國立虎尾科技大學 |

Data driven low-bandwidth intelligent control of a jet engine combustor.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Data driven low-bandwidth intelligent control of a jet engine combustor./
Author:	Toner, Nathan L.
Description:	1 online resource (133 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 78-03(E), Section: B.
Contained By:	Dissertation Abstracts International78-03B(E).
Subject:	Artificial intelligence. -
Online resource:	click for full text (PQDT)
ISBN:	9781369293340

Data driven low-bandwidth intelligent control of a jet engine combustor.
Toner, Nathan L.

Data driven low-bandwidth intelligent control of a jet engine combustor. - 1 online resource (133 pages)

Source: Dissertation Abstracts International, Volume: 78-03(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

This thesis introduces a low-bandwidth control architecture for navigating the input space of an un-modeled combustor system between desired operating conditions while avoiding regions of instability and blow-out. An experimental procedure is discussed for identifying regions of instability and gathering sufficient data to build a data-driven model of the system's operating modes. Regions of instability and blow-out are identified experimentally and a data-driven operating point classifier is designed. This classifier acts as a map of the operating space of the combustor, indicating regions in which the flame is in a "good" or "bad" operating mode. A data-driven predictor is also designed that monitors the combustion process in real time and provides a prediction of what operating mode the flame will be in for the next measurement. A path planning algorithm is then discussed for planning an input trajectory from the current operating condition to the desired operating condition that avoids regions of instability or blow-out in the input space. An adaptive layer is incorporated into the path planning algorithm to ensure that the path planner can update its trajectory when new information about the operating space becomes available.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781369293340Subjects--Topical Terms:

559380
Artificial intelligence.
Index Terms--Genre/Form:

554714
Electronic books.

Data driven low-bandwidth intelligent control of a jet engine combustor.
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245 1 0 $a Data driven low-bandwidth intelligent control of a jet engine combustor.
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300 $a 1 online resource (133 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 78-03(E), Section: B.
500 $a Adviser: Galen B. King.
502 $a Thesis (Ph.D.) $c Purdue University $d 2016.
504 $a Includes bibliographical references
520 $a This thesis introduces a low-bandwidth control architecture for navigating the input space of an un-modeled combustor system between desired operating conditions while avoiding regions of instability and blow-out. An experimental procedure is discussed for identifying regions of instability and gathering sufficient data to build a data-driven model of the system's operating modes. Regions of instability and blow-out are identified experimentally and a data-driven operating point classifier is designed. This classifier acts as a map of the operating space of the combustor, indicating regions in which the flame is in a "good" or "bad" operating mode. A data-driven predictor is also designed that monitors the combustion process in real time and provides a prediction of what operating mode the flame will be in for the next measurement. A path planning algorithm is then discussed for planning an input trajectory from the current operating condition to the desired operating condition that avoids regions of instability or blow-out in the input space. An adaptive layer is incorporated into the path planning algorithm to ensure that the path planner can update its trajectory when new information about the operating space becomes available.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Artificial intelligence. $3 559380
650 4 $a Mechanical engineering. $3 557493
650 4 $a Engineering. $3 561152
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856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10179967 $z click for full text (PQDT)