首页|Reports Outline Machine Learning Findings from West Virginia University (Towards a Machine Learning Model To Predict the Laminar Flame Speed of Fuel Blends and Vented Gases In Lithium-ion Batteries)
Reports Outline Machine Learning Findings from West Virginia University (Towards a Machine Learning Model To Predict the Laminar Flame Speed of Fuel Blends and Vented Gases In Lithium-ion Batteries)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Current study results on Machine Learn ing have been published. According to newsoriginating from Morgantown, West Vir ginia, by NewsRx correspondents, research stated, “A data-drivenmachine learnin g (ML) model for predicting laminar flame speeds (LFS) of common fuel-air mixtur es isdeveloped, with a major advantage of being convenient and prompt to be use d at various temperatures,pressures, equivalence ratios and various composition s for both single and multi-compounds fuels. Specifically,combining (ⅰ) Cantera , an open-source software for modeling chemical kinetics, thermodynamics,and tr ansport processes and (ⅱ) the regression learner model, the newly developed mod el is able to predictthe LFS for various fuels and fuel blends, including those of hydrogen, hydrocarbons such as methane,ethane, propane as well as combustib le gases from lithium-ion batteries.”
MorgantownWest VirginiaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningW est Virginia University
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