , Claus-Dieter Munz
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.
{"title":"Tackling compressible turbulent multi-component flows with dynamic hp-adaptation","authors":"Pascal Mossier , Philipp Oestringer , Steven Jöns , Jens Keim , Catherine Mavriplis , Andrea D. Beck , 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":"2025-11-26","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}
Pub Date : 2025-11-24DOI: 10.1016/j.compfluid.2025.106918
A. Crivellini , A. Ghidoni , E. Mantecca , G. Noventa
This work proposes a modified formulation of the Spalart-Allmaras and 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.
{"title":"Intermittency-based transition models for different flow conditions in a high-order framework","authors":"A. Crivellini , A. Ghidoni , E. Mantecca , 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":"2025-11-24","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}
Pub Date : 2025-11-22DOI: 10.1016/j.compfluid.2025.106919
James G․ Coder
The mathematical and practical behavior of finite-domain filters is explored for applications to turbulent flow simulations. High-order filters are constructed for finite-difference schemes that satisfy summation-by-parts, with calibration that considers the spectral behavior at boundaries and the integration norm of the numerical scheme, leading to both symmetric and asymmetric filters. All filters studied are contractive, but additional analysis of potential transient growth behavior is performed. The filters are applied to the one-dimensional linear advection equation, a reflecting acoustic wave on a finite domain, the inviscid evolution of two-dimensional, compressible turbulence, and transitional flow over an airfoil at moderate Reynolds number. It is observed that symmetric filters offer better overall performance with provable stability properties compared to asymmetric filters calibrated based on spectral behavior, and forgoing spectral calibration in favor of operator symmetry does not decrease solution quality for turbulence simulations.
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