باران باد در نمایی از برج تاریخی: شبیه سازی عددی، اعتبار سنجی تمام عیار و تجزیه و تحلیل حساسیت

Wind-driven rain (WDR) is one of the most important moisture sources that affect the hygrothermal performance and the durability of building facades. The facades of the Dutch monumental building St. Hubertus show severe deterioration caused by WDR. Assessment of the amount and intensity of WDR falling onto the facades is necessary as input for numerical heat-air-moisture (HAM) transfer models to analyse the causes of the moisture problems and the impact of remedial measures. In this study, a numerical simulation method based on Computational Fluid Dynamics (CFD) is used to predict the amount of WDR impinging on the south-west facade of the tower of the building. The paper focuses on the numerical simulation results, the validation of these results and their sensitivity to two parameters: the level of geometrical detailing of the computational building model and the upstream terrain aerodynamic roughness length. Validation is performed by comparison of the numerical results with a dataset obtained from on-site WDR measurements. It is shown that the CFD simulations provide fairly good predictions of the amount of WDR impinging on the south-west facade of the tower, except for the lower part. It is also shown that the local effects of geometrical facade details are significant and can yield differences in WDR exposure up to 40%, while their effect at other positions is negligible. Finally, the sensitivity of WDR simulations to the upstream aerodynamic roughness length is discussed.

Building Physics aims at providing a healthy, comfortable and sustainable indoor and outdoor environment of buildings. Sustainability also involves the durability of the building envelope that separates the indoor and the outdoor environment. Wind-driven rain (WDR) is one of the most important moisture sources that affect the hygrothermal performance and the durability of building facades [1], [2], [3], [4] and [5]. Numerical analysis of the hygrothermal behaviour with so-called HAM (heat-air-moisture) models requires accurate WDR data as boundary condition [1], [2], [3], [4], [5], [6], [7] and [8]. Three categories of methods exist for determining the WDR intensity that impinges on building facades: (1) measurements, (2) semi-empirical methods and (3) numerical methods based on Computational Fluid Dynamics (CFD). A literature review of each of these categories has been provided by Blocken and Carmeliet [5]. Measurements have always been the primary tool in WDR research, but are nowadays only rarely conducted. The most important reason is the fact that WDR measurements can easily suffer from large errors [3], [9], [10] and [11]. Recently, guidelines have been proposed that should be followed for selecting accurate and reliable WDR data from experimental WDR datasets [10] and [11]. The strict character of these guidelines, however, implies that only very few rain events in a WDR dataset are accurate and reliable and hence suitable for WDR studies. Other drawbacks of WDR measurements are the fact that they are time-consuming and the fact that measurements conducted at a particular building site have very limited application to other sites. Semi-empirical methods are an alternative to measurements. The main advantage of semi-empirical methods is their ease-of-use; their main disadvantage is that generally only rough estimates of the WDR exposure can be obtained [5]. Given the limitations of measurements and semi-empirical methods, in the past decades, numerical simulation with CFD has been explored. Choi [12], [13] and [14] developed and applied a steady-state numerical simulation technique based on CFD. It allows determining the spatial distribution of WDR on building facades for given (fixed) values of the wind speed, the wind direction and the horizontal rainfall intensity. Later, Choi's simulation technique was extended into the time domain by Blocken and Carmeliet [15] and [16]. In all of these studies, CFD simulations were based on the Reynolds-Averaged-Navier–Stokes (RANS) equations. Validation is an essential part of RANS CFD simulations. Up to now, only a few attempts have been made for CFD validation with full-scale WDR measurements [3], [5], [15], [17], [18] and [19]. van Mook [3] was the first to compare simulations with full-scale measurements at a few selected positions at the west facade of a wide, high-rise building. Blocken and Carmeliet [15] and [18] performed CFD WDR validation for a low-rise building with a sloped and a flat roof module, based on measurements with 24 WDR gauges installed at the south-west and north-west facades. Tang and Davidson [17] validated numerical simulations for the rather complex Cathedral of Learning with WDR measurements at 16 different positions. Most recently, Abuku et al. [19] employed the WDR measurement data by Nore et al. [20] for the validation of WDR simulations on the west facade of a low-rise rectangular test building with various angles of wind incidence. While some authors found significant discrepancies between simulations and measurements [3], others indicated a fair to good agreement [15], [17], [18] and [19]. While these efforts have provided valuable information, the need for additional WDR measurement data and CFD validation studies is still present, in particular for different types of buildings in different environment topographies [11] and [18]. This need is driven by the complex nature of WDR and the wide range of influencing parameters. Additionally, sensitivity studies are needed concerning the large amount of computational parameters that have to be set by the user in such CFD simulations. Two important questions are: (1) to what extent do geometrical facade details have to be included in the computational model of the building; and (2) how sensitive are the results to the estimate of the aerodynamic roughness length of the upstream terrain. The inclusion of facade details can present a challenge in CFD WDR studies, due to the large difference in length scales between the computational domain (up to several 100 m or km) and the facade details (down to a few cm) and the subsequent need for very fine grids near the building surface. The aerodynamic roughness length determines the shape of the inlet profiles of mean wind speed, turbulent kinetic energy and turbulence dissipation rate, as well as the physical roughness height kS that has to be applied at the bottom of the computational domain [21]. It is generally estimated from the updated Davenport roughness classification by Wieringa [22], based on a fetch (upstream distance) of at least 5 km ( Table 1). However, estimating a representative aerodynamic roughness length for heterogeneous terrain is difficult, and it is therefore rather easy to be one class off in this roughness classification. Table 1. Davenport classification of aerodynamic terrain roughness length as updated by Wieringa [22]. Values can be extracted from the table by a visual determination of the roughness class on condition that it is based on a fetch of at least 5 km. y0 (m) Landscape description 1 0.0002 Open sea or lake (irrespective of the wave size), tidal flat, snow-covered flat plain, featureless desert, tarmac, concrete, with a free fetch of several kilometres. Sea 2 0.005 Featureless land surface without any noticeable obstacles and with negligible vegetation; e.g. beaches, pack ice without large ridges, morass, and snow-covered or fallow open country. Smooth 3 0.03 Level country with low vegetation (e.g. grass) and isolated obstacles with separations of at least 50 obstacle heights; e.g. grazing land without windbreaks, heather, moor and tundra, runway area of airports. Open 4 0.10 Cultivated area with regular cover of low crops, or moderately open country with occasional obstacles (e.g. low hedges, single rows of trees, isolated farms) at relative horizontal distances of at least 20 obstacle heights. Roughly open 5 0.25 Recently-developed “young” landscape with high crops or crops of varying height, and scattered obstacles (e.g. dense shelterbelts, vineyards) at relative distances of about 15 obstacle heights. Rough 6 0.50 “Old” cultivated landscape with many rather large obstacle groups (large farms, clumps of forest) separated by open spaces of about 10 obstacle heights. Also low large vegetation with small interspaces such as bush land, orchards, young densely-planted forest. Very rough 7 1.0 Landscape totally and quite regularly covered with similar-size large obstacles, with open spaces comparable to the obstacle heights; e.g. mature regular forests, homogeneous cities or villages. Closed 8 ≥2.0 Centres of large towns with mixture of low-rise and high-rise buildings. Also irregular large forests with many clearings. Chaotic Table options In this paper, CFD simulations of WDR on the south-west facade of the tower of Hunting Lodge St. Hubertus (Fig. 1a) are performed. It is a monumental building situated in the National Park “De Hoge Veluwe”. Especially the south-west facade of the building shows severe deterioration caused by WDR and subsequent phenomena such as rain penetration, mould growth, frost damage, salt crystallisation and efflorescence, and cracking due to hygrothermal gradients (Fig. 1b–e). The CFD WDR simulations are important to obtain accurate spatial and temporal distribution records of WDR, to be used as input for numerical HAM transfer simulations. These simulations will be used in a later stage to analyse the causes of the moisture problems and to assess the impact of remedial measures. Validation of the CFD simulations is performed by WDR measurements at a few selected locations at the south-west facade. In addition, the influence of neglecting all protruding and recessed facade details and of shifting the estimated upstream aerodynamic roughness length one class higher or lower, is investigated. This can provide some guidance for future CFD WDR simulations. Full-size image (105 K) Fig. 1. (a) Hunting Lodge St. Hubertus and (b–e) moisture damage at the tower due to wind-driven rain: (b) salt efflorescence; (c) cracking/blistering due to salt crystallisation; (d) rain penetration and discolouration; (e) cracking at inside surface. Figure options In Section 2 of the paper, the definitions of the specific catch ratio, the catch ratio and the influencing parameters are given. The building geometry and the surrounding topography are described in Section 3. In Section 4, the measurement set-up for wind, rain and WDR is described. The extended WDR simulation model, the simulation characteristics and settings and the selected rain events are described in Section 5. The simulation results are compared with the measurements in Section 6. This section also contains the results of the sensitivity study. The paper ends with a discussion (Section 7) and the conclusions (Section 8).

