Validation of FDS for the prediction of medium-scale pool fires

Wen, Jennifer X.; Kang, Kwang‐Yong; Donchev, Ted; Karwatzki, J.

doi:10.1016/j.firesaf.2006.08.007

Cited by 99 publications

(49 citation statements)

References 29 publications

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“…Also, the information gained for the model parameter Sc t can be used in future validation processes. Previous studies of Sc t that showed a very small effect on the flow characteristics were performed with few values over a small range [51][52][53][54]. The present analysis checked 50,000 random points over a range of 0.4-10,000 to determine a range of 0.61-1.89 for consistent results.…”

Section: Information Feedback and Feed Forwardmentioning

confidence: 82%

“…Xin et al [51] used an Sc t value of 0.4 in the simulation of a methane pool fire and stated that Sc t did not have a significant influence on simulation output. Other researchers have also claimed that the choice of Sc t is unimportant compared with other variables [53,54]. On the other hand, Yimer et al [52] reported a wide range of values (0.2-0.85) and highlighted their effect on the flow field predictions.…”

Section: Determine I/u Mapmentioning

confidence: 99%

“…This model parameter describes the momentum/scalar interactions in the flow. The modeling difficulty associated with this parameter is highlighted by the wide range of values from the literature shown in Table 4 [51][52][53][54]. Xin et al [51] used an Sc t value of 0.4 in the simulation of a methane pool fire and stated that Sc t did not have a significant influence on simulation output.…”

Section: Determine I/u Mapmentioning

confidence: 99%

See 2 more Smart Citations

Application of a Verification, Validation and Uncertainty Quantification Framework to a Turbulent Buoyant Helium Plume

Jatale

Smith

Thornock

et al. 2015

Flow Turbulence Combust

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A framework for verification and validation/uncertainty quantification of largescale, highly coupled, complex processes is presented. Steps in the framework include code and solution verification, development of an input/uncertainty map, definition of evaluation criteria, development of a surrogate model, performance of a consistency analysis (a mathematical statement limiting error), and the feed forward and feedback of information gain from the analysis. This framework is applied to a turbulent, buoyant, 1 m diameter helium plume. Experimental measurements are obtained from Sandia National Laboratory while the helium plume simulations are performed using Large Eddy Simulation. Both parameter and experimental uncertainty associated with the helium plume are reduced by requiring consistency.

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Section: Information Feedback and Feed Forwardmentioning

confidence: 82%

Section: Determine I/u Mapmentioning

confidence: 99%

Section: Determine I/u Mapmentioning

confidence: 99%

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Application of a Verification, Validation and Uncertainty Quantification Framework to a Turbulent Buoyant Helium Plume

Jatale

Smith

Thornock

et al. 2015

Flow Turbulence Combust

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“…Many researchers [5][6][7][8][9][10] have discussed the comparisons between the FDS numerical model results and the full-scale fire experimental parameters, such as HRR (heat release rate), CO, carbon dioxide (CO 2 ), temperature and soot, in various buildings. In addition, there are room liquid fire simulation [11] and smoke characteristics analysis [12].…”

Section: Introductionmentioning

confidence: 99%

Full-Scale Measurement and Numerical Analysis of Liquefied Petroleum Gas Water Heaters with Ventilation Factors in Balcony

Chiu¹,

Chen²,

Chen³

et al. 2015

JCEA

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This study carried out full-scale gas water heater combustion experiments and adopted FDS (fire dynamics simulator) to simulate three scenarios-different balcony environments when using water heater, such as airtight balcony, indoor door with openings and force ventilation to compare with full-scale combustion experiments. According to FDS simulation results, O 2 , CO and CO 2 simulation concentration value correspond with full-scale experimental results. When the indoor O 2 concentration was lower than 15%, which causes incomplete combustion, the CO concentration would rise rapidly and even reached above 1,500 ppm, causing death in short time. In addition, when the force ventilation model supplied the water heater with enough air to burn, the indoor CO concentration will keep low and harmless to humans. The study also adopted diverse variables, such as the opening area of window, outdoor wind speed and water heater types, to analyze deeply user's safety regarding gas water heater. In a result, while balcony area is larger than 14 m 2 , the volume of water heater is below 16 L (33.1 kW), and the indoor window, connecting balcony with room, is closed, if the opening on the outdoor window of the balcony is larger than 0.2 m 2 , this can ensure the personal security of the indoor space.

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“…However, more effective techniques are being suggested by many researchers and the numerical analysis is increasingly gaining reliability through various case studies on the real fire tests and comparative verification. As a study of the numerical analysis on the flame of laboratory level, Hamins et al [1] analyzed the combustion phenomena of a wax candle, and the study result on the mediumscale pool fire was verified by Wen et al [2]. Recently, heat flux and flame height of the blaze predicted in SBI (single burning item) were researched by Jianping et al [3].…”

Section: Introductionmentioning

confidence: 99%

A study for a fire spread mechanism of residential buildings with numerical modeling

Ahn

Kim

2011

WIT Transactions on the Built Environment

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This study is intended to present a computational standard model for combustibles, compartments and buildings. As performance based design is more popular, fire-intensity and fire-load have turned out to be very important factors for building design and can be predicted through some computational work. To predict and estimate the fire properties of a residential fire, we made some numerical models of combustibles, compartments and a residential building. In a bid to validate the estimate values, research was divided into three parts of step verifications. The first was for combustibles, the second was for compartments and the third was for the building. During each step, computational analysis results from numerical models were compared with real fire tests. For computational analysis, the Fire Dynamics Simulator was used with Large Eddy Simulation model for turbulence. Consequently, fire-intensity was well predicted and flash-over of rooms were successfully estimated.

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Validation of FDS for the prediction of medium-scale pool fires

Cited by 99 publications

References 29 publications

Application of a Verification, Validation and Uncertainty Quantification Framework to a Turbulent Buoyant Helium Plume

Application of a Verification, Validation and Uncertainty Quantification Framework to a Turbulent Buoyant Helium Plume

Full-Scale Measurement and Numerical Analysis of Liquefied Petroleum Gas Water Heaters with Ventilation Factors in Balcony

A study for a fire spread mechanism of residential buildings with numerical modeling

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