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Pragmatic Validation of Numerical Models Used for the Assessment of Radioactive Waste Repositories: A Perspective

doi: 10.3390/en15103585
The safety case for a radioactive waste repository relies heavily on results obtained by numerical models that assess the long-term performance of the engineered and natural barrier systems. Given that important engineering and public policy decisions are based on these models, it is essential that we critically evaluate their abilities and limitations, and thus justify the level of confidence we have in the inferences drawn from the modeling. In this article, we discuss some of the issues surrounding the modeler’s attempts to test, corroborate, confirm, and verify numerical models—a process sometimes referred to as model validation. This wide-ranging topic is approached by first examining its deep roots in the philosophy of science and hypothesis testing. However, the application of these principles to radioactive waste isolation calls for a more pragmatic approach, which has the narrower goal of corroborating site-specific models and their usefulness for a specific purpose. We focus on the practical aspects of validating hydrogeological models that are used to understand the evolution of the repository system. We will make the case that the responsible use of numerical models requires a sufficient understanding of the quality and robustness of the simulation results, with direct implications for how these results need to be interpreted, and how they can (or cannot) be used in support of important policy decisions.
Technology, T, safety assessment, simulation, pragmatic model validation, radioactive waste isolation
Technology, T, safety assessment, simulation, pragmatic model validation, radioactive waste isolation
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).6 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
