Data-Driven Input-Output Models for Reacting, High-Enthalpy Flows
Abstract
ABSTRACT (APPROVED FOR PUBLIC RELEASE)The extraction and description of principal structures and mechanisms in reacting, high-enthal,pyflows is a key component in the understanding of these complex flows. The proposed effort aimsat developing data-driven and model-,based methods for the input-output analysis of compressible,high-speed flows. An encompassing viewpoint, bring together advanced opt,imization methods,randomized algorithms and physics-tailored machine learning, will be taken to develop and benchmarktools for the e,ffective analysis of these types of flows.Turbulent, reacting flows in hypersonic flight applications, both external to the vehicle,and internalto the combustor, rank among the most complex in fluid dynamics, owing to a compoundinteraction of multiple physical pro,cesses (aerodynamics, turbulence, acoustics, shock-dynamics,aerothermochemistry, combustion, flame dynamics, nonequilibrium phenomen,a) over a very widerange of spatio-temporal scales. The reciprocal influence of these processes and the resulting emergenceof domina,nt flow features are crucial to a fundamental understanding of the flow physics.The difficulty in developing predictive models is co,mpounded by the complexities inherent in obtainingexperimental data and providing high-fidelity simulations with detailed thermochem,istryfor external and internal flows relevant to hypersonic vehicles. Recent advances in linear algebra,operator approximation, and,dynamical systems approaches have provided exciting opportunitiesthat enable a detailed understanding of a range of multiscale physi,cal phenomena, including turbulentflows. Reduced-order representations and input-output transfer function relationships derivedfrom,the equations of motion have elucidated low-rank behavior (the naturally-amplified, dynamicallysignificant, hidden structure) in cha,otic flows. In this proposal, our focus lies on reducedcomplexityunderstanding of the physics underlying the interaction of high-spe,ed, turbulent flowswith chemistry using this novel approach to mathematical modeling.We distill the underlying mathematical foundati,ons of these approaches and multiscale modelingto reimagine a modeling strategy with a focus on the specific challenges associated w,ithhigh-speed flow and the complexity of the range of possible interactions between flow aerodynamics,turbulence and chemistry. Our,ultimate objective is a framework to exploit the structure of thegoverning equations to generate reduced-order dynamical systems mod,els for the interaction ofturbulence with chemistry in high-enthalpy flows. We expect that these models will inform futureturbulence, models by characterizing the hidden variables regulating the high-dimensionality of thegoverning equations, with the potential to i,dentify the most propitious forms for sub-grid scalemodels and improved predictability of computational tools in the hypersonic regi,me.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Feb 08, 2022
- Source ID
- N000142212150
Entities
People
- Beverley McKeon
Organizations
- California Institute of Technology
- Office of Naval Research
- United States Navy