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Multiscale Structural Optimization f...

Snyder, Isabella.

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Multiscale Structural Optimization for Applications in Thermal Stability and Actuation.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Multiscale Structural Optimization for Applications in Thermal Stability and Actuation./
作者:	Snyder, Isabella.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
面頁冊數:	70 p.
附註:	Source: Masters Abstracts International, Volume: 86-01.
Contained By:	Masters Abstracts International86-01.
標題:	Mechanical engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31333454
ISBN:	9798383206287

Multiscale Structural Optimization for Applications in Thermal Stability and Actuation.
Snyder, Isabella.

Multiscale Structural Optimization for Applications in Thermal Stability and Actuation. - Ann Arbor : ProQuest Dissertations & Theses, 2024 - 70 p.

Source: Masters Abstracts International, Volume: 86-01.

Thesis (M.S.)--Drexel University, 2024.

This thesis outlines a novel multiscale optimization framework for designing multifunctional structures that must withstand mechanical and thermal loads. The objective is to exploit the unique potential of spatially varying microstructures for enhanced thermal stability and actuation capabilities. The methodology hinges on a three-phase material design within the microstructure, composed of materials with high and low coefficients of thermal expansion (CTE) and void material, to achieve a spectrum of CTE from negative to positive. By optimizing the layout of these microstructures within a macrostructure, it is possible to induce desired thermomechanical behaviors, accommodating extreme conditions and precise deformations.To address the computational challenges inherent in designing complex multiscale structures, the research utilizes a deep neural network (DNN) surrogate model for numerical homogenization, significantly reducing the computational cost. The surrogate model predicts effective material properties, which are confirmed against traditional finite element analysis. The structure is optimized using an objective function and a constraint function. This paper analyzes how the use of compliance as the objective function and displacement as a component of the constraint function affects the results. Presented examples validate the optimization approach for thermal stability, where the target displacement is zero, and actuation, where the target displacement is non-zero.

ISBN: 9798383206287Subjects--Topical Terms:

649730
Mechanical engineering.
Subjects--Index Terms:

Actuation

Multiscale Structural Optimization for Applications in Thermal Stability and Actuation.
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300 $a 70 p.
500 $a Source: Masters Abstracts International, Volume: 86-01.
500 $a Advisor: Najafi, Ahmad Raeisi.
502 $a Thesis (M.S.)--Drexel University, 2024.
520 $a This thesis outlines a novel multiscale optimization framework for designing multifunctional structures that must withstand mechanical and thermal loads. The objective is to exploit the unique potential of spatially varying microstructures for enhanced thermal stability and actuation capabilities. The methodology hinges on a three-phase material design within the microstructure, composed of materials with high and low coefficients of thermal expansion (CTE) and void material, to achieve a spectrum of CTE from negative to positive. By optimizing the layout of these microstructures within a macrostructure, it is possible to induce desired thermomechanical behaviors, accommodating extreme conditions and precise deformations.To address the computational challenges inherent in designing complex multiscale structures, the research utilizes a deep neural network (DNN) surrogate model for numerical homogenization, significantly reducing the computational cost. The surrogate model predicts effective material properties, which are confirmed against traditional finite element analysis. The structure is optimized using an objective function and a constraint function. This paper analyzes how the use of compliance as the objective function and displacement as a component of the constraint function affects the results. Presented examples validate the optimization approach for thermal stability, where the target displacement is zero, and actuation, where the target displacement is non-zero.
590 $a School code: 0065.
650 4 $a Mechanical engineering. $3 649730
650 4 $a Mechanics. $3 525881
650 4 $a Computer engineering. $3 621879
650 4 $a Thermodynamics. $3 517304
653 $a Actuation
653 $a Computational cost
653 $a Deep neural network
653 $a Multiscale structures
653 $a Optimization
653 $a Thermal stability
690 $a 0548
690 $a 0346
690 $a 0464
690 $a 0348
710 2 $a Drexel University. $b Mechanical Engineering and Mechanics (College of Engineering). $3 3168962
773 0 $t Masters Abstracts International $g 86-01.
790 $a 0065
791 $a M.S.
792 $a 2024
793 $a English
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