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Numerical and Deep Learning Study of Transpiration Cooling for Sharp Hypersonic Leading Edge

Abstract

Recent escalating interest in the development of highly maneuverable hypersonic vehicles demands sharp leading edges. However, sharp leading edges induce severe aerothermal conditions where conventional passive or ablative thermal protection systems fail to protect the leading edge. This dissertation investigates transpiration cooling employing oxide coolants as an alternative system to thermally protect sharp leading edges. This dissertation studies two primary objectives that collectively assess the viability of transpiration cooling. The first objective is to characterize the performance of transpiration cooling for various coolant properties, flight conditions, and leading edge radii. We use semi-analytical boundary layer model to predict the surface temperature, evaporative mass flux, and boiling limit of the system. Our findings do not readily align with an optimal set of material properties for transpiration cooling. Instead, certain coolant properties are more appropriate for various flight conditions and leading edge sizes. The second objective is to characterize the liquid coolant flow through porous media for various hypersonic aerothermal conditions. We experimentally and numerically obtain the permeability of representative silicon carbide foams to estimate the necessary pressure gradient and assess the self-pumping capability of various coolants to meet the mass flow rate demands at the surface. We then utilize computationally efficiency deep learning models to characterize the porous media at the pore-scale to facilitate the design of the microstructures of porous leading edge. Our two numerical frameworks cohere both external and internal aspects of the system to evaluate the performance of transpiration cooling and optimize the coolant properties to effectively protect sharp leading edges, which are paramount for highly maneuverable hypersonic vehicles, for various hypersonic applications.

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