| NSF Org: |
CMMI Division of Civil, Mechanical, and Manufacturing Innovation |
| Recipient: |
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| Initial Amendment Date: | July 11, 2016 |
| Latest Amendment Date: | July 11, 2016 |
| Award Number: | 1635167 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Kathryn Jablokow
kjabloko@nsf.gov (703)292-7933 CMMI Division of Civil, Mechanical, and Manufacturing Innovation ENG Directorate for Engineering |
| Start Date: | August 15, 2016 |
| End Date: | December 31, 2019 (Estimated) |
| Total Intended Award Amount: | $261,067.00 |
| Total Awarded Amount to Date: | $261,067.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
261 FOREST DR STE 3000 STATESBORO GA US 30458-6724 (912)478-5465 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
Engineering Building Statesboro GA US 30458-8005 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | ESD-Eng & Systems Design |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.041 |
ABSTRACT
Topology optimization is a computational design framework to idealize materials distribution of complex engineering systems. Uncertainties, unavoidable in manufacturing process and operating environments, often plague those engineering systems, thus need be taken into account during the design process. Conventional deterministic design approaches typically lead to inefficient and overly conservative designs that overcompensate for uncertainties, or unknowingly risky designs due to the inherent uncertainties. This award supports fundamental research on topology optimization of complex engineering structures in the presence of uncertainty. Specifically, it will develop novel methods to determine the ideal material distribution of complex engineering systems with low probabilities of failure corresponding to some critical failure mechanisms. The methods and associated numerical tools will be applicable to a broad, multidisciplinary optimization methodology. The findings will promote growth in additive manufacturing, especially 3D-printing, which is able to manufacture products with complex topology and thus demands efficient topology design methods to bring out its full potential. Other engineering applications include durable design for energy harvest devices, fatigue-resistant design for civil and aerospace applications, and reliable design for green energy industry. The education impact consists of attracting, engaging, and training K-12 and undergraduate students through extensive dissemination and outreach programs.
Technically, this research project aims to create new theoretical foundations and numerical algorithms for large-scale, robust topology optimization (RTO) and reliability-based topology optimization (RBTO) of complex engineering systems. Innovations include: (1) a new adaptive-sparse polynomial dimensional decomposition method designed for statistical moments and reliability analyses of ultra-high-dimensional, stochastic systems; (2) a new topology design sensitivity analysis for RTO and RBTO to enable concurrent evaluation of uncertainties and their design sensitivities; and (3) a new topology optimization algorithm integrating the level-set method for both fast convergence and clear geometry. In addition, proposed research will incorporate utility functions for developing new practical RBTO model for industrial applications. The project will deliver a novel, feasible paradigm-shifting advance toward solving large-scale topology optimization problems under uncertainty.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
The rising of 3D printing technology brings a great potential to manufacture products with very complex geometry. On other hand, it also demands the development of topology design method, which is aiming to figure out material distribution in certain space or domain, allowing proper complex topology (for instance, holes with irregular shape) in the final product. Classical topology design procedures rely on specified safety factors for prevent failures or malfunctions of product. Their product is often plagued by uncertain circumstances in manufacturing and/or operating processes because lack of quantification of the uncertainties.
With the support of this National Science Foundation award, the principal investigator examines possible remedy for classical deterministic topology design methods. More specifically, new approaches to acquire the best-possible material distributions under a large number of random impacts either from external environment, for example, random loads, or from internal characteristics, for example, random material properties. The research includes new theoretical investigations and implementation of new numerical methods connecting various fields including engineering, mechanics, statistics and mathematics. The proposed approached can deal with a large number of random variables and is applicable to 1) robust topology design; and 2) reliability-based topology design. The former seeks for optimal material distributions with minimal propagation of uncertainties. Whereas the latter aims at optimal design with low failure probability. To achieve these objectives, the novel integration of deterministic topology derivative and the proposed mathematical framework are developed for moment analysis, reliability analysis, and their stochastic topology sensitivity analysis with both accuracy and efficiency.
The research results have significant potentials to be applicable to multidisciplinary optimization field including but not limited to 3D printing product design, civil structure design, advanced material design, and aerospace applications. It also provides theoretic bases for further development of CAD/CAE software. Knowledge transfer includes presenting in conferences and meeting of civil, mechanical field, poster in student symposia, and peer-reviewed journal articles, and student training via class projects.
Last Modified: 06/09/2020
Modified by: Xuchun Ren
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