首页|Investigators from University of South Carolina Target Machine Learning (Explain able and Reduced-feature Machine Learning Models for Shape and Drag Prediction o f a Freely Moving Drop In the Sub-critical Weber Number Regime)
Investigators from University of South Carolina Target Machine Learning (Explain able and Reduced-feature Machine Learning Models for Shape and Drag Prediction o f a Freely Moving Drop In the Sub-critical Weber Number Regime)
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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 tonews reporting originating from Columbia, South Carolina, by NewsRx correspondents, research stated,“Accurately predictin g the shape and drag of a moving drop is crucial in many spray applications. However, due to the complex interaction between the drag force and drop shape defor mation, accurateprediction of drop shape and drag from Computational Fluid Dyna mics (CFD) simulation requires largecomputational resources and time.”
ColumbiaSouth CarolinaUnited StatesNorth and Central AmericaComputational Fluid DynamicsCyborgsEmerging Tech nologiesFluid MechanicsMachine LearningUniversity of South Carolina
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