World's Best Scientists 2026 revealed!

D-Index & Metrics

Mechanical and Aerospace Engineering

D-Index
67
Citations
13686
World Ranking
474
National Ranking
69

Shun-Peng Zhu publication distribution in Mechanical and Aerospace Engineering in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Mechanical and Aerospace Engineering in 2026. The highlighted bar marks where Shun-Peng Zhu sits on this spectrum.

47–56 publications: 10 scientists 57–66 publications: 23 scientists 67–76 publications: 32 scientists 77–86 publications: 62 scientists 87–96 publications: 67 scientists 97–106 publications: 91 scientists 107–116 publications: 113 scientists 117–126 publications: 115 scientists 127–136 publications: 130 scientists 137–146 publications: 140 scientists 147–156 publications: 155 scientists 157–166 publications: 132 scientists 167–176 publications: 133 scientists 177–186 publications: 130 scientists 187–196 publications: 140 scientists 197–206 publications: 115 scientists 207–216 publications: 125 scientists 217–226 publications: 117 scientists 227–236 publications: 99 scientists 237–246 publications: 92 scientists 247–256 publications: 100 scientists 257–266 publications: 95 scientists 267–276 publications: 88 scientists 277–286 publications: 77 scientists 287–296 publications: 74 scientists 297–306 publications: 74 scientists 307–316 publications: 62 scientists 317–326 publications: 70 scientists 327–336 publications: 59 scientists 337–346 publications: 58 scientists 347–356 publications: 45 scientists 357–366 publications: 44 scientists 367–376 publications: 36 scientists 377–386 publications: 41 scientists 387–396 publications: 32 scientists 397–406 publications: 23 scientists 407–416 publications: 28 scientists 417–426 publications: 27 scientists 427–436 publications: 25 scientists 437–446 publications: 23 scientists 447–456 publications: 23 scientists 457–466 publications: 20 scientists 467–476 publications: 12 scientists 477–486 publications: 24 scientists 487–496 publications: 18 scientists 497–506 publications: 12 scientists 507–516 publications: 13 scientists 517–526 publications: 21 scientists 527–536 publications: 12 scientists 537–546 publications: 8 scientists 547–556 publications: 16 scientists 557–566 publications: 3 scientists 567–576 publications: 11 scientists 577–586 publications: 6 scientists 587–596 publications: 5 scientists 597–606 publications: 6 scientists 607–616 publications: 7 scientists 617–626 publications: 7 scientists 627–636 publications: 10 scientists 637–646 publications: 4 scientists 647–656 publications: 3 scientists 657–658 publications: 2 scientists 659+ publications: 100 scientists
47 publications 659+

This scientist: 214 publications — 49th percentile

49% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 659 publications or more.

Shun-Peng Zhu D-index placement in Mechanical and Aerospace Engineering in 2026

The chart shows the D-index (discipline H-index) distribution of Mechanical and Aerospace Engineering scientists ranked by Research.com in 2026. The highlighted bar marks where Shun-Peng Zhu sits on this spectrum.

30 D-Index: 83 scientists 31 D-Index: 113 scientists 32 D-Index: 144 scientists 33 D-Index: 153 scientists 34 D-Index: 189 scientists 35 D-Index: 158 scientists 36 D-Index: 139 scientists 37 D-Index: 127 scientists 38 D-Index: 130 scientists 39 D-Index: 126 scientists 40 D-Index: 104 scientists 41 D-Index: 100 scientists 42 D-Index: 107 scientists 43 D-Index: 101 scientists 44 D-Index: 103 scientists 45 D-Index: 79 scientists 46 D-Index: 88 scientists 47 D-Index: 70 scientists 48 D-Index: 83 scientists 49 D-Index: 44 scientists 50 D-Index: 64 scientists 51 D-Index: 56 scientists 52 D-Index: 50 scientists 53 D-Index: 48 scientists 54 D-Index: 58 scientists 55 D-Index: 52 scientists 56 D-Index: 48 scientists 57 D-Index: 42 scientists 58 D-Index: 34 scientists 59 D-Index: 42 scientists 60 D-Index: 37 scientists 61 D-Index: 42 scientists 62 D-Index: 44 scientists 63 D-Index: 22 scientists 64 D-Index: 33 scientists 65 D-Index: 29 scientists 66 D-Index: 23 scientists 67 D-Index: 29 scientists 68 D-Index: 24 scientists 69 D-Index: 19 scientists 70 D-Index: 34 scientists 71 D-Index: 26 scientists 72 D-Index: 19 scientists 73 D-Index: 18 scientists 74 D-Index: 19 scientists 75 D-Index: 14 scientists 76 D-Index: 19 scientists 77 D-Index: 8 scientists 78 D-Index: 18 scientists 79 D-Index: 16 scientists 80 D-Index: 12 scientists 81 D-Index: 17 scientists 82 D-Index: 11 scientists 83 D-Index: 16 scientists 84 D-Index: 7 scientists 85 D-Index: 9 scientists 86 D-Index: 8 scientists 87 D-Index: 6 scientists 88 D-Index: 6 scientists 89 D-Index: 7 scientists 90 D-Index: 10 scientists 91 D-Index: 4 scientists 92 D-Index: 4 scientists 93+ D-Index: 100 scientists
30 D-Index 93+

