Using artificial neural networks to predict the performance of a liquid metal reflux solar receiver: Preliminary results

PDF Version Also Available for Download.

Description

Three and four-layer backpropagation artificial neural networks have been used to predict the power output of a liquid metal reflux solar receiver. The networks were trained using on-sun test data recorded at Sandia National Laboratories in Albuquerque, New Mexico. The preliminary results presented in this paper are a comparison of how different size networks train on this particular data. The results give encouragement that it will be possible to predict output power of a liquid metal receiver under a variety of operating conditions using artificial neural networks.

Physical Description

10 p.

Creation Information

Fowler, M.M. December 31, 1995.

Context

This article is part of the collection entitled: Office of Scientific & Technical Information Technical Reports and was provided by UNT Libraries Government Documents Department to Digital Library, a digital repository hosted by the UNT Libraries. More information about this article can be viewed below.

Who

People and organizations associated with either the creation of this article or its content.

Author

  • Fowler, M.M. North Carolina Agricultural and Technical State Univ., Greensboro, NC (United States). Dept. of Mechanical Engineering

Sponsor

Publisher

  • Sandia National Laboratories
    Publisher Info: Sandia National Labs., Albuquerque, NM (United States)
    Place of Publication: Albuquerque, New Mexico

Provided By

UNT Libraries Government Documents Department

Serving as both a federal and a state depository library, the UNT Libraries Government Documents Department maintains millions of items in a variety of formats. The department is a member of the FDLP Content Partnerships Program and an Affiliated Archive of the National Archives.

Contact Us

What

Descriptive information to help identify this article. Follow the links below to find similar items on the Digital Library.

Description

Three and four-layer backpropagation artificial neural networks have been used to predict the power output of a liquid metal reflux solar receiver. The networks were trained using on-sun test data recorded at Sandia National Laboratories in Albuquerque, New Mexico. The preliminary results presented in this paper are a comparison of how different size networks train on this particular data. The results give encouragement that it will be possible to predict output power of a liquid metal receiver under a variety of operating conditions using artificial neural networks.

Physical Description

10 p.

Notes

OSTI as DE96003753

Source

  • Energy Week `96: American Society of Mechanical Engineers and American Petroleum Institute energy week conference and exhibition, Houston, TX (United States), 21 Jan - 2 Feb 1996

Language

Item Type

Identifier

Unique identifying numbers for this article in the Digital Library or other systems.

  • Other: DE96003753
  • Report No.: SAND--95-2789C
  • Report No.: CONF-960154--1
  • Grant Number: AC04-94AL85000
  • Office of Scientific & Technical Information Report Number: 170578
  • Archival Resource Key: ark:/67531/metadc620992

Collections

This article is part of the following collection of related materials.

Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

Office of Scientific and Technical Information (OSTI) is the Department of Energy (DOE) office that collects, preserves, and disseminates DOE-sponsored research and development (R&D) results that are the outcomes of R&D projects or other funded activities at DOE labs and facilities nationwide and grantees at universities and other institutions.

What responsibilities do I have when using this article?

When

Dates and time periods associated with this article.

Creation Date

  • December 31, 1995

Added to The UNT Digital Library

  • June 16, 2015, 7:43 a.m.

Description Last Updated

  • April 14, 2016, 2:08 p.m.

Usage Statistics

When was this article last used?

Yesterday: 0
Past 30 days: 0
Total Uses: 2

Interact With This Article

Here are some suggestions for what to do next.

Start Reading

PDF Version Also Available for Download.

International Image Interoperability Framework

IIF Logo

We support the IIIF Presentation API

Fowler, M.M. Using artificial neural networks to predict the performance of a liquid metal reflux solar receiver: Preliminary results, article, December 31, 1995; Albuquerque, New Mexico. (digital.library.unt.edu/ark:/67531/metadc620992/: accessed October 17, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.