Pub Date : 2023-10-12DOI: 10.1080/10618562.2023.2259806
Jianbo Zhou, Rui Zhang, Lyu Chen
AbstractAirfoil flow field data acquisition is pivotal to the study of aerodynamics, traditionally relying on time-consuming computational fluid dynamics simulations or expensive wind tunnel tests. Herein, we introduce a new methodology leveraging Transformer Neural Network (TNN), which differs from conventional methodologies by employing self-attention mechanisms, to effectively predict these critical flow field data using historical data. A comprehensive set of experiments demonstrates the TNN model’s exceptional predictive accuracy, achieving over 95% across various airfoils under various operating conditions. Beyond accuracy and efficiency, we introduce an attention principle in our TNN model enhancing its interpretability. By aligning the TNN model’s attention distribution with the aerodynamic principles of airfoils, we illustrate how it utilises these geometric attributes in its predictions, thereby offering theoretical backing to its predictive outcomes. Our TNN model’s commendable accuracy, efficiency and interpretability illuminate the pathway for continued exploration in the fusion of deep learning with computational fluid dynamics.KEYWORDS: Deep learningTransformer Neural Networkairfoilflow field Disclosure statementNo potential conflict of interest was reported by the author(s).Data Availability StatementThe data that support the findings of this study are available from the corresponding author upon reasonable request.Additional informationFundingThis work was supported by Scientific Research Project of Department of Education of Hunan Province [grant number 21C1577]; Natural Science Foundation of Hunan Province [grant number 2022JJ60090].
{"title":"Prediction of Flow Field Over Airfoils Based on Transformer Neural Network","authors":"Jianbo Zhou, Rui Zhang, Lyu Chen","doi":"10.1080/10618562.2023.2259806","DOIUrl":"https://doi.org/10.1080/10618562.2023.2259806","url":null,"abstract":"AbstractAirfoil flow field data acquisition is pivotal to the study of aerodynamics, traditionally relying on time-consuming computational fluid dynamics simulations or expensive wind tunnel tests. Herein, we introduce a new methodology leveraging Transformer Neural Network (TNN), which differs from conventional methodologies by employing self-attention mechanisms, to effectively predict these critical flow field data using historical data. A comprehensive set of experiments demonstrates the TNN model’s exceptional predictive accuracy, achieving over 95% across various airfoils under various operating conditions. Beyond accuracy and efficiency, we introduce an attention principle in our TNN model enhancing its interpretability. By aligning the TNN model’s attention distribution with the aerodynamic principles of airfoils, we illustrate how it utilises these geometric attributes in its predictions, thereby offering theoretical backing to its predictive outcomes. Our TNN model’s commendable accuracy, efficiency and interpretability illuminate the pathway for continued exploration in the fusion of deep learning with computational fluid dynamics.KEYWORDS: Deep learningTransformer Neural Networkairfoilflow field Disclosure statementNo potential conflict of interest was reported by the author(s).Data Availability StatementThe data that support the findings of this study are available from the corresponding author upon reasonable request.Additional informationFundingThis work was supported by Scientific Research Project of Department of Education of Hunan Province [grant number 21C1577]; Natural Science Foundation of Hunan Province [grant number 2022JJ60090].","PeriodicalId":56288,"journal":{"name":"International Journal of Computational Fluid Dynamics","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136012556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 : 2023-10-04DOI: 10.1080/10618562.2023.2264198
Yong Su Jung, Bharath Govindarajan, James Baeder
AbstractA solution algorithm using Hamiltonian paths is presented as a unified grid approach for rotorcraft applications. Hidden line structures are robustly identified on general two- and three-dimensional unstructured grids with mixed elements, providing a framework for line-based solvers. A pure quadrilateral/hexahedral mesh is a prerequisite for line identification and enables approximate factorisation along the lines. The numerical efficiency obtained using the line-implicit method on various unstructured grids is better than that of the point-implicit method. Both finite-difference and gradient reconstructions are possible regardless of grid type. A combined reconstruction method is applied, which uses different reconstructions simultaneously but for different grid directions. Finally, the solution convergence rate is further improved using a preconditioned generalized minimal residual method (GMRES), where the preconditioning step is performed using the efficient line-implicit method.Keywords: Rotorcraft aerodynamicsunstructured gridcomputational efficiencyline-implicit methodgeneralized minimal residual method AcknowledgementsThe authors would like to acknowledge Dr. Roger Strawn (Army AFDD) and Dr. Rajneesh Singh (ARL) for their continued support of HAMSTR development. This work was supported by the National Supercomputing Center with supercomputing resources including technical support (KSC-2022-CRE-0197).Disclosure statementNo potential conflict of interest was reported by the author(s).
