The WUFI® FinMould add-on was developed in collaboration between the Finnish research institute VTT, the Tampere University and Fraunhofer IBP. Hannu Viitanen, Tuomo Ojanen, and in recent years especially Juha Vinha and his team have been dealing with the growth conditions of mould on wood and other building materials. The mathematic-empirical model described in detail in [1, 2, 3] was originally developed for Scots pine and Norway spruce sapwood (the so-called VTT or Viitanen mould model) and was further developed into the “Finnish Mould Growth Model” (FinMould) in a collaboration between VTT and the Tampere University [4, 5, 6].
The model predicts mould growth as a function of substrate material, temperature and relative humidity. For evaluation the so-called Mould Index (MI) was developed, which indicates the intensity of growth using an easy-to-understand scale. Compared to the original model for wood, WUFI® FinMould allows the calculation for all building materials and structures for which the sensitivity class is known: the differences in the materials’ susceptibility are taken into account by four “Sensitivity Classes” and four “Material Classes” (classes indicating the decline of mould growth during periods outside the growth conditions) [4, 5, 6]. The assessment of the mould growth risk according to this approach has meanwhile also been adopted in the North American ASHRAE Standard 160 for the hygrothermal evaluation of building components and buildings.
The add-on allows an easy application of the model using the default setting for many different building materials. Based on measurements or hygrothermal simulation results the material surface can be assessed with just a few simple settings. In addition to the recommended FinMould model, the previous VTT model and the evaluation according to ASHRAE 160 remain available for selection. The temperature and humidity data can be transferred directly from WUFI® or from its film viewer Animation1D or imported from project and text files.
The evaluation is supported by a traffic light scheme [7] which classifies the results considering the user exposure of the surface from uncritical (green) to inacceptable (red). In the yellow range, the user has to decide whether the growth risk for the specific component is still acceptable or already too high.
Further information and regular updates on the Finnish Mould Growth Model (including the assignment of building materials to the different mould sensitivity classes) are provided by Tampere University: https://research.tuni.fi/buildingphysics/finnish-mould-growth-model/
Literature
[1] A. Hukka and H. A. Viitanen, “A mathematical model of mould growth on wooden material”, Wood Science and Technology, vol. 33, no. 6, pp. 475–485, 1999. doi: 10.1007/s002260050131 [2] H. A. Viitanen, A. Hanhijärvi, A. Hukka, and K. Koskela, “Modelling mould growth and decay damages”, in Proceedings of Healthy Buildings, vol. 3, 2000, pp. 341–346. [3] H. A. Viitanen, Factors affecting the development of mould and brown rot decay in wooden material and wooden structures. Uppsala: Swedish University of Agricultural Sciences, Dept. of Forest Products, 1996. [4] H. A. Viitanen, J. Vinha, K. Salminen, T. Ojanen, R. Peuhkuri, L. Paajanen, and K. Lähdesmäki, “Moisture and bio-deterioration risk of building materials and structures”, Journal of Building Physics, vol. 33, no. 3, pp. 201–224, 2010. doi: 10.1177/1744259109343511 [5] T. Ojanen, H. A. Viitanen, R. Peuhkuri, K. Lähdesmäki, J. Vinha, and K. Salminen, “Mold growth modeling of building structures using sensitivity classes of materials”, Proceedings Buildings XI, Florida, 2010. [6] T. Ojanen, R. Peuhkuri, H. A. Viitanen, K. Lähdesmäki, J. Vinha, and K. Salminen, “Classification of material sensitivity – new approach for mould growth modeling”, in 9th Nordic Symposium on Building Physics, Tampere: Tampere University of Technology, 2011, pp. 867–874. [7] H. A. Viitanen, M. Krus, T. Ojanen, V. Eitner, and D. Zirkelbach, “Mold risk classification based on comparative evaluation of two established growth models”, Energy Procedia, vol. 78, pp. 1425–1430, 2015. doi: 10.1016/j.egypro.2015.11.165