Research Categories / Ohmsett

Oil Spill Containment Boom Computational Fluid Dynamics and Physical Modeling Study

This project will investigate towed oil containment boom systems to assess how computational fluid dynamics (CFD) modeling and physical scaled model testing results may predict full-scale boom performance. The contractor will conduct CFD modeling and physical testing of scaled boom systems at multiple scales. Data obtained will be analyzed to determine their consistency when accounting for scale factors.

LiDAR Oil Characterization and Automated Software Development

Under OSRR Project 1091, the NRL performed preliminary experiments to assess pulsed laser light technology (Light Detection And Ranging - LiDAR) ability to detect oil and characterize oil thickness on water. Initial testing conducted at Ohmsett demonstrated the successful application of LiDAR remote sensing to detect and measure the presence of oil at the surface and underwater.

This project will continue the development of the LiDAR system's ability to detect and characterize oil on the surface and varying subsurface layers thickness values and depth in the water.

The Web based General NOAA Oil Modeling Environment (WebGNOME) Anywhere

The current NOAA's WebGNOME platform displays the modeling bounds with available operational forecast models for selected areas. These areas are typically in shoreline areas. This project will expand the availability of forecast models to cover offshore areas where BSEE's regulated facilities reside. This added feature will enable the ability to run WebGNOME more easily, using available operational forecast models.

OIL DETECTION AND THICKNESS ESTIMATION UNDER/IN ICE BASED ON ELECTRICAL CAPACITANCE TOMOGRAPHY (ECT)

This project will study and test the Electrical Capacitance Tomography (ECT) sensor to detect oil in/under ice. For oil detection and thickness estimation under/in ice, where the access to the imaged region is limited to above its surface, AUB proposes a planar sensor design where the electrodes are mounted on a single plane and placed at a relatively close distance above the ice surface. 

Development of an Advancing Skimmer Test Protocol

This project will develop a defined, repeatable test protocol for testing advancing skimmer systems. SL Ross will convene a workgroup of oil response subject matter experts who will develop a general test protocol suitable for use with a variety of advancing skimmers. The test protocol will be developed for use at the Ohmsett facility; however, large-scale tank facilities will be considered to broaden the protocol’s applicability.

Update of the Ohmsett Dispersant Effectiveness Test Protocol

This project updated the Dispersant Effectiveness (DE) test protocol used at Ohmsett, the National Oil Spill Response Research and Renewable Energy Test Facility. Ohmsett is the largest facility of its kind and offers significant advantages for testing response technologies such as dispersants in simulated field conditions. The original DE test protocol was developed between 2000-2003.

Development of an Oil Thickness Sensor Phase II

This project developed oil thickness sensors to quantify the thickness of oil floating on water. Two unique sensors were developed to measure oil thickness greater than 3mm in real time. The first is a hand held unit that can be deployed from a vessel or used in a test environment. Measurements are read by the user directly from the sensor. The second is designed for mounting on a skimmer, buoy, or oil containment boom, and is designed to measure in wave conditions. This sensor transmits oil thickness measurements wirelessly to a user up to a distance of 200-250 meters.

System and Algorithm Development to Estimate Oil Thickness and Emulsification Through an UAS Platform

The focus of this project will be on the design and implementation of two components, the UAS system, and the algorithms for the image processing used on the system. The project will be carried out in two phases:  Phase 1: Development/Implementation of the UAS platform/sensors and its algorithms for oil classification and image processing based on Ohmsett testing.  Phase 1 will involve the following sub-tasks: 1) UAS multisensory array implementation, 2) Controlled experiment (Ohmsett tank testing), and 3) Development of the oil classification and image processing algorithm.

Slick Thickness Characterization Based on Low Noise, Polarized Synthetic Aperture Radar

The project team will use radar technology instead of optical or infrared methods in order to enable 24-hour, weather independent operation that can be deployed in inclement or difficult to access environments, and reduce dependence upon on-site personnel. The team will evaluate the capability of low noise L-band (1.26 GHz) synthetic aperture radar (SAR) imagery acquired by the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) sensor.
 

Estimating Oil Slick Thickness with LiDAR Remote Sensing Technology

The purpose of this study was to assess and evaluate the capabilities and limitations of two above-water laser systems owned and operated by NRL, to detect and characterize oil layers of varying thickness on the surface of the water, in conjunction with an acoustic sensor for in-water detection (oil in the water column).

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