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Particle Dynamics in Fluid Flows

Particle Dynamics in Fluid Flows is a research topic within Ocean Engineering. Science Explorer counts 31k research works in it since 1950. 22.7% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the interaction between turbulent flows and dispersed particles or droplets. It covers various aspects such as particle collision, deposition, clustering, and the effects of turbulence on the behavior of multiphase flows. The research also delves into numerical simulations and modeling techniques for understanding the complex dynamics of turbulent particle interactions.

  • Turbulence
  • Particles
  • Multiphase Flow
  • Particle-Laden Flows
  • Collision
  • Deposition
  • Clustering
  • Two-Phase Flow
  • Lagrangian Simulation
  • Inertial Particles
Research works
31k
fractional, since 1950
In the world top 10%
7.1k
per year above
Top-10% rate
22.7%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
+24%
the tick is no change

Which countries lead Particle Dynamics in Fluid Flows research?

By volume, China and the United States publish the most (1.4k and 659 works in 2022–2025).

By volume, 2022–2025

  1. 1 China 1.4k works
  2. 2 United States 659 works
  3. 3 Russia 276 works
  4. 4 India 269 works
  5. 5 Germany 244 works
  6. 6 France 224 works
  7. 7 Japan 165 works
  8. 8 United Kingdom 156 works
  9. 9 Canada 97 works
  10. 10 Italy 92 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: 39.4%United States: 18.3%Russia: 7.7%India: 7.5%6 others listed: 27.2%39%largest
China1,419 · 39.4%United States659 · 18.3%Russia276 · 7.7%India269 · 7.5%6 others listed979 · 27.2%

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

Which institutions lead Particle Dynamics in Fluid Flows research?

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

Who are the leading researchers in Particle Dynamics in Fluid Flows?

The most-cited researchers publishing on Particle Dynamics in Fluid Flows include Lídia Morawska, Menachem Elimelech and John Newman.

  1. 1 Lídia Morawska Australia 6.4k citations
  2. 2 Menachem Elimelech United States 3k citations
  3. 3 John Newman United States 2.9k citations
  4. 4 John H. Seinfeld United States 2.9k citations

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

Where is Particle Dynamics in Fluid Flows research done?

The largest centres of Particle Dynamics in Fluid Flows research in 2022–2025 are Beijing (China), Xi'an (China), Shanghai (China) and Moscow (Russia). Among places with at least 20 works in it, it is an unusually large share of all research in Magdeburg and Novosibirsk.

Largest cities, 2022–2025

  1. 1 Beijing China 313 works
  2. 2 Xi'an China 102 works
  3. 3 Shanghai China 98 works
  4. 4 Moscow Russia 95 works
  5. 5 Nanjing China 79 works
  6. 6 Hangzhou China 70 works
  7. 7 Paris France 65 works
  8. 8 Novosibirsk Russia 59 works
  9. 9 Wuhan China 49 works
  10. 10 Harbin China 47 works

Where it is the local speciality

  1. MagdeburgDE · 21.1 works16×
  2. NovosibirskRU · 59.0 works11×
← less than its size predictsmore →

Location quotient: how much more of its research is in Particle Dynamics in Fluid Flows than the world average.

See Particle Dynamics in Fluid Flows on the map

Where is the best place to study Particle Dynamics in Fluid Flows?

Among universities, judged by research, Shandong University of Science and Technology, Otto-von-Guericke-Universität Magdeburg and University of Tulsa 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%25%50%75%mean 32.6%fractional works in this node (log) →share in the world top 10% →Shandong University of Science and Technology: 22, 64.4%Otto-von-Guericke-Universität Magdeburg: 20, 34.4%University of Tulsa: 10, 40.0%Eindhoven University of Technology: 16, 31.2%Xi'an Jiaotong University: 47, 26.9%Indian Institute of Technology Madras: 20, 13.2%Northeast Electric Power University: 10, 20.3%Zhejiang Sci-Tech University: 19, 25.2%China University of Mining and Technology: 25, 27.2%Jiangsu University: 17, 43.2%Shandong University …University of TulsaOtto-von-Guericke-Un…Eindhoven University…
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 Shandong University of Science and TechnologyChina 83.164.4%10.3×22 +273.4%
2 Otto-von-Guericke-Universität MagdeburgGermany 68.334.4%27.2×20 +166.0%
3 University of TulsaUnited States 60.440.0%40.3×10 +144.2%
4 Eindhoven University of TechnologyNetherlands 57.631.2%10.5×16 +2.4%
5 Xi'an Jiaotong UniversityChina 56.526.9%6.7×47 +53.3%
6 Indian Institute of Technology MadrasIndia 52.613.2%10.4×20 +92.9%
7 Northeast Electric Power UniversityChina 52.620.3%16.9×10 +176.5%
8 Zhejiang Sci-Tech UniversityChina 52.525.2%14.6×19 +18.9%
9 China University of Mining and TechnologyChina 51.927.2%7.3×25 +75.3%
10 Jiangsu UniversityChina 51.143.2%5.3×17 +88.3%

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 Particle Dynamics in Fluid Flows research growing?

Output in 2018–2022 was 24% higher than in 2013–2017, peaking in 2024. The fastest-growing topics are Particle Dynamics in Fluid Flows.

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.