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Minimize RSR Award Detail

Research Spending & Results

Award Detail

Awardee:UNIVERSITY OF HAWAII SYSTEMS
Doing Business As Name:University of Hawaii
PD/PI:
  • Ersegun D Gedikli
  • (808) 956-7572
  • egedikli@hawaii.edu
Co-PD(s)/co-PI(s):
  • Oceana Francis
Award Date:07/29/2021
Estimated Total Award Amount: $ 793,505
Funds Obligated to Date: $ 793,505
  • FY 2021=$793,505
Start Date:01/01/2022
End Date:12/31/2024
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.050
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:NNA Research: Development of a Nonlinear Reduced Order Modeling Framework for Marine Structures Operating in The Arctic and Sub-Arctic Regions
Federal Award ID Number:2127095
DUNS ID:965088057
Parent DUNS ID:009438664
Program:NNA-Navigating the New Arctic
Program Officer:
  • Mamadou Diallo
  • (703) 292-4257
  • mdiallo@nsf.gov

Awardee Location

Street:2440 Campus Road, Box 368
City:Honolulu
State:HI
ZIP:96822-2234
County:Honolulu
Country:US
Awardee Cong. District:01

Primary Place of Performance

Organization Name:University of Hawaii
Street:2440 Campus Road, Box 368
City:Honolulu
State:HI
ZIP:96822-2234
County:Honolulu
Country:US
Cong. District:01

Abstract at Time of Award

Navigating the New Arctic (NNA) is one of NSF's 10 Big Ideas. NNA projects address convergence scientific challenges in the rapidly changing Arctic. The Arctic research is needed to inform the economy, security and resilience of the Nation, the larger region, and the globe. NNA empowers new research partnerships from local to international scales, diversifies the next generation of Arctic researchers, enhances efforts in formal and informal education, and integrates the co-production of knowledge where appropriate. This award fulfills part of that aim by addressing interactions among natural and environments in the following NNA focus areas: Data and Observation, Education, and Global Impact. Due to rapid warming in the Arctic, sea ice is thinning and retreating, making more Arctic waters increasingly accessible to shipping and transportation, research and exploration, and other economic development activities. Increased maritime activities in the region pose potential risks to the Arctic environment, especially in areas used by fishing vessels, offshore oil, and gas industry and cruise liners. Marine structures operating in the ice-covered part of the region also lead to changes in the Arctic icescape. Using a combination of data from field experiments, models, satellites and observations, this project explores complex interactions between ice and marine structures in the Arctic to develop a conceptual framework for risk assessment and modelling to provide safe shipping and operations in the region. The knowledge acquired through the project benefits a wide range of stakeholders, such as residents, businesses, local, regional, and government agencies, and researchers who are invested in the well-being of the region to ensure a resilient and sustainable Arctic environment. The project also provides research opportunities for graduate and undergraduate students and a postdoctoral researcher and outreach to minority students for learning mechanical engineering. In the Arctic, a major barrier to understanding the relationship between sea ice and marine structures is the complex, evolving nature of Arctic system interactions. Therefore, this project utilizes a reduced-order modeling framework to simplify these complex ice-structure interactions through understanding the dynamics of each parameter and finding the dominant environmental and structural conditions governing these interactions. This project develops a state-of-the-art ice-structure interaction modeling framework and risk assessment concept. Meteorological and oceanographic data from satellite images, data stations, and meteorological institutions, as well as structural response data from marine structures are studied to create data matrices and to facilitate the development of an efficient framework. Using sparsity-promoting multivariate analysis techniques and equation free modeling, the nonlinear nature of such interactions are clarified. The developed model is used to form a novel risk assessment concept, which assists ship operators and people working on other man-made structures to provide informed decisions during operations in the Arctic. 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.

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