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Advancements have been achieved in the optimization of waverider designs with the aid of machine learning to expedite the design process. However, these approaches are hampered by the need for extensive sample sizes and susceptibility to becoming ensnared in local optima. This study undertakes a parametric design based on the wedge-derived, power-law-shaped waverider, increasing configuration diversity and creating a dataset with limited samples by calculating waverider geometry and aerodynamic parameters. At a Mach number of 10, a multi-objective optimization design is implemented using the Young's double-slit experiment-least squares support vector regression (YDSE-LSSVR) surrogate model in conjunction with improved congestion distance multi-objective particle swarm optimization algorithm, focusing on maximizing the lift-to-drag ratio and volumetric efficiency as much as possible. The results indicated that, under conditions of limited samples, the YDSE-LSSVR model outperforms standard models such as support vector regression, LSSVR, Kriging, and Polynomial Chaos Expansions-Kriging regarding prediction accuracy. The Pareto solutions for both concave and convex waveriders, obtained through multi-objective optimization, improve the lift-to-drag ratio by 17.36% and 21.70%, respectively, and increase the volumetric efficiency by 88.89% and 105.56%, in comparison to baseline configurations. In addition, the research examines the impact of various design parameters on the Pareto solutions. Finally, the study applies the K-means method to conduct a cluster analysis of the Pareto solutions, generating three-dimensional waverider configurations based on distinguished solutions from different clusters. [ABSTRACT FROM AUTHOR]

Stable combustion is desirable for efficient operation of scramjet engines at high flight Mach numbers, and being able to reconstruct the flow-field wave patterns in stable combustion facilitates proactive evaluation of engine operating conditions. Proposed here is a multi-level connected shifted-window transformer (MCSwinT) model for reconstructing the flow-field wave patterns of stable combustion in a supersonic combustor. A combustion feature conversion block is used to convert high-dimensional and low-dimensional combustion features; a deep pressure feature extraction block is used to extract the flow-field wave patterns, and MCSwinT blocks enable multi-level fusion, thereby extracting the high-dimensional combustion features of the flow-field wave system. A dynamic loss function unifies spatial content loss and feature space loss, leading to enhanced reconstruction results. Separately, data on the stable combustion process of a hydrogen-fueled scramjet engine were collected in a direct-connect supersonic pulse combustion wind tunnel, and these data are used to validate the robustness and generalization capability of MCSwinT. The experimental results show that the flow-field wave patterns of stable combustion are reconstructed successfully using MCSwinT of different scales. Compared to other models, MCSwinT exhibits lower model complexity while achieving performance improvements of 7% and 17% in peak signal-to-noise ratio and structural similarity index, respectively. Additionally, the high generalization ability of the proposed model is validated in a sparsity experiment. This model effectively reconstructs the flow-field wave patterns of stable combustion, providing a crucial foundation for further research on scramjet engines. [ABSTRACT FROM AUTHOR]

To alleviate the problem of high-fidelity data dependence and inexplicability in pure data-driven neural network models, physical informed neural networks (PINNs) provide a new learning paradigm. This study constructs an efficient, accurate, and robust PINN framework for predicting unsteady combustion flow fields based on Navier–Stokes (NS) equation constraints. To achieve fast prediction of a multi-physical field in a scramjet combustion chamber, we propose a U-shaped residual neural network model based on feature information fusion. The model uses a residual neural network module as the backbone, uses jump connection to improve model generalization, and uses the U-shaped structure to fuse the receptive field features with different scales to enhance the feature expression ability of the model. To prevent improper assumptions from leading to wrong method constraints, we consider the flow characteristic mechanism of each physical field to constrain the neural network and verify its accuracy through numerical simulation of the unsteady flow field in the scramjet combustor with Mach number (Ma) 2.0. This method can accurately predict the multi-physical field of unsteady turbulent combustion based on the time, space, Ma and turbulent eddy viscosity coefficients of a small number of samples. Specially, the proposed physical driven and data driven fusion proxy model can predict the unsteady combustion flow field in milliseconds. It has important reference value to solve the problem of low calculation efficiency of a traditional numerical simulation method of a combustion process. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The Reynolds‐Averaged Navier–Stokes (RANS) model is the main model in engineering applications today. However, the normal value of the closure coefficient of the RANS turbulence model is determined based on some simple basic flows and may no longer be applicable for complex flows. In this paper, the closure coefficient of shear stress transport (SST) turbulence model is recalibrated by combining Bayesian method and particle swarm optimization algorithm, so as to improve the numerical simulation accuracy of wall pressure in supersonic flow. First, the obtained prior samples were numerically calculated, and the Sobol index of the closure coefficient was calculated by sensitivity analysis method to characterize the sensitivity of the wall pressure to the model parameters. Second, combined with the uncertainty of propagation parameters by non‐intrusive polynomial chaos (NIPC). Finally, Bayesian optimization is used to quantify the uncertainty and obtain the maximum likelihood function estimation and optimal parameters. The results show that the maximum relative error of wall pressure predicted by the SST turbulence model decreases from 29.71% to 9.00%, and the average relative error decreases from 9.86% to 3.67% through the parameter calibration of Bayesian optimization method. In addition, the system evaluated the calibration effect of three criteria, and the calibration results parameters under the three criteria were all better than the calculated results of the nominal values. Meanwhile, the velocity profile and density profile of the flow field were also analyzed. Finally, the same calibration method was applied to the supersonic hollow cylinder and BSL (Baseline) turbulence model, and the same calibration results were obtained, which verified the universality of the calibration method. [ABSTRACT FROM AUTHOR]