Pub Date : 2025-10-13DOI: 10.1016/j.compfluid.2025.106873
Hiroyuki Asada, Kanako Maruyama, Soshi Kawai
Low-pass filters designed on unstructured grids are investigated in terms of a transfer function in the wavenumber space. The transfer functions on unstructured grids are derived, and the properties of the low-pass filters for removing high-wavenumber components and inducing phase errors are investigated through the derived transfer functions. The transfer function reveals that the low-pass filters on unstructured grids can achieve the property that higher-wavenumber components are removed more by adjusting a filter coefficient to a small value, whereas large filter coefficients induce unfavorable amplifications of high-wavenumber components. The presence of phase errors induced by the low-pass filters on triangle unstructured cells is also found by the transfer function. Furthermore, the transfer function shows that the numerical methods for evaluating the gradients that appear in the filter formulation affect the characteristics of the low-pass filters and that the simplest central scheme can have an advantage in terms of retaining numerical robustness by removing high-wavenumber components compared to the edge-normal augmentation scheme. The numerical experiments of inviscid Taylor–Green vortex and shock-vortex interaction are also conducted with the low-pass filter coupled with the non-dissipative kinetic energy and entropy preserving (KEEP) scheme on unstructured grids, demonstrating the validity of the present transfer function of the low-pass filter.
{"title":"Transfer function of low-pass filters on unstructured grids","authors":"Hiroyuki Asada, Kanako Maruyama, Soshi Kawai","doi":"10.1016/j.compfluid.2025.106873","DOIUrl":"10.1016/j.compfluid.2025.106873","url":null,"abstract":"<div><div>Low-pass filters designed on unstructured grids are investigated in terms of a transfer function in the wavenumber space. The transfer functions on unstructured grids are derived, and the properties of the low-pass filters for removing high-wavenumber components and inducing phase errors are investigated through the derived transfer functions. The transfer function reveals that the low-pass filters on unstructured grids can achieve the property that higher-wavenumber components are removed more by adjusting a filter coefficient to a small value, whereas large filter coefficients induce unfavorable amplifications of high-wavenumber components. The presence of phase errors induced by the low-pass filters on triangle unstructured cells is also found by the transfer function. Furthermore, the transfer function shows that the numerical methods for evaluating the gradients that appear in the filter formulation affect the characteristics of the low-pass filters and that the simplest central scheme can have an advantage in terms of retaining numerical robustness by removing high-wavenumber components compared to the edge-normal augmentation scheme. The numerical experiments of inviscid Taylor–Green vortex and shock-vortex interaction are also conducted with the low-pass filter coupled with the non-dissipative kinetic energy and entropy preserving (KEEP) scheme on unstructured grids, demonstrating the validity of the present transfer function of the low-pass filter.</div></div>","PeriodicalId":287,"journal":{"name":"Computers & Fluids","volume":"303 ","pages":"Article 106873"},"PeriodicalIF":3.0,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145325679","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 : 2025-10-13DOI: 10.1016/j.compfluid.2025.106876
Layal Jbara , Aashish Goyal , Anthony Wachs
<div><div>We introduce a deep neural network framework that combines machine learning with domain knowledge to model particle–laden flows, specifically focusing on suspensions of non-spherical polyhedral particles. Building upon our flow configuration knowledge, our model leverages Graph Neural networks (GNNs) to capture the intricate spatial, geometrical and relational interactions between particles. The particles are represented as nodes in a directed graph, with pairwise interactions encoded as directed edges, capturing both the local microstructure and inherent symmetries of the flow configuration. A multi-layer perceptron (MLP) function is employed for message passing, and a multi-headed attention mechanism is integrated to weigh the importance