首页|Study Results from Imperial College London Update Understanding of Machine Learn ing (Simulation of Turbulent Premixed Flames With Machine Learning - Tabulated T hermochemistry)
Study Results from Imperial College London Update Understanding of Machine Learn ing (Simulation of Turbulent Premixed Flames With Machine Learning - Tabulated T hermochemistry)
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NETL
NSTL
Elsevier
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on Machine Learning are pre sented in a new report. According to news reporting from London, United Kingdom, by NewsRx journalists, research stated, “The numerical integration of the diffe rential equations describing chemical kinetics consumes the majority of computat ional time in combustion simulations that involve direct coupling of chemistry a nd flow, such as transported probability density function (PDF) methods, direct numerical simulation (DNS), conditional moment closure (CMC), unsteady flamelet, multiple mapping closure (MMC), thickened flame model, linear eddy model (LEM), partially stirred reactor (PaSR) as in OpenFOAM and laminar flame computation. This step can be accelerated by tabulation, and artificial neural networks (ANNs ) have recently emerged as a powerful technique in this domain.”
LondonUnited KingdomEuropeChemistr yCyborgsEmerging TechnologiesMachine LearningThermochemistryImperial C ollege London
NETL
NSTL
Elsevier
