279 lines
11 KiB
Markdown
279 lines
11 KiB
Markdown
# Thesis Ideas 2025-07-30
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Following our group meeting from Monday, July 28th, Dan
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suggested I write down 6 ideas, and from them we shall
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figure out a possible topic idea that I can really start
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working on.
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I used ChatGPT to do some of the heavy lifting based on the
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papers I've been reading, and leveraged the 'deep research'
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feature. Here are some of my favorite ideas, broken down
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into goals, outcomes, impact, and related papers.
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___________________________________________________________
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## **Integrating Shielding into Nuclear Power Control**
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### Goal:
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The goal of this research is develop machine learning
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enabled control algorithims for nuclear power applications
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that incoporate shielding: a formal guarantee of adherence
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to system specifications without augmenting the machine
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learning process.
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### Outcomes:
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For this research to be successful, I will accomplish the
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following:
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1. Identify key controllers in a nuclear power context with
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the most benefit from using an ML-based controller
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2. Translate regulatory and system level requirements into a
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formal specification to synthesize a controller 'shield'.
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This shield monitors the ML controller and intervenes
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whenever a requirement is predicted to be violated.
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3. Evaluate performance of the ML controller with attached
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shield, while assessing the amount of shield useage for
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different operating scenarios (power up, shut down, regular
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load following)
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### Impact:
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Machine learning controllers can outperform PID and
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rule-based controllers by adapting to nonlinear dynamics,
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optimizing over multi-objective cost functions, and changing
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plant conditions. But, these ML controllers are often
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*unexplainable*, meaning that their global behavior is not
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easily understood.This unexplainability prevents ML based
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controllers from being used in high-assurance usecases such
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as nuclear power. Shielding can address this issue, by
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providing a formal runtime assurance, allieviating the
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burden of explainability away from the machine learning
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algorithm. This work would further bring regulatory
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requiremnts into the formal design of control systems and
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help bridge the gap between high assurance systems and the
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start of the art in control.
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### Relevant Papers
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[[safe-reinforcement-learning-via-shielding]]
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[[evaluating-robustness-of-neural-networks-with-mixed-integer-programming]]
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___________________________________________________________
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## **Formally Verified Neural Network Control of Control Rod System**
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### Goals:
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The goal of this research is to use formal methods to ensure that
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a neural network based control rod controller will never violate
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safety guarantees of a reactor trip system. To do this, a
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satisfiability modulo theory method will be applied to
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exhaustively search the network for potential failure modes.
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### Outcomes:
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If this research is successful, I will have accomplished the
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following:
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- Build a neural network controller for real time control of a
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control rod system.
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- Formalize safety guarantees of shutdown margin in a
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satisfiability modulo theory embedding
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- Formally verify that the neural network based controller will
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not violate any shutdown margin restrictions
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### Impact:
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SMT solvers and MILP formulations have been applied to neural
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networks to ensure that the network is resilient to input
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perturbations. I think we can expand this to more general
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considerations of the state space, especially when there are a
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relatively small number of states such as in power contexts. The
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benefit of this system is that we would get closer to saying
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neural network based systems can be high assurance for physical
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systems.
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### Related Papers:
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[[reluplex-an-efficient-smt-solver-for-verifying-deep-neural-networks]]
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[[evaluating-robustness-of-neural-networks-with-mixed-integer-programming]]
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[[formal-verification-of-neural-network-controlled-autonomous-systems]]
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___________________________________________________________
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## **Temporal Logic Specifications for Autonomous Controller Synthesis**
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### Goals:
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The goal of this program is to use temporal logic
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specifications to procedurally generate autonomous
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supervisory controllers for a reactor system.
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### Outcomes:
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If this research is successful, I will have accomplished the
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following:
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- Captured high level safety and operating requirements in a
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temporal logic language such as TLA+ or FRET
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- Synthesize a supervisory controller from the temporal
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logic specification that can be implemented on a real
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control system with minimal user effort.
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- Verify the supervisory controller generated adheres to
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safety specifications using exhaustive model checking.
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### Impact:
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### Related Papers:
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___________________________________________________________
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## **Formally Verified Runtime Monitoring and Fallback**
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### Goals:
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If this research is successful, we will be able to generate
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autonomous controller shields that provably adhere to specifications
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written with temporal logic.
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### Outcomes:
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- Create an intermediary shield that mediates signals between an
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optimal control system and the physical plant (MODBUS)?
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- Translate specifications in a language like TLA+ into an
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executable program
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- Provide proof artifacts that automatically generated
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shield components will not allow an arbitrary controller to
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reach an unsafe state.
