ABSTRACT
Modeling to predict flame spread and fire growth is an active area of research in Fire Safety Engineering. A significant limitation to current approaches has been the lack of thermophysical material properties necessary for the simplified pyrolysis models embedded within the models. Researchers have worked to derive physical properties such as density, specific heat capacity, and thermal conductivity from data obtained using bench-scale fire tests such as Thermo-Gravimetric Analysis (TGA). While Genetic Algorithms (GA) have been successfully used to solve for constants in empirical models, it has been shown that the resulting parameters are not valid individually as material properties, especially for complex materials such as wood. This paper describes an alternate approach using Genetic Programming (GP) to automatically derive a mass loss model directly from TGA data.
- Holladay, K., Robbins, K. and Ronne, J.v. FIFTH™: A stack based GP language for vector processing. Ebner, O. N., Ekart, Vanneschi and Esparcia Alcazar ed. EuroGP 2007, Springer, Valencia, Spain, 2007. Google ScholarDigital Library
- Lautenberger, C., Rein, G. and Fernandez-Pello, C. The Application of a Genetic Algorithm to Estimate Material Properties for Fire Modeling from Bench-Scale Fire Test Data. Fire Safety Journal, 41. 204.Google Scholar
- McGrattan, K., Hostikka, S., Floyd, J., et al. Fire Dynamics Simulator (Version 5), Technical Reference Guide National Institute of Standards and Technology, Gaithersburg, Maryland, 2009.Google Scholar
- Rein, G., Lautenberger, C., Fernandez-Pello, C., et al. Application of Genetic Algorithms and Thermogravimetry to Determine the Kinetics of Polyurethane Foam in Smoldering Combustion. Combustion and Flame, 146 (1-2). 95--108.Google ScholarCross Ref
- Sharp, J. M., Janssens, M. and Holladay, K. Parameter Estimation to Obtain Apparent Thermophysical Properties of Materials Used in Fire-Resistant Construction 12th International Conference on Fire and Materials, San Francisco, CA, 2011.Google Scholar
- Stoliarov, S., Crowley, S. and Walters, R. A Model of Burning for Charring Polymers INTERFLAM 2010 Fire Science & Engineering Conference, University of Nottingham, UK, 2010, 509--518.Google Scholar
- Webster, R., Lazaro, M. and Alvear, D. Limitations in Current Parameter Estimation Techniques for Pyrolysis Modeling 6th International Seminar on Fire and Explosion Hazards, University of Leeds, UK, 2010.Google Scholar
Recommendations
A Comparison of three evolutionary strategies for multiobjective genetic programming
We report what we believe to be the first comparative study of multi-objective genetic programming (GP) algorithms on benchmark symbolic regression and machine learning problems. We compare the Strength Pareto Evolutionary Algorithm (SPEA2), the Non-...
Genetic Programming for Estimation of Heat Flux between the Atmosphere and Sea Ice in Polar Regions
GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary ComputationThe Earth surface and atmosphere exchange heat via turbulent fluxes. An accurate description of the heat exchange is essential in modelling the weather and climate. In these models the heat fluxes are described applying the Monin-Obukhov similarity ...
Neural network crossover in genetic algorithms using genetic programming
AbstractThe use of genetic algorithms (GAs) to evolve neural network (NN) weights has risen in popularity in recent years, particularly when used together with gradient descent as a mutation operator. However, crossover operators are often omitted from ...
Comments