Keywords
approximate near-wall domain decomposition
turbulent flows
How to Cite
Abstract
The simulation of turbulent flows around objects is computationally expensive and requires a balance between accuracy and computational performance. The objective of this work is to construct an operator that would improve the result of a less accurate, but more computationally efficient model using simulation results for similar flows obtained by a slower but more accurate method. The Spalart-Allmaras model is used as the turbulence model. The approximate near-wall domain decomposition (ANDD) approach is used as the fast, less accurate model, while the one-block approach (without decomposition) is used as the baseline, more accurate model. In this work, the operator is constructed with a non-local approach, where the entire input flow field affects every point of the output flow field. The operator is constructed with a convolutional neural network (CNN) of an encoder-decoder architecture. The efficiency and accuracy of the obtained surrogate model are demonstrated with a supersonic flow over a compression corner with different angle α and Reynolds number values. We considered interpolation and extrapolation both by Re and α.
References
Grotjans H., Menter F. R. Wall Functions for Industrial Applications. Computational Fluid Dynamics. 1998;98(1):2.
Utyuzhnikov S., Petrov M., Chikitkin A., Titarev V. On Extension of Near-Wall Non-overlapping Domain Decomposition to Essentially Unsteady Turbulent Flows. Smart Modeling for Engineering Systems: Proceedings of the Conference 50 Years of the Development of Grid-Characteristic Method. 2019;133:199. Springer. DOI: 10.1007/978-3-030-06228-6_17.
Chikitkin A., Utyuzhnikov S., Petrov M., Titarev V. Non-Overlapping Domain Decomposition for Modeling Essentially Unsteady Near-Wall Turbulent Flows. Computers & Fluids. 2020;202:104506. DOI: 10.1016/j.compfluid.2020.104506.
Petrov M., Utyuzhnikov S., Chikitkin A., Titarev V. On Extension of Near-Wall Domain Decomposition to Turbulent Compressible Flows. Computers & Fluids. 2020;210:104629. DOI: 10.1016/j.compfluid.2020.104629.
Hanna B. N., Dinh N. T., Youngblood R. W., Bolotnov I. A. Machine-Learning Based Error Prediction Approach for Coarse-Grid Computational Fluid Dynamics (CG-CFD). Progress in Nuclear Energy. 2020;118:103140. DOI: 10.1016/j.pnucene.2019.103140.
Raissi M., Perdikaris P., Karniadakis G. E. Physics-Informed Neural Networks: A Deep Learning Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations. Journal of Computational Physics. 2019;378:686–707. DOI: 10.1016/j.jcp.2018.10.045.
Guo X., Li W., Iorio F. Convolutional Neural Networks for Steady Flow Approximation. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016;481–490. DOI: 10.1145/2939672.2939738.
Spalart P. R., Allmaras S. R. 30th Aerospace Sciences Meeting and Exhibit. Aerospace Sciences Meetings. American Institute of Aeronautics and Astronautics, Reston, VA. 1992. DOI: 10.2514/6.1992-439.
Petrov M. N., Tambova A. A., Titarev V. A., Utyuzhnikov S. V., Chikitkin A. V. FlowModellium Software Package for Calculating High-Speed Flows of Compressible Fluid. Computational Mathematics and Mathematical Physics. 2018;58(11):1865–1886. DOI: 10.1134/S0965542518110118.
Zhu J. Y., Park T., Isola P., Efros A. A. Unpaired Image-To-Image Translation Using CycleConsistent Adversarial Networks. Proceedings of the IEEE International Conference on Computer Vision. 2017;2223–2232.
He K., Zhang X., Ren S., Sun J. Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016;770–778.
Zhang H., Goodfellow I., Metaxas D., Odena A. Self-Attention Generative Adversarial Networks. International Conference on Machine Learning. 2019;7354–7363.