This scientist: 67 D-Index — 87th percentile

87% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 93 D-Index or more.

Overview

Shun-Peng Zhu is a researcher affiliated with the University of Electronic Science and Technology of China in China. Their work is primarily situated within the field of Engineering, with a focus on several subfields including Mechanical Engineering, Mechanics of Materials, Statistics, Probability and Uncertainty, Civil and Structural Engineering, and Materials Chemistry.

Their research encompasses a variety of topics, notably:

  • Fatigue and fracture mechanics
  • Probabilistic and Robust Engineering Design
  • High Temperature Alloys and Creep
  • Structural Health Monitoring Techniques
  • Non-Destructive Testing Techniques
  • Additive Manufacturing Materials and Processes
  • Mechanical stress and fatigue analysis

Shun-Peng Zhu has contributed to both journal articles and book publications. Their recent papers include:

  • Design of robust superhydrophobic surfaces, 2020, Nature
  • Probabilistic framework for fatigue life assessment of notched components under size effects, 2020, International Journal of Mechanical Sciences
  • Recent advances on notch effects in metal fatigue: A review, 2020, Fatigue & Fracture of Engineering Materials & Structures
  • Probabilistic modeling of uncertainties in fatigue reliability analysis of turbine bladed disks, 2020, International Journal of Fatigue
  • A novel Kriging-model-assisted reliability-based multidisciplinary design optimization strategy and its application in the offshore wind turbine tower, 2022, Renewable Energy

Their book contributions, published by Springer International Publishing, include:

  • Structural Reliability Analysis, 2025
  • Advances in Structural Mechanics and Applications, 2022

Frequent co-authors of Shun-Peng Zhu are:

  • José A.F.O. Correia
  • Ding Liao
  • Abílio M.P. De Jesus
  • Qingyuan Wang
  • Behrooz Keshtegar

Shun-Peng Zhu has published extensively in several academic journals, including:

  • International Journal of Fatigue
  • Engineering Failure Analysis
  • SSRN Electronic Journal
  • Computer Methods in Applied Mechanics and Engineering
  • Proceedings of the Institution of Civil Engineers - Maritime Engineering

Best Publications

  • Design of robust superhydrophobic surfaces

    Dehui Wang;Qiangqiang Sun;Matti J. Hokkanen;Chenglin Zhang

  • Probabilistic framework for fatigue life assessment of notched components under size effects

    Ding Liao;Shun-Peng Zhu;Shun-Peng Zhu;Shun-Peng Zhu;Behrooz Keshtegar;Guian Qian

  • Probabilistic Physics of Failure-based framework for fatigue life prediction of aircraft gas turbine discs under uncertainty

    Shun-Peng Zhu;Hong-Zhong Huang;Weiwen Peng;Hai-Kun Wang

  • Risk evaluation in failure mode and effects analysis of aircraft turbine rotor blades using Dempster–Shafer evidence theory under uncertainty

    Jianping Yang;Hong Zhong Huang;Li Ping He;Shun Peng Zhu

  • Computational-experimental approaches for fatigue reliability assessment of turbine bladed disks

    Shun-Peng Zhu;Qiang Liu;Weiwen Peng;Xian-Cheng Zhang

  • Defect tolerant fatigue assessment of AM materials: Size effect and probabilistic prospects

