1. Hygrothermal behavior modeling of different Lime-Hemp concrete mixes Samuel Dubois PhD Student, Gembloux ABT, Belgium Tokyo, ICCS 2013

2. Lime-Hemp Concretes A sustainable construction material (Low carbon)  Made of hemp shivs + Lime-based binder Cast, sprayed or prefabricated Different proportions depending on final usage

3. Lime-Hemp Concretes Roof, wall, slab or plaster mixes

4. Lime-Hemp Concretes

5. Stated to offer a comfortable indoor climate High porosity and hygroscopicity  Moisture storage and vapor permeability both high High moisture exchange capacity with environment  Potentially good in regulating variations of indoor relative humidity Surrounding Air  Linked latent heat effects +Q

6. How to characterize this behavior? Experimentally : Numerically : Heat Air and Moisture (HAM) Models  PDE Equations  Lots of available models  Different hygrothermal parameters Moisture Buffer Value (MBV) protocol  Sample under cyclic relative humidity sollicitations  Weight variation monitoring

7. Objectives 1.Characterize the behavior of different samples during a MBV test (cyclic RH) 2.Confront the experimental results to a HAM model  Get hygric transfers parameters through inverse modeling

8. Experimental set-up 3 different samples –Variation of portland cement dosage  Quantify a possible effect of hydraulic binder on moisture exchange capacity Sample conditionment –Initially in equilibrium with 50%RH –One unique exchange face 25% PC 75% PC 100% QSC

9. Experimental set-up Climate chamber + sensors –8 hours @ 75%RH followed by 16 hours @ 33%RH –Constant temperature –Continuous weight monitoring –Surface temperature monitoring (Latent heat!) –Indoor air temperature/relative humidity monitoring

10. Hygrothermal model Developed in COMSOL Multiphysics –Advantages concerning interoperability –Coded in MatLab for communication with the inverse modeling tool Mathematical representation –Two balance equations + Boundary conditions MoistureHeat Two variables (temperature and relative humidity) / 1D / Simplification assumptions

11. Inverse Modeling? The opposite of direct modeling Find the best estimates of hygrothermal transfer parameters –We would normally measure first the parameters and then predict the behavior –Here an algorithm compares experimental and numerical results in an optimization process Benefit? –Multiple parameters obtained within one experiment Find parameters which minimize the difference between model output and experimental results Two datasets for the estimation : surface T and weight variation

12. Hygrothermal model What are the parameters to be estimated? –Moisture capacity (storage), vapor permeability and surface resistance –Impossible to estimate heat transfer parameters! –Optimization on 2 datasets with fixed heat parameters Moisture balance Boundary conditions Exchange properties of the boundary layer

13. Results (Experimental) The 3 samples behave similarly

14. Results (Inverse modeling) LH sample Resistance factor and initial conditions well optimized Vapor permeability and moisture capacity highly correlated Inverse modeling

15. Results (Inverse modeling) Model and experimental data comparison

16. Conclusions The hydraulic binder dosage (Portland cement) have little influence on isothermal hygric properties of LHC (in the range 33-75%RH) The proportion hemp/binder is more crucial The MBV protocol is unable to give information about thermal transfer properties but shows latent heat effects Interesting to explore other RH range  other phenomena Inverse modeling is a powerful tool