Using Data Assimilation to Improve Turbulence Modeling for Inclined Jets in Crossflow

Abstract

Abstract Data assimilation (DA) integrating limited experimental data and computational fluid dynamics is applied to improve the prediction accuracy of flow and mixing behavior in inclined jet-in-crossflow (JICF). The ensemble Kalman filter (EnKF) approach is used as the DA technique, and the Reynolds-averaged Navier–Stokes (RANS) modeling serves as the prediction framework. The flow field and scalar mixing characteristics of a cylinder-inclined JICF and a sand dune (SD)-inspired inclined JICF are studied at various velocity ratios (VR = 0.4, 0.8, and 1.2). First, the Spalart–Allmaras (SA) model and the standard k-ɛ model are investigated based on the cylinder configuration at VR = 1.2. An optimized set of model constants are determined for each model using the EnKF-based data assimilation. The SA model shows remarkable improvement and better prediction in flow separation than the standard k-ɛ model after DA. Further exploration demonstrates that this set of the SA model constants can be extended to other VRs and even the SD-inspired configuration, mainly due to the correction of the predicted flow separation in inclined JICF. Finally, an investigation of the concentration field also shows satisfying improvement, resulting from a more appropriate turbulent Schmidt number. The optimized model constants, the revealed extensibility, and the uncovered mechanism of using the EnKF-based DA to improve the simulation of JICF could facilitate the design of related applications such as gas turbine film cooling.