Mapping the wind resource over UK cities

Decentralised energy sources, such as small-scale-wind energy, have a number of well-known advantages. However, within urban areas, the potential for energy generation from the wind is not currently fully utilised. One of the most significant reasons for this is that the complexity of air flows within the urban boundary layer makes accurate predictions of the wind resource difficult to achieve. Without sufficiently accurate methods of predicting this resource, there is a danger that wind turbines will either be installed at unsuitable locations or that many viable sites will be overlooked. In this paper, we compare the accuracy of three different analytical methodologies for predicting above-roof mean wind speeds across a number of UK cities. The first is based upon a methodology developed by the UK Meteorological Office. We then implement two more complex methods which utilise maps of surface aerodynamic parameters derived from detailed building data. The predictions are compared with measured mean wind speeds from a wide variety of UK urban locations. The results show that the methodologies are generally more accurate when more complexity is used in the approach, particularly for the sites which are well exposed to the wind. The best agreement with measured data is achieved when the influence of wind direction is thoroughly considered and aerodynamic parameters are derived from detailed building data. However, some uncertainties in the building data add to the errors inherent within the methodologies. Consequently, it is suggested that a detailed description of both the shapes and heights of the local building roofs is required to maximise the accuracy of wind speed predictions

Low-cost wind resource assessment for small-scale turbine installations using site pre-screening and short-term wind measurements

A two-stage approach to low-cost wind resource assessment for small-scale wind installations has been investigated in terms of its ability to screen for non-viable sites and to provide accurate wind power predictions at promising locations. The approach was implemented as a case study at ten UK locations where domestic-scale turbines were previously installed. In stage one, sites were pre-screened using a boundary-layer scaling model to predict the mean wind power density, including estimated uncertainties, and these predictions were compared to a minimum viability criterion. Using this procedure, five of the seven non-viable sites were correctly identified without direct onsite wind measurements and none of the viable sites were excluded. In stage two, more detailed analysis was carried out using 3 months onsite wind measurements combined with measure-correlate-predict (MCP) approaches. Using this process, the remaining two non-viable sites were identified and the available wind power density at the three viable sites was accurately predicted. The effect of seasonal variability on the MCPpredicted wind resource was considered and the implications for financial projections were highlighted. The study provides a framework for low-cost wind resource assessment in cases where long-term onsite measurements may be too costly or impractical

Optimised mixing and flow resistance during shear flow over a rib roughened boundary

A series of numerical investigations has been performed to study the effect of lower boundary roughness on turbulent flow in a two-dimensional channel. The roughness spacing to height ratio, w/k, has been investigated over the range 0.12 to 402 by varying the horizontal rib spacing. The square roughness elements each have a cross-sectional area of (0.05 H)2, where H is the full channel height. The Reynolds number, Reτ is fixed based on the value of the imposed pressure gradient, dp/dx, and is in the range 6.3 × 103 − 4.5 × 104. A Reynolds Averaged Navier–Stokes (RANS) based turbulence modelling approach is adopted using a commercial CFD code, ANSYS-CFX 14.0. Measurements of eddy viscosity and friction factor have been made over this range to establish the optimum spacings to produce maximum turbulence enhancement, mixing and resistance to flow. These occur when w/k is approximately 7. It is found that this value is only weakly dependent on Reynolds number, and the decay rate of turbulence enhancement as a function of w/k ratio beyond this optimum spacing is slow. The implications for heat transfer design optimisation and particle transport are considered

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease