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- SourceAvailable from: Michael Thompson[Show abstract] [Hide abstract]
ABSTRACT: Corbard & Thompson analyzed quantitatively the strong radial differential rotation that exists in a thin layer near the solar surface. We investigate the role of this radial shear in driving a flux transport dynamo operating with such a rotation profile. We show that despite being strong, near-surface radial shear effectively contributes only ∼1 kG (∼30% of the total) to the toroidal fields produced there unless an abnormally high, surface a-effect is included. While 3 kG spot formation from ∼1–2 kG toroidal fields by convective collapse cannot be ruled out, the evolutionary pattern of these model fields indicates that the polarities of spots formed from the near-surface toroidal field would violate the observed polarity relationship with polar fields. This supports previous results that large-scale solar dynamos generate intense toroidal fields in the tachocline, from which buoyant magnetic loops rise to the photosphere to produce spots. Polar fields generated in flux transport models are commonly much higher than observed. We show here that by adding enhanced diffusion in the supergranulation layer (originally proposed by Leighton), near-surface toroidal fields undergo large diffusive decay preventing spot formation from them, as well as reducing polar fields closer to the observed values. However, the weaker polar fields lead to the regeneration of a toroidal field of less than ∼10 kG at the convection zone base, too weak to produce spots that emerge in low latitudes, unless an additional poloidal field is produced at the tachocline. This is achieved by a tachocline a-effect, previously shown to be necessary for coupling the north and south hemispheres to ensure toroidal and poloidal fields that are antisymmetric about the equator.
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ABSTRACT: By employing the SAFT-VR Mie equation of state, molecular-based models are developed from which the thermodynamic properties of pure (i.e., single-component) organic fluids and their mixtures are calculated. This approach can enable the selection of optimal working fluids in organic Rankine cycle (ORC) applications, even in cases for which experimental data relating to mixture properties are not available. After developing models for perfluoroalkane (n-C4F10+n-C10F22) mixtures, and validating these against available experimental data, SAFT-VR Mie is shown to predict accurately both the single-phase and saturation properties of these fluids. In particular, second-derivative properties (e.g., specific heat capacities), which are less reliably calculated by cubic equations of state (EoS), are accurately described using SAFT-VR Mie, thereby enabling an accurate prediction of important working-fluid properties such as the specific entropy. The property data are then used in thermodynamic cycle analyses for the evaluation of ORC performance and cost. The approach is applied to a specific case study in which a sub-critical, non-regenerative ORC system recovers and converts waste heat from a refinery flue-gas stream with fixed, predefined conditions. Results are compared with those obtained when employing analogue alkane mixtures (n-C4H10+n-C10H22) for which sufficient thermodynamic property data exist. When unlimited quantities of cooling water are utilized, pure perfluorobutane (and pure butane) cycles exhibit higher power outputs and higher thermal efficiencies compared to mixtures with perfluorodecane (or decane), respectively. The effect of the composition of a working-fluid mixture in the aforementioned performance indicators is non-trivial. Only at low evaporator pressures (<10bar) do the investigated mixtures perform better than the pure fluids. A basic cost analysis reveals that systems with pure perfluorobutane (and butane) fluids are associated with relatively high total costs, but are nevertheless more cost effective per unit power output than the fluid mixtures (due to the higher generated power). When the quantity of cooling water is constrained by the application, overall performance deteriorates, and mixtures emerge as the optimal working fluids.
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ABSTRACT: The UK's railway network is extensive and utilised by many millions of passen-gers each day. Passenger and train movements around the network create large amounts of data; details of tickets sold, passengers entering and exiting stations, train movements etc. Knowing how passengers want to use the network is critical in planning services that meet their requirements. However, understanding and managing the vast amounts of data gener-ated daily is not easy using traditional methods. We show how, utilising e-Science methods, it is possible to make understanding this data easier and help the various stakeholders within the rail industry to more accurately plan their operations and offer more efficient services that better meet the requirements of passengers.
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