Abstract：The engine is the core component of the vehicle power system. The dynamic monitoring of the engine flow field can grasp the combustion situation of the internal flow field of the engine, which is of great significance for the vehicle condition monitoring and performance evaluation. Therefore, advanced diagnostic technology is the basis for the development of engine technology and one of the necessary conditions for the development of new aerospace vehicles. The laser absorption spectroscopy technique can realize the measurement of gas parameters in the combustion field, and the absorption spectroscopy wavelength modulation technique (WMS) can improve the signal-to-noise ratio in the harsh flow field environment of the engine. However, the WMS-based methods for solving the integrated absorbance, temperature, and concentration are centered on simulated annealing algorithms (SA) and suffer from long execution times. Based on the intrinsic correlation of the spectral parameters of the flow field evolving and the light distribution as fixed information, a machine learning method is used to model the harmonic signal (S2f/1f) and the integrated absorbance (A), and the extreme learning machine algorithm (ELM) is selected, which has a short training time and fast prediction results. Using the neural network’s property can approximate the true value, the simulation determines S2f/1f and A for different flow field models under light layout and constructs data sets to carry out model training for the neural network. In the method validation, 2 000 data sets were simulated, 1 800 sets were selected as the training set to train the model, and the remaining 200 sets were used as the prediction set. The average relative error of the predicted integrated absorbance of the test set was 1.058%, and the coefficient of determination was 0.999, which verified the reliability of the training model. Random noise of 3% and 5% was added to the input S2f/1f data set, and the average relative errors of predicted integrated absorbance were 1.89% and 3.2%, respectively, which showed that ELM has better noise resistance. Based on this method, experimental validation was carried out on a direct-connected scramjet with a practical test duration of 5 s and about 10GB of collected data, and the integrated absorbance was solved by both ELM and WMS methods respectively, and the results were consistent. Compared with the WMS method, which takes several hours to perform, the ELM predicts the integrated absorbance in about 15 seconds, enabling the rapid measurement of the integrated absorbance of the engine flow field.
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