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The flow field around generic space launch vehicles with hot exhaust plumes is investigated numerically. Reynolds-Averaged Navier-Stokes (RANS) simulations are thermally coupled to a structure solver to allow determination of heat fluxes into and temperatures in the model structure. The obtained wall temperatures are used to accurately investigate the mechanical and thermal loads using Improved Delayed Detached Eddy Simulations (IDDES) as well as RANS. The investigated configurations feature cases both with cold air and hot hydrogen/ water vapour plumes as well as cold and hot wall temperatures. It is found that the presence of a hot plume increases the size of the recirculation region and changes the pressure distribution on the nozzle structure and thus the loads experienced by the vehicle. The same effect is observed when increasing the wall temperatures. Both RANS and IDDES approaches predict the qualitative changes between the configurations, but the reattachment location predicted by IDDES is up to 7% further upstream than that predicted by RANS. Additionally, the heat flux distribution along the nozzle and base surface is analysed and shows significant discrepancies between RANS and IDDES, especially on the nozzle surface and in the base corner.

The sensitivity of hybrid RANS-LES methods like Improved Delayed Detached Eddy Simulation (IDDES) to numerical model parameter variations related to generic space launch vehicle aft-body flows is investigated. In particular, the changes resulting from the choice of the time-step size, the turbulence model, the fluid modelling, the circumferential grid resolution, the filter length definition, and the data collection period is considered. The results are also compared to experimental and numerical data taken from the available literature. The sensitivity to the time-step size and the turbulence model is minuscule with respect to the obtained mean flow field, wall pressure distributions, azimuthal modes, and wall pressure frequency spectra. However, circumferential resolution, fluid model, and filter length definition affect the solution to a higher extent. Buffeting spectra are very sensitive to the data collection period.

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