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Journals
Processes
Special Issues
Advances in Statistical Description of Scalar Turbulence
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Advances in Statistical Description of Scalar Turbulence

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 3296

Special Issue Editors

Dr. Stepan Nosek

E-Mail Website
Guest Editor
‎Institute of Thermomechanics, The Czech Academy of Sciences, v.v.i., Dolejškova 1402/5, 18200 Prague, Czech Republic
Interests: turbulence; pollutant dispersion; atmospheric boundary layer; air quality; experimental fluid mechanics
Prof. Dr. Zbynek Janour

E-Mail Website
Guest Editor
Institute of Thermomechanics, The Czech Academy of Sciences, v.v.i., Dolejškova 1402/5, 18200 Prague, Czech Republic
Interests: mathematical modeling; turbulence modeling; atmospheric physics

Special Issue Information

Dear Colleagues,

The transport of scalar in turbulent flow is essential in numerous engineering applications. However, the processes connected with scalar transport are still not well understood. The lack of understanding makes numerical models mostly fail to predict the scalar concentration levels in many practical situations. The main reason behind this lies in the paradigm that the underlying turbulent flow entirely drives scalar turbulence. Previous studies have demonstrated that passive scalar turbulence statistics might differ substantially from the statistics of turbulent flow. However, the number of studies dealing with a detailed statistical description of so-called scalar turbulence is scarce. Therefore, this Special Issue, “Advances in the Statistical Description of Scalar Turbulence”, aims to enhance the research in that area and exchange knowledge and ideas in this respect. The topics of the invited experimental, numerical, or theoretical studies should focus but are not limited on:

  • Scalar transport and dispersion in engineering and environmental flows;
  • Scalar transport over complex surfaces;
  • Link between the scalar and flow turbulence statistics;
  • The role of flow coherent structures on scalar transport;
  • Detection of scalar coherent structures;
  • New experimental and numerical methods in scalar turbulence.

Dr. Stepan Nosek
Prof. Dr. Zbynek Janour
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • turbulence
  • scalar transport
  • diffusion
  • numerical models
  • experimental fluid mechanics
  • coherent structures

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Published Papers (1 paper)

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Research

26 pages, 18091 KiB  
Article
Machine Learning to Estimate the Mass-Diffusion Distance from a Point Source under Turbulent Conditions
by Takahiro Ishigami, Motoki Irikura and Takahiro Tsukahara
Processes 2022, 10(5), 860; https://doi.org/10.3390/pr10050860 - 26 Apr 2022
Cited by 6 | Viewed by 2625
Abstract
Technologies that predict the sources of substances diffused in the atmosphere, ocean, and chemical plants are being researched in various fields. The flows transporting such substances are typically in turbulent states, and several problems including the nonlinearity of turbulence must be overcome to [...] Read more.
Technologies that predict the sources of substances diffused in the atmosphere, ocean, and chemical plants are being researched in various fields. The flows transporting such substances are typically in turbulent states, and several problems including the nonlinearity of turbulence must be overcome to enable accurate estimations of diffusion-source location from limited observation data. We studied the feasibility of machine learning, specifically convolutional neural networks (CNNs), to the problem of estimating the diffusion distance from a point source, based on two-dimensional, instantaneous information of diffused-substance distributions downstream of the source. The input image data for the learner are the concentration (or luminance of fluorescent dye) distributions affected by turbulent motions of the transport medium. In order to verify our approach, we employed experimental data of a fully developed turbulent channel flow with a dye nozzle, wherein we attempted to estimate the distances between the dye nozzle and downstream observation windows. The inference accuracy of four different CNN architectures were investigated, and some achieved an accuracy of more than 90%. We confirmed the independence of the inference accuracy on the anisotropy (or rotation) of the image. The trained CNN can recognize the turbulent characteristics for estimating the diffusion source distance without statistical processing. The learners have a strong dependency on the condition of learning images, such as window size and image noise, implying that learning images should be carefully handled for obtaining higher generalization performance. Full article
(This article belongs to the Special Issue Advances in Statistical Description of Scalar Turbulence)
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