of neighboring nodes and edge features in the aggregation process. We define the directed edges between the nodes using the incidence function <span><math><msub><mrow><mi>ψ</mi></mrow><mrow><mi>G</mi></mrow></msub></math></span> such that the <span><math><mi>k</mi></math></span>th nearest neighbors of each particle <span><math><msub><mrow><mi>v</mi></mrow><mrow><mi>i</mi></mrow></msub></math></span> are identified using the neighborhood defined by <span><math><mrow><msub><mrow><mi>N</mi></mrow><mrow><mi>k</mi></mrow></msub><mrow><mo>(</mo><msub><mrow><mi>v</mi></mrow><mrow><mi>i</mi></mrow></msub><mo>)</mo></mrow></mrow></math></span> and we test different values of <span><math><mi>k</mi></math></span> to assess the impact of varying the number of neighbors. The convergence of predictions improves with an increasing number of neighbors (<span><math><mi>k</mi></math></span>), highlighting the importance of refining the neighborhood structure for better model performance. Our results demonstrate the effectiveness of the GNN in predicting streamwise drag forces, with <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> values consistently exceeding 0.90 for <span><math><mrow><mi>Δ</mi><msub><mrow><mi>F</mi></mrow><mrow><mi>x</mi></mrow></msub></mrow></math></span>, and exceeding 0.80 for transverse lift force<span><math><mrow><mi>Δ</mi><msub><mrow><mi>F</mi></mrow><mrow><mi>y</mi></mrow></msub></mrow></math></span> and torque <span><math><mrow><mi>Δ</mi><msub><mrow><mi>T</mi></mrow><mrow><mi>z</mi></mrow></msub></mrow></math></span> at all <span><math><mi>κ</mi></math></span> values for low <span><math><mrow><mi>R</mi><mi>e</mi></mrow></math></span> and <span><math><mi>ϕ</mi></math></span>. However, the model’s performance decreases as <span><math><mrow><mi>R</mi><mi>e</mi></mrow></math></span> and <span><math><mi>ϕ</mi></math></span> increase, particularly for transverse forces and torques. We show that the GNN outperforms the literature-reported models that lack incorporation of local physical properties as input parameters and provides comparable or superior performance to Convolutional Neural Networks (CNNs), even when local velocity is included. The GNN excels in cap
{"title":"Graph neural network based model of hydrodynamic closure laws in non-spherical particle–laden flows","authors":"Layal Jbara , Aashish Goyal , Anthony Wachs","doi":"10.1016/j.compfluid.2025.106876","DOIUrl":"10.1016/j.compfluid.2025.106876","url":null,"abstract":"<div><div>We introduce a deep neural network framework that combines machine learning with domain knowledge to model particle–laden flows, specifically focusing on suspensions of non-spherical polyhedral particles. Building upon our flow configuration knowledge, our model leverages Graph Neural networks (GNNs) to capture the intricate spatial, geometrical and relational interactions between particles. The particles are represented as nodes in a directed graph, with pairwise interactions encoded as directed edges, capturing both the local microstructure and inherent symmetries of the flow configuration. A multi-layer perceptron (MLP) function is employed for message passing, and a multi-headed attention mechanism is integrated to weigh the importance of neighboring nodes and edge features in the aggregation process. We define the directed edges between the nodes using the incidence function <span><math><msub><mrow><mi>ψ</mi></mrow><mrow><mi>G</mi></mrow></msub></math></span> such that the <span><math><mi>k</mi></math></span>th nearest neighbors of each particle <span><math><msub><mrow><mi>v</mi></mrow><mrow><mi>i</mi></mrow></msub></math></span> are identified using the neighborhood defined by <span><math><mrow><msub><mrow><mi>N</mi></mrow><mrow><mi>k</mi></mrow></msub><mrow><mo>(</mo><msub><mrow><mi>v</mi></mrow><mrow><mi>i</mi></mrow></msub><mo>)</mo></mrow></mrow></math></span> and we test different values of <span><math><mi>k</mi></math></span> to assess the impact of varying the number of neighbors. The convergence of predictions improves with an increasing number of neighbors (<span><math><mi>k</mi></math></span>), highlighting the importance of refining the neighborhood structure for better model performance. Our results demonstrate the effectiveness of the GNN in predicting streamwise drag forces, with <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> values consistently