東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Estimating extreme responses using a...

Alford, Laura Kay.

FindBook

Google Book

Amazon

博客來

Estimating extreme responses using a non-uniform phase distribution.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Estimating extreme responses using a non-uniform phase distribution./
作者:	Alford, Laura Kay.
面頁冊數:	201 p.
附註:	Adviser: Armin W. Troesch.
Contained By:	Dissertation Abstracts International69-03B.
標題:	Engineering, Marine and Ocean. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3304913
ISBN:	9780549511618

Estimating extreme responses using a non-uniform phase distribution.
Alford, Laura Kay.

Estimating extreme responses using a non-uniform phase distribution. - 201 p.

Adviser: Armin W. Troesch.

Thesis (Ph.D.)--University of Michigan, 2008.

Random processes are often modeled as a summation of a finite number of sinusoidal components. Various individual time series are created through the randomly chosen phase angles associated with each component. A specific event of the random process is characterized by the time at which the event happens and the chosen set of phase angles. Together, the time and the phase angle constitute the phase of each component. If many samples of a given event are cataloged, a histogram of the phases can be generated to produce a phase probability density function (PDF) that relates the event to the spectrum of the random process and the number of components used in the simulation.

ISBN: 9780549511618Subjects--Topical Terms:

1019064
Engineering, Marine and Ocean.

Estimating extreme responses using a non-uniform phase distribution.
LDR:02687nam 2200277 a 45 001 953858
005 20110621
008 110622s2008 ||||||||||||||||| ||eng d
020 $a 9780549511618
035 $a (UMI)AAI3304913
035 $a AAI3304913
040 $a UMI $c UMI
100 1 $a Alford, Laura Kay. $3 1277332
245 1 0 $a Estimating extreme responses using a non-uniform phase distribution.
300 $a 201 p.
500 $a Adviser: Armin W. Troesch.
500 $a Source: Dissertation Abstracts International, Volume: 69-03, Section: B, page: 1877.
502 $a Thesis (Ph.D.)--University of Michigan, 2008.
520 $a Random processes are often modeled as a summation of a finite number of sinusoidal components. Various individual time series are created through the randomly chosen phase angles associated with each component. A specific event of the random process is characterized by the time at which the event happens and the chosen set of phase angles. Together, the time and the phase angle constitute the phase of each component. If many samples of a given event are cataloged, a histogram of the phases can be generated to produce a phase probability density function (PDF) that relates the event to the spectrum of the random process and the number of components used in the simulation.
520 $a Simulation of moderately rare events showed the component phase PDFs to be non-uniform and non-identically distributed. These PDFs were modeled using a single parameter, modified Gaussian distribution and used to generate design time series with a specific event at a specific time. To eliminate the need for Monte Carlo simulation, the single parameter of the phase distribution of each component was determined by comparing the PDF of the rare event as calculated using the non-uniform phase distributions to the PDF of the rare event as calculated using Extreme Value Theory. This approach is convenient and efficient as the phase parameters do not have to be estimated via Monte Carlo simulation; it is useful as the parameters can be generated for extremely rare events as easily as moderately rare events. In addition, the comparison to Extreme Value Theory helps to quantify the risk associated with rare events. An example application involving the springing of a Great Lakes bulk carrier shows how the method of nonuniform phases correctly predicts the build up of waves over several periods that produces a large bending moment.
590 $a School code: 0127.
650 4 $a Engineering, Marine and Ocean. $3 1019064
690 $a 0547
710 2 $a University of Michigan. $3 777416
773 0 $t Dissertation Abstracts International $g 69-03B.
790 $a 0127
790 1 0 $a Troesch, Armin W., $e advisor
791 $a Ph.D.
792 $a 2008
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3304913