    Unknown

  • Recent advances on notch effects in metal fatigue: A review

    Ding Liao;Shun‐Peng Zhu;José A.F.O. Correia;Abílio M.P. De Jesus

  • Hybrid enhanced Monte Carlo simulation coupled with advanced machine learning approach for accurate and efficient structural reliability analysis

    Changqi Luo;Behrooz Keshtegar;Shun Peng Zhu;Osman Taylan

  • Ni-based layered double hydroxide catalysts for oxygen evolution reaction

    L. Yang;L. Yang;L. Yang;Z. Liu;S. Zhu;S. Zhu;L. Feng

  • A novel Kriging-model-assisted reliability-based multidisciplinary design optimization strategy and its application in the offshore wind turbine tower

    Unknown

  • Fatigue reliability assessment of turbine discs under multi‐source uncertainties

    S.P. Zhu;S.P. Zhu;Q. Liu;J. Zhou;Z.Y. Yu

  • Probabilistic modeling of uncertainties in fatigue reliability analysis of turbine bladed disks

    Xiao-Peng Niu;Run-Zi Wang;Ding Liao;Shun-Peng Zhu

  • A modified strain energy density exhaustion model for creep–fatigue life prediction

    Run-Zi Wang;Xian-Cheng Zhang;Shan-Tung Tu;Shun-Peng Zhu

  • Probabilistic fatigue life prediction and reliability assessment of a high pressure turbine disc considering load variations

    Shun-Peng Zhu;Shun-Peng Zhu;Qiang Liu;Qiang Lei;Qingyuan Wang

  • Probabilistic framework for multiaxial LCF assessment under material variability

    S. P. Zhu;S. P. Zhu;Stefano Foletti;Stefano Beretta

  • A generalized energy-based fatigue–creep damage parameter for life prediction of turbine disk alloys

    Shun-Peng Zhu;Hong-Zhong Huang;Li-Ping He;Yu Liu

  • Hybrid and enhanced PSO: Novel first order reliability method-based hybrid intelligent approaches

    Unknown

  • Evaluation and comparison of critical plane criteria for multiaxial fatigue analysis of ductile and brittle materials

    Shun-Peng Zhu;Shun-Peng Zhu;Zheng-Yong Yu;José Correia;Abílio De Jesus

  • Bivariate Analysis of Incomplete Degradation Observations Based on Inverse Gaussian Processes and Copulas

    Weiwen Peng;Yan-Feng Li;Yuan-Jian Yang;Shun-Peng Zhu

  • Multiaxial fatigue analysis of notched components using combined critical plane and critical distance approach

    Ding Liao;Shun-Peng Zhu;Shun-Peng Zhu;Guian Qian

  • Probabilistic modeling of fatigue life distribution and size effect of components with random defects

    Y. Ai;S.P. Zhu;S.P. Zhu;D. Liao;J.A.F.O. Correia

  • Strain energy gradient-based LCF life prediction of turbine discs using critical distance concept

    Shun-Peng Zhu;Shun-Peng Zhu;Yunhan Liu;Qiang Liu;Zheng-Yong Yu

  • Structural reliability analysis and uncertainties-based collaborative design and optimization of turbine blades using surrogate model

    Debiao Meng;Shunqi Yang;Yu Zhang;Shun‐Peng Zhu

  • Mean stress effect correction in strain energy-based fatigue life prediction of metals:

    Shun-Peng Zhu;Qiang Lei;Hong-Zhong Huang;Yuan-Jian Yang

Frequent Co-Authors

Hong-Zhong Huang
Hong-Zhong Huang University of Electronic Science and Technology of China
José A.F.O. Correia
José A.F.O. Correia University of Porto
Behrooz Keshtegar
Behrooz Keshtegar Zabol University
Yan-Feng Li
Yan-Feng Li University of Electronic Science and Technology of China
Abílio M.P. De Jesus
Abílio M.P. De Jesus University of Porto
Yu Liu
Yu Liu University of Electronic Science and Technology of China
Stefano Beretta
Stefano Beretta Polytechnic University of Milan
Shan-Tung Tu
Shan-Tung Tu East China University of Science and Technology
Filippo Berto
Filippo Berto Sapienza University of Rome
Sankaran Mahadevan
Sankaran Mahadevan Vanderbilt University

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