CFD simulations of wind-driven rain (WDR) on the south-west facade of a monumental building tower have been performed. The simulations have been compared with full-scale measurements and a sensitivity analysis has been conducted concerning the level of geometrical facade detailing and the upstream terrain aerodynamic roughness length. The following conclusions are made: The numerical and the experimental results have been compared in terms of the catch ratio value at the end of each rain event. At the upper part of the south-west tower facade, the differences between the catch ratio values range from 14% to 30%. Given the complex nature of WDR and the large number of influencing parameters, this is considered a fair agreement. Larger discrepancies however have been observed at the lower position, where the simulations underestimate the amount of WDR by more than 50%. This underestimation is mainly attributed to the turbulent dispersion of the raindrops, which is not included in the model. This is considered particularly important for the lower part of the facades of high buildings. Including the facade details yields significant improvements (up to 14%) at the measurement positions 5 and 6, which are in the direct vicinity of these details. A more detailed comparison shows that very close to the details i.e. just below the openings at the loggias on the top floor of the tower, catch ratios increase by 40% for U10 = 3 m/s. The effect of the details is less pronounced for higher wind speeds (U10 = 10 m/s). No conclusions can be drawn for lower parts of the facade due to the fact that turbulent dispersion was neglected, and modelling accuracy is low at these locations. When the different inlet mean wind speed profiles are matched at the height of interest, the WDR simulation results at this height appear quite insensitive (differences less than 10%) to changes in upstream aerodynamic terrain roughness length y0 from 0.5 m to 1 m and 2 m. However, wind speed measurements are often conducted at 10 m height and this will generally be the matching height. In that case, changes will probably be significant and a correct estimate of y0 is important. These conclusions apply to the present situation of building and surroundings. More research is needed to expand the validity of these findings.