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### Impact:
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Shielding is one of the preeminent ways to do safe machine
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learning controllers. Instead of putting the proof burden on
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the machine learning component, shielding creates a safe
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boundary in the state space where a safety controller will
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step in if the machine learning controller endangers the
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system. This technology solves a critical problem with high
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assurance systems: high assurance systems have critical
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safety requirements that make scrutiny on autonomous systems
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safety intense. Shielding can provide a safety barrier for
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the controller, allowing the architecture of the control
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laws to be amenable to more efficient machine learning based
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methods. Finally, utilizing an automatic translation from a
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temporal logic formulation of a speculation will allow the
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engineers of these systems to quickly and clearly implement
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a shield, without all of the cumbersome derivation.
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### Related Papers:
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[[on-using-real-time-reachability-for-the-safety-assurance-of-machine-learning-controllers]]
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[[enhancing-cyber-physical-system-dependability-via-synthesis-challenges-and-future-directions]]
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[[safe-reinforcement-learning-via-shielding]]
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___________________________________________________________
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## **Data-Driven Fault Detection Using High-Assurance Digital Twins**
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(8)
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### Goals:
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The goal of this research is to use machine learning to
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identify system faults of a reactor control system during
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runtime. A digital twin will be compared to measurements
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from a real plant to identify issues such as coolant losses,
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sensor and actuator failures, or component degredation so
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that safety strategic decisions about the plant can be made
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autonomously.
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### Outcomes:
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For this research to be successful, I will accomplish the
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following:
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- Create a simulation suite for the Small Modular Advanced
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High Temperature Reactor (SmAHTR) to simulate fault
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conditions of sensors, actuators, and component degradation.
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- Develop a physics informed neural network (PINN) approach
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to evaluate physics discrepancies in measured signals and
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to estimate physically relevant parameters to determine
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real system divergence from the nominal plant.
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- Realize a proof of concept autonomous controller than can
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react to fault conditions by switching to different
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control modes rather than only responding with reactor
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shutdown.
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### Impact:
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The nuclear energy industry's largest expense is operations
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and maintenance (O&M). These costs include typical reactor repair
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and refueling, the labor involved to complete such
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maintenance, and finally the labor involved in operating the
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reactor itself. Currently the largest of these O&M expenses
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is the labor and part cost used in maintenance, while large
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nuclear reactor facilities require a modest reactor operator
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budget per megawatt of energy produced. The advent of small
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modular reactors (SMRs) and microreactors (MRs) will change
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these economics significantly.
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As SMRs and MRs become more common, the cost of repair and
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maintenance should reduce dramatically as nuclear power
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components will become modular, replaceable parts instead of
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the bespoke reactor designs currently operating. Operator
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wages, however, can be expected to increase without
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introducing greater controller autonomy. SMRs and MRs are
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much smaller output designs per reactor core, and if they
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are required to employ the same size reactor operator team
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as a conventional large reactor, will suffer from much
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larger operator expense per megawatt. Greater controller
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autonomy can solve this problem by unloading some reactor
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control responsibilities from the operator, and therein
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reduce labor consumption.
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<# TO DO #>
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Finally reactor safety can be improved by greater autonomy
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yada yada find some reasons to back this up.
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### Related Papers:
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___________________________________________________________
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## **Verified Adaptive Control**
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### Goals:
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The goal of this research is to create an adaptive controller
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that can adjust to system dynamics changes over time to maintain
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an optimal control, while using formal methods to provide strong
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safety guarantees about the malleable control law.
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### Outcomes:
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For this research to be successful, I will accomplish the
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following:
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- Create a simulation suite for the Small Modular Advanced High
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Temperature Reactor (SmAHTR) to simulate component degradation
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such as heat exchanger blockages and fuel concentration burn-up.*
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- Create an adaptive control rod controller to maximize load following
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precision for a simulated power grid demand.
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- Use contract based verification at runtime to ensure that
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learned parameters for the adaptive controller remain within
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safety specification limits
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*Is this actually even a problem for SmAHTR? Figuring the fuel is
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suspended in the salt I'd assume chemistry is pretty strictly
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controlled. I'm sure I can find other examples.
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### Impact:
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Certain reactor control systems are already automatic systems,
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such as constant temperature or pressure controls for operating
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at steady state. These simple controllers are able to follow load
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changes from the power grid on their own, but over will lose
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efficiency as the underlying plant mechanics become less
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efficient, or maintenance is performed and components are
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refreshed. For nuclear power contexts, fine control is ideal to
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maximize profits and to minimize energy wasteage. This is not an
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easy problem to solve, however, as the dynamics of the underlying
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plant are constantly changing. Adaptive control can help address
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this issue, but learnable controllers must come with guarantees
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of safety in order to be attractive to the nuclear industry.
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### Related Papers:
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