Investigating the application of Kalman Filters for real-time accountancy in fusion fuel cycles

Journal Article · 28 January 2022 · Fusion Engineering and Design

DOI: https://doi.org/10.1016/j.fusengdes.2022.113037 · OSTI ID:1856104

Flynn, H. B. ^[1]; Larsen, George ^[1]

---

1. Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL)

Here tritium accountancy in the fusion fuel cycle is a significant concern for the operation of commercial devices. It is expected that a limit on the maximum amount of tritium inventory in the system will be implemented, meaning that accountancy of the tritium inventory in the fuel cycle will need to be as accurate as possible. This is difficult since not all locations along the fuel cycle can benefit from the implementation of a tritium accountancy sensor and measurements will inherently contain error. Commercial fusion plants will also operate continuously, challenging current accountancy techniques that rely on static processes in well-controlled environments. In this paper a simple fusion fuel cycle concept is defined and the tritium inventory over time of each component is modelled using the Euler Approximation of a series of differential Equations in Python. This information is then used to simulate sensor measurements at specific points on the fuel cycle and then passed through a Kalman Filter (KF) to improve the accuracy of the true measurement of the sensors.

• Research Organization: Savannah River National Lab (SRNL), Aiken, SC (United States); Savannah River Site (SRS), Aiken, SC (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Fusion Energy Sciences (FES)
• Grant/Contract Number: 89303321CEM000080
• OSTI ID: 1856104
• Report Number(s): SRNL-STI-2021-00621
• Journal Information: Fusion Engineering and Design, Journal Name: Fusion Engineering and Design Vol. 176; ISSN 0920-3796
• Publisher: ElsevierCopyright Statement
• Country of Publication: United States
• Language: English