Data assimilation and sparse sensing reconstruction of flow field for centrifugal compressors

Accurate prediction of flow field in centrifugal compressors using the present turbulence model remains challenging due to intense nonlinearity and strong adverse pressure gradients inherent in the flow. An ensemble-Kalman-filter-based data assimilation (DA) framework is developed to optimize the RNG (renormalization group) k–ε turbulence models for enhancing prediction accuracy. The DA-optimized RNG k–ε model eliminates anomalous static pressure peaks within high-pressure gradient regions, leading to a more physical pressure distribution. Compared with the original results, the maximum absolute percentage error of static pressure is reduced from 19.98% to 7.24%, and the mean absolute percentage error from 5.12% to 2.77%, achieving a significant improvement in prediction accuracy. Furthermore, constrained by compressor geometry and experimental costs, reconstruction from sparse measurements is critically needed. A sparse-sensing shallow neural network (SS-SNN) is therefore proposed to reconstruct the static pressure field using limited sensing data. The SS-SNN results achieve the maximum absolute percentage error of 7.80% and the mean absolute percentage error of 2.18%. These results indicate that DA-corrected turbulence model and SS-SNN reconstruction offer can rapidly and accurately determine the static pressure distribution in centrifugal compressors.