The paper presents a multi-disciplinary approach for aero-thermal and heat transfer analysis for internal flows. The versatility and potential benefit offered by the approach is described through the application to a realistic low pressure turbine assembly. The computational method is based on a run time code-coupling architecture that allows mixed models and simulations to be integrated together for the prediction of the sub-system aero-thermal performance. In this specific application the model is consisting of two rotor blades, the embedded vanes, the inter-stage cavity and the solid parts. The geometry represents a real engine situation.The key element of the approach is the use of a fully modular coupling strategy that aims to combine (1) flexibility for design needs, (2) variable level of modelling for better accuracy and (3) in memory code coupling for preserving computational efficiency in large system and sub-system simulations.For this particular example Reynolds Averaged Navier-Stokes (RANS) equations are solved for the fluid regions and thermal coupling is enforced with the metal (conjugate heat transfer). Fluid-fluid interfaces use mixing planes between the rotating parts while overlapping regions are exploited to link the cavity flow to the main annulus flow as well as in the cavity itself for mapping of the metal parts and leakages. Metal temperatures predicted by the simulation are compared to those retrieved from a thermal model of the engine, and the results are discussed with reference to the underlying flow physics.
Amirante
GTP-20-1650Recent years have also seen a considerable increase in the use of Conjugate Heat Transfer (CHT) analysis with much of this work applied to blade cooling [2, 3], pre-swirl systems [4, 5] and disc cavities [6,7]. With the increase of computational power, aero-thermal studies have gradually moved from single-component analysis to more complex scenarios involving multiple cavities and transient regimes [8,9,10,11].Despite the recognised improvement achievable with simulations, when considering accuracy, efficiency and reduction of turn-around-time there are still a number of drawbacks that represent challenging hurdles to be addressed. First, heat transfer predictions based on RANS or URANS models suffer from inaccuracies linked to turbulence modelling and are notoriously poor for a variety of flows (most Amirante 3 GTP-20-1650