Showing 81 - 100 results of 124 for search 'layer shock processing', query time: 0.15s Refine Results
  1. 81
  2. 82
  3. 83
  4. 84
  5. 85
  6. 86
  7. 87
  8. 88
  9. 89
  10. 90
  11. 91

    Numerical Investigation on Flame Stabilization of Cavity-Based Scramjet Combustor Using Compressible Modified FGM Model by Qi Zhang, Weibing Zhu, Dongchao Yang, Hong Chen

    Published 2022-01-01
    “…The coupling effect of shock waves and shear layer cause the shear layer to quickly destabilize, resulting in the turbulence effect, which promotes the mixing of air and fuel. …”
    Get full text
    Article
  12. 92
  13. 93

    Prediction of Spectral Response for Explosion Separation Based on DeepONet by Xiaoqi Chen, Zhanlong Qu, Yuxi Wang, Zihao Chen, Ganchao Chen, Xiao Kang, Ying Li

    Published 2025-04-01
    “…Strong shock waves generated during the pyrotechnic separation process of aerospace vehicles can cause high-frequency damage or even structural failure to the vehicle’s structure. …”
    Get full text
    Article
  14. 94

    Systemic Risk Contagion in Reconstructed Financial Credit Network within Banking and Firm Sectors on DebtRank Based Model by Yuetang (Peter) Bian, Yu Wang, Lu Xu

    Published 2020-01-01
    “…The computational simulation on how systemic risk contagious process evolves has been conducted, where the possible influential factors of network structure, agent’s initial risk status, external shock ratio, liquidity flow rate, and different layers of the network are considered. …”
    Get full text
    Article
  15. 95
  16. 96

    YConvFormer: A Lightweight and Robust Transformer for Gearbox Fault Diagnosis with Time–Frequency Fusion by Yihang Peng, Jianjie Zhang, Songpeng Liu, Mingyang Zhang, Yichen Guo

    Published 2025-08-01
    “…It models long-range temporal dependencies through spatial axial modeling, expanding the receptive field of shock features, while channel axial reinforcement strengthens the interaction of harmonics across frequency bands. …”
    Get full text
    Article
  17. 97

    Research on the Reliability of Aluminum Nitride Ceramic Substrate for IGBT by QIAN Jianbo, HUANG Shidong

    Published 2017-01-01
    “…In this paper, the peel strength and thermal shock resistance of AlN ceramic substrate prepared by AMB process and DBC (direct bond copper) process were compared, and the reliability of AMB-AlN substrate can be improved by controlling the thickness of its TiN layer, increasing the depth of its copper edge holes and increasing the side etching of copper coil. …”
    Get full text
    Article
  18. 98

    Influence of impact forces on the strength characteristics of the railway roadbed by O. G. Krasnov, N. N. Astanin

    Published 2017-04-01
    “…It is shown that impulsive shock forces of 360 ... 530 kN, which initiate high-level accelerations in the ballast layer and on the main pad of the subgrade, arise in the presence of defects in the form of sliders and welds with oversized sizes on the rolling surface. …”
    Get full text
    Article
  19. 99

    Numerical Investigation of Spontaneous Ignition During Pressurized Hydrogen Release: Effects of Burst Disk Shape and Opening Characteristics by Wanbing Lin, Zhenhua Wang, Guanghu Wang, Juncheng Jiang, Jingnan Wu, Lei Ni, Ru Zhou, Mingguang Zhang, Liang Ma

    Published 2025-06-01
    “…The 10-step-like opening enhances jet turbulence and promotes flame merging between the boundary layer and jet front, intensifying combustion. Domed structures cause a high-velocity region behind the leading shock wave, altering jet front evolution. …”
    Get full text
    Article
  20. 100

    Machine-learning-based analytics for risk forecasting of anaphylaxis during general anesthesia by Shuang Liu, Yasuyuki Suzuki, Toshihiro Yorozuya, Masaki Mogi

    Published 2022-12-01
    “…To develop a personalized risk forecast platform for general anesthesia-related anaphylaxis, as a first step, we aimed to investigate the feasibility of machine-learning-based classification using clinical features of patients for risk prediction of anesthesia-related anaphylaxis. After data pre-processing, the performance of five classification methods: Logistic Regression Analysis, Support Vector Machine, Random Forest, Linear Discriminant Analysis, and Naïve Bayes), which were integrated with four feature selection methods (Recursive Feature Elimination, Chi-Squared Method, Correlation-based Feature Selection, and Information Gain Ratio), was evaluated using two-layer cross-validation. …”
    Get full text
    Article