Many evolving nuclear energy technologies use advanced predictive multiscale, multiphysics modeling and simulation (M&S) capabilities to reduce the cost and schedule of design and licensing. Historically, the role of experiments has been as a primary tool for the design and understanding of nuclear system behavior, while M&S played the subordinate role of supporting experiments. In the new era of multiscale, multiphysics computational-based technology development, this role has been reversed. The experiments will still be needed, but they will be performed at different scales to calibrate and validate the models leading to predictive simulations for design and licensing. Minimizing the required number of validation experiments produces cost and time savings. The use of multiscale, multiphysics models introduces challenges in validating these predictive tools - traditional methodologies will have to be modified to address these challenges. This paper gives the basic aspects of a methodology that can potentially be used to address these new challenges in the design and licensing of evolving nuclear technology. The main components of the proposed methodology are verification, validation, calibration, and uncertainty quantification - steps similar to the components of the traditional US Nuclear Regulatory Commission (NRC) licensing approach, with the exception of the calibration step. An enhanced calibration concept is introduced here, and is accomplished through data assimilation. The goal of this methodology is to enable best-estimate prediction of system behaviors in both normal and safety-related environments. This goal requires the additional steps of estimating the domain of validation, and quantification of uncertainties, allowing for the extension of results to areas of the validation domain that are not directly tested with experiments. These might include the extension of the M&S capabilities for application to full-scale systems. The new methodology suggests a formalism to quantify an adequate level of validation (predictive maturity) with respect to existing data, so that required new testing can be minimized, saving cost by demonstrating that further testing will not enhance the quality of the predictive tools. The proposed methodology is at a conceptual level. Upon maturity, and if considered favorably by the stakeholders, it could serve as a new framework for the next generation of the best estimate plus uncertainty (BEPU) licensing methodology that the NRC has developed. In order to achieve maturity, the methodology must be communicated to scientific, design, and regulatory stakeholders for discussion and debate. This paper is the first step in establishing that communication.
All Science Journal Classification (ASJC) codes
- Nuclear and High Energy Physics
- Nuclear Energy and Engineering
- Materials Science(all)
- Safety, Risk, Reliability and Quality
- Waste Management and Disposal
- Mechanical Engineering