Journal of Thermophysics and Heat Transfer, Ahead of Print.
The Direct Simulation Monte Carlo Method computations are performed to investigate the heat transfer across highly evacuated cryogenic tank insulation structures. These structures usually consist of one cold and one hot wall with a temperature difference up to 260 K surrounding a rarefied gas which originates from permeating or leaking propellant. To validate the flow solver PICLas for this application, heat transfer results across parallel flat plates with nonflowing gaseous hydrogen and methane are compared to empirical relations of rarefied gas heat transfer and reference computations, showing good agreement with a deviation of less than 11%. Because gas flow usually occurs during and after evacuation, the heat transfer and skin friction coefficient in a symmetrical hydrogen channel flow with a wall distance of 30 mm is compared with literature data, showing a good match with a Nusselt number deviation of less than 20%. Furthermore, honeycomb tank insulation structures are analyzed, which can be used for future cryogenic liquid rocket tanks. Here, rarefied flow simulations are performed for slitted honeycomb structures with and without throughflow of hydrogen gas at a Knudsen number of 1.5 and transitional flow conditions at a Knudsen number of 0.1. The heat transfer results at the honeycomb sandwich are 50 to 70% below empirical relations for heat transfer across flat plates. Throughflow does not affect the heat transfer across the honeycomb because the Peclet number is less than 0.01.
Aerodynamic thermal prediction plays a crucial role in the design of a hypersonic vehicle, particularly with regard to the thermal protection system. Traditional methods of aerodynamic thermal prediction encounter several primary challenges, including slow convergence rates, rigorous computational grid requirements, and the need to simplify by assuming isothermal wall conditions. In this research, we propose using the Convolutional Neural Network (CNN) Hybrid Feature (HF) model to facilitate rapid aerothermal predictions for both isothermal wall conditions with varying wall temperatures and radiation balance wall conditions. The CNN HF model is trained separately for isothermal wall conditions under identical inflow conditions as well as for diverse inflow conditions and radiation balance wall temperature scenarios. The model’s predictions are then compared to numerical simulation results. Our findings demonstrate that the CNN HF model efficiently provides rapid aerothermal predictions by leveraging macroscopic converged flowfield data. In the majority of cases, the model achieves a threefold enhancement in computational efficiency while maintaining predictive accuracy within a 5% range when compared to numerical simulation results. The application of the CNN HF approach in aerothermal prediction for different wall temperatures and radiation balance scenarios has significantly reduced the time required to obtain aerodynamic heating results.
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