Evaluation of a Heat Release Rate based on Massively Generated Simulations and Machine Learning Approach
Mateusz Fliszkiewicz, Adam Krasuski, Karol Krenski
DOI: http://dx.doi.org/10.15439/2014F475
Citation: Position Papers of the 2014 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 3, pages 45–52 (2014)
Abstract. We present an approach for evaluation of a heat release rate of compartment fires. The approach is based on the idea of matching the actual condition of the fire to the pre-generated CFD simulations. We use an IR image of imprint of the temperature on the ceiling as a similarity relationship between actual fire and the set of the simulations. We extract the invariants, features and similarity measures of the fires using machine learning approach.