World's Best Scientists 2026 revealed!

D-Index & Metrics

Mechanical and Aerospace Engineering

D-Index
38
Citations
4697
World Ranking
2318
National Ranking
17

Sinisa Krajnovic 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 Sinisa Krajnovic 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: 217 publications — 50th percentile

50% of scientists in this discipline score the same or lower.

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

Sinisa Krajnovic 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 Sinisa Krajnovic 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: 38 D-Index — 36th percentile

36% of scientists in this discipline score the same or lower.

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

Overview

Sinisa Krajnovic is affiliated with Chalmers University of Technology in Sweden and has a significant body of research primarily focused on engineering and environmental science. Their work spans multiple subfields including aerospace engineering, computational mechanics, environmental engineering, automotive engineering, and ocean engineering.

Their research topics predominantly involve aerodynamics and fluid dynamics, wind and air flow studies, fluid dynamics and vibration analysis, vehicle emissions and performance, fluid dynamics and turbulent flows, ship hydrodynamics and maneuverability, and computational fluid dynamics and aerodynamics.

They have contributed extensively to scientific literature, with notable recent papers such as:

  • Evaluation of LES, IDDES and URANS for prediction of flow around a streamlined high-speed train (2022) published in Journal of Wind Engineering and Industrial Aerodynamics
  • The effect of ground clearance on the aerodynamics of a generic high-speed train (2020) published in Journal of Fluids and Structures
  • Assessment of LES, IDDES and RANS approaches for prediction of wakes behind notchback road vehicles (2021) published in Journal of Wind Engineering and Industrial Aerodynamics
  • Impact of the bogies and cavities on the aerodynamic behaviour of a high-speed train. An IDDES study (2020) published in Journal of Wind Engineering and Industrial Aerodynamics
  • Numerical investigation of the wake bi-stability behind a notchback Ahmed body (2021) published in Journal of Fluid Mechanics

Frequent co-authors collaborating with Sinisa Krajnovic include Kan He, Guangjun Gao, Jia-Bin Wang, Xinchao Su, and Guglielmo Minelli. These partnerships have contributed to a substantial number of publications in the field.

Publication venues where their research most often appears include:

  • Physics of Fluids
  • Journal of Wind Engineering and Industrial Aerodynamics
  • Journal of Fluid Mechanics
  • Ocean Engineering
  • IEEE Communications Magazine

The main fields of study reflected in their work are:

  • Engineering
  • Environmental Science

Subfields of study associated with their contributions are:

  • Aerospace Engineering
  • Computational Mechanics
  • Environmental Engineering
  • Automotive Engineering
  • Ocean Engineering

Main topics addressed in their research include:

  • Aerodynamics and Fluid Dynamics Research
  • Wind and Air Flow Studies
  • Fluid Dynamics and Vibration Analysis
  • Vehicle emissions and performance
  • Fluid Dynamics and Turbulent Flows
  • Ship Hydrodynamics and Maneuverability
  • Computational Fluid Dynamics and Aerodynamics

Best Publications

  • Cluster-based reduced-order modelling of a mixing layer

    Eurika Kaiser;Bernd R. Noack;Laurent Cordier;Andreas Spohn

  • Flow around a simplified car, Part 1: Large eddy simulation

    Siniša Krajnović;Lars Davidson

  • On the need for a nonlinear subscale turbulence term in POD models as exemplified for a high-Reynolds-number flow over an Ahmed body

    Jan Östh;Bernd R. Noack;Siniša Krajnović;Diogo Barros

  • LES study of the influence of the nose shape and yaw angles on flow structures around trains

    Hassan Hemida;Siniša Krajnović

  • Large-Eddy Simulation of the Flow Around a Bluff Body

    Sinisa Krajnovic;Lars Davidson

  • Flow around a tall finite cylinder explored by large eddy simulation

    Siniša Krajnović

  • Flow Around a Simplified Car, Part 2: Understanding the Flow

    Siniša Krajnović;Lars Davidson

  • Near-Wall Formulation of the Partially Averaged Navier-Stokes Turbulence Model

    Branislav Basara;Siniša Krajnovic;Sharath Girimaji;Zoran Pavlovic

  • Large-Eddy Simulation of the Flow Around Simplified Car Model

    Siniša Krajnović;Lars Davidson

  • Large eddy simulation of the flow around a simplified train moving through a crosswind flow

    Siniša Krajnović;Per Ringqvist;Koji Nakade;Branislav Basara

  • An efficient very large eddy simulation model for simulation of turbulent flow

    Xingsi Han;Siniša Krajnović

  • LES Study of the Influence of a Train-Nose Shape on the Flow Structures Under Cross-Wind Conditions

    Hassan Hemida;Siniša Krajnović

  • Dynamic analysis of the effect of nose length on train aerodynamic performance

    Guang Chen;Xiao-Bai Li;Zhen Liu;Dan Zhou

  • Exploring Flow Structures Around a Simplified ICE2 Train Subjected to A 30° Side Wind Using LES

    Hassan Hemida;Siniša Krajnović

  • Influence of floor motions in wind tunnels on the aerodynamics of road vehicles

    Siniša Krajnović;Lars Davidson

  • Large eddy simulation of flows around ground vehicles and other bluff bodies.

    Siniša Krajnović

  • Numerical simulation of the flow around a simplified vehicle model with active flow control

    Siniša Krajnović;João Fernandes

  • Superiority of PANS compared to LES in predicting a rudimentary landing gear flow with affordable meshes

    Siniša Krajnović;Ragnar Lárusson;Branislav Basara

  • A study of the aerodynamics of a generic container freight wagon using Large-Eddy Simulation

    Jan Östh;Siniša Krajnović

  • Evaluation of LES, IDDES and URANS for prediction of flow around a streamlined high-speed train

    Unknown

  • Large-Eddy Simulation of the Flow Around a Simplified High Speed Train Under the Influence of a Cross-Wind

    Hassan Hemida;Sinisa Krajnovic;Lars Davidson

  • Very-Large-Eddy Simulation Based on k-ω Model

    Xingsi Han;Siniša Krajnović

  • Effects of simplifying train bogies on surrounding flow and aerodynamic forces

    Tianyun Dong;Xifeng Liang;Sinisa Krajnović;Xiaohui Xiong

Frequent Co-Authors

Lars Davidson
Lars Davidson Chalmers University of Technology
Hassan Hemida
Hassan Hemida University of Birmingham
Bernd R. Noack
Bernd R. Noack Harbin Institute of Technology
Sharath S. Girimaji
Sharath S. Girimaji Texas A&M University
John Sheridan
John Sheridan Monash University
Paolo Falcone
Paolo Falcone Chalmers University of Technology
Erik Kjellström
Erik Kjellström Swedish Meteorological and Hydrological Institute
Christian Oliver Paschereit
Christian Oliver Paschereit Technical University of Berlin
Robert J. Martinuzzi
Robert J. Martinuzzi University of Calgary

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