{"title":"A Unified Grid Approach Using Hamiltonian Paths for Computing Aerodynamic Flows","authors":"Yong Su Jung, Bharath Govindarajan, James Baeder","doi":"10.1080/10618562.2023.2264198","DOIUrl":"https://doi.org/10.1080/10618562.2023.2264198","url":null,"abstract":"AbstractA solution algorithm using Hamiltonian paths is presented as a unified grid approach for rotorcraft applications. Hidden line structures are robustly identified on general two- and three-dimensional unstructured grids with mixed elements, providing a framework for line-based solvers. A pure quadrilateral/hexahedral mesh is a prerequisite for line identification and enables approximate factorisation along the lines. The numerical efficiency obtained using the line-implicit method on various unstructured grids is better than that of the point-implicit method. Both finite-difference and gradient reconstructions are possible regardless of grid type. A combined reconstruction method is applied, which uses different reconstructions simultaneously but for different grid directions. Finally, the solution convergence rate is further improved using a preconditioned generalized minimal residual method (GMRES), where the preconditioning step is performed using the efficient line-implicit method.Keywords: Rotorcraft aerodynamicsunstructured gridcomputational efficiencyline-implicit methodgeneralized minimal residual method AcknowledgementsThe authors would like to acknowledge Dr. Roger Strawn (Army AFDD) and Dr. Rajneesh Singh (ARL) for their continued support of HAMSTR development. This work was supported by the National Supercomputing Center with supercomputing resources including technical support (KSC-2022-CRE-0197).Disclosure statementNo potential conflict of interest was reported by the author(s).","PeriodicalId":56288,"journal":{"name":"International Journal of Computational Fluid Dynamics","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135646263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 : 2023-01-02DOI: 10.1080/10618562.2023.2260763
Ondřej Bublík, Václav Heidler, Aleš Pecka, Jan Vimmr
AbstractWe design and implement a physics-informed convolutional neural network (CNN) to solve fluid flow problems on a parametrised domain. The goal is to compare the effectiveness of training based solely on CFD-generated training data with purely physics-informed training and training based on a combination of both. We consider the problem of incompressible fluid flow in a convergent-divergent channel with variable wall shape. A speciality of the designed neural network is the incorporation of the boundary condition directly in the CNN. A physics-informed CNN that uses a non-Cartesian mesh poses a challenge when evaluating partial derivatives. We propose a gradient layer that approximates the first and second partial derivatives by finite differences using Lagrange interpolation. Our analysis shows that the convergence of purely physics-informed training is slow. However, using a small training dataset in combination with physics-informed training can achieve results comparable to physics-uninformed training with a considerably larger training dataset.Keywords: Physics-informed neural networkconvolutional neural networkU-Netincompressible fluid flowfluid dynamics Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis research is supported by project GA21-31457S ‘Fast flow-field prediction using deep neural networks for solving fluid-structure interaction problems’ of the Grant Agency of the Czech Republic.
{"title":"Fluid Flow Modelling Using Physics-Informed Convolutional Neural Network in Parametrised Domains","authors":"Ondřej Bublík, Václav Heidler, Aleš Pecka, Jan Vimmr","doi":"10.1080/10618562.2023.2260763","DOIUrl":"https://doi.org/10.1080/10618562.2023.2260763","url":null,"abstract":"AbstractWe design and implement a physics-informed convolutional neural network (CNN) to solve fluid flow problems on a parametrised domain. The goal is to compare the effectiveness of training based solely on CFD-generated training data with purely physics-informed training and training based on a combination of both. We consider the problem of incompressible fluid flow in a convergent-divergent channel with variable wall shape. A speciality of the designed neural network is the incorporation of the boundary condition directly in the CNN. A physics-informed CNN that uses a non-Cartesian mesh poses a challenge when evaluating partial derivatives. We propose a gradient layer that approximates the first and second partial derivatives by finite differences using Lagrange interpolation. Our analysis shows that the convergence of purely physics-informed training is slow. However, using a small training dataset in combination with physics-informed training can achieve results comparable to physics-uninformed training with a considerably larger training dataset.Keywords: Physics-informed neural networkconvolutional neural networkU-Netincompressible fluid flowfluid dynamics Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis research is supported by project GA21-31457S ‘Fast flow-field prediction using deep neural networks for solving fluid-structure interaction problems’ of the Grant Agency of the Czech Republic.","PeriodicalId":56288,"journal":{"name":"International Journal of Computational Fluid Dynamics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135798728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 : 2023-01-02DOI: 10.1080/10618562.2023.2246391
