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  • 1.
    Häggkvist, Kenneth
    et al.
    SMHI, Professional Services.
    Svensson, Urban
    SMHI, Research Department, Oceanography.
    Taesler, Roger
    SMHI, Research Department.
    NUMERICAL SIMULATIONS OF PRESSURE FIELDS AROUND BUILDINGS1989In: Building and Environment, ISSN 0360-1323, E-ISSN 1873-684X, Vol. 24, no 1, p. 65-72Article in journal (Refereed)
  • 2. Nik, Vahid M.
    et al.
    Kalagasidis, Angela Sasic
    Kjellström, Erik
    SMHI, Research Department, Climate research - Rossby Centre.
    Assessment of hygrothermal performance and mould growth risk in ventilated attics in respect to possible climate changes in Sweden2012In: Building and Environment, ISSN 0360-1323, E-ISSN 1873-684X, Vol. 55, p. 96-109Article in journal (Refereed)
    Abstract [en]

    Most of the last 20 years in Sweden have been mild and wet compared to the 1961-1990 climate reference period. After a few relatively cold years in the mid-1980s, practically all years have been warmer than the preceding 30 years average. During the indicated period, an increase of moisture-related problems (mould growth) was observed in ventilated attics, a moisture sensitive building part. This work investigates hygrothermal performance of ventilated attics in respect to possible climate change. Hygrothermal simulations of attics were performed numerically in Matlab. Four attic constructions are investigated - a conventional attic and three alternative constructions suggested by practitioners. Simulations were done for the period of 1961-2100 using the weather data of RCA3 climate model. Effects of three different emissions scenarios are considered. Hygrothermal conditions in the attic are assessed using a mould growth model. Based on the results the highest risk of mould growth was found on the north roof of the attic in Gothenburg, Sweden. Results point to increment of the moisture problems in attics in future. Different emissions scenarios do not influence the risk of mould growth inside the attic due to compensating changes in different variables. Assessing the future performance of the four attics shows that the safe solution is to ventilate the attic mechanically, though this solution inevitably requires extra use of electrical energy for running the fan. Insulating roofs of the attic can decrease the condensation on roofs, but it cannot decrease the risk of mould growth considerably, on the wooden roof underlay. (C) 2012 Elsevier Ltd. All rights reserved.

  • 3. Nik, Vahid M.
    et al.
    Kalagasidis, Angela Sasic
    Kjellström, Erik
    SMHI, Research Department, Climate research - Rossby Centre.
    Statistical methods for assessing and analysing the building performance in respect to the future climate2012In: Building and Environment, ISSN 0360-1323, E-ISSN 1873-684X, Vol. 53, p. 107-118Article in journal (Refereed)
    Abstract [en]

    Global warming and its effects on climate are of great concern. Climate change can affect buildings in different ways. Increased structural loads from wind and water, changes in energy need and decreased moisture durability of materials are some examples of the consequences. Future climate conditions are simulated by global climate models (GCMs). Downscaling by regional climate models (RCMs) provides weather data with suitable temporal and spatial resolutions for direct use in building simulations. There are two major challenges when the future climate data are used in building simulations. The first is to handle and analyse the huge amount of data. The second challenge is to assess the uncertainties in building simulations as a consequence of uncertainties in the future climate data. In this paper two statistical methods, which have been adopted from climatology, are introduced. Applications of the methods are illustrated by looking into two uncertainty factors of the future climate; operating RCMs at different spatial resolutions and with boundary data from different GCMs. The Ferro hypothesis is introduced as a nonparametric method for comparing data at different spatial resolutions. The method is quick and subtle enough to make the comparison. The parametric method of decomposition of variabilities is described and its application in data assessment is shown by considering RCM data forced by different GCMs. The method enables to study data and its variations in different time scales. It provides a useful summary about data and its variations which makes the comparison between several data sets easier. (C) 2012 Elsevier Ltd. All rights reserved.

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