王攀(西北工業大學，教授)

王攀(西北工業大學，教授)

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王攀，西北工業大學，力學與交通運載工程學院，教授，博導

基本介紹

  • 中文名：王攀
  • 國籍：中國
  • 民族：漢
  • 出生地：陝西
  • 畢業院校：西北工業大學
  • 學位/學歷：博士
  • 職業：教師
  • 職務：教授
人物經歷,研究方向,主要成就,獲獎記錄,學術成果,主講課程,

人物經歷

2025.03-至今，西北工業大學 力學與土木建築學院 長聘教授
2020.07-至今，西北工業大學 力學與土木建築學院 博導
2018.06-至今，西北工業大學 力學與土木建築學院 碩導
2015.07-2025.03，西北工業大學 力學與土木建築學院 副教授

研究方向

隨著飛行器等裝備研製需求的不斷提升，複雜環境下典型結構機構壽命短、可靠性差的問題十分突出。由於結構機構可靠性評估與設計的難度大、試驗驗證成本高，高可信度的分析設計方法和試驗驗證技術已經成為保障結構機構可靠性的重要手段。團隊面向國家重大戰略需求，針對飛行器結構機構（起落架、艙門、襟縫翼、彈射座椅等），航空發動機渦輪部件、調節機構，太空飛行器典型結構、摺疊機構等對象，開展了複雜裝備的結構機構可靠性分析設計及驗證等方面的工作。

主要成就

主持國家自然科學基金、國家重大科技專項專題、某領域重大項目專題等國家級項目8項，航空科學基金、航天科學基金、陝西省創新能力提升計畫等省部級項目5項；在RESS、AST、MSSP、APM等國際頂級期刊上發表學術論文60餘篇，以第一完成人授權/受理髮明專利13項；獲國家科技進步二等獎（5/10）、國防科技進步一等獎（7/15）等科研獎勵4項；入選陝西省青年科技新星、陝西省高校青年傑出人才、西北工業大學“翱翔新星”、西北工業大學優秀青年教師等；擔任某領域質量監督管理專家組成員，JRSE、套用力學學報、失效分析與預防等期刊青年編委。

獲獎記錄

科研獎勵
國家科技進步二等獎，2019，排名第5
國防科技進步一等獎，2022，排名第7
國防科技進步二等獎，2017，排名第9
陝西省科技進步二等獎，2016，排名第8
個人榮譽
國家級青年人才，2024
陝西省青年科技新星，2022
陝西省高校青年傑出人才，2022
西北工業大學優秀青年教師（吳亞軍獎）一等獎，2023
西北工業大學“翱翔新星”，2019
陝西省優秀博士論文，2017

