• Proceedings of the Korea Institute of Fire Science and Engineering Conference
• /
• 1996.11a
• /
• pp.15-18
• /
• 1996

A general description of sprinkler activation time in compartment-fire-generated smoke layers is made. For calculation of the time hot layer temperature is obtained from two-layer zonal model and time constant of sprinkler is measured. Upper-layer thickness at the instant of sprinkler activation is also presented with changes of opening area. The outputs of the present study provide inputs for the interaction modeling of sprinkler spray and compartment fire environment, which simulates fire suppression phenomena.

• 한국전산유체공학회:학술대회논문집
• /
• 2008.03b
• /
• pp.358-361
• /
• 2008

A study is made of thermal plume flow model for the development of helicopter simulator over the forest fire. For numerical analysis, the Boussinesq fluid approximation and line fire model, which is assumed by the shape of forest fire spreading, are adopted. Comparing 3-D full numerical solutions with 2-D similarity solution, it has been built a new model that is capable of temperature prediction along the symmetric vertical axis in both cases of laminar and turbulent flows.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
• /
• 2002.05a
• /
• pp.174-179
• /
• 2002

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
• /
• 2002.05a
• /
• pp.3-9
• /
• 2002

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
• /
• 2012.04a
• /
• pp.397-399
• /
• 2012

• Fire Science and Engineering
• /
• v.21 no.2 s.66
• /
• pp.111-117
• /
• 2007

Full scale fire suppression test by water mist system were performed in machinery engine room ($20m{\times}15m{\times}10m$) according to IMO MSC/circ. 1165. The K-factor and operating pressure were 2.4 and 80 bar respectively. To assess the prediction capability of numerical simulation, FDS simulation was performed at the same operating condition with the full scale experiment. It was found that FDS simulation had the limitation for the fire extinguishing time prediction but was able to predict the spatial temperature distribution.

• Journal of Korean Society of Forest Science
• /
• v.87 no.4
• /
• pp.528-534
• /
• 1998

There are many parameters in prediction of forest fire spread. Among others wind and slope factors are considered to be the important parameters in spread of forest fire. Generally, all the inclined planes with same slopes can not have the same wind velocity in complex mountain area. But this effect has been disregarded in complex geometry. In this paper, wind values which have velocity and direction is calculated by applying computational fluid dynamics to the forest geometry. These results are applied for forest fire spreading algorithm with experimental Korean ROS(Rate Of Spread). Finally, the comparison between the simulation and the real forest fire has correspondence about 90%.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.23 no.1
• /
• pp.65-70
• /
• 2023

In this paper, we used a deep learning model to detect and segment flame and smoke in real time from fires. To this end, well known U-NET was used to separate and divide the flame and smoke of the fire using multi-class. As a result of learning using the proposed technique, the values of loss error and accuracy are very good at 0.0486 and 0.97996, respectively. The IOU value used in object detection is also very good at 0.849. As a result of predicting fire images that were not used for learning using the learned model, the flame and smoke of fire are well detected and segmented, and smoke color were well distinguished. Proposed method can be used to build fire prediction and detection system.

• Steel and Composite Structures
• /
• v.44 no.6
• /
• pp.769-788
• /
• 2022

In this paper a mathematical model for the prediction of the fire resistance of slim-floor steel beams based on an Artificial Neural Network modeling procedure is presented. The artificial neural network models are trained and tested using an analytical database compiled for this purpose from analytical results based on FEM. The proposed model was selected as the optimum from a plethora of alternatives, employing different activation functions in the context of Artificial Neural Network technique. The performance of the developed model was compared against analytical results, employing several performance indices. It was found that the proposed model achieves remarkably improved predictions of the fire resistance of slim-floor steel beams. Moreover, based on the optimum developed AN model a closed-form equation for the estimation of fire resistance is derived, which can prove a useful tool for researchers and engineers, while at the same time can effectively support the teaching of this subject at an academic level.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2013.05a
• /
• pp.65-66
• /
• 2013

As a result of executing Cone Calorimeter experiment on 12 samples among combustibles of domestic residential facilities, flooring materials showed higher HRR and THR than wall papers, and in case of toxicity and SPR, wall papers having adhesive components in one side by considering use conveniences were measured high.