Review of Modeling Techiniques for Marine Debris Flows Conference Paper uri icon

abstract

  • © 2018 IEEE. As a result of the recent severe storm events in the Gulf of Mexico and along the East Coast there is increasing concern over the volume of man-made debris that has been observed entering the coastal marine ecosystems. Understanding the movement and accumulation patterns of debris within a flow is important for gaining knowledge on how the presence of debris affects the performance of marine facilities. Runoff from storm water discharge introduces a considerable amount of debris into waterways that either float on the surface or travel within the water column. This discharge impacts coastal areas and contributes to the accumulation of trash in the oceans. This research study reports on some of the published field data on flow density of natural and anthropogenic debris that has been reported in the open literature. This includes data reported on the amounts, types, and distributions of marine debris. Several predictive models that have been created to simulate the transport and accumulation of marine debris are discussed. These models were developed to address local knowledge of ocean and river currents and wind patterns. Studies on the influence of debris accumulation at riverine and coastal structures such as bridge piers and hydroelectric dams are also noted. The accumulation of debris at these structures is particularly important due to the risk of blockage, scour, or structural failure. In one of these studies a probabilistic model that investigates the interaction of marine life with hydrokinetic devices is discussed that is based on a limited amount of field data. Of particular concern was the presence of a rotating device and the additional risk of the quantification of various aspects including injury to aquatic life and damage to the structure. This study identifies the need to better understand the distribution and behavior of hard and soft debris and its distribution within a flow in order to more accurately predict its interaction and accumulation patterns about a wide range of constructed facilities.

author list (cited authors)

  • Brown, A. H., & Niedzwecki, J. M.

citation count

  • 1

publication date

  • October 2018

publisher