Shutdown Dose Rate Modeling for Radiation Requirements Development and Design Trend Analysis in the ARC Fusion Device

To achieve commercial viability, Commonwealth Fusion System’s ARC device must maximize its availability to produce power, thus demanding a rapid maintenance process to replace radiation-damaged components. Designing robotic systems to operate in this radiation environment requires understanding the...

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Bibliographic Details
Main Author: Murphy, Daniel T.
Other Authors: Whyte, Dennis G.
Format: Thesis
Published: Massachusetts Institute of Technology 2024
Online Access:https://hdl.handle.net/1721.1/156046
Description
Summary:To achieve commercial viability, Commonwealth Fusion System’s ARC device must maximize its availability to produce power, thus demanding a rapid maintenance process to replace radiation-damaged components. Designing robotic systems to operate in this radiation environment requires understanding the expected radiation levels and how design decisions impact those levels. This thesis uses the Rigorous Two-Step (R2S) methodology to scope the radiation environment and provide data for those design trade-offs that must be considered in future ARC design iterations. The first trend is Vanadium’s lower dose rate than Eurofer as a Vacuum Vessel and Blanket Tank material in all configurations, making it the preferred candidate from a radiation perspective. Second, the model indicates that the choice in Blanket Tank material contributes non-trivially to the maintenance radiation environment. Third, the trends demonstrate minimal additional reduction in radiation levels from delaying the start of maintenance beyond 14 days after fusion ceases. The final trend shows the reduction in the radiation field from the removal of the Blanket Tank with the Vacuum Vessel warrants future study. Finally, this thesis incorporates historical nuclear robotics experience to establish an iterative process by which to develop robotic radiation requirements and assess maintenance decision effects on ARC-level optimality.