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

Roof, wall, slab or plaster mixes

Lime-Hemp Concretes

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 (Experimental)

• The 3 samples behave similarly

Results (Inverse modeling)

• LH sample

• Resistance factor and initial conditions well optimized

• Vapor permeability and moisture capacity highly correlated

Inversemodeling

Results (Inverse modeling)

• Model and experimental data comparison

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