首页|Findings in Machine Learning Reported from Pacific Northwest National Laboratory (Machine Learning Methods for Particle Stress Development In Suspension Poiseuille Flows)
Findings in Machine Learning Reported from Pacific Northwest National Laboratory (Machine Learning Methods for Particle Stress Development In Suspension Poiseuille Flows)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - Investigators discuss new findings in Machine Learning. According to news originatingfrom Richland, Washington, by NewsRx correspondents, research stated, “Numerical simulations are usedto study the dynamics of a developing suspension Poiseuille flow with monodispersed and bidispersedneutrally buoyant particles in a planar channel, and machine learning is applied to learn the evolvingstresses of the developing suspension. The particle stresses and pressure develop on a slower time scalethan the volume fraction, indicating that once the particles reach a steady volume fraction profile, theyrearrange to minimize the contact pressure on each particle.”
RichlandWashingtonUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningPacific Northwest National Laboratory
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