Donald P. Rizzetta, Daniel J. Garmann
AbstractWall-resolved large-eddy simulations were carried out for the flow over a Gaussian bump configuration. The geometry and flow conditions were motivated by an experimental investigation, which was conducted in order to provide data for validating numerical modelling. The present computations were initiated as benchmark results that are accessible via wall-resolved large-eddy simulation. It was found that by increasing the bump height, the Reynolds number could be reduced and flow separation would occur. The modified bump then serves as a surrogate for the original Gaussian bump producing a smooth separated flow. Solutions to the unsteady three-dimensional compressible Navier-Stokes equations were obtained utilising a high-fidelity computational scheme and an implicit time-marching approach. Large-eddy simulations were performed and grid resolution studies were carried to ensure quality of computed results. Features of the flowfields are elucidated, and it was found that the time-mean surface streamline pattern had similar features to that of the experiment.Keywords: Smooth-Body separationGaussian bumplarge-eddy simulationhigh-order numerical methodcompact-differencing scheme Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis material is based upon work supported by the Air Force Office of Scientific Research under an award monitored by G. Abate. Computational resources were supported in part by grants of supercomputer time from the U. S. Department of Defense Supercomputing Resource Centers at the Stennis Space Center, MS, Vicksburg, MS, and Wright-Patterson AFB, OH.
{"title":"Wall-Resolved Large-Eddy Simulation of Flow Over a Three-Dimensional Gaussian Bump","authors":"Donald P. Rizzetta, Daniel J. Garmann","doi":"10.1080/10618562.2023.2246391","DOIUrl":"https://doi.org/10.1080/10618562.2023.2246391","url":null,"abstract":"AbstractWall-resolved large-eddy simulations were carried out for the flow over a Gaussian bump configuration. The geometry and flow conditions were motivated by an experimental investigation, which was conducted in order to provide data for validating numerical modelling. The present computations were initiated as benchmark results that are accessible via wall-resolved large-eddy simulation. It was found that by increasing the bump height, the Reynolds number could be reduced and flow separation would occur. The modified bump then serves as a surrogate for the original Gaussian bump producing a smooth separated flow. Solutions to the unsteady three-dimensional compressible Navier-Stokes equations were obtained utilising a high-fidelity computational scheme and an implicit time-marching approach. Large-eddy simulations were performed and grid resolution studies were carried to ensure quality of computed results. Features of the flowfields are elucidated, and it was found that the time-mean surface streamline pattern had similar features to that of the experiment.Keywords: Smooth-Body separationGaussian bumplarge-eddy simulationhigh-order numerical methodcompact-differencing scheme Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis material is based upon work supported by the Air Force Office of Scientific Research under an award monitored by G. Abate. Computational resources were supported in part by grants of supercomputer time from the U. S. Department of Defense Supercomputing Resource Centers at the Stennis Space Center, MS, Vicksburg, MS, and Wright-Patterson AFB, OH.","PeriodicalId":56288,"journal":{"name":"International Journal of Computational Fluid Dynamics","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135799622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a new method that accurately captures the interface of gas–liquid two-phase flow using a neural network-based lattice Boltzmann front-tracking interface capturing method. The motion of a single free-falling droplet is simulated using the Front Tracking Method (FTM), enabling the acquisition of information regarding the velocity field and interface points. The velocity field and interface points from the simulations are then utilised to generate input and output datasets for training the neural network (NN) models. Subsequently, the trained Bayesian regularised Back Propagation Neural Network (BRBPNN) model is integrated into the Lattice Boltzmann method (LBM), utilising the velocity field obtained from LBM simulation as input. The predicted LBM interface exhibits remarkable agreement with the FTM interface, as evidenced by a high correlation coefficient of 0.99945 for the ordinate value of the interface point in both methods. Therefore, the proposed method achieves precise positioning of the phase interface of LBM.
