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ISSN Online: 2379-1748

ISBN Flash Drive: 978-1-56700-469-4

ISBN Online: 978-1-56700-470-0

Second Thermal and Fluids Engineering Conference
April, 2-5, 2017, Las Vegas, NV, USA

CONTAMINATED FUEL FIRES: PARAMETRIC SENSITIVITY OF RESUSPENSION AND BOILING PARTICLE EVOLUTION

Get access (open in a dialog) pages 497-511
DOI: 10.1615/TFEC2017.cbf.017709

Abstract

The safety requirements for handling hazardous materials are important, as they dictate costs and designs associated with related facilities and operations. One such concern relates to respirable hazards that sometimes can become mixed with a flammable solvent (i.e., gasoline) and ignited. As part of maintaining health, safety, and security, the U.S. Department of Energy (DOE) maintains a number of documents to ensure safety across the DOE complex. A DOE Handbook (DOE-HDBK-3010) provides boundary estimates for various accident types, including fire, explosion, and seismic events. These estimates allow safety analysts to incorporate hazards in evaluating safety systems to protect people in the event of an accident of this nature. The basis for the recommendations relates to historical tests. Current computing capabilities offer new methods to assess the hazards. In the interest of updating and assessing the DOE handbook recommendations, we are re-visiting historical experimental tests and modeling the scenarios. This paper focuses on a historical test where a gasoline pool fire was doped with a solid contaminant. To better simulate this scenario, we have implemented some new capability in our simulation codes. We have added a multi-component evaporation model, and we have developed a model for particle adhesion and re-suspension. The new capabilities contribute to the understanding of the physical tests. We identify the duration of the active boiling regime and the intensity of turbulence parameters to be the most critical parameters to the quantitative outcome of the predictions. The newly implemented model capabilities are important code improvements that allow models to better predict the physics of this type of scenario.