Science Explorer Interactive view Map

Fluid Dynamics and Heat Transfer

Fluid Dynamics and Heat Transfer is a research topic within Computational Mechanics. Science Explorer counts 29k research works in it since 1950. 19.6% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the dynamics of drop impact on surfaces, including phenomena such as drop splashing, spreading, and coalescence. It explores topics related to surface tension, multiphase flow, level set methods, spray cooling, and the Leidenfrost phenomenon.

  • Drop Impact
  • Surface Tension
  • Fluid Dynamics
  • Multiphase Flow
  • Level Set Method
  • Spray Cooling
  • Interface Reconstruction
  • Liquid Jet Instability
  • Coalescence of Drops
  • Leidenfrost Phenomenon
Research works
29k
fractional, since 1950
In the world top 10%
5.6k
per year above
Top-10% rate
19.6%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
+35%
the tick is no change

Which countries lead Fluid Dynamics and Heat Transfer research?

By volume, China and the United States publish the most (2.2k and 567 works in 2022–2025).

By volume, 2022–2025

  1. 1 China 2.2k works
  2. 2 United States 567 works
  3. 3 India 381 works
  4. 4 Russia 267 works
  5. 5 France 263 works
  6. 6 Germany 238 works
  7. 7 Japan 214 works
  8. 8 United Kingdom 171 works
  9. 9 South Korea 129 works
  10. 10 Canada 115 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

China: 48.9%United States: 12.4%India: 8.3%Russia: 5.8%6 others listed: 24.7%49%largest
China2,241 · 48.9%United States567 · 12.4%India381 · 8.3%Russia267 · 5.8%6 others listed1,131 · 24.7%

Shares of the rows listed above, not of the whole node.

Which institutions lead Fluid Dynamics and Heat Transfer research?

By volume in 2022–2025, Xi'an Jiaotong University publishes the most Fluid Dynamics and Heat Transfer research, followed by Shanghai Jiao Tong University and Tsinghua University.

Who are the leading researchers in Fluid Dynamics and Heat Transfer?

The most-cited researchers publishing on Fluid Dynamics and Heat Transfer include Lei Jiang, Rolf D. Reitz and Parviz Moin.

  1. 1 Lei Jiang China 3.8k citations
  2. 2 Rolf D. Reitz United States 2.5k citations
  3. 3 Parviz Moin United States 2.4k citations
  4. 4 Issam Mudawar United States 2.4k citations
  5. 5 D.D. Ganji Iran 2.3k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

Where is Fluid Dynamics and Heat Transfer research done?

The largest centres of Fluid Dynamics and Heat Transfer research in 2022–2025 are Beijing (China), Shanghai (China), Xi'an (China) and Nanjing (China). Among places with at least 20 works in it, it is an unusually large share of all research in Tomsk, Darmstadt and Novosibirsk.

Largest cities, 2022–2025

  1. 1 Beijing China 451 works
  2. 2 Shanghai China 176 works
  3. 3 Xi'an China 173 works
  4. 4 Nanjing China 118 works
  5. 5 Hangzhou China 94 works
  6. 6 Harbin China 90 works
  7. 7 Wuhan China 87 works
  8. 8 Moscow Russia 87 works
  9. 9 Paris France 84 works
  10. 10 Tianjin China 75 works

Where it is the local speciality

  1. TomskRU · 34.0 works12×
  2. DarmstadtDE · 30.2 works11×
  3. NovosibirskRU · 66.6 works10×
  4. ZhenjiangCN · 58.8 works9.9×
  5. MianyangCN · 28.6 works9.5×
  6. KharagpurIN · 24.3 works9.0×
← less than its size predictsmore →

Location quotient: how much more of its research is in Fluid Dynamics and Heat Transfer than the world average.

See Fluid Dynamics and Heat Transfer on the map

Where is the best place to study Fluid Dynamics and Heat Transfer?

Among universities, judged by research, Harbin Engineering University, Xi'an Jiaotong University and City University of Hong Kong score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%20%40%mean 27.86%fractional works in this node (log) →share in the world top 10% →Harbin Engineering University: 46, 31.5%Xi'an Jiaotong University: 82, 24.1%City University of Hong Kong: 18, 31.3%Dalian University of Technology: 50, 29.3%Jiangsu University: 44, 22.6%Otto-von-Guericke-Universität Magdeburg: 11, 37.4%Indian Institute of Technology Madras: 30, 14.2%Shanghai Jiao Tong University: 71, 25.4%Beijing Institute of Technology: 40, 25.3%Hong Kong Polytechnic University: 13, 37.5%Harbin Engineering U…City University of H…Dalian University of…Xi'an Jiaotong Unive…
above the meannear itbelow it

One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.

#UniversityScoreTop 10%SpecialisationWorksGrowth
1 Harbin Engineering UniversityChina 72.031.5%16.2×46 +70.0%
2 Xi'an Jiaotong UniversityChina 70.224.1%9.4×82 +61.5%
3 City University of Hong KongHong Kong 68.831.3%5.8×18 +257.1%
4 Dalian University of TechnologyChina 68.629.3%9.8×50 +32.6%
5 Jiangsu UniversityChina 66.822.6%10.8×44 +82.7%
6 Otto-von-Guericke-Universität MagdeburgGermany 66.637.4%11.9×11 +83.3%
7 Indian Institute of Technology MadrasIndia 64.014.2%12.3×30 +202.2%
8 Shanghai Jiao Tong UniversityChina 62.925.4%6.1×71 +93.9%
9 Beijing Institute of TechnologyChina 62.025.3%6.3×40 +163.9%
10 Hong Kong Polytechnic UniversityHong Kong 61.237.5%2.8×13 +245.2%

Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.

Is Fluid Dynamics and Heat Transfer research growing?

Output in 2018–2022 was 35% higher than in 2013–2017, peaking in 2024. The fastest-growing topics are Fluid Dynamics and Heat Transfer.

19801990200020102020
grewheldshrank

The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.