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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]

Gully erosion poses a significant global concern due to its role in land degradation and soil erosion, particularly pronounced in Northeast China's diverse agro-geomorphic regions. However, there is a lack of comprehensive studies on gully characteristics, development rates, and the topographic threshold of gully formation in these areas. To address this gap, we selected three different agro-geomorphic watersheds, named HL (Hailun), ML (Muling), and YKS (Yakeshi), with areas of 30.88 km2, 31.53 km2, and 21.98 km2, respectively. Utilizing high-resolution (2.1 m, 2 m) remote sensing imagery (ZY-3, GF-1), we analyzed morphological parameters (length, width, area, perimeter, etc.) and land use changes for all permanent gullies between 2013 and 2023. Approximately 30% of gullies were selected for detailed study of the upstream drainage area and gully head slopes to establish the topographic threshold for gully formation (S = a·A−b). In HL, ML, and YKS, average gully lengths were 526.22 m, 208.64 m, and 614.20 m, respectively, with corresponding widths of 13.28 m, 8.45 m, and 9.32 m. The gully number densities in the three areas were 3.14, 25.18, and 0.82/km2, respectively, with a gully density of 1.65, 5.25, and 0.50 km km−2, and 3%, 5%, and 1% of the land has disappeared due to gully erosion, respectively. YKS exhibited the highest gully head retreat rate at 17.50 m yr−1, significantly surpassing HL (12.24 m yr−1) and ML (7.11 m yr−1). Areal erosion rates were highest in HL (277.79 m2 yr−1) and lowest in YKS (105.22 m2 yr−1), with ML intermediate at 243.36 m2 yr−1. However, there was no significant difference in gully expansion rate (0.37–0.42 m yr−1) among the three areas (p > 0.05). Differences in gully development dynamics among the three regions were influenced by land use, slope, and topographic factors. The topographic threshold (S = a·A−b) for gully formation varied: HL emphasized drainage area (a = 0.052, b = 0.52), YKS highlighted soil resistance (a = 0.12, b = 0.36), and the parameters a and b of ML fell within the range between these of HL and YKS (a = 0.044, b = 0.27). This study has enriched the scope and database of global gully erosion research, providing a scientific basis for gully erosion prevention and control planning in Northeast China. [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]