EVENT DETECTION IN MULTI-VARIATE SCIENTIFIC SIMULATIONS USING FEATURE ANOMALY METRICS
Abstract not provided.
Publications
Machine Learning Models of Errors in LES Predictions of Surface Pressure Fluctuations
Abstract not provided.
Machine Learning Models of Errors in Large Eddy Simulation Predictions of Surface Pressure Fluctuations
Abstract not provided.
Convolutional Neural Networks for Frequency Response Predictions
Abstract not provided.
Investigating Turbulent Wall Pressure Fluctuations using Machine Learning Techniques
Abstract not provided.
Using Feature Importance Metrics to Detect Events of Interest in Scientific Computing Applications
Abstract not provided.
Development of machine learning models for turbulent wall pressure fluctuations
AIAA SciTech Forum - 55th AIAA Aerospace Sciences Meeting
In many aerospace applications, it is critical to be able to model fluid-structure interactions. In particular, correctly predicting the power spectral density of pressure fluctuations at surfaces can be important for assessing potential resonances and failure modes. Current turbulence modeling methods, such as wall-modeled Large Eddy Simulation and Detached Eddy Simulation, cannot reliably predict these pressure fluctuations for many applications of interest. The focus of this paper is on efforts to use data-driven machine learning methods to learn correction terms for the wall pressure fluctuation spectrum. In particular, the non-locality of the wall pressure fluctuations in a compressible boundary layer is investigated using random forests and neural networks trained and evaluated on Direct Numerical Simulation data.
Development of Machine Learning Models for Turbulent Wall Pressure Fluctuations
Abstract not provided.
Development of Machine Learning Models for Turbulent Wall Pressure Fluctuations
Abstract not provided.
9 Results