Simulations of Vortex Evolution and Phase Slip in Oscillatory Potential Flow of the Superfluid Component of Helium-4 Through an Aperture

The evolution of semicircular quantum vortex loops in oscillating potential flow emerging from an aperture is simulated in some highly symmetrical cases. As the frequency of potential flow oscillation increases, vortex loops that are evolving so as eventually to cross all of the streamlines of potential flow are drawn back toward the aperture when the flow reverses. As a result, the escape size of the vortex loops, and hence the net energy transferred from potential flow to vortex flow in such 2 Pi phase-slip events, decreases as the oscillation frequency increases. Above some aperture-dependent and flow-dependent threshold frequency, vortex loops are drawn back into the aperture. Simulations are preformed using both radial potential flow and oblate-spheroidal potential flow.Comment: 18 pages, 6 figures, sequel to cond-mat/050203

Nitrate stable isotopes and major ions in snow and ice samples from four Svalbard sites

Increasing reactive nitrogen (N-r) deposition in the Arctic may adversely impact N-limited ecosystems. To investigate atmospheric transport of N-r to Svalbard, Norwegian Arctic, snow and firn samples were collected from glaciers and analysed to define spatial and temporal variations (1 10 years) in major ion concentrations and the stable isotope composition (delta N-15 and delta O-18) of nitrate (NO3-) across the archipelago. The delta N-15(NO3-) and delta O-18(NO3-) averaged -4 parts per thousand and 67 parts per thousand in seasonal snow (2010-11) and -9 parts per thousand and 74 parts per thousand in firn accumulated over the decade 2001-2011. East-west zonal gradients were observed across the archipelago for some major ions (non-sea salt sulphate and magnesium) and also for delta N-15(NO3-) and delta O-18(NO3-) in snow, which suggests a different origin for air masses arriving in different sectors of Svalbard. We propose that snowfall associated with long-distance air mass transport over the Arctic Ocean inherits relatively low delta N-15(NO3-) due to in-transport N isotope fractionation. In contrast, faster air mass transport from the north-west Atlantic or northern Europe results in snowfall with higher delta N-15(NO3-) because in-transport fractionation of N is then time-limited

Machine learning for estimation of building energy consumption and performance:a review

Ever growing population and progressive municipal business demands for constructing new buildings are known as the foremost contributor to greenhouse gasses. Therefore, improvement of energy eciency of the building sector has become an essential target to reduce the amount of gas emission as well as fossil fuel consumption. One most eective approach to reducing CO2 emission and energy consumption with regards to new buildings is to consider energy eciency at a very early design stage. On the other hand, ecient energy management and smart refurbishments can enhance energy performance of the existing stock. All these solutions entail accurate energy prediction for optimal decision making. In recent years, articial intelligence (AI) in general and machine learning (ML) techniques in specic terms have been proposed for forecasting of building energy consumption and performance. This paperprovides a substantial review on the four main ML approaches including articial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance