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A deep “long short-term memory” (LSTM) model, which is a special recurrent neural network (RNN) in deep learning, was established. Data from orthogonal experiments of the micro-combustor was used for training the model. A nonlinear mapping of output power to the geometric parameters of the micro-combustor was built. After changing the geometric parameters of the micro-combustor, the output power was predicted, and the corresponding results were analyzed. The results show that the model from the deep LSTM has a relative error of 1.1% in power prediction, which is within the scope of engineering requirements.

Study of a deep LSTM model for power prediction of micro-combustor