Heat Losses Caused by the Temporary Influence of Wind in Timber Frame Walls Insulated with Fibrous Materials

Abstract

The paper presents the results of research concerning three fiber materials—mineral wool, hemp fiber and wood wool—as loose-fill thermal insulation materials. The analysis used the material parameters determined in previous works conducted by the authors, such as thermal conductivity and air permeability in relation to bulk density. These materials exhibit open porosity; thus, convection is an essential phenomenon in the heat transfer process. The paper aimed at conducting thermal simulations of various frame wall variants which were filled with the above-mentioned insulation materials. The simulations were performed with the Control Volume Method using the Delphin 5.8 software. The studies accounted for the effect of wind pressure and the time of its influence on a wall insulated by means of fiber material with a thickness of 150 as well as 250 mm. The simulation enabled us to obtain such data as maximal R-value reduction and time to return to equilibrium after filtration for the analyzed materials. The study proved that heat transfer in these insulations strongly depends on the bulk density, thickness of the insulation and wind pressure. The decrease in R is reduced as the density increases. This results from the decreased air permeability characterizing the material. Wind washing causes lower R reduction than air filtration in all models. The greater the thickness, the longer it takes for the models to return to the equilibrium state following air filtration (and wind washing). This period is comparable for air filtration and wind washing. Hemp fibers were characterized with the strongest susceptibility to air filtration; in the case of wood wool, it was also high, but lower than for hemp fibers, while mineral wool was characterized with the lowest.

Heat Transfer in Straw-Based Thermal Insulating Materials

“…Both straw types show a similar trend in terms of changing bulk density, and we measured the lowest thermal conductivity for both Type-1 and Type-2 straw bulks at 120 kg/m 3 bulk density, which can be considered the optimum bulk density. This optimum bulk density is higher than that of mineral wool or bagasse (100 kg/m 3 ), hemp or palm fiber (90 kg/m 3 ), flax fiber (80 kg/m 3 ), feather fiber (60 kg/m 3 ) or wood wool (50 kg/m 3 ), respectively [ 20 , 50 , 64 , 65 ].…”

“…However, the models were created for mineral wools and aerogel blankets, but the equations prescribed for these materials had to be changed or supplemented to be able to describe the presented straw material properly. For example, a straw model needs a more accurate material property description because of the nature of the stems and because some of the natural state or loose-filled thermal natural thermal insulations are especially sensitive to bulk density changes [ 20 , 48 , 49 , 50 ]. In the case of the first base model [ 45 ], the material density and the bulk density of the insulation were sufficient because the glass fibers are both solids.…”

Moisture Transport in Loose Fibrous Insulations under Steady-State Boundary Conditions

“…The correlation with the number of measurement repetitions is in the range of 0.059-0.929, but for three samples, it is over 0.802. The lowest Figure 10c presents a comparison between the measured results of thermal conductivity for the tested samples and the dry samples tested for a similar density range [40]. The large visible range of measured values for samples 2-4 and the concentration of the values for sample 1 are characteristic.…”

“…This means that the greatest difference is for the sample with the highest density, the second for the lowest density sample, the third for the second most dense sample, and finally, the smallest for the third most dense, sample 1. Figure 10c presents a comparison between the measured results of thermal conductivity for the tested samples and the dry samples tested for a similar density range [40]. The large visible range of measured values for samples 2-4 and the concentration of the values for sample 1 are characteristic.…”