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Title: Method and system for analyzing gas leak based on machine learning

Abstract

Embodiments of the present invention provide a system for estimating a location of a gas leak, based on machine learning from forward gas concentration data provided by an analog or scale model including a gas source. The system improves significantly over previous systems by providing high quality, physically accurate forward modeling data inexpensively. During operation, the system configures an aerosol source at a first location to emit a gaseous aerosol. The system then configures a laser source to illuminate the aerosol with a laser sheet. The system may then obtain an image of a reflection of the laser sheet from the aerosol. The system may then analyze the image to quantify a three-dimensional concentration distribution of the aerosol. The system may then estimate, based on solving an inverse problem and an observed second gas concentration, a second location of a second gas source.

Inventors:
; ; ;
Issue Date:
Research Org.:
Palo Alto Research Center Incorporated, Palo Alto, CA (United States)
Sponsoring Org.:
USDOE Advanced Research Projects Agency - Energy (ARPA-E)
OSTI Identifier:
1464218
Patent Number(s):
10,031,040
Application Number:
15/472,018
Assignee:
Palo Alto Research Center Incorporated (Palo Alto, CA)
DOE Contract Number:  
AR0000542
Resource Type:
Patent
Resource Relation:
Patent File Date: 2017 Mar 28
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; 03 NATURAL GAS

Citation Formats

Smith, Clinton J., Saha, Bhaskar, Beck, Victor A., and Schwartz, David E. Method and system for analyzing gas leak based on machine learning. United States: N. p., 2018. Web.
Smith, Clinton J., Saha, Bhaskar, Beck, Victor A., & Schwartz, David E. Method and system for analyzing gas leak based on machine learning. United States.
Smith, Clinton J., Saha, Bhaskar, Beck, Victor A., and Schwartz, David E. Tue . "Method and system for analyzing gas leak based on machine learning". United States. https://www.osti.gov/servlets/purl/1464218.
@article{osti_1464218,
title = {Method and system for analyzing gas leak based on machine learning},
author = {Smith, Clinton J. and Saha, Bhaskar and Beck, Victor A. and Schwartz, David E.},
abstractNote = {Embodiments of the present invention provide a system for estimating a location of a gas leak, based on machine learning from forward gas concentration data provided by an analog or scale model including a gas source. The system improves significantly over previous systems by providing high quality, physically accurate forward modeling data inexpensively. During operation, the system configures an aerosol source at a first location to emit a gaseous aerosol. The system then configures a laser source to illuminate the aerosol with a laser sheet. The system may then obtain an image of a reflection of the laser sheet from the aerosol. The system may then analyze the image to quantify a three-dimensional concentration distribution of the aerosol. The system may then estimate, based on solving an inverse problem and an observed second gas concentration, a second location of a second gas source.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {7}
}

Patent:

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