| description 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. | |
