High resolution dual-plane stereo-PIV for validation of subgrid scale models in large-eddy simulations of turbulent premixed flames
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文摘
A measurement strategy for the experimental validation of subgrid scale (SGS) models for large-eddy simulations (LES) of turbulent premixed flames is presented. The approach is based on a dual-plane stereo-PIV technique. The measurement of the flow field is performed in two parallel planes which allows the determination of velocity gradients in all three directions. The flame front position in the PIV images is deduced from the clearly observable step in the particle number density between burnt and unburnt gases. This facilitates the single-shot based evaluation of important quantities for reacting flows, e.g., the density weighted rate-of-strain tensor. Also filtered quantities like the SGS scalar flux of the reaction progress variable can be directly determined by spatial averaging over several regions of interest which reproduces the application of the filter function in LES. Moreover, the measured data allows the direct interpretation of SGS model formulations since besides the filtered values, also resolved data are generated. Thus, statistical a-priori tests of SGS models are possible. The measurement strategy is explained, a statistical evaluation of the density weighted rate-of-strain tensor is given, and exemplarily an instantaneous distribution of the measured radial SGS scalar flux is compared with predictions of two models, the gradient diffusion model and the Clark model. Starting from a reference operation point of a turbulent V-shaped flame the following three parameters – Reynolds number, fuel–air ratio and fuel type – have been varied independently. First results show that the gradient diffusion model fails completely, while the Clark model predictions show a high degree of correlation to the directly determined flux components, especially in the reactant zone. More advanced modeling, however, may be needed, to incorporate for instance heat-release effects more closely.

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