Assembling Multiple Models Within the RAVEN Framework

Abstract

RAVEN (Risk Analysis Virtual Environment) is an INL-developed software tool that can be used to identify and increase the safety margin in complex systems. As a generic software framework, RAVEN is designed to perform parametric and probabilistic analysis based on the response of complex system codes. RAVEN is capable of investigating the system response as well as the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused toward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. RAVEN is currently able to construct multi-targets Reduced Order Models (ROMs), which are aimed to represent the response of a system (in a fixed configuration) for multiple Figures of Merits (FOMs) and time-dependent ROMs. These capabilities represent the initial steps for a larger implementation about the interaction of multiple models. In fact, in several cases, multiple models need to interface with each other since the initial conditions of one are dependent on the outcomes of another. To better understand the problem that here is solved, it is useful to consider a simple example: The following problem is considered: a weather forecast simulation code 'a' is used to compute the external (i.e. ambient) temperature in a certain location. A second model 'b' is inquired to compute the average temperature in a room having as boundary condition, among several others, the external ambient temperature. The response of the model 'b' depends on the outcome of the model 'a'. The reported example is only aimed to illustrate the reason why the creation of a framework to make interact different models is a key development for the advancement of RAVEN as a comprehensive calculation flow driver. Before reporting how the ensemble-models have been implemented, it is necessary to briefly describe the representative Model 'entities' that are available in RAVEN.