40 lines
1.8 KiB
Markdown
40 lines
1.8 KiB
Markdown
# A random thesis idea I had
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This is kind of connected to the high assurance digital twin
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idea, but I am currently in the middle of reading and needed
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to get this out of my head.
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Here's the situation:
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Manyu's work made a lot of progress to apply contract based
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formal methods to nuclear power. To do this, an assumption
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of a certain components output is fed into the input of the
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next component. Math is done, and then the output of that
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component becomes a guarantee, which is then the assumption
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for the next component in line after that.
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But here's a question: how do you know that your assumptions
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and guarantee's are valid on a real system, in real time?
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These contracts are based on having a model of the system
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with which you can evaluate the assumptions/guarantee pairs.
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But, real systems never will line up perfectly with a model,
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and over time or different conditions, will absolutely have
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different physical behaviors. Knowing if the contracts still
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hold for the real system is a significant problem.
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Here's where some online modeling in simulation can come in.
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Perhaps, we can use a digital twin to estimate what the
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critical model parameters for the contract methods are in
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the real system. This is probably most easily accomplished
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with either a physics informed neural network (PINN) or some
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sort of particle filter bayesian nonsense. Once those
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parameters are identified, we can reevaluate the contracts
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to know a) if our system is safe, b) what our new
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assumptions and safe operating range are, and c) make
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strategic decisions about the plant control if necessary.
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This relates to the [autonomous framework paper](/Zettelkasten/Literature%20Notes/albertiAutomationLevelsNuclear2023.md)
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that talks about getting to higher levels of automation.
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Level 3 is exactly this, the automated reactor operation
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system being able to detect and diagnose what an error is.
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