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Modeling and Control Design for One ...

Wilson, Kellie.

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Modeling and Control Design for One Stage Axial Flow Compressor.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Modeling and Control Design for One Stage Axial Flow Compressor./
Author:	Wilson, Kellie.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
Description:	179 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-10, Section: B.
Contained By:	Dissertations Abstracts International85-10B.
Subject:	Mechanical engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31237968
ISBN:	9798382317557

Modeling and Control Design for One Stage Axial Flow Compressor.
Wilson, Kellie.

Modeling and Control Design for One Stage Axial Flow Compressor. - Ann Arbor : ProQuest Dissertations & Theses, 2024 - 179 p.

Source: Dissertations Abstracts International, Volume: 85-10, Section: B.

Thesis (Ph.D.)--Idaho State University, 2024.

Studying the characteristics of complex nonlinear systems and designing control strategies necessitates the development of computationally efficient methods. In particular, compressor systems benefit from accurate models that capture the nonlinear dynamics. Many numerical approaches have been created using the Moore-Greitzer (MG) model. A model of a small experimental compressor rig is developed using the Toolbox for the Modeling and Analysis of Thermodynamic Systems (T-MATS). To verify the accuracy and capabilities of this toolbox, experimental test data and data generated through simulation using the T-MATS model is compared. The error is minor between the experimental data and the simulation data, which indicates utility of such simulation models for use in further research. In this dissertation, an overview of neural networks is given with a focus on Long-Short Term Memory (LSTM) networks for dynamic systems. A simple test system is presented for verifying the LSTM approach for single input single output (SISO) systems. The proposed LSTM approach is demonstrated for the multiple input multiple output (MIMO) axial compressor model. This dissertation investigates control systems designed using MG models. Linearization of the MG model at various locations is executed to create segments for controller design. At each of these linearized sections, a controller is designed and optimized using a Genetic Algorithm (GA). By combining these, a controller network is produced. To investigate the performance, this network is implemented with the T-MATS model and the LSTM model. It is shown that the controller network performs well with good results on both experimentally based models. The results show that a controller network can be designed on a mathematical model of an axial compressor that will perform well when applied to experimentally based models.

ISBN: 9798382317557Subjects--Topical Terms:

649730
Mechanical engineering.
Subjects--Index Terms:

Compressors

Modeling and Control Design for One Stage Axial Flow Compressor.
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245 1 0 $a Modeling and Control Design for One Stage Axial Flow Compressor.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 179 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-10, Section: B.
500 $a Advisor: Schoen, Marco P.
502 $a Thesis (Ph.D.)--Idaho State University, 2024.
520 $a Studying the characteristics of complex nonlinear systems and designing control strategies necessitates the development of computationally efficient methods. In particular, compressor systems benefit from accurate models that capture the nonlinear dynamics. Many numerical approaches have been created using the Moore-Greitzer (MG) model. A model of a small experimental compressor rig is developed using the Toolbox for the Modeling and Analysis of Thermodynamic Systems (T-MATS). To verify the accuracy and capabilities of this toolbox, experimental test data and data generated through simulation using the T-MATS model is compared. The error is minor between the experimental data and the simulation data, which indicates utility of such simulation models for use in further research. In this dissertation, an overview of neural networks is given with a focus on Long-Short Term Memory (LSTM) networks for dynamic systems. A simple test system is presented for verifying the LSTM approach for single input single output (SISO) systems. The proposed LSTM approach is demonstrated for the multiple input multiple output (MIMO) axial compressor model. This dissertation investigates control systems designed using MG models. Linearization of the MG model at various locations is executed to create segments for controller design. At each of these linearized sections, a controller is designed and optimized using a Genetic Algorithm (GA). By combining these, a controller network is produced. To investigate the performance, this network is implemented with the T-MATS model and the LSTM model. It is shown that the controller network performs well with good results on both experimentally based models. The results show that a controller network can be designed on a mathematical model of an axial compressor that will perform well when applied to experimentally based models.
590 $a School code: 0320.
650 4 $a Mechanical engineering. $3 649730
650 4 $a Aerospace engineering. $3 1002622
650 4 $a Computer engineering. $3 621879
650 4 $a Applied mathematics. $3 2122814
650 4 $a Thermodynamics. $3 517304
653 $a Compressors
653 $a Controllers
653 $a Long-Short Term Memory
653 $a Moore-Greitzer model
653 $a Neural network
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690 $a 0538
690 $a 0464
690 $a 0348
690 $a 0364
710 2 $a Idaho State University. $b College of Science and Engineering - Mech Eng. $3 3772308
773 0 $t Dissertations Abstracts International $g 85-10B.
790 $a 0320
791 $a Ph.D.
792 $a 2024
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31237968