Dynamic modelling of a wind catcher/tower turret for natural ventilation

This paper discusses experimental and theoretical investigations and Computational Fluid Dynamics (CFD) modelling considerations to evaluate the performance of a square section wind catcher system connected to the top of a test room for the purpose of natural ventilation. The magnitude and distribution of pressure coefficients (C-p) around a wind catcher and the air flow into the test room were analysed. The modelling results indicated that air was supplied into the test room through the wind catcher's quadrants with positive external pressure coefficients and extracted out of the test room through quadrants with negative pressure coefficients. The air flow achieved through the wind catcher depends on the speed and direction of the wind. The results obtained using the explicit and AIDA implicit calculation procedures and CFX code correlate relatively well with the experimental results at lower wind speeds and with wind incidents at an angle of 0 degrees. Variation in the C-p and air flow results were observed particularly with a wind direction of 45 degrees. The explicit and implicit calculation procedures were found to be quick and easy to use in obtaining results whereas the wind tunnel tests were more expensive in terms of effort, cost and time. CFD codes are developing rapidly and are widely available especially with the decreasing prices of computer hardware. However, results obtained using CFD codes must be considered with care, particularly in the absence of empirical data.

On variational data assimilation in continuous time

Variational data assimilation in continuous time is revisited. The central techniques applied in this paper are in part adopted from the theory of optimal nonlinear control. Alternatively, the investigated approach can be considered as a continuous time generalization of what is known as weakly constrained four-dimensional variational assimilation (4D-Var) in the geosciences. The technique allows to assimilate trajectories in the case of partial observations and in the presence of model error. Several mathematical aspects of the approach are studied. Computationally, it amounts to solving a two-point boundary value problem. For imperfect models, the trade-off between small dynamical error (i.e. the trajectory obeys the model dynamics) and small observational error (i.e. the trajectory closely follows the observations) is investigated. This trade-off turns out to be trivial if the model is perfect. However, even in this situation, allowing for minute deviations from the perfect model is shown to have positive effects, namely to regularize the problem. The presented formalism is dynamical in character. No statistical assumptions on dynamical or observational noise are imposed.

Sparse polynomial approximation in positive order Sobolev spaces with bounded mixed derivatives and applications to elliptic problems with random loading

In the present paper we study the approximation of functions with bounded mixed derivatives by sparse tensor product polynomials in positive order tensor product Sobolev spaces. We introduce a new sparse polynomial approximation operator which exhibits optimal convergence properties in L2 and tensorized View the MathML source simultaneously on a standard k-dimensional cube. In the special case k=2 the suggested approximation operator is also optimal in L2 and tensorized H1 (without essential boundary conditions). This allows to construct an optimal sparse p-version FEM with sparse piecewise continuous polynomial splines, reducing the number of unknowns from O(p2), needed for the full tensor product computation, to View the MathML source, required for the suggested sparse technique, preserving the same optimal convergence rate in terms of p. We apply this result to an elliptic differential equation and an elliptic integral equation with random loading and compute the covariances of the solutions with View the MathML source unknowns. Several numerical examples support the theoretical estimates.

The impact of milk proteins and peptides on blood pressure and vascular function: a review of evidence from human intervention studies.

CVD are the leading cause of death worldwide. Hypertension, a major controllable risk factor of CVD, is intimately associated with vascular dysfunction, a defect which is also now recognised to be a major, modifiable risk factor for the development of CVD. The purpose of the present review was to critically evaluate the evidence for the effects of milk proteins and their associated peptides on blood pressure (BP) and vascular dysfunction. After a detailed literature search, the number of human trials evaluating the antihypertensive effects of casein-derived peptides (excluding isoleucine-proline-proline and valine-proline-proline) was found to be limited; the studies were preliminary with substantial methodological limitations. Likewise, the data from human trials that examined the effects of whey protein and peptides were also scarce and inconsistent. To date, only one study has conducted a comparative investigation on the relative effects of the two main intact milk proteins on BP and vascular function. While both milk proteins were shown to reduce BP, only whey protein improved measures of arterial stiffness. In contrast, a growing number of human trials have produced evidence to support beneficial effects of both milk proteins and peptides on vascular health. However, comparison of the relative outcomes from these trials is difficult owing to variation in the forms of assessment and measures of vascular function. In conclusion, there is an accumulating body of evidence to support positive effects of milk proteins in improving and/or maintaining cardiovascular health. However, the variable quality of the studies that produced this evidence, and the lack of robust, randomised controlled intervention trials, undermines the formulation of firm conclusions on the potential benefits of milk proteins and peptides on vascular health.

Production of milk foams by steam injection: the effects of steam pressure and nozzle design

Foam properties depend on the physico-chemical characteristics of the continuous phase, the method of production and process conditions employed; however the preparation of barista-style milk foams in coffee shops by injection of steam uses milk as its main ingredient which limits the control of foam properties by changing the biochemical characteristics of the continuous phase. Therefore, the control of process conditions and nozzle design are the only ways available to produce foams with diverse properties. Milk foams were produced employing different steam pressures (100-280 kPa gauge) and nozzle designs (ejector, plunging-jet and confined-jet nozzles). The foamability of milk, and the stability, bubble size and texture of the foams were investigated. Variations in steam pressure and nozzle design changed the hydrodynamic conditions during foam production, resulting in foams having a range of properties. Steam pressure influenced foam characteristics, although the net effect depended on the nozzle design used. These results suggest that, in addition to the physicochemical determinants of milk, the foam properties can also be controlled by changing the steam pressure and nozzle design.