## 3 Results
### Development of a General Systematic Approach
#### 3.1.2 Classification of Approaches for Testing and Identification of Optimization Potential
However, often cycling is also done simply the way the equipment allows, sometimes even done just as fast as possible to save time. The analysis thus shows that standardization of testing should not mean to try to define testing in a way that testing is always done with the same methods at the same conditions. This is not applicable as already different applications impose different conditions. But it is also not possible to test everything for reasons of time and experimental effort. Therefore, it is clear that testing should be tailored to the individual case. As the analysis has shown, this is already done in some aspects, however not systematically. Therefore, here, a systematic approach to tailor and optimize testing is developed. The following sections identify first the basic functions and related properties that might be subject to testing, e.g., selected with a specific application in mind (Section 3.1.3), and then an approach is given to find the possible degradation effects and underlying mechanisms to allow tailoring and optimizing test procedures (Section 3.1.4). Before, however, another reason why the degradation effects and mechanisms must be studied in more detail related to testing is discussed for completeness. Selective testing reduces the number of variations that need to be tested, but this is not always sufficient. For example, a PCM in a building application is supposed to be used for 10, 20, maybe even 30 years. For testing a final product at application-typical conditions, several months, maybe even four seasons, might be accepted. But during development, testing again at various development steps, a month is long. Even to test a single set of conditions, the available test time is a fraction of the application-typical time. The easiest approach is then to observe degradation within the available test time and then extrapolate, e.g., to full life-time, life-usages (like phase changes), sometimes even calculate rates. Figure 2 shows five measured data points (black) within test time and extrapolation assuming a linear correlation. A linear correlation, thus degradation at constant rate, is commonly used in corrosion as the corrosion rate. A disadvantage of extrapolation is not knowing if it is justified, e.g., it is not justified in corrosion if there is passivation. Degradation might be accelerated by applying conditions outside application-typical conditions, e.g., higher temperatures. Conditions that accelerate degradation are called \"acceleration factor\". By testing outside application-typical conditions, an attempt can be made to increase the \"stress\" and thereby accelerate degradation (Figure 2, red data points), in turn shortening the test time compared to application-typical conditions. Tested is degradation versus time, at different values of conditions, e.g., different temperatures. The correlation between observed times and values of conditions, described by a function, allows one to derive degradation times at application-typical conditions, so non-accelerated, from those observed when accelerated by a change in conditions. Thus, the correlation allows estimates for degradation at application-typical conditions in shorter test times. Accelerated testing is \"accelerated degradation testing\" or \"accelerated life testing\" [17]. In the last one, specifically the life time is observed, with end-of-life defined by \"failure\", meaning a property failing specification. Often the function describing the observed correlation, which is also called \"lifetime-relationship model\", is just an empirical fit to observed data, used without scientific basis. However, ideally the function should be based on a \"physical or chemical model\" for the \"mechanism\" behind it (e.g., Arrhenius model). A good example is again the function of heat storage, described by the property of specific enthalpy, and focus on a degradation mechanism by temperature. Let's assume a PCM has an application-typical number of 1000 cycles; too long to test.