Award Abstract # 2142290
CAREER: Bridging the Gap between Deterministic and Stochastic Structures for Mixed Stochasticity System Design

NSF Org: CMMI
Division of Civil, Mechanical, and Manufacturing Innovation
Recipient: UNIVERSITY OF CONNECTICUT
Initial Amendment Date: December 12, 2021
Latest Amendment Date: June 3, 2022
Award Number: 2142290
Award Instrument: Continuing Grant
Program Manager: Harrison Kim
harkim@nsf.gov
 (703)292-7328
CMMI
 Division of Civil, Mechanical, and Manufacturing Innovation
ENG
 Directorate for Engineering
Start Date: January 1, 2022
End Date: December 31, 2026 (Estimated)
Total Intended Award Amount: $536,740.00
Total Awarded Amount to Date: $536,740.00
Funds Obligated to Date: FY 2022 = $536,740.00
History of Investigator:
  • Hongyi Xu (Principal Investigator)
    hongyi.3.xu@uconn.edu
Recipient Sponsored Research Office: University of Connecticut
438 WHITNEY RD EXTENSION UNIT 1133
STORRS
CT  US  06269-9018
(860)486-3622
Sponsor Congressional District: 02
Primary Place of Performance: University of Connecticut
191 Auditorium Rd.
Storrs
CT  US  06269-3139
Primary Place of Performance
Congressional District:
02
Unique Entity Identifier (UEI): WNTPS995QBM7
Parent UEI:
NSF Program(s): EDSE-Engineering Design and Sy,
CAREER: FACULTY EARLY CAR DEV
Primary Program Source: 01002223DB NSF RESEARCH & RELATED ACTIVIT
01002324DB NSF RESEARCH & RELATED ACTIVIT

010V2122DB R&RA ARP Act DEFC V
Program Reference Code(s): 024E, 068E, 1045, 8029, 8043
Program Element Code(s): 072Y00, 104500
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.041

ABSTRACT

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).

Systems or products composed of parts that have random (or stochastic) characteristics pose a significant challenge to the design of complex engineering systems. There is a lack of systematic methods for designing artifacts utilizing this type of mixed stochasticity structure. This Faculty Early Career Development Program (CAREER) project will establish a novel computational framework that bridges the gap between random and regular structural patterns to enable: 1) generative design of structures with tailorable stochasticity and desired properties and 2) design of mixed stochasticity structural systems that consist of mixed stochastic structural units to achieve optimal performance. Two sets of engineering applications will be used to evaluate the methodology and demonstrate its benefit to society: design of microstructural materials in energy storage systems and design of a mixed stochasticity structural system for vehicle impact safety. The broader application of the research developments will benefit a wide range of industries, such as high-capacity energy storage materials, structural safety and reliability, and automotive lightweighting, which rely on materials and structures with mixed stochasticity. Furthermore, the results of this research will contribute theories and methodologies that benefit the reliability design of mixed stochasticity systems, such as power grid systems, water distribution systems, and transportation systems. The education and outreach objective of this project is to create a prototype of education-research-industry integration and apply it to K-12 education outreach, outreach to local businesses, and university education. The K-12 education outreach will motivate students to pursue education and careers in science and engineering fields. The outreach to local businesses will assist small businesses and small manufacturers to compete in the post?COVID-19 world. The integration among research, industry practice, and education will strengthen the undergraduate and graduate students? learning experiences.

The overarching research goal of this project is to create a novel computational framework that bridges the gap between deterministic and stochastic structures by establishing a unified design space that covers structural patterns whose stochasticity ranges from random to regular. This framework enables generative design of microstructures/structures with tailorable properties and stochasticity, as well as design of a mixed stochasticity structural system that consists of deterministic and stochastic structural units to achieve optimal performance. This research will transform the discovery and design of structural/microstructural systems in three ways. First, a first-of-its-kind design representation method will be developed for mixed stochasticity structures that will enable the design of structures with tailorable stochasticity. Second, the research will provide a theoretical foundation for transferring knowledge between deterministic and stochastic systems to inspire new designs that achieve target properties. Third, a new design framework will be established for the robustness and reliability-based design of mixed stochasticity structural system. The education and outreach plan includes a Science, Technology, Engineering, Arts, and Math (STEAM) project for K-12 teachers and students in underrepresented minority schools, research dissemination to local small businesses and small manufacturers, and integration among university education, research, and industrial collaboration.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Feng, Zhengkun and Lei, Weijun and Xu, Leidong and Chen, Shikui and Xu, Hongyi "Automated de novo design of architectured materials: Leveraging eXplainable Artificial Intelligence (XAI) for inspiration from stochastic microstructure outliers" Extreme Mechanics Letters , v.73 , 2024 https://doi.org/10.1016/j.eml.2024.102269 Citation Details
Naghavi_Khanghah, Kiarash and Wang, Zihan and Xu, Hongyi "Reconstruction and Generation of Porous Metamaterial Units via Variational Graph Autoencoder and Large Language Model" Journal of Computing and Information Science in Engineering , 2024 https://doi.org/10.1115/1.4066095 Citation Details
Wang, Ying and Song, Jihun and Huang, Luyao and Xu, Leidong and Xu, Hongyi and Zhu, Juner and Zhu, Hongli "Bioinspired Hierarchical Porous Architecture for Enhanced Kinetics and Mechanical Integrity in Thick Cathode" Small , 2024 https://doi.org/10.1002/smll.202406058 Citation Details
Wang, Zihan and Bray, Austin and Naghavi_Khanghah, Kiarash and Xu, Hongyi "Designing Connectivity-Guaranteed Porous Metamaterial Units Using Generative Graph Neural Networks" Journal of Mechanical Design , v.147 , 2025 https://doi.org/10.1115/1.4066128 Citation Details
Wang, Zihan and Daeipour, Mohamad and Xu, Hongyi "Quantification and propagation of Aleatoric uncertainties in topological structures" Reliability Engineering & System Safety , v.233 , 2023 https://doi.org/10.1016/j.ress.2023.109122 Citation Details
Wang, Zihan and Xu, Hongyi "Manufacturability-aware deep generative design of 3D metamaterial units for additive manufacturing" Structural and Multidisciplinary Optimization , v.67 , 2024 https://doi.org/10.1007/s00158-023-03732-4 Citation Details
Xu, Leidong and Hoffman, Nathaniel and Wang, Zihan and Xu, Hongyi "Harnessing structural stochasticity in the computational discovery and design of microstructures" Materials & Design , v.223 , 2022 https://doi.org/10.1016/j.matdes.2022.111223 Citation Details
Xu, Leidong and Naghavi Khanghah, Kiarash and Xu, Hongyi "Designing Mixed-Category Stochastic Microstructures by Deep Generative Model-based and Curvature Functional-based Methods" Journal of Mechanical Design , 2023 https://doi.org/10.1115/1.4063824 Citation Details
Xu, Leidong and Naghavi_Khanghah, Kiarash and Xu, Hongyi "Design of Mixed-Category Stochastic Microstructures: A Comparison of Curvature Functional-Based and Deep Generative Model-Based Methods" , 2023 https://doi.org/10.1115/DETC2023-114601 Citation Details

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