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RUI: TRANSPORT OF INERTIAL PARTICLES IN TIME-DEPENDENT AND STOCHASTIC FLOWS
Agency:
NSF
NSF
National Science Foundation
Project Number:
1418956
1418956
Contact PI / Project Leader:
FORGOSTON, ERIC
FORGOSTON, ERIC
Awardee Organization:
MONTCLAIR STATE UNIVERSITY STUDENT GOVERNMENT
MONTCLAIR STATE UNIVERSITY STUDENT GOVERNMENT
Description
Abstract Text:
Objects in moving fluids rarely go with the flow. Instead they may sink, swim or steer in order to reach a destination, or they may respond to other influences, including their own sizes and shapes. Taking advantage of the ways that real objects interact with flows enables a wide range of important technologies. On small scales, micro robots may be steered inside the human body to perform surgery. On the largest scales, ocean drifters may efficiently monitor currents, marine life or global weather patterns. Each example presents challenges originating from the complex fluid flow patterns and from the difficulty in planning the most efficient navigation strategy. This project concentrates on the challenges of positioning autonomous vehicles in the ocean, where unpredictable and variable currents, seasonal variability, weather events, and other random influences must also be accounted for. Computer models of fluid flows and mathematical models of control will be combined to find optimal strategies to position autonomous ocean vehicles. Laboratory experiments will use precisely tuned fluid flows and remotely controlled particles to capture the important effects of the vehicles' mass, size, and shape. The project payoff is significant in that a better monitored ocean is advantageous to fishing and shipping, the military, and environmental monitoring. The project will involve and support undergraduate and graduate students in leading-edge research. Significantly, the student population at Montclair State University, and in particular, the Department of Mathematical Sciences, includes a substantial proportion who are members of groups underrepresented in STEM disciplines (including women and minorities) and the research program will leverage existing programs directed to these students. The outcome of the research will be disseminated through seminars, presentations at meetings, and publications in peer-reviewed journals.
In this project computer models of fluid flows and mathematical models of transport and control will be combined to find optimal control strategies for autonomous ocean vehicles, which will be modeled both as inertial and non-inertial objects. Laboratory experiments on precisely tuned flows and magnetically controlled particles will be used both to validate and guide the investigations. The goal is to use experimental and computed flow fields to identify critical transport features and integrate these features into control algorithms that optimally position particles. This project will improve transport control capabilities by developing models for transport and control of inertial objects in canonical flows subject to time-dependent and stochastic perturbations. Flow data will be generated by the numerical simulation of gyre flows, jets, and boundary currents. Inertial particles will be modeled directly using a state-of-the-art interfacial multi-phase numerical code. Laboratory experiments have been designed so that similar flows can be generated by reconfiguring the geometric forcing devices. Through high resolution particle imaging velocimetry (PIV) and particle tracking, experimental flow fields and their transport properties will be correlated with those of the associated model flows. Additionally, control strategies will be implemented using ferromagnetic tracer particles and magnetic pulses. To gain insight into the flow transport properties, flow and tracer data will be analyzed using a variety of geometric and probabilistic methods including finite-time Lyapunov exponents, inertial particle models, almost invariant sets, and finite-time coherent sets. These techniques will directly result in the ability to identify loitering regions and their boundaries and to determine maximal transport rates. This information will be leveraged to develop simple predictive models of transport and trajectory control that can be efficiently adapted to emergent applications.
In this project computer models of fluid flows and mathematical models of transport and control will be combined to find optimal control strategies for autonomous ocean vehicles, which will be modeled both as inertial and non-inertial objects. Laboratory experiments on precisely tuned flows and magnetically controlled particles will be used both to validate and guide the investigations. The goal is to use experimental and computed flow fields to identify critical transport features and integrate these features into control algorithms that optimally position particles. This project will improve transport control capabilities by developing models for transport and control of inertial objects in canonical flows subject to time-dependent and stochastic perturbations. Flow data will be generated by the numerical simulation of gyre flows, jets, and boundary currents. Inertial particles will be modeled directly using a state-of-the-art interfacial multi-phase numerical code. Laboratory experiments have been designed so that similar flows can be generated by reconfiguring the geometric forcing devices. Through high resolution particle imaging velocimetry (PIV) and particle tracking, experimental flow fields and their transport properties will be correlated with those of the associated model flows. Additionally, control strategies will be implemented using ferromagnetic tracer particles and magnetic pulses. To gain insight into the flow transport properties, flow and tracer data will be analyzed using a variety of geometric and probabilistic methods including finite-time Lyapunov exponents, inertial particle models, almost invariant sets, and finite-time coherent sets. These techniques will directly result in the ability to identify loitering regions and their boundaries and to determine maximal transport rates. This information will be leveraged to develop simple predictive models of transport and trajectory control that can be efficiently adapted to emergent applications.
Project Terms:
Accounting; Algorithms; Arts; Code; Complex; Computer Simulation; Data; design; Destinations; Devices; Environmental Monitoring; Event; Fishes; fluid flow; Goals; graduate student; Human body; Image; improved; insight; interfacial; Investigation; Journals; Laboratories; Life; Liquid substance; Magnetism; Marines; mathematical model; mathematical sciences; meetings; member; Methods; Military Personnel; Minority; Modeling; Monitor; Oceans; Operative Surgical Procedures; Outcomes Research; particle; Pattern; Peer Review; Phase; Physiologic pulse; Population; Positioning Attribute; predictive modeling; programs; Property; Publications; Research; research study; Resolution; Robot; Shapes; Shipping; Ships; simulation; STEM field; Students; Swimming; Techniques; Technology; Time; Tracer; Underrepresented Minority; Universities; Velocimetries; Weather; weather patterns; Woman
Details
Contact PI / Project Leader Information:
Name:
FORGOSTON, ERIC
Other PI Information:
BILLINGS, LORA; YECKO, PHILIP
Awardee Organization:
Name: MONTCLAIR STATE UNIVERSITY STUDENT GOVERNMENT
City: MONTCLAIR
Country: UNITED STATES
Congressional District:
State Code: NJ
District: 11
Other Information:
Fiscal Year: 2014
Award Notice Date: 04-Jul-2014
Award Notice Date: 04-Jul-2014
DUNS Number: 053506184
Project Start Date: 15-Jul-2014
Budget Start Date:
Project Start Date: 15-Jul-2014
Budget Start Date:
CFDA Code: 47.049
Project End Date: 30-Jun-2017
Budget End Date:
Project End Date: 30-Jun-2017
Budget End Date:
Agency: ?
National Science Foundation
Agency: The entity responsible for the administering of a research grant, project, or contract. This may represent a federal department, agency, or sub-agency (institute or center). Details on agencies in Federal RePORTER can be found in the FAQ page.
Project Funding Information for 2014:
| Year | Agency Agency: The entity responsible for the administering of a research grant, project, or contract. This may represent a federal department, agency, or sub-agency (institute or center). Details on agencies in Federal RePORTER can be found in the FAQ page. |
FY Total Cost |
|---|---|---|
| 2014 |
NSF National Science Foundation |
$299,987 |
Results
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