Aero-engine intakes play a critical role in the performance of modern high-bypass turbofan engines. It is their function to provide uniformly distributed, steady air flow to the engine fan face under a variety of flow conditions. However, during situations of high incidence, high curvature of the intake lip can accelerate flow to supersonic speeds, terminating with a shock wave. This produces undesirable shock wave boundary layer interactions (SWBLIs).

Reynolds-Averaged Navier Stokes (RANS) turbulence models have been shown to be insensitive to the effects of boundary layer relaminarisation present in these highly-accelerated flows. Further, downstream of the SWBLI, RANS methods fail to capture the distorted flow that propagates towards the engine fan face.

The present work describes simulations of a novel experimental intake rig model that replicates the key physics found in a real intake- namely acceleration, shock and SWBLI. The model is a simple geometric configuration resembling a lower intake lip at incidence. Simulations are carried out at two angles of attack, $\alpha =23\circ$ and $\alpha =25\circ$, with the more aggressive $\alpha =25\circ$ possessing a high degree of shock oscillation.

RANS, Large Eddy Simulations (LES) and hybrid RANS-LES are carried out in this work. Modifications to the one-equation Spalart-Allmaras (SA) RANS turbulence model are proposed to account for the effects of re-laminarisation and curvature. The simulation methods are validated against two canonical test cases. The first is a subsonic hump model where RANS modifications give a noticeable improvement in surface pressure predictions, even for this mild acceleration case. However, RANS is shown to over-predict the separation size. LES performs much better here, as long as the Smagorinsky-Lilly SGS model is not used. The $\sigma$-SGS model is found to perform best, and is used to run a hybrid RANS-LES that predicts a separation bubble size within $4\%$ of LES.

The second canonical test case is a transonic hump that features a normal shockwave and SWBLI. RANS performs well here, predicting shock location, surface pressure and separation with good agreement with experimental measurements. Hybrid RANS-LES also performs well, but predicts a shock downstream of that measured by experiment. The use of an improved shock sensor here is able to maintain solution accuracy.

Simulations of the intake rig are then run. RANS modifications provide a significant improvement in prediction of the shock location and lip surface pressure compared to the standard SA model. However, RANS models fail to reproduce the post shock interaction flow well, giving incorrect shape of the flow distortion. Further, RANS is inherently unable to capture the unsteady shock oscillations and related flow features.

LES and hybrid RANS-LES predict the shock location and SWBLI well, with the downstream flow distortion also in very good agreement with experimental measurements. LES and hybrid RANS-LES are able to reproduce the time averaged smearing of the shock which RANS cannot. However, shock oscillations in the $\alpha =25\circ$ case present a particular challenge for costly LES, requiring long simulation time to obtain time averaged flow statistics. Hybrid RANS-LES offers a significant saving in computational expense, costing approximately $20\%$ of LES. The work proposes recommendations for simulation strategy for intakes at incidence based on computational cost and performance of simulation methods.