• Journal of the Korea Society for Simulation
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• v.22 no.2
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• pp.101-107
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• 2013

Because forest fires are predicted to increase in severity and frequency under global climate change with important environmental implications, an understanding of fire dynamics is critical for mitigation of these negative effects. For the reason, researchers with different background, such as ecologists, physicists, and mathematical biologists, have developed the simulation models to mimic the forest fire spread patterns. In this study, we suggested a novel model considering the wind effect. Our theoretical forest was comprised of two different tree species with varying probabilities of transferring fire that were randomly distributed in space at densities ranging from 0.0 (low) to 1.0 (high). We then studied the distributional patterns of burnt trees using a two-dimensional stochastic cellular automata model with minimized local rules. We investigated the time, T, that the number of burnt trees reaches 25% of the whole trees for different values of the initial tree density, fire transition probability, and the degree of wind strength. Simulation results showed that the values of T decreased with the increase of tree density, and the wind effect decreased in the case of too high or low tree density. We believe that our model can be a useful tool to explore forest fire spreading patterns.

• Fire Science and Engineering
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• v.34 no.2
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• pp.86-93
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• 2020

This study aims to maintain the safety of an outdoor storage tank through the fundamental case analysis of explosion and fire accidents in the storage tank. We consider an accident caused by the explosion of fire inside the tank, as a result of the gradual spreading of the residual fire generated by wind lamps flying off a workplace in the storage tank yard. To determine the cause of the accident, atmospheric diffusion conditions were derived through CCTV image analysis, and the wind direction was analyzed using computational fluid dynamics. Additionally, the amount of oil vapor inside the tank when the floating roof was at the lowest position, and the behavior of the vapor inside the tank when the floating roof was at the highest position were investigated. If the cause of the explosion in the storage tank is identified and the level of the storage tank is maintained below the internal floating roof, dangerous liquid fills the storage tank, and the vapor in the space may stagnate on the internal floating roof. We intend to improve the operation procedure such that the level of the storage tank is not under the Pontoon support, as well as provide measures to prevent flames from entering the storage tank by installing a flame arrester in the open vent of the tank.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.4
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• pp.50-58
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• 2017

Recently, some methods of converging heterogeneous fire sensor data have been proposed for effective fire detection, but the rule-based methods have low adaptability and accuracy, and the fuzzy inference methods suffer from detection speed and accuracy by lack of consideration for images. In addition, a few image-based deep learning methods were researched, but it was too difficult to rapidly recognize the fire event in absence of cameras or out of scope of a camera in practical situations. In this paper, we propose a novel fire detection system combining a deep learning algorithm based on CNN and fuzzy inference engine based on heterogeneous fire sensor data including temperature, humidity, gas, and smoke density. we show it is possible for the proposed system to rapidly detect fire by utilizing images and to decide fire in a reliable way by utilizing multi-sensor data. Also, we apply distributed computing architecture to fire detection algorithm in order to avoid concentration of computing power on a server and to enhance scalability as a result. Finally, we prove the performance of the system through two experiments by means of NIST's fire dynamics simulator in both cases of an explosively spreading fire and a gradually growing fire.

• Wind and Structures
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• v.5 no.2_3_4
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• pp.291-300
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• 2002

An efficient large eddy simulation algorithm is used to compute surface pressure distributions on an eleven story (target) building on the NIST campus. Local meteorology, neighboring buildings, topography and large vegetation (trees) all play an important part in determining the flows and therefore the pressures experienced by the target. The wind profile imposed at the upstream surface of the computational domain follows a power law with an exponent representing a suburban terrain. This profile accounts for the flow retardation due to friction from the surface of the earth, but does not include fluctuations that would naturally occur in this flow. The effect of neighboring buildings on the time dependent surface pressures experienced by the target is examined. Comparison of the pressure fluctuations on the single target building alone with those on the target building in situ show that, owing to vortices shed by the upstream buildings, fluctuations are larger when such buildings are present. Even when buildings are lateral to or behind the target, the pressure disturbances generate significantly different flows around this building. A simple grid-free mathematical model of a tree is presented in which the trunk and the branches are each represented by a collection of spherical particles strung together like beads on a string. The drag from the tree, determined as the sum of the drags of the component particles, produces an oscillatory, spreading wake of slower fluid, suggesting that the behavior of trees as wind breakers can be modeled usefully.