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Zettelkasten/Permanent Notes/thesis-ideas.md
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@ -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: