Moisture Transport In Materials Research Articles

HYSTERESIS AND TEMPERATURE DEPENDENCY OF WATER VAPOR SORPTION

Moisture in porous building materials plays an important role in almost all durability problems. The sorption characteristics of building materials exhibit hysteresis in the way the equilibrium curves develop between adsorption and desorption. The sorption curves are also somewhat temperature-dependent. These facts are most often neglected in models for combined heat and moisture transport in materials. This study provides the sorption isotherm and its hysteresis of different porous building materials. The paper seeks to contribute to the knowledge base about such sorption characteristic by presenting some new measurements of hysteresis and temperature dependency of the moisture sorption characteristics of different porous building materials: concrete, porous concrete, cement plaster, limes cement plaster, brick, and spruce. Scanning curves are measured for all materials where periods with adsorption and desorption interrupt each other intermittently between 0% and 97% of relative humidity.

Adaptive Neutron Radiography Correlation for Simultaneous Imaging of Moisture Transport and Deformation in Hygroscopic Materials

Neutron radiography is a key non-destructive testing technology for the investigation of moisture transport in materials. However, quantitative moisture measurements in swelling materials are currently challenging due to the lack of referencing between moist and dry state radiographs. A novel adaptive texture correlation algorithm is presented to simultaneously image inhomogeneous moisture distributions and moisture-induced strain fields. The proposed method provides a valuable tool for the study of time- and position-dependent hygromechanical interactions. Moreover, it requires no modification of existing neutron installations. The method was validated against gravimetric moisture content and optic surface deformation measurements. Its applicability was demonstrated for two actual topics in wood science, the investigation of moisture gradients within the growth ring microstructure and the study of moisture transport processes in wood-fiber composites. The algorithm can be widely used to characterize hygroscopic materials with heterogeneous texture, as frequently found in wood constructions, food industry, engineering and soil science.

Influence of uncertainty in heat–moisture transport properties on convective drying of porous materials by numerical modelling

The influence of uncertainties in heat–moisture transport properties, due to measurement errors and material heterogeneity, on the numerical simulation results of convective drying of two capillary-saturated porous materials is investigated by a transport-property parameter analysis (TP-PA), based on the Monte Carlo method. Here, the heat–air–moisture transfer model is evaluated many times, each time using a random set of one or multiple input parameters (i.e. transport properties), by which a stochastic model output is generated. The propagation of these transport-property uncertainties to the hygrothermal behaviour of the drying system is evaluated by statistical analysis of the model simulation output. The spread on the hygrothermal response is found to be strongly material dependent and is related to the dominant mode of moisture transport in the material, i.e. liquid or vapour transport. The TP-PA results clearly indicate that uncertainties in the heat–moisture transport properties can lead to significant differences in drying behaviour predictions, where differences of the total drying time with respect to its mean value up to 200% are found for the materials considered. Therefore, numerical modelling of heat and moisture transport in porous materials should preferably include a quantification of the propagation of these uncertainties, for example by means of the proposed transport-property parameter analysis. Such analysis, however, additionally requires detailed (a priori) experimental material characterisation to determine realistic uncertainty ranges.