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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Investigation of the physical characteristics of fluidized graphite moderators

Halliday, Samuel Lee, 1931- January 1962 (has links)
No description available.
2

Study of GaAs as a possible field assisted positron moderator

沈躍躍, Shan, Yueyue. January 1994 (has links)
published_or_final_version / Physics / Master / Master of Philosophy
3

Neural network calibration of moderator temperature coefficient measurements in pressurized water nuclear reactors /

Adams, Joseph T., January 1993 (has links)
Thesis (M.S.)--Virginia Polytechnic Institute and State University, 1993. / Vita. Abstract. Includes bibliographical references (leaves 78-80). Also available via the Internet.
4

Study of GaAs as a possible field assisted positron moderator /

Shan, Yueyue. January 1994 (has links)
Thesis (M. Phil.)--University of Hong Kong, 1994. / Includes bibliographical references.
5

Optimized accelerator based epithermal neutron beams for boron neutron capture therapy /

Kudchadker, Rajat, January 1996 (has links)
Thesis (Ph. D.)--University of Missouri-Columbia, 1996. / Typescript. Vita. Includes bibliographical references (leaves 146-151). Also available on the Internet.
6

Optimized accelerator based epithermal neutron beams for boron neutron capture therapy

Kudchadker, Rajat, January 1996 (has links)
Thesis (Ph. D.)--University of Missouri-Columbia, 1996. / Typescript. Vita. Includes bibliographical references (leaves 146-151). Also available on the Internet.
7

Neural network calibration of moderator temperature coefficient measurements in pressurized water nuclear reactors

Adams, Joseph T. 04 December 2009 (has links)
Neural networks have been shown to be capable of predicting the moderator temperature coefficient in a nuc1ear reactor by using the frequency response functions between the in-core neutron flux signal and the ex-core thermocouple signal as inputs. In this work, actual data from a nuc1ear reactor is used by neural networks to estimate the moderator temperature coefficient at different times during a fuel cycle. Along with the conventional method of training neural networks, a new method of training that better models the use of neural networks in predicting the moderator temperature coefficient is also successfully demonstrated. The results show that neural networks are effective at estimating the moderator temperature coefficient if the domain of prediction is within the training domain of the network. The advantage of using the autoregression method to create the frequency response patterns used as inputs to the neural network as opposed to frequency response functions calculated by the Fourier transform method is also shown. / Master of Science

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