exceeding 0.90 for <span><math><mrow><mi>Δ</mi><msub><mrow><mi>F</mi></mrow><mrow><mi>x</mi></mrow></msub></mrow></math></span>, and exceeding 0.80 for transverse lift force<span><math><mrow><mi>Δ</mi><msub><mrow><mi>F</mi></mrow><mrow><mi>y</mi></mrow></msub></mrow></math></span> and torque <span><math><mrow><mi>Δ</mi><msub><mrow><mi>T</mi></mrow><mrow><mi>z</mi></mrow></msub></mrow></math></span> at all <span><math><mi>κ</mi></math></span> values for low <span><math><mrow><mi>R</mi><mi>e</mi></mrow></math></span> and <span><math><mi>ϕ</mi></math></span>. However, the model’s performance decreases as <span><math><mrow><mi>R</mi><mi>e</mi></mrow></math></span> and <span><math><mi>ϕ</mi></math></span> increase, particularly for transverse forces and torques. We show that the GNN outperforms the literature-reported models that lack incorporation of local physical properties as input parameters and provides comparable or superior performance to Convolutional Neural Networks (CNNs), even when local velocity is included. The GNN excels in cap","PeriodicalId":287,"journal":{"name":"Computers & Fluids","volume":"303 ","pages":"Article 106876"},"PeriodicalIF":3.0,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145325683","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 : 2025-10-13DOI: 10.1016/j.compfluid.2025.106875
Giulio Gori
We propose to rely on a flat Dirichlet distribution to sample the eigenvalues of the Reynolds Stress Tensor in RANS simulations. The goal is to forward propagate the uncertainty inherent the structure of the turbulence closure to targeted QoIs. The flat Dirichlet distribution is defined over the 2-dimensional simplex delimiting the Reynolds Stress Tensor realizability conditions. This ensures the tensor positive-definiteness and serves the uncertainty forward propagation by means of diverse techniques e.g., Monte Carlo or Polynomial Chaos Expansions. Simulations are performed using a modified version of the open-source SU2 suite. Results are obtained for two reference test cases. Namely, the subsonic air flow over a backward facing step and the NACA0012 airfoil operating in subsonic conditions and with a variable angle of attack.
{"title":"Turbulence model uncertainty estimation via Monte Carlo perturbation of the Reynolds Stress Tensor","authors":"Giulio Gori","doi":"10.1016/j.compfluid.2025.106875","DOIUrl":"10.1016/j.compfluid.2025.106875","url":null,"abstract":"<div><div>We propose to rely on a flat Dirichlet distribution to sample the eigenvalues of the Reynolds Stress Tensor in RANS simulations. The goal is to forward propagate the uncertainty inherent the structure of the turbulence closure to targeted QoIs. The flat Dirichlet distribution is defined over the 2-dimensional simplex delimiting the Reynolds Stress Tensor realizability conditions. This ensures the tensor positive-definiteness and serves the uncertainty forward propagation by means of diverse techniques e.g., Monte Carlo or Polynomial Chaos Expansions. Simulations are performed using a modified version of the open-source SU2 suite. Results are obtained for two reference test cases. Namely, the subsonic air flow over a backward facing step and the NACA0012 airfoil operating in subsonic conditions and with a variable angle of attack.</div></div>","PeriodicalId":287,"journal":{"name":"Computers & Fluids","volume":"303 ","pages":"Article 106875"},"PeriodicalIF":3.0,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145325678","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}
The central-upwind weighted essentially non-oscillatory (WENO) scheme introduces the downwind substencil to reconstruct the numerical flux, where the smoothness indicator for the downwind substencil is of critical importance in maintaining high order in smooth regions and preserving the essentially non-oscillatory behavior in shock capturing. In this study, we design the smoothness indicator for the downwind substencil by simply summing up all local smoothness indicators and taking the average, which includes the regularity information of the whole stencil. Accordingly the JS-type and Z-type nonlinear weights, based on simple local smoothness indicators, are developed for the fourth-order central-upwind WENO scheme. The accuracy, robustness, and high-resolution properties of our proposed schemes are demonstrated in a variety of one- and two-dimensional problems.
