首页|University of Bayreuth Researcher Provides New Data on Machine Learning (The imp act of turbulent transport efficiency on surface vertical heat fluxes in the Arc tic stable boundary layer predicted from similarity theory and machine-learning)
University of Bayreuth Researcher Provides New Data on Machine Learning (The imp act of turbulent transport efficiency on surface vertical heat fluxes in the Arc tic stable boundary layer predicted from similarity theory and machine-learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on ar tificial intelligence. According to news originating from Bayreuth, Germany, by NewsRx correspondents, research stated, "We analyzed 14 days of observations fro m sonic anemometry and high-resolution fiber optic distributed sensing collected in the stable polar boundary layer (SBL). The study sought to evaluate if and u nder which conditions the sensible heat flux is related to the temperature gradi ent." Our news editors obtained a quote from the research from University of Bayreuth: "Machine learning methods were employed to identify drivers of and model heat f luxes. We found the recently proposed coupling metric O defined as the ratio of the buoyancy length scale and measurement height to delineate physically meaning ful transport regimes. The regime transition marks the point where static stabil ity in addition to the vertical turbulence strength control the heat transport, which is rather gradual than abrupt. The maximum downward heat flux is reached w hen one third of turbulent eddies exceed the opposing buoyancy forces in the SBL . We found evidence that even for large O a substantial fraction of the turbulen t transport is non-equilibrium. The non-dimensional temperature gradient is bett er explained by variations in O than z = zL-1 from Monin-Obukhov Similarity theory."
University of BayreuthBayreuthGerman yEuropeCyborgsEmerging TechnologiesMachine Learning
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