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Noah L Ryder

Publications and source records attributed to Noah L Ryder.

2 recordsLinked to original sources

Near and far field contamination modeling in a large scale enclosure: Fire Dynamics Simulator comparisons with measured observations.

The occurrence of a fire, no matter how small, often exposes objects to significant levels of contamination from the products of combustion. The production and dispersal of these contaminants has been an issue of relevance in the field of fire science for many years, though little work has been done to examine the contamination levels accumulated within an enclosure some time after an incident. This phenomenon is of great importance when considering the consequences associated with even low level contamination of sensitive materials, such as food, pharmaceuticals, clothing, electrical equipment, etc. Not only does such exposure present a localized hazard, but also the shipment of contaminated goods places distant recipients at risk. It is the intent of this paper to use a well-founded computational fluid dynamic (CFD) program, the Fire Dynamics Simulator (FDS), a large eddy simulation (LES) code developed by National Institute of Standards and Technology (NIST), to model smoke dispersion in order to assess the subject of air contamination and post fire surface contamination in a warehouse facility. Measured results are then compared with the results from the FDS model. Two components are examined: the production rate of contaminates and the trajectory of contaminates caused by the forced ventilation conditions. Each plays an important role in determining the extent to which the products of combustion are dispersed and the levels to which products are exposed to the contaminants throughout the enclosure. The model results indicate a good first-order approximation to the measured surface contamination levels. The proper application of the FDS model can provide a cost and time efficient means of evaluating contamination levels within a defined volume.

Computer Simulation↗

Consequence modeling using the fire dynamics simulator.

The use of Computational Fluid Dynamics (CFD) and in particular Large Eddy Simulation (LES) codes to model fires provides an efficient tool for the prediction of large-scale effects that include plume characteristics, combustion product dispersion, and heat effects to adjacent objects. This paper illustrates the strengths of the Fire Dynamics Simulator (FDS), an LES code developed by the National Institute of Standards and Technology (NIST), through several small and large-scale validation runs and process safety applications. The paper presents two fire experiments--a small room fire and a large (15 m diameter) pool fire. The model results are compared to experimental data and demonstrate good agreement between the models and data. The validation work is then extended to demonstrate applicability to process safety concerns by detailing a model of a tank farm fire and a model of the ignition of a gaseous fuel in a confined space. In this simulation, a room was filled with propane, given time to disperse, and was then ignited. The model yields accurate results of the dispersion of the gas throughout the space. This information can be used to determine flammability and explosive limits in a space and can be used in subsequent models to determine the pressure and temperature waves that would result from an explosion. The model dispersion results were compared to an experiment performed by Factory Mutual. Using the above examples, this paper will demonstrate that FDS is ideally suited to build realistic models of process geometries in which large scale explosion and fire failure risks can be evaluated with several distinct advantages over more traditional CFD codes. Namely transient solutions to fire and explosion growth can be produced with less sophisticated hardware (lower cost) than needed for traditional CFD codes (PC type computer verses UNIX workstation) and can be solved for longer time histories (on the order of hundreds of seconds of computed time) with minimal computer resources and length of model run. Additionally results that are produced can be analyzed, viewed, and tabulated during and following a model run within a PC environment. There are some tradeoffs, however, as rapid computations in PC's may require a sacrifice in the grid resolution or in the sub-grid modeling, depending on the size of the geometry modeled.

Computer Simulation↗