Developing the Algorithms for moisture control in roofs

Computer simulations are quite effective in predicting the ability of a roofing system to prevent problems with moisture accumulation. However, it is necessary to set up, run and analyze a computer simulation in order to determine the results. Algorithms were therefore developed in order to predict the moisture control performance of a roofing system without having to perform and analyze the results of a computer simulation. These algorithms can then be included in a fast, user-friendly program, or performed using a hand calculator, which makes the information available to a much wider group. This will enable the roofing professional in the US to quickly and accurately determine if a roof constructed with a given type of membrane, insulation material and deck will be moisture-tolerant in a given location on a building controlled to a specific indoor relative humidity, without the need to set up and run a computer simulation.

The algorithms were developed using a database of 600 simulations. Five different climates were analyzed: Bismarck, Chicago, Knoxville, Miami, and Seattle. These were selected to represent the range of heating degree days (HDD) seen in the continental US. Indoor relative humidities of 40%, 50%, and 60% with an indoor temperature of 68ˇăF were used in the study.

The range of roofing configurations evaluated included 1-inch and 3-inch thick wood fiberboard, 1-inch and 3-inch polyisocyanurate (PIR) insulation, and a 3-inch composite of the two. Four metal decks with permeances of 0.64, 1, 5, and 10 English perms were included. Two values for membrane absorptance of 0.7 for a white roof and 0.1 for a black roof were also used. All possible combinations of the above parameters were simulated using the finite-difference model. Table 1 shows the roof properties and environmental conditions analyzed [1].

This database was analyzed for each of the quantifiable moisture control requirements to develop the predictive algorithms. Multiple linear regression was done using combinations of first, second, third order and inverse terms of each of the variables to develop the necessary correlations. See Reference [9] for details regarding the production of these algorithms.

References

Desjarlais, A.O. (1995). Self-Drying Roofs: What! No Dripping! Proceedings of the ASHRAE/DOE/BETEC Thermal Performance of the Exterior Envelopes of Buildings VI.

NRCA. 1996. The NRCA Roofing and Waterproofing Manual, Fourth Edition. Rosemount, IL: National Roofing Contractors Association.

Desjarlais, A.O. and N. Byars. 1997. Moisture Design to Improve the Durability of Low-Slope Roofing Systems. Proceedings of the Sustainable Roofing Workshop (ORNL CONF-9610200). Oak Ridge, TN: Oak Ridge National Laboratory.

ASHRAE. 1993. ASHRAE Handbook of Fundamentals. Atlanta, GA: American Society of Heating, Refrigerating and Air-Conditioning Engineers.

Tobiasson, W. and M. Harrington. 1986. Vapor drive maps of the U.S. Proceedings of the ASHRAE/DOE/BTECC Thermal Performance of the Exterior Envelopes of Buildings III, pp. 663-672.

Tobiasson, W. 1988. Vapor Retarders for Membrane Roofing Systems. Proceedings of the 9th Conference on Roofing Technology. Rosemount, IL: National Roofing Contractors Association.

Kyle, D.M., and Desjarlais, A.O. 1994. Assessment of Technologies for Constructing Self-Drying Low-Slope Roofs, Oak Ridge National Laboratory Report ORNL/CON-380, Oak Ridge TN.

Black, W.Z. and Hartley, J.G. 1991 Thermodynamics, Harper Collins Publishers.

Desjarlais, A.O. and Byars, N.A. 1997. A New Look at Moisture Control in Low-Slope Roofing. Proceedings of the 4th International Symposium on Roofing Technology. Rosemount, IL: National Roofing Contractors Association, to be published.