World's Best Scientists 2026 revealed!

D-Index & Metrics

Mechanical and Aerospace Engineering

D-Index
50
Citations
8182
World Ranking
1193
National Ranking
147

Ming Jia 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 Ming Jia 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: 260 publications — 63rd percentile

63% of scientists in this discipline score the same or lower.

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

Ming Jia 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 Ming Jia 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: 50 D-Index — 67th percentile

67% of scientists in this discipline score the same or lower.

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

Overview

Ming Jia is affiliated with the Dalian University of Technology in China. Their research primarily focuses on engineering and chemical engineering, with significant contributions in computational mechanics and fluid flow and transfer processes. Additional subfields include biomedical engineering, materials chemistry, and electrical and electronic engineering.

The scientist's work concentrates on several key topics, including:

  • Advanced Combustion Engine Technologies
  • Combustion and flame dynamics
  • Heat transfer and supercritical fluids
  • Catalytic Processes in Materials Science
  • Fluid Dynamics and Heat Transfer
  • Biodiesel Production and Applications
  • Vehicle emissions and performance

Ming Jia has published extensively in various academic venues, with frequent appearances in:

  • SSRN Electronic Journal
  • Fuel
  • Energy
  • Combustion and Flame
  • Proceedings of the Combustion Institute

Recent papers showcase the range of their work:

  • "Towards a comprehensive optimization of engine efficiency and emissions by coupling artificial neural network (ANN) with genetic algorithm (GA)," 2021, Energy
  • "Numerical investigation on heat transfer of water spray cooling from single-phase to nucleate boiling region," 2020, International Journal of Thermal Sciences
  • "Spray-turbulence-chemistry interactions under engine-like conditions," 2021, Progress in Energy and Combustion Science
  • "A conceptual model of polyoxymethylene dimethyl ether 3 (PODE3) spray combustion under compression ignition engine-like conditions," 2024, Combustion and Flame
  • "Development of a practical reaction model of polycyclic aromatic hydrocarbon (PAH) formation and oxidation for diesel surrogate fuel," 2020, Fuel

Ming Jia collaborates regularly with multiple coauthors, including:

  • Yaopeng Li
  • Yachao Chang
  • Yanzhi Zhang
  • Huiquan Duan
  • Yikang Cai

Best Publications

  • Enhancement on a Skeletal Kinetic Model for Primary Reference Fuel Oxidation by Using a Semidecoupling Methodology

    Yao-Dong Liu;Ming Jia;Mao-Zhao Xie;Bin Pang

  • Numerical study on the combustion and emission characteristics of a methanol/diesel reactivity controlled compression ignition (RCCI) engine

    Yaopeng Li;Ming Jia;Yaodong Liu;Maozhao Xie

  • A reduced toluene reference fuel chemical kinetic mechanism for combustion and polycyclic-aromatic hydrocarbon predictions

    Hu Wang;Hu Wang;Mingfa Yao;Zongyu Yue;Ming Jia

  • Parametric study and optimization of a RCCI (reactivity controlled compression ignition) engine fueled with methanol and diesel

    Yaopeng Li;Ming Jia;Yachao Chang;Yaodong Liu

  • Development of a new skeletal mechanism for n-decane oxidation under engine-relevant conditions based on a decoupling methodology

    Yachao Chang;Ming Jia;Yaodong Liu;Yaopeng Li

  • Development of a skeletal mechanism for diesel surrogate fuel by using a decoupling methodology

    Yachao Chang;Ming Jia;Yaopeng Li;Yaodong Liu

  • Combustion and particle number emissions of a direct injection spark ignition engine operating on ethanol/gasoline and n-butanol/gasoline blends with exhaust gas recirculation

    Zhijin Zhang;Tianyou Wang;Ming Jia;Qun Wei

  • A chemical kinetics model of iso-octane oxidation for HCCI engines

    Ming Jia;Maozhao Xie

  • The effect of injection timing and intake valve close timing on performance and emissions of diesel PCCI engine with a full engine cycle CFD simulation

    Ming Jia;Maozhao Xie;Tianyou Wang;Zhijun Peng

  • Thermodynamic energy and exergy analysis of three different engine combustion regimes

    Yaopeng Li;Yaopeng Li;Ming Jia;Yachao Chang;Sage L. Kokjohn

  • Development of a reduced n-dodecane-PAH mechanism and its application for n-dodecane soot predictions

    Hu Wang;Youngchul Ra;Ming Jia;Rolf D. Reitz

  • Towards a comprehensive optimization of engine efficiency and emissions by coupling artificial neural network (ANN) with genetic algorithm (GA)

    Yaopeng Li;Yaopeng Li;Ming Jia;Xu Han;Xue Song Bai

  • Multiple-objective optimization of methanol/diesel dual-fuel engine at low loads: A comparison of reactivity controlled compression ignition (RCCI) and direct dual fuel stratification (DDFS) strategies

    Yaopeng Li;Yaopeng Li;Ming Jia;Leilei Xu;Leilei Xu;Xue Song Bai

  • Development of a New Skeletal Chemical Kinetic Model of Toluene Reference Fuel with Application to Gasoline Surrogate Fuels for Computational Fluid Dynamics Engine Simulation

    Yao-Dong Liu;Ming Jia;Mao-Zhao Xie;Bin Pang

  • Experimental and modeling study of liquid fuel injection and combustion in diesel engines with a common rail injection system

    Leilei Xu;Leilei Xu;Xue Song Bai;Ming Jia;Yong Qian

  • DEVELOPMENT OF A NEW SPRAY/WALL INTERACTION MODEL FOR DIESEL SPRAY UNDER PCCI-ENGINE RELEVANT CONDITIONS

    Yanzhi Zhang;Ming Jia;Hong Liu;Maozhao Xie

  • Towards a comprehensive understanding of the influence of fuel properties on the combustion characteristics of a RCCI (reactivity controlled compression ignition) engine

    Yaopeng Li;Ming Jia;Yachao Chang;Maozhao Xie

  • Neural network prediction of biodiesel kinematic viscosity at 313 K

    Xiangzan Meng;Ming Jia;Tianyou Wang

  • Development of a skeletal oxidation mechanism for biodiesel surrogate

    Yachao Chang;Ming Jia;Yaopeng Li;Yanzhi Zhang

  • Application of a Decoupling Methodology for Development of Skeletal Oxidation Mechanisms for Heavy n-Alkanes from n-Octane to n-Hexadecane

    Yachao Chang;Ming Jia;Yaodong Liu;Yaopeng Li

Frequent Co-Authors

Maozhao Xie
Maozhao Xie Dalian University of Technology
Tianyou Wang
Tianyou Wang Tianjin University
Rolf D. Reitz
Rolf D. Reitz University of Wisconsin–Madison
Hu Wang
Hu Wang Tianjin University
Kai H. Luo
Kai H. Luo University College London
Wei-Haur Lam
Wei-Haur Lam Tianjin University
Xingcai Lu
Xingcai Lu Shanghai Jiao Tong University
Zhixia He
Zhixia He Jiangsu University
Xue-Song Bai
Xue-Song Bai Lund University
Javier Monsalve-Serrano
Javier Monsalve-Serrano Universitat Politècnica de València

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