Enhanced Line Integral Convolution with Flow Feature Detection

Okada, Arthur; Lane, David; Ames Research Center

doi:10.21949/1403673

Advanced Search

Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.

Search our Collections & Repository

Advanced Search
Custom Query

All these words:

For very narrow results

This exact word or phrase:

When looking for a specific result

Any of these words:

Best used for discovery & interchangable words

None of these words:

Recommended to be used in conjunction with other fields

Language:

Dates

Publication Date Range:

to

Document Data

Title:

Document Type:

Library

Collection:

Series:

People

Author:

Clear All

Query Builder

Query box

Clear All

For additional assistance using the Custom Query please check out our Help Page

i

Enhanced Line Integral Convolution with Flow Feature Detection

1995-01-01
By Okada, Arthur ; Lane, David

[PDF-22.95 KB]

English

Details You May Also Like

Details:

Creators:

Okada, Arthur ; Lane, David

Okada, Arthur ; Lane, David Less -
Corporate Creators:

Ames Research Center
Corporate Contributors:

United States. National Aeronautics and Space Administration
Subject/TRT Terms:

[+]

Analysis Aviation Flow Flow Distribution Flow Feature Detection Flow Fields Flow Patterns Flow Separation And Reattachments Flow Simulations Flow Visualization Image Enhancements LIC Method Unsteady Flow
DOI:

https://doi.org/10.21949/1403673
Resource Type:

Research Paper
Geographical Coverage:

United States
Corporate Publisher:

Ames Research Center
NTL Classification:

NTL-AVIATION-AVIATION
Abstract:

Prepared ca. 1995. The Line Integral Convolution (LIC) method, which blurs white noise textures along a vector field, is an effective way to visualize overall flow patterns in a 2D domain [Cabral & Leedom '93]. The method produces a flow texture image based on the input velocity field defined in the domain. Because of the nature of the algorithm, the texture image tends to be blurry. This some-times makes it difficult to identify boundaries where flow separation and reattachments occur. We present techniques to enhance LIC texture images and use colored texture images to highlight flow separation and reattachment boundaries. Our techniques have been applied to several flow fields defined in 3D curvilinear multi-block grids and scientists have found the results to be very useful.
Format:

PDF
Funding:

NAS 2-14303
Collection(s):

US Transportation Collection
Main Document Checksum:

[+]

urn:sha256:14b816470e77f9f7c6f0f4aceae2a13eff7480f91ae70d1ca90409b7c6141e3a
Download URL:

https://rosap.ntl.bts.gov/view/dot/12957/dot_12957_DS1.pdf
File Type:

No Additional Files

More +

Enhanced Line Integral Convolution with Flow Feature Detection

Details:

Supporting Files

You May Also Like