{"title":"JS-type and Z-type weights for fourth-order central-upwind weighted essentially non-oscillatory schemes","authors":"Jiaxi Gu , Xinjuan Chen , Kwanghyuk Park , Jae-Hun Jung","doi":"10.1016/j.compfluid.2025.106867","DOIUrl":"10.1016/j.compfluid.2025.106867","url":null,"abstract":"<div><div>The central-upwind weighted essentially non-oscillatory (WENO) scheme introduces the downwind substencil to reconstruct the numerical flux, where the smoothness indicator for the downwind substencil is of critical importance in maintaining high order in smooth regions and preserving the essentially non-oscillatory behavior in shock capturing. In this study, we design the smoothness indicator for the downwind substencil by simply summing up all local smoothness indicators and taking the average, which includes the regularity information of the whole stencil. Accordingly the JS-type and Z-type nonlinear weights, based on simple local smoothness indicators, are developed for the fourth-order central-upwind WENO scheme. The accuracy, robustness, and high-resolution properties of our proposed schemes are demonstrated in a variety of one- and two-dimensional problems.</div></div>","PeriodicalId":287,"journal":{"name":"Computers & Fluids","volume":"303 ","pages":"Article 106867"},"PeriodicalIF":3.0,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145325685","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 : 2025-10-09DOI: 10.1016/j.compfluid.2025.106858
Amareshwara Sainadh Chamarthi
The paper proposes a physically consistent numerical discretization approach for simulating viscous compressible multicomponent flows. It has two main contributions. First, a contact discontinuity (and material interface) detector is developed. In those regions of contact discontinuities, the THINC (Tangent of Hyperbola for INterface Capturing) approach is used for reconstructing appropriate variables (phasic densities). For other flow regions, the variables are reconstructed using the Monotonicity-preserving (MP) scheme (or Weighted essentially non-oscillatory scheme (WENO)). For reconstruction in the characteristic space, the THINC approach is used only for the contact (or entropy) wave and volume fractions. For the reconstruction of primitive variables, the THINC approach is used for phasic densities and volume fractions only, offering an effective solution for reducing dissipation errors near contact discontinuities. The numerical results of the benchmark tests show that the proposed method captured the material interface sharply compared to existing methods. The second contribution is the development of an algorithm that uses a central reconstruction scheme for the tangential velocities, as they are continuous across material interfaces in viscous flows. In this regard, the Ducros sensor (a shock detector that cannot detect material interfaces) is employed to compute the tangential velocities using a central scheme across material interfaces. Using the central scheme does not produce any oscillations at the material interface. The proposed approach is thoroughly validated with several benchmark test cases for compressible multicomponent flows, highlighting its advantages. The physics appropriate approach also shown to prevent spurious vortices, despite being formally second-order accurate for nonlinear problems, on a coarser mesh than a genuinely high-order accurate method.
{"title":"Physics appropriate interface capturing reconstruction approach for viscous compressible multicomponent flows","authors":"Amareshwara Sainadh Chamarthi","doi":"10.1016/j.compfluid.2025.106858","DOIUrl":"10.1016/j.compfluid.2025.106858","url":null,"abstract":"<div><div>The paper proposes a physically consistent numerical discretization approach for simulating viscous compressible multicomponent flows. It has two main contributions. First, a contact discontinuity (and material interface) detector is developed. In those regions of contact discontinuities, the THINC (Tangent of Hyperbola for INterface Capturing) approach is used for reconstructing appropriate variables (phasic densities). For other flow regions, the variables are reconstructed using the Monotonicity-preserving (MP) scheme (or Weighted essentially non-oscillatory scheme (WENO)). For reconstruction in the characteristic space, the THINC approach is used only for the contact (or entropy) wave and volume fractions. For the reconstruction of primitive variables, the THINC approach is used for phasic densities and volume fractions only, offering an effective solution for reducing dissipation errors near contact discontinuities. The numerical results of the benchmark tests show that the proposed method captured the material interface sharply compared to existing methods. The second contribution is the development of an algorithm that uses a central reconstruction scheme for the tangential velocities, as they are continuous across material interfaces in viscous flows. In this regard, the Ducros sensor (a shock detector that cannot detect material interfaces) is employed to compute the tangential velocities using a central scheme across material interfaces. Using the central scheme does not produce any oscillations at the material interface. The proposed approach is thoroughly validated with several benchmark test cases for compressible multicomponent flows, highlighting its advantages. The physics appropriate approach also shown to prevent spurious vortices, despite being formally second-order accurate for nonlinear problems, on a coarser mesh than a genuinely high-order accurate method.</div></div>","PeriodicalId":287,"journal":{"name":"Computers & Fluids","volume":"303 ","pages":"Article 106858"},"PeriodicalIF":3.0,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145264410","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 : 2025-10-08DOI: 10.1016/j.compfluid.2025.106870
