R-Values alone do not accurately predict real-world performance. Here is why our EPS foam is far superior to other forms of insulation even with equivalent R-Values.
Performance predictions using heat transfer calculations based on tested and published R-values alone do not accurately predict real-world performance. R-Value is a term given to the property of any material to “resist” the conduction of heat. R-Value as a metric of measurement is legitimate. The functional design of the laboratory determination of this property, relative to insulation materials, is flawed at a fundamental level – yielding what can be called the R-Value myth. This flaw forces the engineering community, bound to adhere to the scientific formulas dictated by their governing body (ASHRAE), to calculate heat loads using the “myth” that this R-Value property of various insulation materials derived by flawed test methods can accurately predict the performance of the insulation material in real life. Not true.
Let’s look at the flawed test criteria used in the laboratories to determine R-values. The first flaw relates to “standard temperature”. This stipulates that the test be conducted at a constant temperature of 75-degrees Fahrenheit. Does it seem odd that a test to determine the performance values for insulation materials for HVAC equipment would be designed around a 75-degree temperature? Who uses heating or cooling when it’s 75 degrees? It isn’t surprising that fiberglass performs well at 75-degrees. However, the efficiency of fiberglass dramatically decreases at temperatures that are above or below 75-degrees while foam insulation performs very well and its efficiency actually increases the colder it gets.
The second flaw of the test criteria requires that R-value testing not start until the materials reach what is called “steady state”. Steady state occurs when a material is exposed to a heat source and allowed to become thermally saturated so that for every single unit of heat entering on one side of the material a single unit of heat exits the opposite side. This seems very scientific. It appears logical, but it misses a single important issue relevant to predicting real world performance: The amount of time it takes to reach steady state.