學術成果

期刊論文
  1. SC Dong, TY Yuan, L Li, P Wang, et al. A random interval coupling-based active learning Kriging with meta-model importance sampling method for hybrid reliability analysis under small failure probability. Computer Methods in Applied Mechanics and Engineering, 2025, 441, 117992 中科院一區TOP
  2. 胡歡歡，王攀*，朱韋達，王琦銳. 航空發動機錐齒輪典型失效分析及可靠性最佳化設計. 失效分析與預防，2025
  3. FK Xin, P Wang*, Y Chen, et al. A new uncertainty reduction-guided single-loop Kriging coupled with subset simulation for time-dependent reliability analysis. Reliability Engineering & System Safety, 2025, 260: 111065 中科院一區TOP
  4. HH Hu, P Wang*, HQ Chang, et al. A hybrid single-loop approach combining the target beta-hypersphere sampling and active learning Kriging for reliability-based design optimization. Aerospace Science and Technology, 2025, 161, 110136 中科院一區TOP
  5. HH Hu, P Wang*, FK Xin, et al. Hybrid adaptive moment estimation based performance measure approach for complex reliability-based design optimization. Reliability Engineering & System Safety, 2024, 252, 110481 中科院一區TOP
  6. XY Huang, P Wang*, Qirui Wang, et al. An improved adaptive Kriging method for the possibility-based design optimization and its application to aeroengine turbine disk. Aerospace Science and Technology, 2024, 153, 109495 中科院一區TOP
  7. FK Xin, P Wang*，HH Hu, et al. An efficient system reliability analysis method for flap mechanism under random-interval hybrid uncertainties. Structural and Multidisciplinary Optimization, 2024, 67(8), 135 中科院二區
  8. FK Xin, P Wang*，Qirui Wang, et al. Parallel adaptive ensemble of metamodels combined with hypersphere sampling for rare failure events. Reliability Engineering & System Safety, 2024, 246(18):110090 中科院一區TOP
  9. HH Hu, P Wang*, FK Xin, et al. Failure probability function estimation in augmented sample space combined active learning Kriging and adaptive sampling by Voronoi cells. Mechanical Systems and Signal Processing, 2024, 206: 110897 中科院一區TOP
  10. 張政, 王攀*, 周涵淵. 含區間分布參數的尾噴管調節機構可靠性分析. 北京航空航天大學學報, 2023, 49(12): 3378-3386
  11. XY Huang, P Wang*, FK Xin, et al. Line sampling based fuzzy simulation coupled with adaptive Kriging for estimating failure possibility of simplified turbine disk. Aerospace Science and Technology, 2023, 142: 108613 中科院一區TOP
  12. HH Li, P Wang*, HH Hu, et al. Data-driven reliability assessment with scarce samples considering multidimensional dependence. Probabilistic Engineering Mechanics, 2023, 72: 103440 中科院二區
  13. XY Huang, P Wang*, HH Hu, et al. A novel safety measure with random and fuzzy variables and its solution by combing Kriging with truncated candidate region. Aerospace Science and Technology, 2023, 132: 108049 中科院一區TOP
  14. H Liu, X He, P Wang. Time-dependent reliability analysis method based on ARBIS and Kriging surrogate model. Engineering with Computers, 2023, 39: 2035-2048 中科院二區TOP
  15. FK Xin, P Wang*, HH Hu, et al. An application of dependent Kriging combined with spherical decomposition sampling for the system reliability analysis of flap mechanism. Structural and Multidisciplinary Optimization, 2022, 65: 345 中科院二區
  16. YS Liu, LY Li*, ZM Chang, P Wang. Kriging-based analytical technique for global sensitivity analysis of systems with multivariate output. Structural and Multidisciplinary Optimization, 2022, 65:288 中科院二區
  17. 王攀*, 辛富康, 鄧亞權, 張浩. 基於自適應神經網路的多模式小失效機率分析方法. 機械工程學報, 2022, 58(23): 39-50
  18. Z Zhang, P Wang*, H Hu, et al. Efficient reliability-based design optimization for hydraulic pipeline with adaptive sampling region. Reliability Engineering & System Safety, 2022, 226: 108698 中科院一區TOP
  19. P Wang*, H Zhou, H Hu, et al. A novel method for reliability analysis with interval parameters based on active learning Kriging and adaptive radial‐based importance sampling. International Journal for Numerical Methods in Engineering, 2022, 123: 3264-3284 中科院二區
  20. P Wang*, C Li, H Zhou, et al. Global sensitivity analysis of failure probability of structures with uncertainties of random variable and their distribution parameters. Engineering with Computers, 2022, 38: 4367-4385 中科院二區TOP
  21. P Wang*, Z Zhang, X Huang, H ZHou. An application of active learning Kriging for the failure probability and sensitivity functions of turbine disk with imprecise probability distributions. Engineering with Computers, 2022, 38: 3417-3437 中科院二區TOP