Aaron English , Renato Vacondio , Susanna Dazzi , José M. Domínguez
In this work, Smoothed Particle Hydrodynamics (SPH) is assessed for the modelling of flow past bridges. An improved pressure extrapolation method and a no-slip extension for the widely used modified Dynamic Boundary Condition (mDBC) are presented. The no-slip condition is validated with benchmark test cases of Poiseuille flow and flow past a cylinder. The ability to simulate river flows past bridges is assessed by comparing with experimental measurements for two model bridges with multiple discharges. The results are also evaluated against numerical results from 2D Shallow Water Equation (SWE) simulations, which is the leading approach for this kind of flow. While both methods shows good agreement with the experimental data away from the bridge, the SWE assumptions fail in the immediate vicinity of the bridge. In this region, the SPH method demonstrates higher accuracy, captures additional flow features and offers deeper insight into local hydraulic behaviour. A new SPH restart procedure has been developed that enables high-resolution simulations to be initialized using results from lower-resolution simulations. This greatly reduces simulation run times for large and complex transient flow such as rivers. Advanced DualSPHysics boundary generation and pre-processing tools allow for easier creation of boundaries through STL files, and GPU acceleration on the latest hardware allow for faster simulation with larger domains. With all these features, the first full-scale SPH simulation of a real river flow past a bridge is presented, including the riverbed bathymetry and model of Ponte Vecchio on the Arno River (Italy).
{"title":"Smoothed particle hydrodynamics modelling of river flows past bridges","authors":"Aaron English , Renato Vacondio , Susanna Dazzi , José M. Domínguez","doi":"10.1016/j.compfluid.2025.106870","DOIUrl":"10.1016/j.compfluid.2025.106870","url":null,"abstract":"<div><div>In this work, Smoothed Particle Hydrodynamics (SPH) is assessed for the modelling of flow past bridges. An improved pressure extrapolation method and a no-slip extension for the widely used modified Dynamic Boundary Condition (mDBC) are presented. The no-slip condition is validated with benchmark test cases of Poiseuille flow and flow past a cylinder. The ability to simulate river flows past bridges is assessed by comparing with experimental measurements for two model bridges with multiple discharges. The results are also evaluated against numerical results from 2D Shallow Water Equation (SWE) simulations, which is the leading approach for this kind of flow. While both methods shows good agreement with the experimental data away from the bridge, the SWE assumptions fail in the immediate vicinity of the bridge. In this region, the SPH method demonstrates higher accuracy, captures additional flow features and offers deeper insight into local hydraulic behaviour. A new SPH restart procedure has been developed that enables high-resolution simulations to be initialized using results from lower-resolution simulations. This greatly reduces simulation run times for large and complex transient flow such as rivers. Advanced DualSPHysics boundary generation and pre-processing tools allow for easier creation of boundaries through STL files, and GPU acceleration on the latest hardware allow for faster simulation with larger domains. With all these features, the first full-scale SPH simulation of a real river flow past a bridge is presented, including the riverbed bathymetry and model of Ponte Vecchio on the Arno River (Italy).</div></div>","PeriodicalId":287,"journal":{"name":"Computers & Fluids","volume":"303 ","pages":"Article 106870"},"PeriodicalIF":3.0,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145325686","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 : 2025-10-08DOI: 10.1016/j.compfluid.2025.106868
Naman Bartwal , Somnath Roy , Surya Pratap Vanka
Mixed convection is ubiquitous in nature and industrial processes that involve the combination of both natural and forced convective flows. It plays an important role in broad range of engineering applications such as in cooling of electronics, heat exchangers, HVAC systems, etc. Optimizing the thermal management systems is crucial for achieving effective cooling or heating in industrial equipments. By comprehending and utilizing the phenomenon of mixed convection, one can effectively design thermal systems that attain superior overall performance. Here, we present detailed investigations on the influence of four rotating circular cylinders on mixed convection within a square cavity. We investigate the effects of various parameters such as Richardson number (Ri), Reynolds number (Re) and location and direction of rotation of cylinders. These factors are shown to influence the heat transfer rates significantly, which is shown via streamlines and isotherms pattern within the cavity for varying values of Re and Ri. A radial basis function based meshless method is developed for the simulation of mixed convection scenarios. High-order accuracy is demonstrated by first simulating the benchmark case of cylindrical Couette flow. We have also provided detailed validation and verification for thermal convection problems by comparing our findings to experimental and numerical results in the published literature.
