4.3 Article

Future Use of Large Eddy Simulation in Aero-engines

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ASME
DOI: 10.1115/1.4029363

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  1. EPSRC [EP/H001395/1] Funding Source: UKRI
  2. Engineering and Physical Sciences Research Council [EP/H001395/1] Funding Source: researchfish

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Computational fluid dynamics (CFD) has become a critical tool in the design of aero-engines. Increasing demand for higher efficiency, performance, and reduced emissions of noise and pollutants has focused attention on secondary flows, small scale internal flows, and flow interactions. In conjunction with low order correlations and experimental data, RANS (Reynolds-averaged Navier-Stokes) modeling has been used effectively for some time, particularly at high Reynolds numbers and at design conditions. However, the range of flows throughout an engine is vast, with most, in reality being inherently unsteady. There are many cases where RANS can perform poorly, particularly in zones characterized by strong streamline curvature, separation, transition, relaminarization, and heat transfer. The reliable use of RANS has also been limited by its strong dependence on turbulence model choice and related ad-hoc corrections. For complex flows, large-eddy simulation (LES) methods provide reliable solutions, largely independent of turbulence model choice, and at a relatively low cost for particular flows. LES can now be used to provide in depth knowledge of flow physics, for example, in areas such as transition and real wall roughness effects. This can be used to inform RANS and lower order modeling (LOM). For some flows, LES can now even be used for design. Existing literature is used to show the potential of LES for a range of flows in different zones of the engine. Based on flow taxonomy, best practices including RANS/LES zonalization, meshing requirements, and turbulent inflow conditions are introduced, leading to the proposal of a tentative expert system for industrial use. In this way, LES becomes a well controlled tool, suitable for design use and reduces the burden on the end user. The problem sizes tackled however have lagged behind potential computing power, hence future LES use at scale requires substantial progress in several key areas. Current and future solver technologies are thus examined and the potential current and future use of LES is considered.

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