From b6fdcf7a86358037592d5c6d0f8b4b597bc56523 Mon Sep 17 00:00:00 2001 From: Dane Sabo Date: Mon, 11 Aug 2025 16:54:01 -0400 Subject: [PATCH] vault backup: 2025-08-11 16:54:01 --- Zettelkasten/Permanent Notes/thesis-ideas.md | 33 ++++++++++---------- 1 file changed, 17 insertions(+), 16 deletions(-) diff --git a/Zettelkasten/Permanent Notes/thesis-ideas.md b/Zettelkasten/Permanent Notes/thesis-ideas.md index 8d33fec52..825e3ec8b 100644 --- a/Zettelkasten/Permanent Notes/thesis-ideas.md +++ b/Zettelkasten/Permanent Notes/thesis-ideas.md @@ -258,15 +258,16 @@ ___________________________________________________________ ### Goals: The goal of this research is to develop a high-assurance, -digital twin–based methodology for runtime fault detection -in a reactor control system. A physics-based digital twin -will be continuously compared with live plant measurements -to detect anomalies such as coolant losses, sensor and -actuator faults, and abnormal component degradation. Discrepancies +digital twin–based fault detection and mitigation framework +for a reactor control system. A physics-based digital twin +will be run in parallel with plant measurements to detect +anomalies such as coolant losses, sensor and actuator +faults, and abnormal component degradation. Discrepancies between the digital twin and plant data will be analyzed -using physics-informed machine learning models to diagnose -the underlying fault and trigger appropriate autonomous -control actions. +using residual0based and physics-informed machine learning +models to diagnose the underlying fault and trigger +appropriate graded autonomous control actions ranging from +control adjustments to safe shutdown. ### Outcomes: @@ -274,19 +275,19 @@ For this research to be successful, I will accomplish the following: 1. Create a simulation suite for the Small Modular Advanced - High Temperature Reactor (SmAHTR) to simulate fault -conditions including sensor and actuator failures, and -component degradation. + High Temperature Reactor (SmAHTR) to simulate high-impact +fault types such as secondary loop coolant losses, heat +exchanger fouling, and sensor drifting. -2. Implement a physics-informed neural network (PINN) - framework to estimate key plant parameters, detect +2. Implement a residual-based physics-informed neural + network (PINN) to estimate key plant parameters, detect discrepancies between predicted and measured signals, and -identify probable fault conditions. +identify probable fault condition and severity. 3. Integrate the fault detection developed with a proof-of-concept autonomous supervisory controller -capable of implementing graded responses to fault -conditions. +capable of implementing graded responses to fault conditions +aligned with NRC safety protocols. ### Impact: