RPSO 알고리즘을 이용한 탄화 재료의 열분해 물성치 추정Estimation of the Properties for a Charring Material Using the RPSO Algorithm
- Authors
- 장희철; 박원희; 윤경범; 김태국
- Issue Date
- Feb-2011
- Publisher
- 한국유체기계학회
- Keywords
- Charring material(탄화 재료); Genetic algorithm(유전 알고리즘); Inverse material Estimation(역물성치 추정); Pyrolysis model(열분해 모델); Repulsive particle swarm optimization(반발 입자 군집 최적화); Charring material(탄화 재료); Genetic algorithm(유전 알고리즘); Inverse material Estimation(역물성치 추정); Pyrolysis model(열분해 모델); Repulsive particle swarm optimization(반발 입자 군집 최적화)
- Citation
- 한국유체기계학회 논문집, v.14, no.1, pp 34 - 41
- Pages
- 8
- Journal Title
- 한국유체기계학회 논문집
- Volume
- 14
- Number
- 1
- Start Page
- 34
- End Page
- 41
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/38092
- ISSN
- 2287-9706
- Abstract
- Fire characteristics can be analyzed more realistically by using more accurate properties related to the fire dynamics and one way to acquire these fire properties is to use one of the inverse property estimation techniques. In this study two optimization algorithms which are frequently applied for the inverse heat transfer problems are selected to demonstrate the procedure of obtaining pyrolysis properties of charring material with relatively simple thermal decomposition. Thermal decomposition is occurred at the surface of the charring material heated by receiving the radiative energy from external heat sources and in this process the heat transfer through the charring material is simplified by an unsteady 1-dimensional problem. The basic genetic algorithm(GA) and repulsive particle swarm optimization(RPSO) algorithm are used to find the eight properties of a charring material; thermal conductivity(virgin, char), specific heat(virgin, char), char density, heat of pyrolysis, pre-exponential factor and activation energy by using the surface temperature and mass loss rate history data which are obtained from the calculated experiments. Results show that the RPSO algorithm has better performance in estimating the eight pyrolysis properties than the basic GA for problems considered in this study.
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