{"title":"A Lattice Boltzmann Front-Tracking Interface Capturing Method based on Neural Network for Gas-Liquid Two-Phase Flow","authors":"Bozhen Lai, Zhaoqing Ke, Zhiqiang Wang, Ronghua Zhu, Ruifeng Gao, Yu Mao, Ying Zhang","doi":"10.1080/10618562.2023.2246398","DOIUrl":"https://doi.org/10.1080/10618562.2023.2246398","url":null,"abstract":"This paper presents a new method that accurately captures the interface of gas–liquid two-phase flow using a neural network-based lattice Boltzmann front-tracking interface capturing method. The motion of a single free-falling droplet is simulated using the Front Tracking Method (FTM), enabling the acquisition of information regarding the velocity field and interface points. The velocity field and interface points from the simulations are then utilised to generate input and output datasets for training the neural network (NN) models. Subsequently, the trained Bayesian regularised Back Propagation Neural Network (BRBPNN) model is integrated into the Lattice Boltzmann method (LBM), utilising the velocity field obtained from LBM simulation as input. The predicted LBM interface exhibits remarkable agreement with the FTM interface, as evidenced by a high correlation coefficient of 0.99945 for the ordinate value of the interface point in both methods. Therefore, the proposed method achieves precise positioning of the phase interface of LBM.","PeriodicalId":56288,"journal":{"name":"International Journal of Computational Fluid Dynamics","volume":"11 1","pages":"49 - 66"},"PeriodicalIF":1.3,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73241043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 : 2023-01-02DOI: 10.1080/10618562.2023.2242271
J. Muralha, L. Eça, C. Klaij
This paper presents Solution Verification exercises with the pressure-based compressible flow solver ReFRESCO for five test cases available in the NASA Turbulence Modeling Resource: the two-dimensional flows over a flat plate, a bump-in-channel, a DSMA661 airfoil and a multi-element airfoil and the three-dimensional flow of a bump-in-channel. Simulations are performed with the Favre-averaged continuity and Navier-Stokes equations using the Spalart & Allmaras turbulence model. ReFRESCO results are compared with reference data from density-based compressible flow solvers (CFL3D and FUN3D). two aspects of the implementation of the turbulence model are addressed: the calculation of the distance to the wall and the discretization scheme used in the convective terms of the turbulence model transport equation. Results of this study show perfect consistency with the reference data for the test cases that are not affected by the determination of the distance to the wall.
{"title":"Verification of a Pressure-Based Compressible Flow Solver","authors":"J. Muralha, L. Eça, C. Klaij","doi":"10.1080/10618562.2023.2242271","DOIUrl":"https://doi.org/10.1080/10618562.2023.2242271","url":null,"abstract":"This paper presents Solution Verification exercises with the pressure-based compressible flow solver ReFRESCO for five test cases available in the NASA Turbulence Modeling Resource: the two-dimensional flows over a flat plate, a bump-in-channel, a DSMA661 airfoil and a multi-element airfoil and the three-dimensional flow of a bump-in-channel. Simulations are performed with the Favre-averaged continuity and Navier-Stokes equations using the Spalart & Allmaras turbulence model. ReFRESCO results are compared with reference data from density-based compressible flow solvers (CFL3D and FUN3D). two aspects of the implementation of the turbulence model are addressed: the calculation of the distance to the wall and the discretization scheme used in the convective terms of the turbulence model transport equation. Results of this study show perfect consistency with the reference data for the test cases that are not affected by the determination of the distance to the wall.","PeriodicalId":56288,"journal":{"name":"International Journal of Computational Fluid Dynamics","volume":"8 1","pages":"1 - 27"},"PeriodicalIF":1.3,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81391573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Influence of the Downstream Vehicle Length on Train Aerodynamics Subjected to Crosswind","authors":"Zhuang Tianci, Li Wenhui, Liu Tanghong","doi":"10.36959/717/661","DOIUrl":"https://doi.org/10.36959/717/661","url":null,"abstract":"","PeriodicalId":56288,"journal":{"name":"International Journal of Computational Fluid Dynamics","volume":"25 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80753220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Thermal Radiation, Chemical Reaction and Viscous Dissipation Effects on MHD Mixed Convection Flow of Micro Polar Fluid with Stretching Surface in the Presence of Heat Generation/Absorption","authors":"Zigta Binyam","doi":"10.36959/717/662","DOIUrl":"https://doi.org/10.36959/717/662","url":null,"abstract":"","PeriodicalId":56288,"journal":{"name":"International Journal of Computational Fluid Dynamics","volume":"18 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84918205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 : 2022-11-26DOI: 10.1080/10618562.2023.2225416
Aref Zanjani, A. Tahsini, Kimia Sadafi, Fatemeh Ghavidel Mangodeh
Shape optimisation of supersonic nozzles is of crucial importance in designing propulsion systems and space thrusters. In order to optimise the profile of a supersonic nozzle, the properties of the flow inside the nozzle should be obtained. This paper proposes and verifies a new methodology for analysing flows and designing supersonic nozzles. Flow analysis has been conducted using the method of characteristics, Ansys Fluent and convolutional neural networks. It is shown that deep convolutional neural networks can reach high levels of accuracy in predicting supersonic flow behaviour inside the nozzle. Also, shape optimisation of the supersonic nozzle has been conducted using the genetic algorithm in Ansys Fluent and artificial neural networks. The proposed ANN can optimise the shape of a supersonic nozzle for the given throat diameter, outlet diameter and nozzle length with high accuracy.
