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Project
Investigation of aerothermodynamic phenomena of ground testing facilities (FWOSB128)
Thermal Protection System (TPS) is vital to protect capsules from the
extreme heat fluxes encountered during atmospheric re-entry. The
design of TPS is performed in plasma wind tunnels capable to
reproduce the high enthalpy, dissociated flow of the hypersonic flight.
However, our knowledge of these physicochemical phenomena is still
limited, and testing conditions need to be better addressed for a
truthful flight-to-ground extrapolation. Previous studies pointed out
that the kinetic mechanisms modeling the chemical non-equilibrium
of the flow, and the unsteadiness features of the jet may significantly
influence the testing conditions, but they are usually disregarded.
Kinetic mechanisms will be investigated in this research project by
using robust uncertainty quantification analysis based on a Bayesian
inference framework. The plasma fluctuations will be studied through
a dedicated experimental campaign carried out in the Plasmatron
facility of the von Karman Institute. Time-resolved measurements will
be post-processed using data-driven methodologies capturing the
main features of the flow, their frequency contents, and spatial
distribution. The coupling between unsteadiness and kinetic
mechanisms will be investigated through the development of a new
framework using machine learning classification methodologies to
investigate the impact on the testing conditions. Results are
extrapolated to flight and verified using high-fidelity CFD codes and
validated with real flight data
extreme heat fluxes encountered during atmospheric re-entry. The
design of TPS is performed in plasma wind tunnels capable to
reproduce the high enthalpy, dissociated flow of the hypersonic flight.
However, our knowledge of these physicochemical phenomena is still
limited, and testing conditions need to be better addressed for a
truthful flight-to-ground extrapolation. Previous studies pointed out
that the kinetic mechanisms modeling the chemical non-equilibrium
of the flow, and the unsteadiness features of the jet may significantly
influence the testing conditions, but they are usually disregarded.
Kinetic mechanisms will be investigated in this research project by
using robust uncertainty quantification analysis based on a Bayesian
inference framework. The plasma fluctuations will be studied through
a dedicated experimental campaign carried out in the Plasmatron
facility of the von Karman Institute. Time-resolved measurements will
be post-processed using data-driven methodologies capturing the
main features of the flow, their frequency contents, and spatial
distribution. The coupling between unsteadiness and kinetic
mechanisms will be investigated through the development of a new
framework using machine learning classification methodologies to
investigate the impact on the testing conditions. Results are
extrapolated to flight and verified using high-fidelity CFD codes and
validated with real flight data
Date:1 Jan 2022 → Today
Keywords:hypersonic flight, plasma wind tunnel, data-driven / machine learning methodologies plasma instabilities, chemistry kinetics
Disciplines:Aerodynamics, Astronautical engineering, Fluid mechanics and fluid dynamics, Thermodynamic processes