Доступ предоставлен для: Guest
Портал Begell Электронная Бибилиотека e-Книги Журналы Справочники и Сборники статей Коллекции
Journal of Flow Visualization and Image Processing
SJR: 0.161 SNIP: 0.312 CiteScore™: 0.1

ISSN Печать: 1065-3090
ISSN Онлайн: 1940-4336

Том 27, 2020 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

DOI: 10.1615/JFlowVisImageProc.v9.i1.30
27 pages


John C. Patterson
Centre for Wind, Waves and Water, School of Civil Engineering, The University of Sydney Darlington, NSW 2006, Australia
G. B. Brassington
College of Oceanic and Atmospheric Sciences, Oregon State University, 104 Ocean Admin. Bldg., Corvallis, OREGON 97331, USA
M. Lee
School of Engineering, James Cook University

Краткое описание

A new algorithm for constructing shadowgraph images from approximate density fields is presented with the primary motivation of performing accurate laboratory shadowgraph analysis. Available image construction algorithms are noisy and produce discontinuous errors even for small gradient density fields. Discontinuity errors are serious, being indistinguishable from real optical focusing which occurs frequently. The new algorithm completely eliminates these errors. Image improvements are demonstrated for realistic synthetic refractive index fields. Favorable comparisons of the new algorithm are also demonstrated with laboratory shadowgraph of natural convection flows in a cavity which feature large density gradients. A second motivation of the paper is to accurately analyze approximate shadowgraph images derived from a linearized analytical model for refraction. The linearized shadowgraph images are correlated with the artificial shadowgraph images of the new algorithm. Preliminary results indicate that quantitative information from shadowgraph images of larger gradient density fields could be obtained by iterating about the linear solution.