Journal of Thermophysics and Heat Transfer最新文献

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.

An analytical expression is developed for transient thermal spreading resistance from an isothermal circular source in a cylindrical flux tube as a function of constriction ratio and time. The flux tube is semi-infinite. The spreading resistance expression is obtained from the temperature expression by solving the heat equation. For short times, the dimensionless transient spreading resistance is proportional to dimensionless time based on the square root of the source area. For long times, the dimensionless spreading resistance approaches the values of the corresponding steady-state expression in the literature. For small constriction ratios, dimensionless spreading resistance approaches the classic isothermal half-space limit. A numerical analysis is presented which shows excellent agreement with the analytical solution. Approximate correlations for dimensionless resistance are also presented for both the isothermal and the isoflux cases.

Plasma generation in hypersonic flows is analyzed using a two-temperature model of nonequilibrium air. The uncertainties in electron number density predictions are assessed for flow scenarios that correspond to both strongly shocked and strongly expanded flows, and the dependencies of the calculated uncertainties on individual input parameters are quantified. Ionization levels behind 5 and 7 km/s normal shocks are found to be most sensitive to the associative ionization reactions producing ${\mathrm{O}}_2^{+}$ and ${\mathrm{NO}}^{+}$ in the region of peak electron number density, with nitric oxide kinetics dominating the uncertainty downstream. The higher levels of ionization behind a 9 km/s shock are found to strongly depend on the electron impact ionization of atomic nitrogen as well as the charge exchange between ${\mathrm{N}}_2^{+}$ and N. Recombining flow scenarios depend on many of the same processes that influence the shocked flows, with the notable addition of the reassociation reaction ${\mathrm{O}}^{+}+{\mathrm{N}}_2\leftrightarrow {\mathrm{NO}}^{+}+\mathrm{N}$, which is responsible for large uncertainties in electron number density in net recombining flows. The results provide valuable insight into the typical magnitude of uncertainty associated with plasma formation predictions in hypersonic flows and identify the parameters that should be targeted in efforts to reduce those uncertainties.