Low-Complexity Stochastic Modeling and Control of Turbulent Flows
Abstract
Experimental and numerical datasets are becoming increasingly available for a wide range of flow configurations and Reynolds numbers and advanced machine learning techniques are being used to extract patterns from multimodal datasets. However, unreliable measurements and data anomalies challenge the viability of the resulting data-driven models. Furthermore,data-driven techniques are agnostic to the underlying physics and are not robust to outliers that are not accounted for in the training process. This compromises the performance of data driven models in regimes that were not contained in the available data and limits their utility for flow control. On the other hand, dynamical models of turbulent flows that are based on the Navier-Stokes (NS) equations typically have a large number of degrees of freedom and are not suitable for optimization and control design.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 30, 2022
- Accession Number
- AD1230449
Entities
People
- Mihailo R. Jovanović
Organizations
- University of Southern California