The proposed research opportunity aims to improve accuracy and quantify uncertainty of optical diagnostic techniques by integrating physics-informed models with optical measurements. Optical diagnostic techniques offer non-intrusive insights but often measure quantities which do not faithfully represent the flow physics of interest, such as tracer particles which experience momentum and drag relative to their parent flow. Ill-posed inverse problems such as tomography utilize numerical regularization such as smoothness and symmetry, which do not reflect the physics of time-varying, turbulent, and shock-laden flows of interest to the hypersonic community. This research will focus on the integration of physics-informed models with advanced optical diagnostic measurements such as laser-induced fluorescence (LIF), particle image velocimetry (PIV), particle tracking velocimetry (PTV), background-oriented schlieren (BOS), and their higher-dimensional counterparts like time-accurate tomography. The use of physics-informed processing and analysis techniques will extract more information and insights than what can be achieved through traditional techniques. This enhanced fidelity can be leveraged for increased accuracy, quantifiable uncertainty, and novel deployment in challenging environments.

Distribution Statement A: Approved for Public Release; Distribution is Unlimited. PA# AFRL-2024-5784

key words

non-intrusive diagnostics; optical diagnostics; laser diagnostics; high-speed; high-Reynolds number; turbulent; turbulence; supersonic; hypersonic; flow physics; machine vision; machine learning; neural network; physics informed; pinn; inverse problem; tomography;

Citizenship:  Open to U.S. citizens

Level:  Open to Postdoctoral and Senior applicants