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Hygrothermal behavior modeling of different Lime-Hemp concrete mixes
Samuel DuboisPhD Student, Gembloux ABT, Belgium
Tokyo, ICCS 2013
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
Lime-Hemp Concretes
• Stated to offer a comfortable indoor climate• High porosity and hygroscopicity
Moisture storage and vapor permeability both high
High moisture exchange capacity with environmentHigh moisture exchange capacity with environment
Potentially good in regulating variations of indoor relative humidity
Potentially good in regulating variations of indoor relative humidity
Surrounding Air
Linked latent heat effectsLinked latent heat effects
+Q
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
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
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
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
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
Moisture Heat
Two variables (temperature and relative humidity) / 1D / Simplification assumptions
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 variationTwo datasets for the estimation : surface T and weight variation
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
Results (Inverse modeling)
• LH sample
• Resistance factor and initial conditions well optimized
• Vapor permeability and moisture capacity highly correlated
Inversemodeling
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