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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. 9.9% 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%
3.1k
per year above
Top-10% rate
9.9%
share of its works in the world top 10%
Growth, 2018-21 → 2022-25
+5%
the tick is no change
Largest producer
China
40% of the 10 largest, 2022-now

Which countries lead Particle Dynamics in Fluid Flows research?

By volume, China and the United States publish the most (1.6k and 723 works in 2022-now).

By volume, 2022-now

  1. 1 China 1.6k works
  2. 2 United States 723 works
  3. 3 Russia 287 works
  4. 4 India 286 works
  5. 5 Germany 272 works
  6. 6 France 242 works
  7. 7 Japan 179 works
  8. 8 United Kingdom 168 works
  9. 9 Canada 104 works
  10. 10 Italy 99 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: 40.1%United States: 18.4%Russia: 7.3%India: 7.3%6 others listed: 27.0%40%largest
China1,580 · 40.1%United States723 · 18.4%Russia287 · 7.3%India286 · 7.3%6 others listed1,064 · 27.0%

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

Which institutions lead Particle Dynamics in Fluid Flows research?

By volume in 2022-now, Russian Academy of Sciences publishes the most Particle Dynamics in Fluid Flows research, followed by Centre National de la Recherche Scientifique and Chinese Academy of Sciences.

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

The most-cited researchers publishing on Particle Dynamics in Fluid Flows include Xiaogang Wang, Lídia Morawska and Olaf Ronneberger.

  1. 1 Xiaogang Wang China 260k citations
  2. 2 Lídia Morawska Australia 244k citations
  3. 3 Olaf Ronneberger Germany 188k citations
  4. 4 Qiang Zhang China 184k citations
  5. 5 Jacob Cohen Israel 170k citations

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

What are the most cited papers in Particle Dynamics in Fluid Flows?

The most cited paper in Particle Dynamics in Fluid Flows is "Bubbles, drops and particles" (L.G. Leal, 1979), with 5,330 citations. Next come "Particle Image Velocimetry: A Practical Guide" (5,098) and "Equation of motion for a small rigid sphere in a nonuniform flow" (3,459).

Most cited papers
#PaperReleasedCitations
1 Bubbles, drops and particles L.G. Leal · International Journal of Multiphase Flow Jun 1979 5,330
2 Particle Image Velocimetry: A Practical Guide Markus Raffel 11 Jun 2002 5,098
3 Equation of motion for a small rigid sphere in a nonuniform flow MARTIN R. MAXEY, James J. Riley · The Physics of Fluids Apr 1983 3,459
4 The lift on a small sphere in a slow shear flow Philip Geoffrey Saffman · Journal of Fluid Mechanics Jun 1965 3,412
5 Particle-Imaging Techniques for Experimental Fluid Mechanics Ronald J. Adrian · Annual Review of Fluid Mechanics 1991 3,261
All 100 most cited papers, with filters and sorting

Where is Particle Dynamics in Fluid Flows research done?

The largest centres of Particle Dynamics in Fluid Flows research in 2022-now 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-now

  1. 1 BeijingChina 341 works
  2. 2 Xi'anChina 115 works
  3. 3 ShanghaiChina 107 works
  4. 4 MoscowRussia 97 works
  5. 5 NanjingChina 85 works
  6. 6 HangzhouChina 78 works
  7. 7 ParisFrance 70 works
  8. 8 NovosibirskRussia 61 works
  9. 9 WuhanChina 53 works
  10. 10 HarbinChina 50 works

Where it is the local speciality

  1. MagdeburgDE · 22.5 works16×
  2. NovosibirskRU · 61.2 works11×
← less than its size predicts1×more →

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, Xi'an Jiaotong University and Otto-von-Guericke-Universität Magdeburg 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%mean 22.33%fractional works in this field (log) →share of work in the world top 10% →Shandong University …Otto-von-Guericke-Un…China University of …Xi'an Jiaotong Unive…
above the meannear itbelow it

One dot per university in the first ten of the table below, at the default weights. The upper left is the interesting corner: small places doing unusually strong work.

Excellenceshare of its work in the world top 10%, 2022-now and all time averaged
40%
Specialisationhow much of its work is in this field, counted up to 10× the world share
10%
Sizeits output in this field, on a log scale
30%
Growthoutput growth, counted from -20% to +20%
10%
Internationalshare of its work with a co-author abroad
10%
Each slider sets a weight; the share it gets of the score is beside it.
#UniversityScoreTop 10%SpecialisationWorksGrowth
1Shandong University of Science and TechnologyChina 77.857.2%11.2×23 +48.6%
2Xi'an Jiaotong UniversityChina 58.213.0%7.0×51 +27.4%
3Otto-von-Guericke-Universität MagdeburgGermany 54.122.9%27.6×21 +20.6%
4China University of Mining and TechnologyChina 54.019.4%8.4×27 +85.2%
5Tsinghua UniversityChina 51.610.8%5.5×53 +1.9%
6Shanghai Jiao Tong UniversityChina 51.511.6%3.5×42 +55.0%
7Jiangsu UniversityChina 49.628.9%5.6×18 +109.1%
8Eindhoven University of TechnologyNetherlands 48.814.9%12.2×18 +18.9%
9Kunming University of Science and TechnologyChina 47.221.0%7.8×18 +249.5%
10Okinawa Institute of Science and Technology Graduate UniversityJapan 46.623.6%39.6×8 +325.6%

Universities only. The score blends excellence (40%), size (30%), specialisation (10%), growth (10%), international reach (10%), 2022-now; growth compares 2018-21 with 2022-25 and counts up to +20%, since a jump from a small base is mostly noise.

Every institution in Particle Dynamics in Fluid Flows, country by country

Is Particle Dynamics in Fluid Flows research growing?

Output in 2022-2025 was 5% higher than in 2018-2021, peaking in 2024. The fastest-growing topics are Particle Dynamics in Fluid Flows.

still counting19801990200020102020
grewheldshrank

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

Its share of all research

Growing output can still be a shrinking share: all of science grows. This is Particle Dynamics in Fluid Flows as a percentage of every work published that year, 0.022% in 2024.

still counting0.025%0.050%0.075%Particle Dynamics in Fluid Flows0.019% in 202519801990200020102020

Fractional works of Particle Dynamics in Fluid Flows over all fractional works that year. How the balance of science has shifted.

How much of it reaches the world top 10%

Year by year.

still counting5%10%15%Particle Dynamics in Fluid Flows9.8% in 2025200020102020

Of each year's works in it, the share that reached the world's 10% most cited for their field and year. A field is the world for its own topics, so there is no other line to hold it against.

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.

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