Traditional turbulence models suffer from low accuracy and weak applicability when predicting complex separated flows, such as those that occur in shock boundary layers. To overcome this problem, the present paper considers a cavity-ramp structure and calibrates the turbulence model parameters using a deep neural network (DNN) surrogate model and a genetic algorithm (GA). The non-intrusive polynomial chaos expansion method is used to quantify the uncertainty of the shear stress transport (SST) turbulence model parameters and determine the effects of these parameters on the wall pressure, allowing suitable feature identification parameters to be selected for the DNN turbulence surrogate model. The DNN is compared with the traditional polynomial chaos expansion method, and the results highlight the advantages of using the DNN method to construct the surrogate model. Finally, a GA is used to optimize and calibrate the SST turbulence model parameters based on the surrogate model and experimental data. Experimental results show that the DNN turbulence surrogate model is highly accurate, with the predicted wall pressure, achieving a coefficient of determination above 0.998. The DNN has higher precision, stronger feature extraction ability, and faster prediction times than the traditional polynomial chaos expansion method. The calibrated SST turbulence model produces wall pressures that are close to the experimental data, verifying the feasibility of the proposed method. It is expected that the approach proposed in this paper will improve the calculation accuracy of the SST turbulence model.

In this work, the effects of cavity flameholder configurations on flameholding and performances of kerosene fueled scramjet combustor were studied experimentally and numerically. For experiments, a directly connected ground facility was used and clean high enthalpy air, with a total temperature of 800 K and a total pressure of 800 Kpa, was provided by an electricity resistance heater. To investigate the effects of cavity configurations on flameholding capacity and reacting-flow characteristics, three different flameholders, one single cavity flameholder and two tandem cavity flameholders, were used in experiments. For the two combustors with tandem cavity flameholders, the location and configurations of its up-stream cavity were same with the single cavity flameholder, and the length-to-depth ratios for down-stream cavities were 9 and 11 respectively. The experimental results showed that stabilize kerosene combustion were achieved for combustor with tandem cavity flameholders mounted, and none for that with single cavity flameholder. The none-reacting and reacting flows of combustor models with tandem cavity flameholders were compared and studied with numerical and experimental results. The results showed that higher combustion efficiencies and pressure recovery ratios were achieved for the combustor with down-stream cavity length-to-depth ratio of 9.

The development of high-performance aeroengine combustion chambers strongly depends on the accuracy and reliability of efficient numerical models. In the present work, a reacting solver with a steady laminar flamelet model and spray model has been developed in OpenFOAM and the solver details are presented. The solver is firstly validated by Sandia/ETH-Zurich flames. Furthermore, it is used to simulate nonpremixed kerosene/air spray combustion in an aeroengine combustion chamber with the RANS method. A comparison with available experimental data shows good agreement and validates the capability of the new developed solver in OpenFOAM.