| NSF Org: |
CMMI Division of Civil, Mechanical, and Manufacturing Innovation |
| Recipient: |
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| Initial Amendment Date: | August 8, 2012 |
| Latest Amendment Date: | August 8, 2012 |
| Award Number: | 1234113 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Atul Kelkar
CMMI Division of Civil, Mechanical, and Manufacturing Innovation ENG Directorate for Engineering |
| Start Date: | September 1, 2012 |
| End Date: | August 31, 2016 (Estimated) |
| Total Intended Award Amount: | $270,165.00 |
| Total Awarded Amount to Date: | $270,165.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
77 MASSACHUSETTS AVE CAMBRIDGE MA US 02139-4301 (617)253-1000 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
77 Massachusetts Avenue Cambridge MA US 02139-4307 |
| 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): |
APPLIED MATHEMATICS, DYNAMICAL SYSTEMS |
| 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
The research objective of this collaborative award is the advancement and application of a novel approach for identifying transport barriers in complex fluid flows based on the mathematical discipline of Braid Theory. This approach has the potential to enable a complex flow field to be decomposed into the key domains that organize material transport, using only sparse trajectory data. To investigate the utility and robustness of the approach, this collaborative research project will combine fundamental mathematical research with testing via laboratory experiments, numerical simulations, and field data sets. Deliverables include the development, validation and assessment of a new technique for identifying transport barriers in fluid flows, the creation of new fluid dynamics analysis tools, the training of a graduate student and a postdoc, research experience for undergraduate and high school students, and the organization of a major international workshop.
Transport barriers organize material transport in the Earth's oceans, lakes and rivers. It is a longstanding challenge to identify these dynamical structures, however, since a principal source of data is the trajectories of Lagrangian drifters, which are inevitably sparsely distributed due to the large physical scales involved. The Braid Theory approach has the potential to extract valuable information on the shape and location of transport barriers from sparse drifter data sets. The resulting methodology also supports efficient, real-time implementation; a potentially transformative application is to improve time-sensitive decision-making strategies for man-made and naturally occurring environmental scenarios, including oil spills, radioactive leaks and algal blooms. The results will be disseminated to researchers in academia, government and industry, in particular via a week-long international workshop at the Banff Center, and the academic and personal careers of a high-school student, an undergraduate student, a graduate student and a postdoc will benefit from training in this field.
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.
In complex geophysical flows, transport barriers guide the passage of debris and pollutants and influence biologically important processes such as algal blooms There is a pressing need to develop reliable and efficient methods that can effectively process drifter data to uncover transport barriers. The identification of transport barriers in dynamical systems is a longstanding practical and mathematical challenge that has widespread application, particularly to complex fluid flow problems in oceanography and limnology. The ability to reliably identify evolving transport barriers from available field data during the Deepwater Horizon and Fukushima disasters, for example, would have greatly assisted decision-making strategies. Leading mathematical tools developed over the last decade, however, require substantial computational processing of extensive spatio-temporal velocity field data, making them currently unrealistic for the vast majority of the Earth’s oceans and lakes, where circulation data is predominantly in the form of trajectories of a relatively sparse distribution of Lagrangian drifters.
The intellectual merit of the proposed work is in the advancement and utilization of a new braid theoretic approach to identify transport barriers in complex flows from two-dimensional Lagrangian drifter data. The ability to do so enables a complex flow field to be decomposed into the key components that govern transport, providing a framework for improved understanding and prediction. The PI and a postdoc pursued mathematical developments that the co-PI tested in laboratory experiments, numerical simulations and through application to field data obtained from large-scale, nonlinear, oceanic flows.
The primary broader impact of this research is the benefit to the scientific community that comes from the development of a new analytical tool for identifying transport barriers from drifter data sets. The method, validated by laboratory experiments and numerical simulation, was integrated into a MATLAB package that is freely available. This package has already benefited users in several disciplines, such as geophysics, plasma physics, robotics, and the study of flocking of birds. The wide range of topics shows the wide impact of the research on many disciplines. The project also supported the training of a postdoctoral researcher and a female graduate student and postdoc.
Last Modified: 11/30/2016
Modified by: Thomas Peacock
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