{"title":"Application of a high-order meshless method to study mixed convection heat transfer in a cavity with rotating circular cylinders","authors":"Naman Bartwal , Somnath Roy , Surya Pratap Vanka","doi":"10.1016/j.compfluid.2025.106868","DOIUrl":"10.1016/j.compfluid.2025.106868","url":null,"abstract":"<div><div>Mixed convection is ubiquitous in nature and industrial processes that involve the combination of both natural and forced convective flows. It plays an important role in broad range of engineering applications such as in cooling of electronics, heat exchangers, HVAC systems, etc. Optimizing the thermal management systems is crucial for achieving effective cooling or heating in industrial equipments. By comprehending and utilizing the phenomenon of mixed convection, one can effectively design thermal systems that attain superior overall performance. Here, we present detailed investigations on the influence of four rotating circular cylinders on mixed convection within a square cavity. We investigate the effects of various parameters such as Richardson number (Ri), Reynolds number (Re) and location and direction of rotation of cylinders. These factors are shown to influence the heat transfer rates significantly, which is shown via streamlines and isotherms pattern within the cavity for varying values of Re and Ri. A radial basis function based meshless method is developed for the simulation of mixed convection scenarios. High-order accuracy is demonstrated by first simulating the benchmark case of cylindrical Couette flow. We have also provided detailed validation and verification for thermal convection problems by comparing our findings to experimental and numerical results in the published literature.</div></div>","PeriodicalId":287,"journal":{"name":"Computers & Fluids","volume":"303 ","pages":"Article 106868"},"PeriodicalIF":3.0,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145325682","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 : 2025-10-08DOI: 10.1016/j.compfluid.2025.106856
Takahito Asaga , Yuichi Kuya
This paper proposes numerical methods to obtain converged flow solutions by quantum annealing. The proposed quantum annealing methods are developed for lattice gas automata (LGA) and finite difference methods (FDMs). The quadratic unconstrained binary optimization (QUBO) model for LGA consists of the cost functions for the steady-state flow condition, collision law condition, boundary condition, and flow field condition. In contrast, the QUBO model for FDMs is built directly from the discretized governing equations expressed in a binary form. In the numerical experiments of channel flows, both proposed methods successfully extract the converged velocity profiles from a large number of flow state combinations by quantum annealing. The obtained solutions closely match those obtained by the conventional or analytical approach. Since, due to the difference in characteristics between LGA and FDMs, FDMs can reduce the scale of combinatorial optimization problems more efficiently than LGA, the proposed FDM-based method obtains more accurate solutions than the proposed LGA-based method.
{"title":"Obtaining converged flow solutions using quantum annealing","authors":"Takahito Asaga , Yuichi Kuya","doi":"10.1016/j.compfluid.2025.106856","DOIUrl":"10.1016/j.compfluid.2025.106856","url":null,"abstract":"<div><div>This paper proposes numerical methods to obtain converged flow solutions by quantum annealing. The proposed quantum annealing methods are developed for lattice gas automata (LGA) and finite difference methods (FDMs). The quadratic unconstrained binary optimization (QUBO) model for LGA consists of the cost functions for the steady-state flow condition, collision law condition, boundary condition, and flow field condition. In contrast, the QUBO model for FDMs is built directly from the discretized governing equations expressed in a binary form. In the numerical experiments of channel flows, both proposed methods successfully extract the converged velocity profiles from a large number of flow state combinations by quantum annealing. The obtained solutions closely match those obtained by the conventional or analytical approach. Since, due to the difference in characteristics between LGA and FDMs, FDMs can reduce the scale of combinatorial optimization problems more efficiently than LGA, the proposed FDM-based method obtains more accurate solutions than the proposed LGA-based method.</div></div>","PeriodicalId":287,"journal":{"name":"Computers & Fluids","volume":"303 ","pages":"Article 106856"},"PeriodicalIF":3.0,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145325681","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 : 2025-10-08DOI: 10.1016/j.compfluid.2025.106857
Shashi Shekhar Roy , S.V. Raghurama Rao
In this paper, we present a kinetic model with flexible velocities that satisfy positivity preservation conditions for the Euler equations. Our 1D kinetic model consists of two velocities and employs both the asymmetrical and symmetrical models. Switching between the two models is governed by our formulation of kinetic relative entropy, together with an additional criterion that ensures a robust and accurate scheme yielding entropic results. In 2D, we introduce a novel three-velocity kinetic model, defined to ensure a locally one-dimensional formulation for the resulting macroscopic normal flux. For first order accuracy, we also obtain a limit on the time step that ensures positivity preservation. The resulting numerical scheme captures grid-aligned steady shocks exactly. Several benchmark compressible flow test cases are solved in 1D and 2D to demonstrate the efficacy of the proposed solver.
