Pub Date : 2021-10-08DOI: 10.1186/s42774-021-00082-x
H. Qi, Xinliang Li, Changping Yu, Fulin Tong
{"title":"Direct numerical simulation of hypersonic boundary layer transition over a lifting-body model HyTRV","authors":"H. Qi, Xinliang Li, Changping Yu, Fulin Tong","doi":"10.1186/s42774-021-00082-x","DOIUrl":"https://doi.org/10.1186/s42774-021-00082-x","url":null,"abstract":"","PeriodicalId":33737,"journal":{"name":"Advances in Aerodynamics","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2021-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42552543","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 : 2021-10-05DOI: 10.21203/rs.3.rs-907888/v1
Momoha Nishimura, M. Yamakawa, S. Asao, Seiichi Takeuchi, Mehdi Badri Ghomizad
This study proposes a method for modelling the flow interaction of multiple moving objects where the flow field variables are communicated between multiple separate moving computational domains. Instead of using the conventional approach with a single fixed computational domain covering the whole flow field, this method advances the moving computational domain (MCD) method in which the computational domain itself moves in line with the motions of an object inside. The computational domains created around each object move independently, and the flow fields of each domain interact where the flows cross. This eliminates the spatial restriction for simulating multiple moving objects. Firstly, a shock tube test verifies that the overset implementation and grid movement do not adversely affect the results and that there is communication between the grids. A second test case is conducted in which two spheres are crossed, and the forces exerted on one object due to the other’s crossing at a short distance are calculated. The results verify the reliability of this method and show that it is applicable to the flow interaction of multiple moving objects.
{"title":"Moving computational multi-domain method for modelling the flow interaction of multiple moving objects","authors":"Momoha Nishimura, M. Yamakawa, S. Asao, Seiichi Takeuchi, Mehdi Badri Ghomizad","doi":"10.21203/rs.3.rs-907888/v1","DOIUrl":"https://doi.org/10.21203/rs.3.rs-907888/v1","url":null,"abstract":"This study proposes a method for modelling the flow interaction of multiple moving objects where the flow field variables are communicated between multiple separate moving computational domains. Instead of using the conventional approach with a single fixed computational domain covering the whole flow field, this method advances the moving computational domain (MCD) method in which the computational domain itself moves in line with the motions of an object inside. The computational domains created around each object move independently, and the flow fields of each domain interact where the flows cross. This eliminates the spatial restriction for simulating multiple moving objects. Firstly, a shock tube test verifies that the overset implementation and grid movement do not adversely affect the results and that there is communication between the grids. A second test case is conducted in which two spheres are crossed, and the forces exerted on one object due to the other’s crossing at a short distance are calculated. The results verify the reliability of this method and show that it is applicable to the flow interaction of multiple moving objects.","PeriodicalId":33737,"journal":{"name":"Advances in Aerodynamics","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2021-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41379668","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 : 2021-10-01DOI: 10.1186/s42774-021-00086-7
Kaloki L. Nabutola, S. Boetcher
{"title":"Assessment of conventional and air-jet wheel deflectors for drag reduction of the DrivAer model","authors":"Kaloki L. Nabutola, S. Boetcher","doi":"10.1186/s42774-021-00086-7","DOIUrl":"https://doi.org/10.1186/s42774-021-00086-7","url":null,"abstract":"","PeriodicalId":33737,"journal":{"name":"Advances in Aerodynamics","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46309134","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 : 2021-08-20DOI: 10.21203/rs.3.rs-818524/v1
Chunfei Fang, Jinglei Xu
Wall roughness significantly influences both laminar-turbulent transition process and fully developed turbulence. A wall roughness extension for the KDO turbulence/transition model is developed. The roughness effect is introduced via the modification of the k and ν t boundary conditions. The wall is considered to be lifted to a higher position. The difference between the original position and the higher position, named as equivalent roughness height, is linked to the actual roughness height. The ratio between the two heights is determined by reasoning. With such a roughness extension, the predictions of the KDO RANS model agree well with the measurements of turbulent boundary layer with a sand grain surface, while the KDO transition model yields accurate cross-flow transition predictions of flow past a 6:1 spheroid.
