Moving Source Identiication in an Uncertain Marine Flow: Mediterranean Sea Application
thesis - Abed
AuthorsHammoud, Mohamad Abed ElRahman
Committee membersHoteit, Ibrahim
Im, Hong G.
KAUST DepartmentPhysical Science and Engineering (PSE) Division
Embargo End Date2021-04-27
Permanent link to this recordhttp://hdl.handle.net/10754/662684
MetadataShow full item record
Access RestrictionsAt the time of archiving, the student author of this thesis opted to temporarily restrict access to it. The full text of this thesis will become available to the public after the expiration of the embargo on 2021-04-27.
AbstractIdentifying marine pollutant sources is essential in order to assess, contain and minimize their risk. We propose a Lagrangian Particle Tracking algorithm (LPT) to study the transport of passive tracers continuously released from fixed and moving sources and to identify their source in a backward mode. The LPT is designed to operate with uncertain flow fi elds, described by an ensemble of realizations of the sea currents. Starting from a region of high probability, re- verse tracking is used to generate inverse maps. A probability-weighted distance between the resulting inverse maps and the source trajectory is then minimized to identify the likely source of pollution. We conduct realistic simulations to demonstrate the efficiency of the proposed algorithm in the Mediterranean Sea using ocean data available from Copernicus Marine Environment Monitoring Services. Passive tracers are released along the path of a ship and propagated with an ensemble of flow fi elds forward in time to generate a probability map, which is then used for the inverse problem of source identi fication. Our experiments suggest that the algorithm is able to efficiently capture the release time and source, with some test cases successfully pinpointing the release time and source up to two weeks back in time.
CitationHammoud, M. A. E. R. (2020). Moving Source Identiication in an Uncertain Marine Flow: Mediterranean Sea Application. KAUST Research Repository. https://doi.org/10.25781/KAUST-U5V1U