• Proceedings of the Korean Institute of Building Construction Conference
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• 2020.11a
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• pp.111-112
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• 2020

In the case of, the ignition of flammable external materials by the radiant flame and the accompanying fire in the upper layer are occurring every year, and in the case of the Flashover prediction formula, the limit is reached through the surface area of the space and the factor. Predicts heat release rate. In this study, the critical heat release rate of each prediction formula was calculated based on the ISO 9705 model.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2005.05a
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• pp.203-209
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• 2005

• Fire Science and Engineering
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• v.22 no.2
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• pp.63-69
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• 2008

The characteristics of the spread of a forest fire are generally related to the attributes of combustibles, geographical features, and meteorological conditions, such as wind conditions. The most common methodology used to create a prediction model for the spread of forest fires, based on the numerical analysis of the development stages of a forest fire, is an analysis of heat energy transmission by the stage of heat transmission. When a forest fire breaks out, the analysis of the transmission velocity of heat energy is quantifiable by the spread velocity of flame movement through a physical and chemical analysis at every stage of the fire development from flame production and heat transmission to its termination. In this study, the formula used for the 1-D surface forest fire behavior prediction model, derived from a numerical analysis of the surface flame spread rate of solid combustibles, is introduced. The formula for the 1-D surface forest fire behavior prediction model is the estimated equation of the flame spread velocity, depending on the condition of wind velocity on the ground. Experimental and theoretical equations on flame duration, flame height, flame temperature, ignition temperature of surface fuels, etc., has been applied to the device of this formula. As a result of a comparison between the ROS(rate of spread) from this formula and ROSs from various equations of other models or experimental values, a trend suggesting an increasing curved line of the exponent function under 3m/s or less wind velocity condition was identified. As a result of a comparison between experimental values and numerically analyzed values for fallen pine tree leaves, the flame spread velocity reveals a prediction of an approximately 10% upward tendency under wind velocity conditions of 1 to 2m/s, and of an approximately 20% downward tendency under those of 3m/s.

• Proceedings of the KSR Conference
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• 2008.06a
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• pp.2101-2108
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• 2008

Most of train fires which occur in usual cases do not grow up significantly on a large scale enough to bring about casualties and harmful damages. However, the consequence of some train fire accidents can be devastating disaster so that it would be even recorded in history in unusual cases. Accordingly, such a probability of fire disaster cannot be ignored in aspect of the railway safety assesment. A scale of injury and damage is very difficult to predict and analyze. Because it is depend on various factors, i.e. fire load, burning period, facilities, environment condition, and so on. Thus, a prediction of fire load could be understood as a one methodology to estimate railway safety assesment. The summation method which is one of them is used to evaluate the overall fire load by assuming that sum of heat release rate per unit area or mass of each composite material equals the total. However, since the train fire is classified into a compartment fire in under-ventilation condition. The summation method do not estimate a fire load completely. In this journal, Various methods to predict fire load are introduced and evaluated. Especially the fire simulation tool FDS(Fire Dynamics Simulator)which is based on the CFD(Computational Fluid Dynamics) is introduced, too. Through the FDS simulation, numerical analyses for the fire load and flame spread are performed. Then, these results of the simulation are validated through the comparison study with the experimental data. Then, limitations and approximations including in simulation process are discussed. The future direction of research is proposed.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2008.11a
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• pp.434-437
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• 2008

In this study, we developed a fuel moisture variation prediction model on each day after precipitation during a spring forest fire exhibition period. For this research, we selected plots in pine forest on Sam-Chuck si and Dong-hae si in Kangwon do according to a forest density(low, mediate, high) and classified a surface woody fuel by a diameter.(below 0.6cm, $0.6{\sim}3cm$, $3{\sim}6cm$, and above 6cm). A validity of this model was verified by applying a fuel moisture variation after precipitation in this spring. In the result, $R^2$ was $0.76{\sim}0.92$. This model will be a useful for improvement of a forest fire danger rate forcast through a prediction a fule moisture in forest.

• Journal of the Korean Association of Geographic Information Studies
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• v.6 no.1
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• pp.12-23
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• 2003

The expansion of internet and the development of communication technology have brought about an explosive increasement of data. Further progress has led to the increasing demand for efficient and effective data analysis tools. According to this demand, data mining techniques have been developed to find out knowledge from a huge amounts of raw data. This paper suggests a generalization based classification method which explores rules from real world data appearing repeatedly. Also, it analyzed the relation between weather data and forest fire, and efficiently predicted through it as a prediction model by applying the suggested generalization based classification method to forest fire data. Additionally, the proposed method can be utilized variously in the important field of real life like the analysis and prediction on natural disaster occurring repeatedly, the prediction of energy demand and so forth.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2008.04a
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• pp.33-36
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• 2008

Recently with the increasing economics, many hish-rise and complicated building with new architecture features are constructed in Korea. These higher and bigger building have many difficulties of fire safety concerns, especially evacuation plan. In this paper, aims to present logical basis for forecasting egress behavior of occupants and systematized Egress-Prediction Model by analysing expectable decisive factors among influential factors each step until completing egress from perception of fire cues.

• Fire Protection Technology
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• s.10
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• pp.19-26
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• 1991

This reoprt intended to apprehend the principle for combustible phenomena in the environments and the prediction of its hazard in the virtual fire. So we first explained the basic machanism for the combustion, and discovered the tendency of the conbustion in the condition of the environmental factors(Humidity, Wind, Temperature) by means of some sxperiments about the wood as example.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2006.04a
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• pp.231-237
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• 2006

• Fire Science and Engineering
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• v.33 no.4
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• pp.50-58
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• 2019

The prediction performance of B-RISK was evaluated for the fire behaviors of combustibles in a compartment using Fire Dynamics Simulator (FDS). First of all, to predict the heat release rate (HRR) for two combustible sets, the HRR for one combustible set and the design fire curve were used as input values for B-RISK. Comparing results of B-RISK calculations with experimental data for two combustible sets, it was found that B-RISK results predicted insufficiently for fire growth rate of experimental data but there was good agreement for maximum HRR and total HRR with the experimental data. And the B-RISK results were used for input values of FDS to evaluate the fire behaviors of B-RISK results. Comparing results of FDS calculations with experimental data, the simulation results showed that the temperature and concentrations of O₂, CO₂ in the fire growth phase were different from the experimental data. However, when using the B-RISK result for percentile 70%, the simulation results sufficiently predicted the overall fire behaviors.