Kaufman, Zoe | Search NASA

DOE OSTI2024

Moisture Performance of Unvented Attics With Vapor Diffusion Ports and Buried Ducts in Hot, Humid Climates

Energy efficiency measures, such as cool roofs, radiant barriers, interior radiative control coatings, and buried ducts are increasing in popularity and are promoted by energy codes because of their energy-saving potential. However, these strategies can also pose moisture risks in attics by lowering surface temperatures and increasing condensation potential and moisture accumulation. Of particular concern in hot-humid climates is dripping condensation on cold air-conditioning ductwork in the summer - commonly referred to as duct "sweating" - which threatens the attic floor with conditions conducive to mold growth and rot. One strategy to mitigate these moisture issues is to wrap ductwork in thicker duct-wrap insulation with an integrated exterior vapor barrier, but thick duct wrap can be difficult to come by, expensive, and unwieldy to work with. This study explores an alternative strategy of reducing moisture issues while embracing energy efficiency by using unvented attics with vapor diffusion ports and buried ductwork in hot-humid climates. Vapor diffusion ports have been studied so far in a wide range of U.S. climates, mostly in the context of conditioned attics. In this study, the strategy is implemented in the novel context of hot-humid climates with ductwork sitting atop blown-in attic floor insulation in unconditioned attics. Using a combination of field experiments and hygrothermal modeling, the findings of this project indicate that an unvented attic with vapor diffusion ports and buried ducts may be a key part of a successful low-cost method for reducing the attic moisture load by venting excess moisture out of the attic, keeping duct-jacket surfaces above dew point temperature, and keeping the roof deck safe from winter moisture accumulation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Physics-informed hybrid modeling methodology for building infiltration

Infiltration is responsible for one-third to one-half of the space conditioning load of a typical residential home, but the modeling of infiltration for building energy modeling is either represented by over-simplified equations or dependent on over-generalized rules of thumb. Here, this paper develops a physics-informed data-driven methodology for modeling infiltration using building-specific empirical measurements. The developed hybrid methodology combines machine-learning categorization and grey-box sub-modeling to improve the accuracy and generalization of commonly used grey-box infiltration models. The developed methodology excels at predicting infiltration by improving the ability to predict infiltration under unseen environmental conditions using machine learning algorithms with physical significance. In a case study conducted using the iUnit, a modular studio apartment experimental test facility located at the National Renewable Energy Laboratory, we use empirical airtightness measurements to fit an infiltration model using the developed methodology. We find that the developed methodology can improve the overall model accuracy by 43% and improve extrapolation by 38%, compared with the model based on the common grey-box infiltration equation. We also notice that the selected features can improve the performance of a pure machine-learning model, indicating that our methodology identifies the features with the most physical significance to infiltration modeling.

97 MATHEMATICS AND COMPUTING↗