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Design Optimization Methods for Complex Electromechanical Systems : = Addressing User Preferences and Uncertainties.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Design Optimization Methods for Complex Electromechanical Systems :/
其他題名:	Addressing User Preferences and Uncertainties.
作者:	Malinga, Bongani.
面頁冊數:	1 online resource (126 pages)
附註:	Source: Dissertation Abstracts International, Volume: 78-08(E), Section: B.
標題:	Mechanical engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9781369638677

Design Optimization Methods for Complex Electromechanical Systems : = Addressing User Preferences and Uncertainties.
Malinga, Bongani.

Design Optimization Methods for Complex Electromechanical Systems :Addressing User Preferences and Uncertainties. - 1 online resource (126 pages)

Source: Dissertation Abstracts International, Volume: 78-08(E), Section: B.

Thesis (Ph.D.)--North Carolina State University, 2016.

Includes bibliographical references

The accelerated development of electromechanical technologies brings with it increased design complexity: system designs are required to have more functionality, higher performance, better quality and better reliability for less cost with less development time. These demands have become so intense that traditional design approaches are no longer sufficient; new design paradigms that reduce or eliminate iterative refinements and revisions are needed. System level requirements have to be clearly understood, and customer or end-user expectations have to be aggregated so that the marketplace can influence the design from an early stage.

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

Mode of access: World Wide Web

ISBN: 9781369638677Subjects--Topical Terms:

557493
Mechanical engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Design Optimization Methods for Complex Electromechanical Systems : = Addressing User Preferences and Uncertainties.
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500 $a Source: Dissertation Abstracts International, Volume: 78-08(E), Section: B.
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502 $a Thesis (Ph.D.)--North Carolina State University, 2016.
504 $a Includes bibliographical references
520 $a The accelerated development of electromechanical technologies brings with it increased design complexity: system designs are required to have more functionality, higher performance, better quality and better reliability for less cost with less development time. These demands have become so intense that traditional design approaches are no longer sufficient; new design paradigms that reduce or eliminate iterative refinements and revisions are needed. System level requirements have to be clearly understood, and customer or end-user expectations have to be aggregated so that the marketplace can influence the design from an early stage.
520 $a Technological advances frequently increase design complexity and introduce uncertainty into the design process: parametric uncertainties, unmodeled dynamics, variations in manufacturing tolerances, changing operating conditions, sensing errors and disturbances. Understanding and optimizing the behavior of these complex electromechanical systems is further complicated by the nonlinear, sometimes hysteretic, constitutive relationships between system variables.
520 $a This dissertation presents application-oriented methodologies that can effectively address optimization challenges and uncertainties inherent in the design and control of electromechanical systems. Additionally, methods for multi-attribute decision-making that enable the incorporation of user preferences in the design process are detailed. These methods are developed and demonstrated in the context of three specific applications.
520 $a The first application involves the development of a novel design optimization strategy for a prescribed vibration system (PVS) used to mechanically filter solids from fluids in oil and gas drilling operations. A dynamic model of the PVS is developed, and the effects of disturbance torques are detailed. This model is used to predict the effects of design parameters on system performance and efficiency, as quantified by system attributes. Conjoint value analysis (CVA), a statistical technique commonly used in marketing science, is utilized to incorporate designer preferences. This approach effectively quantifies and optimizes preference-based trade-offs in the design process. The effects of designer preferences on system performance and efficiency are simulated. This novel optimization strategy yields improvements in all system attributes across all simulated vibration profiles, and is applicable to other industrial electromechanical systems.
520 $a The influence of designer preferences is investigated by comparing PVS design alternatives that result from different preference rankings. Monte Carlo-based uncertainty and sensitivity studies are performed to provide additional information on the candidate designs. By understanding how small changes in the values of optimized parameters influence the system attributes, sensitivity analysis and uncertainty analyses can be used as design robustness measures. The optimal PVS design is therefore not based exclusively on the performance objectives, but also on the resulting system robustness, which is valuable considering manufacturing variations and tolerance stacks.
520 $a Next, the synthesis of an L1 adaptive controller for a shape memory alloy (SMA) actuated flexible beam is detailed. The controller manipulates applied voltage, which alters SMA tendon temperature, to track reference bending angles. Simulated and experimental results show that the L1 adaptive controller provides precise tracking of the reference trajectories and effectively compensates for the nonlinear hysteretic relationship between SMA Joule heating and bending angle without explicitly modeling these characteristics. A simulation model whose results closely resemble the experimental performance results is presented. As a first step towards the development of L1 adaptive control implementation guidelines, a complete description of the L 1 control parameters and their correlation to tracking performance is presented.
520 $a Despite the breadth of the applications considered in this dissertation, the methodologies are broadly applicable to the control and design optimization of electromechanical systems in general.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Mechanical engineering. $3 557493
655 7 $a Electronic books. $2 local $3 554714
690 $a 0548
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a North Carolina State University. $3 845424
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10585395 $z click for full text (PQDT)