{"title":"Extension of the KDO turbulence/transition model to account for roughness","authors":"Chunfei Fang, Jinglei Xu","doi":"10.21203/rs.3.rs-818524/v1","DOIUrl":"https://doi.org/10.21203/rs.3.rs-818524/v1","url":null,"abstract":"Wall roughness significantly influences both laminar-turbulent transition process and fully developed turbulence. A wall roughness extension for the KDO turbulence/transition model is developed. The roughness effect is introduced via the modification of the k and ν t boundary conditions. The wall is considered to be lifted to a higher position. The difference between the original position and the higher position, named as equivalent roughness height, is linked to the actual roughness height. The ratio between the two heights is determined by reasoning. With such a roughness extension, the predictions of the KDO RANS model agree well with the measurements of turbulent boundary layer with a sand grain surface, while the KDO transition model yields accurate cross-flow transition predictions of flow past a 6:1 spheroid.","PeriodicalId":33737,"journal":{"name":"Advances in Aerodynamics","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2021-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48257720","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 : 2021-08-01DOI: 10.1186/s42774-021-00077-8
Hao Wang, Yiqin Dai, Jie Yu, Yong Dong
{"title":"Predicting running time of aerodynamic jobs in HPC system by combining supervised and unsupervised learning method","authors":"Hao Wang, Yiqin Dai, Jie Yu, Yong Dong","doi":"10.1186/s42774-021-00077-8","DOIUrl":"https://doi.org/10.1186/s42774-021-00077-8","url":null,"abstract":"","PeriodicalId":33737,"journal":{"name":"Advances in Aerodynamics","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41962774","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}
To overcome the defects of traditional rarefied numerical methods such as the Direct Simulation Monte Carlo (DSMC) method and unified Boltzmann equation schemes and extend the covering range of macroscopic equations in high Knudsen number flows, data-driven nonlinear constitutive relations (DNCR) are proposed first through the machine learning method. Based on the training data from both Navier-Stokes (NS) solver and unified gas kinetic scheme (UGKS) solver, the map between responses of stress tensors and heat flux and feature vectors is established after the training phase. Through the obtained off-line training model, new test cases excluded from training data set could be predicated rapidly and accurately by solving conventional equations with modified stress tensor and heat flux. Finally, conventional one-dimensional shock wave cases and two-dimensional hypersonic flows around a blunt circular cylinder are presented to assess the capability of the developed method through various comparisons between DNCR, NS, UGKS, DSMC and experimental results. The improvement of the predictive capability of the coarse-graining model could make the DNCR method to be an effective tool in the rarefied gas community, especially for hypersonic engineering applications.
{"title":"Data-driven nonlinear constitutive relations for rarefied flow computations","authors":"Wenwen Zhao, Lijian Jiang, Shaobo Yao, Weifang Chen","doi":"10.21203/rs.3.rs-735668/v1","DOIUrl":"https://doi.org/10.21203/rs.3.rs-735668/v1","url":null,"abstract":"To overcome the defects of traditional rarefied numerical methods such as the Direct Simulation Monte Carlo (DSMC) method and unified Boltzmann equation schemes and extend the covering range of macroscopic equations in high Knudsen number flows, data-driven nonlinear constitutive relations (DNCR) are proposed first through the machine learning method. Based on the training data from both Navier-Stokes (NS) solver and unified gas kinetic scheme (UGKS) solver, the map between responses of stress tensors and heat flux and feature vectors is established after the training phase. Through the obtained off-line training model, new test cases excluded from training data set could be predicated rapidly and accurately by solving conventional equations with modified stress tensor and heat flux. Finally, conventional one-dimensional shock wave cases and two-dimensional hypersonic flows around a blunt circular cylinder are presented to assess the capability of the developed method through various comparisons between DNCR, NS, UGKS, DSMC and experimental results. The improvement of the predictive capability of the coarse-graining model could make the DNCR method to be an effective tool in the rarefied gas community, especially for hypersonic engineering applications.","PeriodicalId":33737,"journal":{"name":"Advances in Aerodynamics","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49503041","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 : 2021-07-27DOI: 10.21203/rs.3.rs-729114/v1
SiJie Zeng, X. Duan, Jiangtao Chen, Liang Yan
Sparse Polynomial Chaos Expansion (PCE) is widely used in various engineering fields to quantitatively analyse the influence of uncertainty, while alleviating the problem of dimensionality curse. However, current sparse PCE techniques focus on choosing features with the largest coefficients, which may ignore uncertainties propagated with high order features. Hence, this paper proposes the idea of selecting polynomial chaos basis based on information entropy, which aims to retain the advantages of existing sparse techniques while considering entropy change as output uncertainty. A novel entropy-based optimization method is proposed to update the state-of-the-art sparse PCE models. This work further develops an entropy-based synthetic sparse model, which has higher computational efficiency. Two benchmark functions and a computational fluid dynamics (CFD) experiment are used to compare the accuracy and efficiency between the proposed method and classical methods. The results show that entropy-based methods can better capture the features of uncertainty propagation, improving accuracy and reducing sparsity while avoiding over-fitting problems.
{"title":"Optimized sparse polynomial chaos expansion with entropy regularization","authors":"SiJie Zeng, X. Duan, Jiangtao Chen, Liang Yan","doi":"10.21203/rs.3.rs-729114/v1","DOIUrl":"https://doi.org/10.21203/rs.3.rs-729114/v1","url":null,"abstract":"Sparse Polynomial Chaos Expansion (PCE) is widely used in various engineering fields to quantitatively analyse the influence of uncertainty, while alleviating the problem of dimensionality curse. However, current sparse PCE techniques focus on choosing features with the largest coefficients, which may ignore uncertainties propagated with high order features. Hence, this paper proposes the idea of selecting polynomial chaos basis based on information entropy, which aims to retain the advantages of existing sparse techniques while considering entropy change as output uncertainty. A novel entropy-based optimization method is proposed to update the state-of-the-art sparse PCE models. This work further develops an entropy-based synthetic sparse model, which has higher computational efficiency. Two benchmark functions and a computational fluid dynamics (CFD) experiment are used to compare the accuracy and efficiency between the proposed method and classical methods. The results show that entropy-based methods can better capture the features of uncertainty propagation, improving accuracy and reducing sparsity while avoiding over-fitting problems.","PeriodicalId":33737,"journal":{"name":"Advances in Aerodynamics","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45560716","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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