CDS&E: Physics Guided Super-Resolution for Turbulent Transport

University Of Pittsburgh, Pittsburgh PA

Investigators

Abstract

Understanding turbulence phenomenon is the key to our comprehension of many natural and technological processes in diverse fields including aerodynamics, hydraulics, astrophysics, propulsion, atmospherics, oceanics, medicine, and many others. Direct numerical simulation (DNS) of the Navier-Stokes equations is widely recognized as the prime computational methodology with the highest fidelity in capturing the intricacies of turbulent transport. However, the wide range of flow's time and length scales makes DNS prohibitively expensive and time consuming even on the most advanced high-performance supercomputers. Large eddy simulation (LES), which filters out the very small-scale transport, provides an alternative with a much lower computational cost. However, the data generated by LES are of lower accuracy due to the associated filtering, and it is desirable to reconstruct the original true DNS results from the filtered LES data. This proposal describes a novel physics-guided machine learning methodology to perform this reconstruction with a systematic assessment for a variety of turbulent flows. This project aims to advance the restoration of high-fidelity turbulent flows via three innovations. First, a new physics-guided deep learning model will be developed to reconstruct fine-resolution turbulent flow data from low-resolution coarse LES data. Additional physical learning objectives and relationships will be incorporated to ensure that the proposed model meets specific physical constraints associated with the flow, and it is also generalizable for large scale simulations. Second, a novel deep learning scheme will be developed and utilized to construct high fidelity super-resolution fields from the most reliable LES that can be currently conducted. Finally, a wide variety of turbulent flows will be considered, ranging from passive incompressible, to chemically reactive compressible for comprehensive model assessments. This project has the potential to improve our capability to efficiently simulate high-resolution turbulent flows in many scientific and engineering domains. The research results will be used to develop materials for both undergraduate and graduate education, and for K-12 outreach. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.