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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Plan for Development and Application of Risk Assessment Approach for Transportation Package Approval of an MNPP for Domestic Highway Shipment

For nuclear reactors, Probabilistic Risk Assessment (PRA) has been conducted since the 1970s [e.g., see WASH-1400 (NRC 1975), NUREG-1150 (NRC 1990), and NUREG-1935 (Chang et al. 2012)]. PRA has also been used to assess a dry cask storage system at a nuclear power plant [see NUREG-1864 (NRC 2007)]. PRA techniques have also been applied to the transportation of spent nuclear fuel, most notably in NUREG/CR-4829 (Fischer et al. 1987), NUREG/CR-6672 (Sprung et al. 2000), and NUREG-2125 (NRC 2014). Additional guidance is provided in International Atomic Energy Agency (IAEA) IAEA-TECDOC-1346 (2003). Transportation PRA was also used in the evaluations of transportation impacts in reports such as the Repository Final Environmental Impact Statement (DOE 2002) and the Repository Final Supplemental Environmental Impact Statement (DOE 2008).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Bayesian Optimization of Non-Invariant Systems with Constraints Developed for Application to the ECR Ion Source VENUS

In this work, we consider the optimization of non-invariant systems with both safety and control constraints. We present a new approach based on Bayesian optimization for the dynamic, safe and controlled optimization of such systems. Although there are other possible use cases, we focus on the application to the electron cyclotron resonance ion source VENUS. From experimental data, we have observed that VENUS behaves to first order as a non-invariant dynamic system with moving areas of instability. Our novel approach aims at providing a tool that can maintain system optimization in a safe way. This is accomplished by making sure the objective function, the beam current in the case of VENUS, does not fall under an operational minimum, while simultaneously requiring the optimization to avoid areas where VENUS is unstable. We compare the result of our approach on synthetic data modeled to mimic the behavior of VENUS with two methods from the literature, a standard Bayesian optimizer and a safe Bayesian optimizer, both adapted to deal with dynamic systems. A cross Student T-test is conducted to show the significance of the improvement given by the new method we introduce here, regarding the two preexisting methods we compared to. The results of the tests conducted on synthetic data show that the proposed method succeeds at maintaining the system optimized and obeys the predefined constraints better than the literature methods explored.

Bayesian optimization↗