126th NIA CFD Seminar: Mohammad Zandsalimy (02-22-2022) скачать в хорошем качестве

126th NIA CFD Seminar: Mohammad Zandsalimy (02-22-2022)

Presentation file (pdf) is available at https://nationalia-my.sharepoint.com/... Title: Solution Mode Analysis for Mesh Optimization and Stability Improvement of Finite-Volume Simulations Speaker: Mohammad Zandsalimy, PhD Candidate, Mechanical Engineering, University of British Columbia Abstract: This work prototypes novel methods to enhance the efficiency of a CFD stability improvement approach based on mesh modification. The problematic parts of the unstructured mesh are identified through the unstable eigenvectors and a few vertices are selected for adjustment. The improved vertex selection methodology ensures a fast and reliable optimization procedure. Movement vectors are calculated using the gradients of unstable eigenmodes with respect to the movement of the selected vertices. A novel approach is presented to remediate the opposing eigenmodes in larger problems. Further, the feasibility of residual vector analysis for unstable solution mode identification is studied. Unsupervised classification models in the form of outlier detectors are used to identify anomalous vector elements. A novel method is presented to construct synthetic vectors that resemble unstable eigenvectors. The residual vector helps substantially in reducing the computational cost of the optimization algorithm by removing the eigenanalysis module. In comparison to the full eigenanalysis of the Jacobian matrix, residual vector analysis requires much fewer computational resources. This methodology is used for the first time for the stability improvement of finite-volume simulations. Bio: Mohammad is a Ph.D. candidate in Mechanical Engineering at the University of British Columbia, Vancouver, BC. His main area of research is the stability analysis and improvement of large-scale Computational Fluid Dynamics. Currently, he is working on mesh optimization for improved stability and convergence rate of finite-volume methods through Eigenanalysis of the semi-discrete Jacobian matrix and Principal Component Analysis of solution vectors. He is employing different Machine Learning pipelines to prototype methods for computational cost reduction of the presented optimization algorithms.