Evaluation of relationships between particle orientation and thermal conductivity in bark insulation boards by means of CT and discrete modeling

Transcription

1 Evaluation of relationships between particle orientation and thermal conductivity in bark insulation boards by means of CT and discrete modeling Günther Kain, Bernhard Plank, Marius-Catalin Barbu, Klaus Richter, Alexander Petutschnigg

2 Relevance 10 % of a tree is bark Austria: 1.8 million solid m³ annually Interesting natural properties Search for products with a higher value added 2

3 Structure of research (1) Panel - production (1) Kain et al. (2012) Substantial bark use as insulation material. Forest Products Journal (IF 0.494) (2) Resin Tannin Density related properties (mechanical, thermal) Feasibility Mechanical characterization Thermal characterization (4) Thermal performance Experimental (2) Kain et al. (2014) Density related properties of bark insulation boards bonded with tannin hexamine resin. Eur. J. Wood Prod. (IF 1.105) (3) Kain et al. (2015) Effect of different flavonoid extracts in the optimization of tannin-glued bark insulation boards. Journal of Wood and Fiber Science (submitted) (IF 0.875) (4) Kain et al. (2013) Using bark as heat insulation material. Bioresources (IF 1.549) (3) Resin optimization Tannin type Application on buildings (5) Kain et al. (2015) Analyzing wood bark insulation board structure using x-ray computed tomography and modeling its thermal conductivity by means of finite difference method (FDM). Journal of Composite Materials (IF 1.257) Resin formulation Investigation of polymer (5) Structure studies / Abstract modeling Pore size distribution Source for IF: JCR Science Edition 2013 (Thomson und Reuters) Kain et al. (2013) Softwood bark for modern composites. Pro Ligno 2D/3D models Particle orientation Kain et al. (2012) Stoffliche Rindennutzung in Form von Dämmstoffen. Holztechnologie 3

4 Research question Structure Tree bark insulation composite Microstructure Structure based modeling Volume data 4

5 Potential applications Solar Decathlon

6 Insulation panels Panel production Tannin-hexamine resin UF or muf resin 6

7 Bark panels transient heat flow Panel production Thermal diffusivity of insulation panels by comparison (data apart that of bark according to Pfundstein et al. 2007, pp. 8-9) dt dt = λ c p ρ d2 T dx 2 λ ρ c p T x thermal conductivity in W/(m*K) panel density in kg/m³ specific heat storage capacity in J/(kg*K) temperature in K wall position in m 7

8 CT-scanning X-ray tomograms, sample size 50x50x20 (30) mm³, Larix decidua, density 302 kg/m³, resolution 30.7 um Nanotom 180 NF CT (GE phoenix/x-ray), 60 kv voltage, 410 μa measurement current 8

9 Material structure Outer bark Inner bark Void 5 mm Larix decidua, cross section (2.5 magnif.) Larch bark board, X-ray-tomogram (resolution 30.7 um) 9

10 Thresholding algorithm Relative frequency Grey value 3 J i=1 j=1 g ij g)² h ij = Void Inner bark Outer bark Grey values CT images 3 J i=1 j=1 g ij g i )² h ij + MS W = 3 i=1 J j=1 3 i=1 j=1 Parameters of theoretical normal distributions J g ij g i )² h ij h i g i g)² h i min μ 1 = 28.40; σ 1 = 3.79 μ 2 = 42.56; σ 2 = 4.41 μ 3 = 60.66; σ 3 =

11 CT structure analysis X-ray tomograms, sample size 50x50x20 mm, Larix decidua, density 302 kg/m³, resolution 30.7 um 11

12 Pore size distribution 1 Relative area portion kg/m³ 319 kg/m³ 358 kg/m³ Pore size in mm² Porosity in mm³/mm³ R² = Panel density in kg/m³ 12

13 Pore size distribution 2 Panel center, 302 kg/m³, thickness = 20 mm Larix decidua Pores 1 mm² 1 cm Panel center, 302 kg/m³, thickness = 20 mm Larix decidua Pores 1 mm² 13

14 Modeling heat flow (FDM) 17.5 C Panel center, 280 kg/m³, thickness = 20 mm 2.5 C 1 cm Heat flow in W/m² Larix decidua Average heat flow 56 W/m², thermal conductivity W/(m*K) 17.5 C Panel center, 580 kg/m³, thickness = 20 mm 2.5 C 1 cm Heat flow in W/m² Larix decidua Average heat flow 81 W/m², thermal conductivity W/(m*K) 14

15 Model fit Thermal conductivity in mw/(m*k) Density in kg/m³ Number of observations: 11 Mean absolute relative deviation: 3.4 % Real thermal conductivity Numeric model 15

