Главная Книги е-Книги Журналы Справочники & Сборники Авторы, Редакторы, Рецензенты А - Я индекс Награды
Journal of Flow Visualization and Image Processing
SJR: 0.11 SNIP: 0.312 CiteScore™: 0.1

ISSN Print: 1065-3090
ISSN Online: 1940-4336

Том 26, 2019

Том 25, 2018

Том 24, 2017

Том 23, 2016

Том 22, 2015

Том 21, 2014

Том 20, 2013

Том 19, 2012

Том 18, 2011

Том 17, 2010

Том 16, 2009

Том 15, 2008

Том 14, 2007

Том 13, 2006

Том 12, 2005

Том 11, 2004

Том 10, 2003

Том 9, 2002

Том 8, 2001

Том 7, 2000

Том 6, 1999

Том 5, 1998

Том 4, 1997

Том 3, 1996

Том 2, 1995

Том 1, 1993

Journal of Flow Visualization and Image Processing

Editor-in-Chief: Krishnamurthy Muralidhar

О журнале

The Journal of Flow Visualization and Image Processing is a quarterly refereed research journal that publishes original papers to disseminate and exchange knowledge and information on the principles and applications of flow visualization techniques and related image processing algorithms.
 Flow visualization and quantification have emerged as powerful tools in velocity, pressure, temperature and species concentration measurements, combustion diagnostics, and process monitoring related to physical, biomedical, and engineering sciences. Measurements were initially based on lasers but have expanded to include a wider electromagnetic spectrum. Numerical simulation is a second source of data amenable to image analysis. Direct visualization in the form of high speed, high resolution imaging supplements optical measurements. A combination of flow visualization and image processing holds promise to breach the holy grail of extracting instantaneous three dimensional data in transport phenomena.
 Optical methods can be enlarged to cover a wide range of measurements, first by factoring in the applicable physical laws and next, by including the principle of image formation itself. These steps help in utilizing incomplete data and imperfect visualization for reconstructing a complete scenario of the transport process.
 Many applications involve gas-liquid interfaces that move in time and over a solid surface. Locating such boundaries from image sequences is an important step that can provide considerable insight. Flow visualization data can also be the starting point of inversion algorithms for retrieval of material properties and boundary conditions such as wall heat fluxes and shear stresses.
 Images acquired in an experiment or from simulation contain a wealth of data and on multiple scales. Useful information can then be extracted using statistical and image processing tools. New developments such as tomographic reconstruction, digital correlation technique, and data mining algorithms, including AI, are quite appropriate for interpreting flow visualization data.
 The journal will promote academic and industrial advancement and improvement of flow imaging techniques internationally. It seeks to convey practical information in this field covering all areas in science, technology, and medicine for engineers, scientists, and researchers in industry, academia, and government.