A unique attribute of the nuclear industry is the extent by which designers, developers, operators, and regulators pay attention to demonstrating the safety case. Nuclear installation resiliency or safety takes overriding priority over function and performance. The preparation of the safety case heavily relies on extensive experimental campaigns that challenge the target design under a spectrum of postulated accident scenarios. For safety reasons, these experiments, typically conducted in subscale test apparatuses intended to reproduce the postulated accident scenarios and event sequences realistically, but subject to proven similarity criteria, are often referred to as scaling analyses. Since the dawn of the nuclear industry, significant resources have been committed to the construction and operation of such test facilities, and a massive amount of data has been generated. Such test data have been and are still used to validate the fundamental assumptions of nuclear power plant phenomena.

As we move into the digital world, capturing this multiple decades worth of information in a transparent, easily accessible, and usable manner for the public and industry stakeholders is of paramount importance. Over the last few years, FPoliSolutions has been actively developing an enterprise digital data ontology platform (OGMA) intended to do just that. The goal is to help designers use experimental data to assess their safety case evaluation models. The platform or application programming interface (API) was designed to ontologically organize the experimental data, as well guide the user in the complex analytics associated with the interpretation and use of such test results. The OGMA API supports the user in accessing a library of sophisticated mathematical procedures for performing scaling analyses. For this purpose, the dynamical system scaling (DSS) procedure invented by Dr. Jose’ Reyes was formulated as one of the services in the digital platform.

These methods have been demonstrated to provide an excelled mathematical apparatus to identify similarity criteria or quantify distortions between measured data and the target prototypical conditions. Moreover, DSS can provide a level of synthesis across separate effect tests and integral effect tests that has the promise of yielding efficiency in the evaluation code assessment activities, as setting up evaluation models that support the safety case of current and future nuclear power plants is often a daunting and expensive task.