Progress in quantifying validation data

There are a number of reasons why a numerical value is a desirable outcome for the validation of numerical models: these include the desire to report objectively and succinctly on the comparison with a benchmark result, and the need to rank order implementations of a canonical problem. The key challenge is to develop techniques which provide a numerical output that do not challenge excessively the interpretations of experienced engineers and which presents both sufficient discrimination between comparisons and consistency across the various sub-disciplines of EMC. While this paper concentrates on the subject of numerical code generation, the rationale and associated concepts are just as relevant to other aspects of EMC such as quantifying experimental repeatability. The purpose of this paper is to present some of the work done to date on generating a numerical value representing the overall quality of comparison for two sets of data. This paper reviews and contrasts various techniques currently available to produce this numerical overview. The techniques include correlograms, feature selective validation (FSV), integrated error against log frequency (IELF) and reliability factors. The paper concludes that the most taxing aspect of the work required to develop such techniques is benchmarking against human interpretation, a rating scale is discussed which can assist in obtaining this information.