During hazard analyses of oxygen-enriched systems, the flammability of polymers is commonly assessed by reviewing standard limiting oxygen index (LOI) data available in the literature and summarized in ASTM Standard Guide for Evaluating Nonmetallic Materials for Oxygen Service (ASTM G 63). The data provided by this standard may not correlate with many real-life fire scenarios. Two of the most common issues relate to the direction of flame propagation recommended by the method and the test environment temperature. Modifications of these two parameters could render most polymers flammable, which under standard testing conditions set forth by ASTM G 63 would be considered nonflammable. Modified LOI tests were conducted on 16 polymers to determine the magnitude of these effects. The results indicate that the existing data presented in ASTM G 63 should be used with caution. Furthermore, modified oxygen index data should be included in standard guides for oxygen systems design.
ASTM G 86 and ASTM G 74 are commonly used to evaluate materials susceptibility to ignition in liquid and gaseous oxygen systems. However, the methods have been known for their lack of repeatability. The inherent problems identified with the test logic would either not allow precise identification or the magnitude of problems related to running the tests, such as lack of consistency of systems performance, lack of adherence to procedures, etc. Excessive variability leads to increasing instances of accepting the null hypothesis erroneously, and so to the false logical deduction that problems are nonexistent when they really do exist. This paper attempts to develop and recommend an approach that could lead to increased accuracy in problem diagnostics by using the 50% reactivity point, which has been shown to be more repeatable. The initial tests conducted indicate that PTFE and Viton ® A (for pneumatic impact) and Buna S (for mechanical impact) would be good choices for additional testing and consideration for inter-laboratory evaluations. The approach presented could also be used to evaluate variable effects with increased confidence and tolerance optimization.
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