Wall-resolved versus wall-modeled LES of the flow field and surface forced convective heat transfer for a low-rise building

Large eddy simulation (LES) is widely used to investigate the aerodynamics and convective heat transfer (CHT) at the surfaces of sharp-edged bluff bodies for a wide range of Reynolds (Re) numbers. Due to the heavy computational costs associated with implicit filtering in LES at high Reynolds number flows (Re ≥ 105), wall-modeled (WM) rather than wall-resolved (WR) LES is often adopted. However, the performance of LES-WM for such applications has not yet been systematically investigated. Therefore, this study evaluates the performance of LES-WM and LES-WR for the flow and thermal field at the facades of a low-rise building immersed in an atmospheric boundary layer. Four grids are constructed for LES-WM, each employing different resolution at the building surfaces reaching maximum non-dimensional wall distance y+ = 43, 57, 70, and 95. In addition, the performance of two wall functions, namely the Werner and Wengle and the enhanced wall function is investigated. The results show that the use of LES-WM can result in significant deviations in the predicted near-facade flow pattern and the surface convective heat transfer coefficient (CHTC). Grid resolution significantly impacts the CHTC results and deviations go up to 88% (at the base of the windward facade). Considerable deviations among the employed wall functions are apparent only on the finest grid. In this case, the implementation of the enhanced wall function indicates better performance compared to the non-blended law of the wall (combined with the Werner and Wengle) for CHTC in the regions of the leeward facade where the flow remains attached to the wall. The deviation of the enhanced wall function for surface-averaged CHTC is found to be 10.8% against the wall-resolved LES results, while for the non-blended law of the wall this is 19.2%.</p

Mapping mean total annual precipitation in Belgium, by investigating the scale of topographic control at the regional scale

Accurate precipitation maps are essential for ecological, environmental, element cycle and hydrological models that have a spatial output component. It is well known that topography has a major influence on the spatial distribution of precipitation and that increasing topographical complexity is associated with increased spatial heterogeneity in precipitation. This means that when mapping precipitation using classical interpolation techniques (e.g. regression, kriging, spline, inverse distance weighting, etc.), a climate measuring network with higher spatial density is needed in mountainous areas in order to obtain the same level of accuracy as compared to flatter regions. In this study, we present a mean total annual precipitation mapping technique that combines topographical information (i.e. elevation and slope orientation) with average total annual rain gauge data in order to overcome this problem. A unique feature of this paper is the identification of the scale at which topography influences the precipitation pattern as well as the direction of the dominant weather circulation. This method was applied for Belgium and surroundings and shows that the identification of the appropriate scale at which topographical obstacles impact precipitation is crucial in order to obtain reliable mean total annual precipitation maps. The dominant weather circulation is determined at 260°. Hence, this approach allows accurate mapping of mean annual precipitation patterns in regions characterized by rather high topographical complexity using a climate data network with a relatively low density and/or when more advanced precipitation measurement techniques, such as radar, aren't available, for example in the case of historical data

Assessing the performance of UAS-compatible multispectral and hyperspectral sensors for soil organic carbon prediction

Soil laboratory spectroscopy has proved its reliability for the estimation of soil organic carbon (SOC) by exploiting the relationship between electromagnetic radiation and key spectral features of organic matter located in the VIS-NIR-SWIR (350-2500 nm) region. It currently allows estimating soil variables at sampled points, however geo-statistical techniques have to be used to infer continuous spatial information on soil properties. In this regard, the use of proximal or remote sensing data could be very useful to provide detailed spectral sampling on soil spatial variability at the field or even regional scale. However, the factors affecting the quality of spectral acquisition in outdoor conditions need to be taken into account. In this perspective, we designed a study to investigate the capabilities of two portable hyperspectral sensors (STS-VIS and STS-NIR), and two multispectral cameras with narrow bands in the VIS-NIR region (Parrot Sequoia and Mini-MCA6), against a more sensitive reference hyper-spectral sensor (ASD Fieldspec-Pro 3) to provide data for SOC modelling from ground-based measurements. The aim of the comparison was to assess the performance of Partial Least Squares Regression (PLSR) models, when moving from laboratory to outdoor conditions, namely changing illumination, air conditions and sensor distance. Moreover, to verify the transferability of the prediction models between different measurement setups, we tested a methodology to align spectra acquired under different conditions (laboratory and outdoor) or by different instruments, by means of a calibration factor based on an internal soil standard. The results, in terms of Ratio of Performance to Deviation (RPD), showed that: i) the best performance for SOC modelling under outdoor conditions were obtained using the VIS-NIR range (RPD: 4.2), while the addition of the SWIR region resulted in a worsening of the prediction accuracy (RPD: 2.9); ii) modelling on the narrow bands of the two multispectral cameras (Parrot Sequoia and Tetracam Mini-MCA6) gave better performances (RPD: 4.2 and 3.4 respectively) than with the STS hyperspectral sensors (RPD: 2.6); iii) the STS employment in the outdoor benefitted from a laboratory model calibration adopting a spectral transfer using an internal soil standard, with the RPD increasing from 1.4 to 2.9 after the alignment. We therefore suggest that the employment of VIS-NIR-based portable instrument could be a strategy to obtain accurate and spatially distributed SOC data. Moreover, the perspective of their employment on UAS could represent a cost-effective solution for precision farming applications