  22. HH Hu, P Wang*, HY Zhou. Sequential Reliability Analysis for the Adjusting Mechanism of Tail Nozzle Considering Wear Degradation. Machines, 2022, 10(8): 613
  23. H Li, P Wang*, X Huang, et, al. Vine copula-based parametric sensitivity analysis of failure probability-based importance measure in the presence of multidimensional dependencies. Reliability Engineering & System Safety, 2021, 215: 107898 中科院一區TOP
  24. 黃曉宇, 王攀*, 李海和，張政等. 具有模糊失效狀態的渦輪盤疲勞可靠性及靈敏度分析. 西北工業大學學報，2021, 39(6): 1312-1319
  25. P Wang*, H Li, X Huang, Z Zhang, S Xiao. Numerical decomposition for the reliability-oriented sensitivity with dependent variables using vine copulas. Journal of Mechanical Design, 2021, 143: 081701 中科院二區
  26. H Li, P Wang*, Q Chang, C Zhou, Z Yue. Hybrid multiplicative dimension reduction method for uncertainty analysis of engineering structure. Proceedings of the Institution of Mechanical Engineers Part O: Journal of Risk & Reliability, 2021, 235(1): 144-155
  27. P Wang*, H Li, S Tan, X Huang. Multivariate global sensitivity analysis for casing string using neural network. International Journal of Computational Methods, 2020, 17: 1940015
  28. L Li, Y Liu，Z Shi，P Wang. Multivariate sensitivity analysis for dynamic models with both random and random process inputs. Applied Mathmatical Modelling, 2020, 81: 92-112 中科院一區TOP
  29. W Yun，Z Lu，K Feng，X Jiang，P Wang，L Li. Two Efficient AK-Based Global Reliability Sensitivity Methods by Elaborative Combination of Bayes' Theorem and the Law of Total Expectation in the Successive Intervals Without Overlapping. IEEE Transactions on Reliability, 2020, 69(1): 260-276 中科院二區
  30. F Liu, P Wei, C Tang, P Wang, Zhufeng Yue. Global sensitivity analysis for multivariate outputs based on multiple Gaussian process model. Reliability Engineering & System Safety, 2019, 189: 297-298 中科院一區TOP
  31. P Wang*, Z Lu, K Zhang, S Xiao, Z Yue. Copula-based decomposition approach for the derivative-based sensitivity of variance contributions with dependent variables. Reliability Engineering & System Safety, 2018, 169: 437-450 中科院一區TOP
  32. P Wang*, Z Lu, S Xiao. Variance-based sensitivity analysis with the uncertainties of the input variables and their distribution parameters. Communications in Statistics-Simulation and Computation, 2018, 47(4): 1103-1125
  33. 王攀*, 張磊剛, 呂震宙等. 基於Copula函式的失效機率靈敏度分解方法. 機械工程學報, 2018, 54(16):178-185
  34. S Xiao, Z Lu, P Wang. Global sensitivity analysis based on distance correlation for structural systems with multivariate output. Engineering Structures, 2018, 167: 74-83 中科院一區TOP
  35. S Xiao, Z Lu, P Wang. Multivariate global sensitivity analysis for dynamic models based on wavelet analysis. Reliability Engineering & System Safety, 2018, 170: 20-30 中科院一區TOP
  36. S Xiao, Z Lu, P Wang. Multivariate global sensitivity analysis for dynamic models based on energy distance. Structural and Multidisciplinary Optimization, 2018, 57 (1): 279-291中科院二區
  37. S Xiao, Z Lu, P Wang. Multivariate Global Sensitivity Analysis Based on Distance Components Decomposition. Risk Analysis, 2018, 38(12): 2703-2721 中科院二區
  38. P Wang*, Z Lu, S Xiao. A generalized separation for the variance contributions of input variables and their distribution parameters. Applied Mathematical Modelling, 2017, 47: 381-399 中科院一區TOP
  39. P Wang*, Z Lu. Efficient variance reduction approach based on the variation of the input importance. Journal of Statistical Computation and Simulation, 2016, 86(14): 2856-2873
  40. ZC Tang, Z Lu, P Wang, Y Xia, et al. Efficient numerical simulation method for evaluations of global sensitivity analysis with parameter uncertainty, Applied Mathematical Modelling, 2016, 40 (1): 597-611 中科院一區TOP
  41. ZY Lu, Z Lu, P Wang. A new learning function for Kriging and its applications to solve reliability problems in engineering. Computers & Mathematics with Applications, 2015, 70(5): 1182-1197 中科院二區
  42. L Zhang, Z Lu, P Wang. Efficient structural reliability analysis method based on advanced Kriging method. Applied Mathematical Modelling, 2015, 39(2): 781-793 中科院一區TOP
發明專利
以第一完成人授權受理國家發明專利13項
部分代表性發明專利：
一種彈射系統聯鎖裝置的最佳化設計方法，發明專利，ZL111143970A，排名第1
輪軌接觸中滑軌的疲勞壽命可靠性評估方法，發明專利，ZL111062112A，排名第1
防冰腔結構可靠性設計方法，發明專利，ZL110990939A，排名第1
飛機襟縫翼運動機構可靠性測試方法，發明專利，ZL110667885A，排名第1
齒輪齒條結構的可靠性分析方法及裝置，發明專利，ZL110688717A，排名第1

主講課程

本科生課程：《可靠性工程基礎》
研究生課程：《航空安全工程概論》、《結構系統可靠性設計基礎》
學堂線上MOOC：宋述芳，呂震宙，喻天翔，王攀，可靠性工程基礎，2023年

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