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Adjoint-Based Uncertainty Quantifica...

Seifried, Jeffrey Edwin.

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Adjoint-Based Uncertainty Quantification with MCNP.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Adjoint-Based Uncertainty Quantification with MCNP./
作者:	Seifried, Jeffrey Edwin.
面頁冊數:	151 p.
附註:	Source: Dissertation Abstracts International, Volume: 75-07(E), Section: B.
Contained By:	Dissertation Abstracts International75-07B(E).
標題:	Nuclear engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3616252
ISBN:	9781303830754

Adjoint-Based Uncertainty Quantification with MCNP.
Seifried, Jeffrey Edwin.

Adjoint-Based Uncertainty Quantification with MCNP. - 151 p.

Source: Dissertation Abstracts International, Volume: 75-07(E), Section: B.

Thesis (Ph.D.)--University of California, Berkeley, 2011.

This work serves to quantify the instantaneous uncertainties in neutron transport simulations born from nuclear data and statistical counting uncertainties. Perturbation and adjoint theories are used to derive implicit sensitivity expressions. These expressions are transformed into forms that are convenient for construction with MCNP6, creating the ability to perform adjoint-based uncertainty quantification with MCNP6. These new tools are exercised on the depleted-uranium hybrid LIFE blanket, quantifying its sensitivities and uncertainties to important figures of merit. Overall, these uncertainty estimates are small (< 2%). Having quantified the sensitivities and uncertainties, physical understanding of the system is gained and some confidence in the simulation is acquired.

ISBN: 9781303830754Subjects--Topical Terms:

595435
Nuclear engineering.

Adjoint-Based Uncertainty Quantification with MCNP.
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520 $a This work serves to quantify the instantaneous uncertainties in neutron transport simulations born from nuclear data and statistical counting uncertainties. Perturbation and adjoint theories are used to derive implicit sensitivity expressions. These expressions are transformed into forms that are convenient for construction with MCNP6, creating the ability to perform adjoint-based uncertainty quantification with MCNP6. These new tools are exercised on the depleted-uranium hybrid LIFE blanket, quantifying its sensitivities and uncertainties to important figures of merit. Overall, these uncertainty estimates are small (< 2%). Having quantified the sensitivities and uncertainties, physical understanding of the system is gained and some confidence in the simulation is acquired.
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