CONSTRUCTION AND VALIDATION OF A WIDE-DOMAIN ENGINE FLAMELET COMBUSTION MODEL BASED ON DEEP LEARNING TABLE BUILDING

The development of future next-generation aircraft powered by a new wide domain engine urgently requires CFD methods for efficient and high-precision auxiliary design. This article combines the traditional flamelet/progress variable combustion model with deep learning and neural network methods to construct a new intelligent improved combustion model, which is tested and validated by numerical cases. This improves the prediction accuracy while ensuring computational efficiency. Firstly, the construction method of the artificial neural network in this article were proposed in details, including database partitioning, data normalization, and model training; Then, the impact of different function structures on the new model was tested and analyzed, and the optimization of memory usage under the CPU and GPU based solver framework was discussed; Finally, the intelligent model was coupled to a GPU solver for numerical simulation of three engine cases with flight Mach numbers 4 ~ 12. The results show that the intelligent improved model can replace the traditional flame surface/progress variable database to achieve high-dimensional parameter modeling and model improvement, and can successfully run on GPU; The intelligent improved model has an average error reduction of over 50% compared to traditional models for all mentioned cases, and the maximum reduction in case error can reach 57.2%.