LONG TERM THERMAL RESISTANCE (LTTR)

Aged R-Value (a/k/a Long-Term Thermal Resistance)

Extruded Polystyrene Rigid Foam Insulation products (XPS) have a closed cellular structure and are filled with a blowing agent, HCFC 142b, specifically selected for its low thermal conductivity to enhance the thermal performance of the foam.

Over a long period of time (50 to 75 years), the blowing agent diffuses through the thickness of the foam, and air diffuses into these same closed cellular volumes. Because of this gas movement, the overall thermal resistance (R-value) of the insulation product changes over time.

This phenomenon is typically called 'aging'.

Accurately identifying the aged R-value of XPS is important for a variety of reasons:

• Designers need accurate thermal performance data to determine the heating and cooling loads for buildings and appliances.

• Insulation products are compared to one another based on price and thermal performance

• The need to develop new thermal data due to blowing agent changes dictated by environmental issues

• The current 180 day aging requirement does not accurately represent the long term thermal performance of foam insulation products

XPSA does not promote or recommend the use of LTTR-values generated per the CAN/ULC S770-00 thin slicing method. XPSA recommends retaining the use of an aged R-value of 5 per inch (RSI 0.87/25 mm) for all design purposes.

CAN/ULC S770

The Canadian standard, CAN/ULC S770, defines the long-term thermal resistance (LTTR) of foam products as the 5-year R-value after storage in a laboratory environment. LTTR is a property used to compare different foam products to one another.

The following table lists the minimum LTTR values for extruded polystyrene foam insulations when tested according to CAN/ULC S770-00.

Note: In accordance with the revisions to CAN/ULC S770, the following information on bias is given by the standard: "The bias of this test method has not been determined.Some preliminary data reported by the industry are showing that in certain cases, the bias could be an over prediction."