TSCALE · MULTI-SCALE MODELLING FOR TURBOMACHINERY FLOWS USING HIGH-FIDELITY COMPUTATIONAL DATA

MULTI-SCALE MODELLING FOR TURBOMACHINERY FLOWS USING HIGH-FIDELITY COMPUTATIONAL DATA

The challenge of decarbonising power generation and aviation is vast. While the future energy systems are likely to be reliant on renewable energy sources, a suite of low carbon solutions are needed to absorb the intermittency and fluctuations in wind and solar power generation. Gas turbines are uniquely positioned to complement the energy mix due to their high-power density and flexibility in operation. The operation of gas turbines for power generation will also shift towards more fuel flexibility, on-demand operation and efficient part-load operation. These requirements impose additional constraints on gas turbine design and require better consideration of its operation at off-design conditions which historically were not as critical for power generation operation. Current design methods rely on semi-analytical and experimental correlations to predict losses and deviation angles which are critical for gas turbine operation. However, these methods are not reliable for evaluating compressor designs outside the well explored design space. In such cases, more advanced modelling techniques such as (U)RANS can be used, but even they are known to have problems accurately predicting losses and deviation angles at high incidences. This is exacerbated in multi-stage environments where single blade-row inaccuracies compound. Further still, current design trends often render methods such as URANS inappropriate due to a lack of the spectral gap between deterministic and stochastic unsteadiness. Experiments and high-fidelity simulations are commonly used to inform design tools at off-design conditions, but often assume simplified inflow conditions. In contrast, real multi-stage compressors exhibit complex unsteadiness due to interactions and accumulation of wakes and turbulence from upstream passages (see Figure 1). Capturing this requires high-fidelity data across a wide range of representative conditions. However, due to the computational cost, such simulations are impractical for routine design use. Therefore, it is essential to extract meaningful physical insights from the large datasets generated. This project addresses that gap by applying a data-driven flow decomposition framework to turbomachinery flows. The methodology enables the study of inter-scale energy transfers and the role of various dynamics in turbulence production, dissipation, and energy redistribution within the cascade. By combining multiple high-fidelity datasets representative of engine-relevant conditions, the project aims to define a new modelling paradigm that captures the full 3D unsteady flowfield. The ultimate goal is to develop a set of low-order models, each targeting specific flow scales, to better represent compressor flow physics. These results are expected to offer critical insights into multi-scale interactions in complex flows such as those in aero-engines.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз