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Journal of Flow Visualization and Image Processing
SJR: 0.161 SNIP: 0.312 CiteScore™: 0.1

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

Volumen 27, 2020

Volumen 26, 2019

Volumen 25, 2018

Volumen 24, 2017

Volumen 23, 2016

Volumen 22, 2015

Volumen 21, 2014

Volumen 20, 2013

Volumen 19, 2012

Volumen 18, 2011

Volumen 17, 2010

Volumen 16, 2009

Volumen 15, 2008

Volumen 14, 2007

Volumen 13, 2006

Volumen 12, 2005

Volumen 11, 2004

Volumen 10, 2003

Volumen 9, 2002

Volumen 8, 2001

Volumen 7, 2000

Volumen 6, 1999

Volumen 5, 1998

Volumen 4, 1997

Volumen 3, 1996

Volumen 2, 1995

Volumen 1, 1993

Journal of Flow Visualization and Image Processing

Editor-in-Chief: Krishnamurthy Muralidhar

Ziele und Zweck

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.