 | | | | Application of neural networks trained with multizone models for fast detection of contaminant source position in buildings.(Technical report): An article from: ASHRAE Transactions |  | Authors: Vladimir Vukovic, Jelena Srebric
Publisher: Thomson Gale
Category: Book
Buy New: $9.95
Format: Html
Media: Digital
Pages: 21
ASIN: B00122SHK0
Publication Date: July 1, 2007
Release Date: December 28, 2007
Availability: Available for download now
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This digital document is an article from ASHRAE Transactions, published by Thomson Gale on July 1, 2007. The length of the article is 6028 words. The page length shown above is based on a typical 300-word page. The article is delivered in HTML format and is available in your Amazon.com Digital Locker immediately after purchase. You can view it with any web browser.
From the author: This study presents an overview of the development of a novel approach for real-time detection of contaminant source locations in buildings. The approach uses the ability of parallel computational architectures, neural networks, to perform nonlinear mapping of indoor contaminant concentration patterns to source locations. Such an approach is inverse compared to the traditionally used techniques for predicting contaminant dispersion from the known source position. The existing realtime prediction methods for contaminant dispersion include (1) utilization of powerful supercomputers and computational fluid dynamics, (2) application of statistical techniques to precalculated databases of possible contaminant release scenarios, (3) genetic algorithm classification in combination with multizone or outdoor dispersion models, and (4) measuring techniques, such as computed tomography. This study presents advancements from our initial investigation to include more complex applications and neural network generalization. The initial investigation successfully detected a contaminant source within nine indoor zones based on the contaminant concentrations computed by multizone models. This success initiated a generalization of the neural network procedure into the Locator of Contaminant Sources algorithm. Furthermore, the applicability of the developed tool was extended to include a sensor optimization algorithm. Prospective practical applications of the developed algorithms include detection of chemical, biological, or radiological contaminant sources during incidental events, as well as determination of a minimum number and allocation of sensors required for such detection.
Citation Details
Title: Application of neural networks trained with multizone models for fast detection of contaminant source position in buildings.(Technical report)
Author: Vladimir Vukovic
Publication: ASHRAE Transactions (Magazine/Journal)
Date: July 1, 2007
Publisher: Thomson Gale
Volume: 113 Issue: 2 Page: 154(9)
Article Type: Technical report
Distributed by Thomson Gale
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