UBC Theses and Dissertations
Detecting missing flow separation and predicting error in drag using supervised machine learning Kamalhedayat, Amirpasha
Accuracy of flow simulations is a major concern in Computational Fluid Dynamics (CFD) applications. A possible outcome of inaccuracy in CFD results is missing a major feature in the flow field. Many methods have been proposed to reduce numerical errors and increase overall accuracy, but these are not always efficient or even feasible. In this study, a purely data-driven approach is proposed to assess flow simulations both in a qualitative and a quantitative manner. In this regard, Principal Component Analysis (PCA) has been performed on compressible flow simulations around an airfoil to map the high-dimensional CFD data to a lower-dimensional PCA subspace. A machine learning classifier based on the extracted principal components has been developed to detect the simulations that miss the separation bubble behind the airfoil. The evaluative measures indicate that the model is able to detect most of the simulations where the separation region is poorly resolved. Besides the classifier, a machine learning regressor has been trained on the same PCA subspace to predict the error in the output drag coefficient. The predictions reveal that the regression model estimates accurate errors with a tight uncertainty bound. Further, more efficient models built on top of fewer PCA modes have been implemented that show similar performance. In addition, the developed models were used to inspect simulations solved on a different mesh configuration from the one the models were trained on. This generalization framework gives rise to some challenges that are thoroughly discussed. Overall, the results demonstrate that machine learning models based on the principal components of the data set are promising tools to detect possible missing flow features and predict numerical errors in CFD.
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