Implementation strategies and uncertainties of moisture transport properties in heat,air and moisture (HAM) models: Comparative insights from WUFI and DELPHIN
Restricted accessResearch articleFirst published online 2026
Implementation strategies and uncertainties of moisture transport properties in heat,air and moisture (HAM) models: Comparative insights from WUFI and DELPHIN
Heat, air and moisture (HAM) models are essential for predicting the hygrothermal behavior of building components. However, their outputs can vary significantly due to differences in how hygrothermal material properties are implemented. This study systematically examines the implementation strategies and related uncertainties of moisture transport properties in two widely applied HAM models, WUFI and DELPHIN, focusing on their parameterization logics governed by different driving potentials: relative humidity and capillary pressure. The first part of the study consolidates the characterization methods, data processing procedures and implementation strategies to bridge experimental data to model inputs. The second part applies this setup in two comparative simulation scenarios: one under “extreme” exposure, representing liquid-dominated transport beyond the hygroscopic range in a single loadbearing material with a finishing layer; and another under “service” conditions, involving an internally insulated concrete wall exposed to 50%–98% relative humidity (RH). Results show that simplified liquid transport formulations markedly distort predictions under over-hygroscopic conditions, while the choice between integral or separate representations of vapor and liquid transport significantly alters the coupled heat–moisture balance. This synthesis identifies inconsistencies between material characterization and its practical implementation and quantifies how these discrepancies affect hygrothermal predictions across different regimes. While the findings are specific to the tested regimes, they demonstrate how modeling assumptions and data-handling strategies shape prediction accuracy. By aligning the characterization–processing–implementation chain, the study offers a diagnostic showcase for assessing implementation uncertainty, supporting more robust and physically consistent HAM modeling.
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