{"title":"A kinetic scheme based on positivity preservation with exact shock capture","authors":"Shashi Shekhar Roy , S.V. Raghurama Rao","doi":"10.1016/j.compfluid.2025.106857","DOIUrl":"10.1016/j.compfluid.2025.106857","url":null,"abstract":"<div><div>In this paper, we present a kinetic model with flexible velocities that satisfy positivity preservation conditions for the Euler equations. Our 1D kinetic model consists of two velocities and employs both the asymmetrical and symmetrical models. Switching between the two models is governed by our formulation of kinetic relative entropy, together with an additional criterion that ensures a robust and accurate scheme yielding entropic results. In 2D, we introduce a novel three-velocity kinetic model, defined to ensure a locally one-dimensional formulation for the resulting macroscopic normal flux. For first order accuracy, we also obtain a limit on the time step that ensures positivity preservation. The resulting numerical scheme captures grid-aligned steady shocks exactly. Several benchmark compressible flow test cases are solved in 1D and 2D to demonstrate the efficacy of the proposed solver.</div></div>","PeriodicalId":287,"journal":{"name":"Computers & Fluids","volume":"303 ","pages":"Article 106857"},"PeriodicalIF":3.0,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145325684","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 : 2025-10-03DOI: 10.1016/j.compfluid.2025.106855
N. Smirnova , S. Utyuzhnikov , V. Titarev , M. Petrov
In turbulence modeling the resolution of near-wall boundary layer requires most of computing time. The near-wall domain decomposition (NDD) approach proved to be efficient in tackling this problem. It represents a trade-off between computing time and accuracy. In this method, the computational domain is divided into two non-overlapping subdomains: the inner layer and region outside. The interface boundary conditions of Robin type are set by transferring the boundary conditions from the wall to the interface boundary. In contrast to the exact NDD, in the approximate NDD a simplified system of equations is solved in the near-wall subdomain. In the current paper a variant of the exact NDD is proposed, that uses an operator corresponding to the approximate NDD approach as a preconditioner. To improve the efficiency of NDD methods the GMRES method is applied. The efficacy of NDD algorithms are compared against for the low-Reynolds-number model.
{"title":"Convergence acceleration algorithms for non-overlapping domain decomposition in near-wall turbulence modeling","authors":"N. Smirnova , S. Utyuzhnikov , V. Titarev , M. Petrov","doi":"10.1016/j.compfluid.2025.106855","DOIUrl":"10.1016/j.compfluid.2025.106855","url":null,"abstract":"<div><div>In turbulence modeling the resolution of near-wall boundary layer requires most of computing time. The near-wall domain decomposition (NDD) approach proved to be efficient in tackling this problem. It represents a trade-off between computing time and accuracy. In this method, the computational domain is divided into two non-overlapping subdomains: the inner layer and region outside. The interface boundary conditions of Robin type are set by transferring the boundary conditions from the wall to the interface boundary. In contrast to the exact NDD, in the approximate NDD a simplified system of equations is solved in the near-wall subdomain. In the current paper a variant of the exact NDD is proposed, that uses an operator corresponding to the approximate NDD approach as a preconditioner. To improve the efficiency of NDD methods the GMRES method is applied. The efficacy of NDD algorithms are compared against for the low-Reynolds-number model.</div></div>","PeriodicalId":287,"journal":{"name":"Computers & Fluids","volume":"303 ","pages":"Article 106855"},"PeriodicalIF":3.0,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145263969","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}
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