Enhanced fidelity predictions of hypersonic transition: Embedded measurements and optimal sensing
Abstract
Accurate predictions of hypersonic boundary layer transition are difficult because the onset of turbulence is a chaotic phenomenon that is very sensitive to environmental conditions. Small changes in the free stream perturbation spectrum can lead to quantitative and qualitative changes in transition, i.e. its location and mechanism. This difficulty plagues computations and experiments alike. In the former, predictions even with the most accurate methods (DNS, NPSE, LES) are only as reliable as the prescribed free stream condition which is often unknown in reality. In laboratory and flight experiments, placement of limited number of sensors and interpretation of their signals in order to accurately establish the origin of transition in uncertain (and noisy) environments are difficult. We will tackle these challenges by developing new techniques that (i) determine the most dangerous environmental conditions, (ii) deduce the true disturbance environment by optimally interpreting measurement data and (iii) provide a framework for optimal sensor placement and weighting. The configuration is hypersonic boundary layer transition over cones with slender and blunt leading edges, and in noisy environments. When even limited data are available (e.g. surface pressure at discrete locations), we will use optimization techniques and high fidelity simulations to solve the inverse problem of identifying the free stream condition that caused these measurements. Due to the nonlinearity of the governing equations and their irreversibility, this problem is both mathematically stiff and ill posed, i.e. without a unique solution. However, once solved, not only do we discover the free stream condition, but we can also compute the entire flow field far beyond the original measurements and sensing capabilities. And by optimizing the placement and weighting of sensors, we will minimize the uncertainty in the interpretation of their signals and maximize the accuracy of our predictions.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jan 14, 2022
- Source ID
- FA95501910230
Entities
People
- Tamer A. Zaki
Organizations
- Air Force Office of Scientific Research
- Johns Hopkins University
- United States Air Force