16 Particle orientation 1 cm 1 cm Sample 7H3 383 kg/m³ Average heat flow: W/m² Average thermal conductivity: W/(m*K) Sample 5V2 382 kg/m³ Average heat flow: W/m² Average thermal conductivity: W/(m*K) 16

17 3d-Data Sample 6H1, 326 kg/m³ Porosity 0.33 Sample 5V2, 382 kg/m³ Porosity 0.20 Sample 5H6, 206 kg/m³ Porosity

18 Set up of model 1 Q y,in Q x,in Q z,out Q x,out Q z,in z x y Q y,out 18

19 Set up of model 2 y d x λ void = λ inner bark = [W/(m*K)] λ outer bark = d y z df y α xyz x d z (λ x,y 1,z λ x,y,z )*(T x,y 1,z T x,y,z ) F y + (λ x,y+1,z λ x,y,z ) (T x,y+1,z T x,y,z ) F y +(λ x 1,y,z λ x,y,z ) (T x 1,y,z T x,y,z ) F x ++(λ x+1,y,z λ x,y,z ) (T x+1,y,z T x,y,z ) F x +(λ x,y,z 1 λ x,y,z ) (T x,y,z 1 T x,y,z ) F x +(λ x,y,z+1 λ x,y,z ) (T x,y,z+1 T x,y,z ) F x =0 19

20 Model fit y = x R² = y = x R² = % Thermal conductivity in W/(m*K) y = x R² = y = x R² = Panel density in kg/m³ experiment vert. particles experiment horiz. particles 3d model vert. 3D model horiz. 20

21 3d-Modeling Sample 5V2, 382 kg/m³ Gradient 0.6 K/mm average heat flow W/m² thermal conductivity W/(m*K) W/m² 21

22 Heat flow orientation Average heat flow density in y-direction in W/m² R² = R² = Panel density in kg/m³ av- y-flow vert. particles av. x-flow av. y-flow hor. particles av. z-flow Average heat flow density in x/z-direction in W/m² Average heat flow deviation from y- direction in degrees R² = R² = Panel density in kg/m³ vert. particles hor. particles 22

23 Heat flow orientation Thermal conductivity in W/(m*K) R² = R² = Absolute deviation of heat flow from y-direction in degrees vert. particles horiz. particles 23

24 Heat flow orientation 1 cm Heat flow in W/m² Deviation (from y-dir.) in degrees Sample 5V2, 382 kg/m³, average heat flow W/m², average heat flow angle 6.39 degrees, thermal conductivity W/(m*K) 1 cm Heat flow in W/m² Deviation (from y-dir.) in degrees Sample 7H3, 383 kg/m³, average heat flow W/m², average heat flow angle 7.10 degrees, thermal conductivity W/(m*K) 24

25 Heat flow orientation Overlaid heat flow density in W/m² (sample 5V and 7H) Overlaid heat flow deviation in degrees (sample 5V2 and 7H) 25

26 Conclusions 3d-structure Characterization - material optimization Q x, y, z) = λ x, y, z) T x, y, z) ANOVAsegmentation δ p0 G x, y, z) = μ x, y, z p x, y, z) Discrete thermal modeling 26

27 Thank you for your attention!

28 References Kain G, Teischinger A, Musso M, Barbu MC, Petutschnigg (2012) Stoffliche Rindennutzung in Form von Dämmstoffen. Holztechnologie 53(4): Kain G, Heinzmann B, Barbu MC, Petutschnigg A (2013) Softwood bark for modern composites. Pro Ligno 9(4): Kain G, Barbu MC, Teischinger A, Musso M, Petutschnigg A (2013) Substantial bark use as insulation material. Forest Products Journal 62(6): Kain G, Barbu MC, Hinterreiter S, Richter K, Petutschnigg A (2013) Using bark as a heat insulation material. Bioresources 8(3): Kain G, Güttler V, Barbu MC, Petutschnigg A, Richter K, Tondi G (2014) Density related properties of bark insulation boards bonded with tannin hexamine resin. European Journal of Wood and Wood Products 72: Kain G, Charwat-Pessler J, Barbu MC, Plank B, Richter K, Petutschnigg A (2015) Analyzing Wood bark insulation board structure using X-ray computed tomography and modling its thermal conductivity by means of finite difference method (FDM). Journal of Composite Materials (accepted for publication) Petutschnigg A, Pferschy U, Katz H, Kain G, Teischinger A (2009) Algorithms to define limits for wood property categorization. Forest Products Journal 59(7/8): Pfundstein M, Gellert R, Spitzner MH, Rudolphi A (2007) Insulation materials basics, materials, applications. Department for international architecture ducumentation corporation, Munich, 112 pp. (in German) Proceedings: Kain et al. (2014) Use of tree bark as insulation material. PTF BPI, Salzburg, Austria Kain et al. (2013) Using tree bark as insulation material. Werkstoffkongress, Leoben, Austria 28