首页|Reports from Bhabha Atomic Research Centre Add New Study Findings to Research in Machine Learning [Generic flame extension model development based on machine learning for NPPs fire hazard analysis (FHA)]
Reports from Bhabha Atomic Research Centre Add New Study Findings to Research in Machine Learning [Generic flame extension model development based on machine learning for NPPs fire hazard analysis (FHA)]
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NETL
NSTL
Walter De Gruyter
Data detailed on artificial intelligence have been presented. According to news originating from Mumbai, India, by NewsRx correspondents, research stated, “In enclosed fire situations, the flame interacts with ceiling and its length extension take place, altering the fire severity significantly. Fire safety analysis demand a generic flame extension model.” The news editors obtained a quote from the research from Bhabha Atomic Research Centre: “A generic reliable model is not available as the construct of flame itself had wide variation in literature. The present paper aims to develop a Machine Learning (ML) based generic model of non-dimensional flame extension as a function of non-dimensional Heat Release Rate (HRR). The non-dimensional scaling reduces the number of parameter and also provide a generic nature. Literature review was utilized to collect the data from various open literature sources. This eliminates the limitations of individual correlations and gives a best optimized model which is valid for a wide range of flow regimes and conditions as compared to a specific correlation. Various simple ML models are compared for their performance against test data and a MARS based model was finally recommended. The MARS model was tested against the data which was not used in training and also against the other reported correlation.”
Bhabha Atomic Research CentreMumbaiIndiaAsiaCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
Walter De Gruyter