{"title":"Shape Optimization and Flow Analysis of Supersonic Nozzles Using Deep Learning","authors":"Aref Zanjani, A. Tahsini, Kimia Sadafi, Fatemeh Ghavidel Mangodeh","doi":"10.1080/10618562.2023.2225416","DOIUrl":"https://doi.org/10.1080/10618562.2023.2225416","url":null,"abstract":"Shape optimisation of supersonic nozzles is of crucial importance in designing propulsion systems and space thrusters. In order to optimise the profile of a supersonic nozzle, the properties of the flow inside the nozzle should be obtained. This paper proposes and verifies a new methodology for analysing flows and designing supersonic nozzles. Flow analysis has been conducted using the method of characteristics, Ansys Fluent and convolutional neural networks. It is shown that deep convolutional neural networks can reach high levels of accuracy in predicting supersonic flow behaviour inside the nozzle. Also, shape optimisation of the supersonic nozzle has been conducted using the genetic algorithm in Ansys Fluent and artificial neural networks. The proposed ANN can optimise the shape of a supersonic nozzle for the given throat diameter, outlet diameter and nozzle length with high accuracy.","PeriodicalId":56288,"journal":{"name":"International Journal of Computational Fluid Dynamics","volume":"95 1","pages":"875 - 891"},"PeriodicalIF":1.3,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87690820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 : 2022-11-26DOI: 10.1080/10618562.2023.2221645
Dawei Peng, Lanhao Zhao, Chuanyuan Zhou, Jia Mao
Pile safety has received increasing attention in marine engineering, especially in the field of local scour. In this paper, a finite element numerical model is established for local scour around a cylinder in steady currents. The flow is described by unsteady Reynolds–averaged Navier–Stokes equations with a traditional turbulent closure model. The proposed scour model takes bed load into account. The Exner equation is solved to determine the bed variation and the moving mesh approach is used to capture the evolution of the bed. When the resulting slope exceeds the angle of repose, a novel sand-slide model based on Rodrigues' rotation formula is used to prevent simulation distortion. All the equations are discretized by the two-step Taylor–Galerkin algorithm, and the resulting approach is fast to implement with second-order accuracy in space. The numerical results are found to be in good agreement with the experimental data.
桩的安全问题在海洋工程领域，尤其是局部冲刷领域受到越来越多的关注。本文建立了稳定流条件下圆柱局部冲刷的有限元数值模型。用非定常reynolds - average Navier-Stokes方程和传统的湍流闭包模型来描述流动。提出的冲刷模型考虑了河床荷载。通过求解Exner方程确定床层的变化，采用移动网格法捕捉床层的演变。当产生的坡度超过休止角时，采用基于Rodrigues旋转公式的新型滑坡模型来防止模拟失真。采用两步Taylor-Galerkin算法对所有方程进行离散化，得到的方法在空间上具有二阶精度，实现速度快。数值计算结果与实验数据吻合较好。
{"title":"Finite Element Numerical Simulation of Local Scour of a Three-Dimensional Cylinder under Steady Flow","authors":"Dawei Peng, Lanhao Zhao, Chuanyuan Zhou, Jia Mao","doi":"10.1080/10618562.2023.2221645","DOIUrl":"https://doi.org/10.1080/10618562.2023.2221645","url":null,"abstract":"Pile safety has received increasing attention in marine engineering, especially in the field of local scour. In this paper, a finite element numerical model is established for local scour around a cylinder in steady currents. The flow is described by unsteady Reynolds–averaged Navier–Stokes equations with a traditional turbulent closure model. The proposed scour model takes bed load into account. The Exner equation is solved to determine the bed variation and the moving mesh approach is used to capture the evolution of the bed. When the resulting slope exceeds the angle of repose, a novel sand-slide model based on Rodrigues' rotation formula is used to prevent simulation distortion. All the equations are discretized by the two-step Taylor–Galerkin algorithm, and the resulting approach is fast to implement with second-order accuracy in space. The numerical results are found to be in good agreement with the experimental data.","PeriodicalId":56288,"journal":{"name":"International Journal of Computational Fluid Dynamics","volume":"12 1","pages":"892 - 907"},"PeriodicalIF":1.3,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75219110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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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