Modeling of scramjet combustors based on model migration and process similarity

Abstract

Contributed by the low cost, the simulation method is considered an attractive option for the optimization and design of the supersonic combustor. Unfortunately, accurate and satisfactory modeling is time-consuming and cost-consuming because of the complex processes and various working conditions. To address this issue, a mathematical modeling for the combustor on the basis of the clustering algorithm, machine learning algorithm, and model migration strategy is developed in this paper. A general framework for the migration strategy of the combustor model is proposed among the similar combustors, and the base model, which is developed by training the machine learning model with data from the existing combustion processes, is amended to fit the unexampled combustor using the model migration strategy with a few data. The simulation results validate the effectiveness of the development strategy, and the migrated model is proved to be suitable for the new combustor in higher accuracy with less time and calculation.