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Journal of Fluid Mechanics
H-index 56

Journal of Fluid Mechanics

0022-1120

Published by: Cambridge University Press

https://www.cambridge.org/core/journals/journal-of-fluid-mechanics

Ranking & Metrics

Discipline name Position Best Scientists Publications D-Index
Mechanical and Aerospace Engineering 18 333 1509 50
Physics 52 22 173 31
Engineering and Technology 122 234 804 40

Additional Metrics

Number of Best Scientists*: 780
Documents by Best Scientists*: 2424
Top 100 Ranked Scientists*: 27
SCIMAGO H-index: 271
SCIMAGO SJR: 1.471
Impact Factor: 3.9

Overview

Top Research Topics at Journal of Fluid Mechanics?

Journal of Fluid Mechanics is organized to address concerns in the fields of Mechanics, Classical mechanics, Turbulence, Reynolds number and Vortex. Instability, Boundary layer, Vorticity, Flow (mathematics) and Laminar flow are some of the study areas of Mechanics discussed. Journal of Fluid Mechanics links adjacent topics like Instability with Wavenumber.

Boundary layer thickness, Flow separation, Blasius boundary layer and Boundary layer control studies are all carried out as a component of the study in Boundary layer presented. Classical mechanics research featured in it incorporates concerns from various other topics such as Amplitude, Mathematical analysis and Nonlinear system. Turbulence studies presented include Turbulence kinetic energy, Direct numerical simulation, Reynolds stress, K-epsilon turbulence model and Open-channel flow.

The studies on Reynolds number discussed can also contribute to research in the domains of Drag, Wake, Flow (psychology) and Pipe flow. The studies tackled, which mainly focus on Vortex, apply to Vortex shedding as well.

  • Mechanics (69.72%)
  • Classical mechanics (25.98%)
  • Turbulence (22.64%)

What are the most cited papers published in the journal?

  • On the identification of a vortex (4451 citations)
  • Turbulence statistics in fully developed channel flow at low reynolds number (3918 citations)
  • Progress in the development of a Reynolds-stress turbulence closure (3210 citations)

Research areas of the most cited articles at Journal of Fluid Mechanics:

The published articles focus largely on the fields of Mechanics, Classical mechanics, Turbulence, Reynolds number and Vortex. The published papers connects the study in Mechanics with the closely related areas of Optics. The published papers explore topics in Classical mechanics which can be helpful for research in disciplines like Flow (mathematics), Drag and Mathematical analysis.

Papers citation over time

A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.

The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.

The top authors publishing in Journal of Fluid Mechanics (based on the number of publications) are:

  • Detlef Lohse (136 papers) published 17 papers at the last edition the same number as at the previous edition,
  • John W. Miles (131 papers) absent at the last edition,
  • Andrew W. Woods (104 papers) published 5 papers at the last edition the same number as at the previous edition,
  • Herbert E. Huppert (102 papers) published 1 paper at the last edition,
  • R. A. Antonia (101 papers) absent at the last edition.

The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.

Only papers with recognized affiliations are considered

The top affiliations publishing in Journal of Fluid Mechanics (based on the number of publications) are:

  • University of Cambridge (1406 papers) published 36 papers at the last edition, 4 less than at the previous edition,
  • Imperial College London (721 papers) published 36 papers at the last edition, 7 more than at the previous edition,
  • Massachusetts Institute of Technology (688 papers) published 14 papers at the last edition, 2 more than at the previous edition,
  • California Institute of Technology (534 papers) published 21 papers at the last edition, 9 more than at the previous edition,
  • Stanford University (452 papers) published 15 papers at the last edition, 11 less than at the previous edition.

The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.

Publication chance based on affiliation

The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.

The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.

During the most recent 2021 edition, 33.55% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 19.80% were posted by at least one author from the top 10 institutions publishing in the journal. Another 10.02% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 16.99% of all publications and 53.18% were from other institutions.

Returning Authors Index

A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.

The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.

Returning Institution Index

The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.

The experience to innovation index

Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).

The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:

  • Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
  • Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
  • Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
  • Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
  • Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).

The chart below illustrates experience levels of first authors in cases of publications with multiple authors.

Top Publications

  • The flow physics of COVID-19

    Rajat Mittal;Rui Ni;Jung Hee Seo

    (2020)
    830 Citations
  • Effects of ventilation on the indoor spread of COVID-19

    Rajesh K. Bhagat;M. S. Davies Wykes;Stuart B. Dalziel;P. F. Linden

    (2020)
    434 Citations
  • Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows

    Kai Fukami;Koji Fukagata;Kunihiko Taira

    (2021)
    212 Citations
  • Flow over an espresso cup: Inferring 3D velocity and pressure fields from tomographic background oriented schlieren videos via physics-informed neural networks

    Shengze Cai;Zhicheng Wang;Frederik Fuest;Young-Jin Jeon

    (2021)
    207 Citations
  • Unsupervised deep learning for super-resolution reconstruction of turbulence

    Hyojin Kim;Junhyuk Kim;Sungjin Won;Changhoon Lee

    (2021)
    192 Citations
  • Convolutional-network models to predict wall-bounded turbulence from wall quantities

    Luca Guastoni;Alejandro Güemes;Andrea Ianiro;Stefano Discetti

    (2021)
    185 Citations
  • Dynamics and control of premixed combustion systems based on flame transfer and describing functions

    Thierry Schuller;Thierry Poinsot;Sébastien Candel

    (2020)
    152 Citations
  • Direct numerical simulation of hypersonic turbulent boundary layers: effect of spatial evolution and Reynolds number

    Unknown

    (2022)
    152 Citations
  • Prediction of turbulent heat transfer using convolutional neural networks

    Junhyuk Kim;Changhoon Lee

    (2020)
    141 Citations

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Best Scientists Contributing to This Journal

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