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流动显示和图像处理期刊
Editor-in-Chief: Krishnamurthy Muralidhar

ISSN 打印: 1065-3090

ISSN 在线: 1940-4336

SJR: 0.214 SNIP: 0.312 CiteScore™: 1.1

目标与范围

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.

Call for Papers: Visualization of Complex Flow Structures in Jets and Wakes


Special Issue of Journal of Flow Visualization and Image Processing
Submission Deadline: August, 31, 2021


Guest Editors:

Arun K. Saha, IIT Kanpur, India
K. Muralidhar, IIT Kanpur, India

Reviews expected in two months after submission. Paper length (recommended maximum): 5000 words text; 8 full page figures.

Jets and bluff-body wakes are flow phenomena seen in several important applications. The details of the accompanying flow fields determine the performance of engineering equipment along with rates of energy and species transport in nature. Flow distribution in jets and wakes of bluff objects are quite complex and are known to carry built-in structures. These structures provide understanding of various instability mechanisms and transitions experienced by a simple jet of water from a tap to the complex exhaust of gas turbines. With the development of high-speed imaging systems as well as efficient CFD simulation tools, it is now possible to record details of flow structures and their evolution in diverse contexts. New generation visualization algorithms vividly represent experimental as well as numerical data. The applicable flow physics underlying complex flow structures emerges naturally from such analysis, contributing to fundamental understanding of the subject. The special issue is intended to provide the readers with new developments in the exciting field of flow visualization in jets and wakes of bluff bodies.
Explaining new flow phenomena, both in two- and three-dimensions, particularly in the context of flow structures formed within jets and bluff-body wakes constitutes the theme of this call. The special issue will assemble reviews and original research articles emerging from cross-disciplinary advances in experimental and computational studies that imaginatively reveal flow structures in jets and wakes.

Submit papers via the Begell House submission system. Log in or register here.

Call for Papers: Numerical Flow Visualization: From the Perspectives of Computer Graphics and Machine Learning


Special Issue of Journal of Flow Visualization and Image Processing Submission Deadline: September 30, 2021


Guest Editors:

Zhanping Liu, Old Dominion University, USA
K. Muralidhar, IIT Kanpur, India

Reviews expected within two months after submission. Paper length (recommended maximum): 5,000 words in text; 4-6 figures

The Journal of Flow Visualization and Image Processing has a well-established readership base, while authors see it primarily as a journal of flow imaging from the measurement viewpoint. Image formation from experiments and simulations is a fundamental component of the journal. Flow data arising from numerical simulation encodes a wealth of information that needs to be extracted and displayed so as to gain insight into the pattern. In this respect, it is an increasingly important task to process, represent, visualize, and analyze flow data. This paradigm maps an array of discrete data values either to geometric elements followed by graphics rendering or directly to pixels. Coupled with this visualization pipeline is an emerging pre-processing stage of great promise that unleashes the power of artificial intelligence, particularly machine learning, to perform feature detection and pattern recognition. In light of the importance of computer graphics and machine learning to data analysis, we bring in numerical flow visualization to serve our core purpose of extracting and exploiting information towards scientific discovery.

Numerical flow visualization is focused on velocity vector data (accompanied by scalar variables such as pressure and temperature) resulting from computational fluid dynamics (CFD) simulation of complex systems, processes, or phenomena, e.g., wind/air flows, ocean currents, combustion, electromagnetic fields, and blood flows. Also addressed are velocity vector data obtained from laboratory-scale experiments or imaging modalities, e.g., diffusion tensor imaging (DTI) of cardiovascular blood flows and neural fibers in the human body. As flow data range from planar to surface and further to volumetric domains, from steady to unsteady cases, from structured to unstructured grids, and from megabyte to terabyte and even to exabyte scales, these factors pose daunting challenges to representation, display, exploration, and analysis of complex structures. Algorithm research and system development revolving around these topics, among others, will contribute to advances in numerical flow visualization.

This special issue is an initiative for significantly expanding the scope of the journal to include computer graphics and machine learning so that a variety of numerical methodologies are utilized to achieve effective flow visualization. The editors welcome original submissions and review articles from researchers working in this exciting field.

Submit papers via the Begell House submission system. Log in or register here.
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