DESC0025138

A general lack of public trust in the government, the stigma surrounding nuclear waste, and the development of new rail systems for the transportation of nuclear waste has created a need to demonstrate to the public, in a clear and simple manner, the consequences of train derailments involving spent nuclear fuel casks. Visual demonstrations of derailment and crash scenarios through physical testing have been key to garnering public confidence in the performance of these storage and transportation systems. A more cost-effective and practical method of providing these demonstrations is required. The problem will be overcome by applying proven modeling and simulation techniques to develop digital twins of the train system and storage casks. These models will be validated against their physical counterparts. The models will then be used to simulate derailment crashes of interest to calculate any damage occurring to the casks and quantify the risk of material release. These simulations have the added benefit of providing visual representations of train and cask motion and potential damage that can be used in the future to support the creation of high-quality videos for public outreach campaigns. In Phase I accurate digital twin models of the novel Atlas rail vehicles and a spent nuclear fuel cask will be created. The digital twins will be used to replicate physical test conditions such as drop tests to verify that their structural response is accurate. Five derailment conditions will then be simulated and post-processed to quantify any damage to the casks and associated risk of material release, if any. High quality videos of the derailment simulations will be created that demonstrate the type of visuals that could be used in the future to support public education and outreach efforts. The digital twin technology proven here can be applied to product development workflow, lifecycle management, crash reconstruction, and strategic planning related to structural performance of vehicles, structures, and equipment.