World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
60
Citations
15165
World Ranking
2159
National Ranking
685

Juan J. Alonso publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Juan J. Alonso sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 311 publications — 78th percentile

78% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Juan J. Alonso D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Juan J. Alonso sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 60 D-Index — 78th percentile

78% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Overview

Juan J. Alonso is a researcher affiliated with Stanford University in the United States, specializing in fields related to engineering, with a significant focus on computational mechanics and aerospace engineering. Their research contributes substantially to areas including applied mathematics, global and planetary change, and statistics, probability, and uncertainty.

The main topics of Juan J. Alonso's work include:

  • Computational Fluid Dynamics and Aerodynamics
  • Gas Dynamics and Kinetic Theory
  • Advanced Aircraft Design and Technologies
  • Aerodynamics and Acoustics in Jet Flows
  • Probabilistic and Robust Engineering Design
  • Fluid Dynamics and Turbulent Flows
  • Wind and Air Flow Studies

Their recent publications reflect a range of themes in fluid mechanics, aerospace applications, and design under uncertainty, highlighted by:

  • "A universal velocity profile for turbulent wall flows including adverse pressure gradient boundary layers" (2021), published in Journal of Fluid Mechanics
  • "Lithium-Ion Battery Modeling for Aerospace Applications" (2021), published in Journal of Aircraft
  • "SU2-NEMO: An Open-Source Framework for High-Mach Nonequilibrium Multi-Species Flows" (2021), published in Aerospace
  • "Design exploration and optimization under uncertainty" (2020), published in Physics of Fluids
  • "A Low-Cost Aero-Propulsive Analysis of Distributed Electric Propulsion Aircraft" (2021), published in AIAA Scitech 2021 Forum

Juan J. Alonso frequently collaborates with several coauthors, notably:

  • Matthew Clarke
  • Jayant Mukhopadhaya
  • Racheal M. Erhard
  • Walter Maier
  • Catarina Garbacz

Their work is regularly published in venues focused on aerospace and engineering topics, including:

  • AIAA SCITECH 2022 Forum
  • AIAA Scitech 2021 Forum
  • AIAA Scitech 2020 Forum
  • AIAA SCITECH 2023 Forum
  • AIAA AVIATION 2021 FORUM

Best Publications

  • CFD Vision 2030 Study: A Path to Revolutionary Computational Aerosciences

    Jeffrey P Slotnick;Abdollah Khodadoust;Juan Alonso;David Darmofal

  • SU2: An Open-Source Suite for Multiphysics Simulation and Design

    Thomas D. Economon;Francisco Palacios;Sean R. Copeland;Trent W. Lukaczyk

  • The complex-step derivative approximation

    Joaquim R. R. A. Martins;Peter Sturdza;Juan J. Alonso

  • Stanford University Unstructured (SU 2 ): An open-source integrated computational environment for multi-physics simulation and design

    Francisco Palacios;Juan Alonso;Karthikeyan Duraisamy;Michael Colonno

  • Constrained Multipoint Aerodynamic Shape Optimization Using an Adjoint Formulation and Parallel Computers

    James Reuther;Antony Jameson;Juan Jose Alonso;Mark J. Rimlinger

  • High-Fidelity Aerostructural Design Optimization of a Supersonic Business Jet

    Joaquim R. R. A. Martins;Juan J. Alonso;James J. Reuther

  • Aircraft Gas Turbine Engine Simulations

    William Reynolds;Juan Alonso;Massimiliano Fatica

  • A Machine Learning Strategy to Assist Turbulence Model Development

    Brendan D. Tracey;Karthikeyan Duraisamy;Juan J. Alonso

  • Liszt: a domain specific language for building portable mesh-based PDE solvers

    Zachary DeVito;Niels Joubert;Francisco Palacios;Stephen Oakley

  • Fully-implicit time-marching aeroelastic solutions

    Juan Alonso;Antony Jameson

  • A Coupled-Adjoint Sensitivity Analysis Method for High-Fidelity Aero-Structural Design

    Joaquim R.R.A. Martins;Juan J. Alonso;James J. Reuther

  • AN AUTOMATED METHOD FOR SENSITIVITY ANALYSIS USING COMPLEX VARIABLES

    Joaquim R. R. A. Martins;Ilan M. Kroo;Juan J. Alonso

  • Application of a Non-Linear Frequency Domain Solver to the Euler and Navier-Stokes Equations

    Matthew McMullen;Antony Jameson;Juan J. Alonso

  • Stanford University Unstructured (SU2): Analysis and Design Technology for Turbulent Flows

    Francisco Palacios;Thomas D. Economon;Aniket Aranake;Sean R. Copeland

  • ADjoint: An Approach for the Rapid Development of Discrete Adjoint Solvers

    Charles A. Mader;Joaquim R. R. A. Martins;Juan J. Alonso;Edwin van der Weide

  • Using gradients to construct cokriging approximation models for high-dimensional design optimization problems

    H.-S. Chung;J. Alonso

  • Airfoil design optimization using reduced order models based on proper orthogonal decomposition

    Patrick LeGresley;Juan Alonso

  • Investigation of non-linear projection for POD based reduced order models for Aerodynamics

    Patrick LeGresley;Juan Alonso

  • Aerodynamic shape optimization of supersonic aircraft configurations via an adjoint formulation on distributed memory parallel computers

    J. Reuther;J.J. Alonso;M.J. Rimlinger;A. Jameson

  • Active Subspaces for Shape Optimization

    Trent Lukaczyk;Francisco Palacios;Juan J. Alonso;Paul G. Constantine

  • Constrained multipoint aerodynamic shape optimization using an adjoint formulation and parallel computers

    J. Reuther;A. Jameson;J. Alonso;M. Rimlinger

Frequent Co-Authors

Antony Jameson
Antony Jameson Texas A&M University
Heinz Pitsch
Heinz Pitsch RWTH Aachen University
Joaquim R. R. A. Martins
Joaquim R. R. A. Martins University of Michigan–Ann Arbor
Xiaohua Wu
Xiaohua Wu University of Geneva
Gianluca Iaccarino
Gianluca Iaccarino Stanford University
Ilan Kroo
Ilan Kroo Stanford University
Rafael Palacios
Rafael Palacios Imperial College London
Dimitri J. Mavriplis
Dimitri J. Mavriplis University of Wyoming
Parviz Moin
Parviz Moin Stanford University

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