Split
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- TACTICAL: Improved sentence-level clarity per Gopen principles - Stronger verb choices (cannot vs lacks tools, cannot vs fails) - Better issue-point positioning (new info at sentence end) - Topic-stress consistency (familiar info at start) - Eliminated weak constructions (that are → direct adjectives) - OPERATIONAL: Enhanced paragraph and section flow - Added transition sentences between subsections - Improved coherence in state-of-the-art progression - Clearer bridges between risk categories - Better linkage from discrete to continuous verification - STRATEGIC: Reinforced Heilmeier catechism alignment - Made 'What is new?' and 'Why will it succeed?' explicit - Added 'State of the art' and 'The gap' headers - Consistent question format (How do we...? vs How we...) - Added roadmap at end of Goals section No content changes—only editorial improvements for clarity and impact.
76 lines
5.1 KiB
TeX
76 lines
5.1 KiB
TeX
\section{Broader Impacts}
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\textbf{Who cares and why now:} The nuclear industry, datacenter operators, and clean energy advocates all face the same economic constraint: high operating costs driven by staffing requirements. AI infrastructure demands—growing exponentially—have made this constraint urgent.
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Nuclear power presents both a compelling application domain and an urgent
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economic challenge. Recent interest in powering artificial intelligence
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infrastructure has renewed focus on small modular reactors (SMRs), particularly
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for hyperscale datacenters requiring hundreds of megawatts of continuous power.
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Deploying SMRs at datacenter sites minimizes transmission losses and
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eliminates emissions from hydrocarbon-based alternatives. Nuclear power
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economics at this scale, however, demand careful attention to operating costs.
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According to the U.S. Energy Information Administration's Annual Energy Outlook
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2022, advanced nuclear power entering service in 2027 is projected to cost
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\$88.24 per megawatt-hour~\cite{eia_lcoe_2022}. Datacenter electricity demand is
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projected to reach 1,050 terawatt-hours annually by
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2030~\cite{eesi_datacenter_2024}. If this demand were supplied by nuclear power,
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the total annual cost of power generation would exceed \$92 billion. Within this
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figure, operations and maintenance represents a substantial component. The EIA
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estimates that fixed O\&M costs alone account for \$16.15 per megawatt-hour,
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with additional variable O\&M costs embedded in fuel and operating
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expenses~\cite{eia_lcoe_2022}. Combined, O\&M-related costs represent
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approximately 23--30\% of the total levelized cost of electricity, translating
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to \$21--28 billion annually for projected datacenter demand.
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\textbf{What difference does it make?} This research directly addresses the
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\$21--28 billion annual O\&M cost challenge through high-assurance autonomous
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control, making small modular reactors economically viable for datacenter power.
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Current nuclear operations require full control room staffing for each
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reactor—whether large conventional units or small modular designs. These staffing requirements drive the high O\&M costs
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that make nuclear power economically challenging, particularly for smaller
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reactor designs where the same staffing overhead must be spread across lower
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power output. Synthesizing provably correct hybrid controllers from formal
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specifications automates routine operational sequences that currently require
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constant human oversight. This enables a fundamental shift from direct operator
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control to supervisory monitoring, where operators oversee multiple autonomous
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reactors rather than manually controlling individual units.
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The correct-by-construction methodology is critical for this transition.
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Traditional automation approaches cannot provide sufficient safety guarantees
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for nuclear applications, where regulatory requirements and public safety
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concerns demand the highest levels of assurance. Formally verifying both the
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discrete mode-switching logic and the continuous control behavior, this research
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will produce controllers with mathematical proofs of correctness. These
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guarantees enable automation to safely handle routine operations---startup
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sequences, power level changes, and normal operational transitions---that
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currently require human operators to follow written procedures. Operators will
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remain in supervisory roles to handle off-normal conditions and provide
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authorization for major operational changes, but the routine cognitive burden of
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procedure execution shifts to provably correct automated systems that are much
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cheaper to operate.
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SMRs represent an ideal deployment target for this technology. Nuclear
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Regulatory Commission certification requires extensive documentation of control
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procedures, operational requirements, and safety analyses written in structured
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natural language. As described in our approach, these regulatory documents can
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be translated into temporal logic specifications using tools like FRET, then
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synthesized into discrete switching logic using reactive synthesis tools, and
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finally verified using reachability analysis and barrier certificates for the
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continuous control modes. The infrastructure of requirements and specifications
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already exists as part of the licensing process, creating a direct pathway from
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existing regulatory documentation to formally verified autonomous controllers.
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Beyond reducing operating costs for new reactors, this research will establish a
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generalizable framework for autonomous control of safety-critical systems. The
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methodology of translating operational procedures into formal specifications,
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synthesizing discrete switching logic, and verifying continuous mode behavior
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applies to any hybrid system with documented operational requirements. Potential
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applications include chemical process control, aerospace systems, and autonomous
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transportation, where similar economic and safety considerations favor increased
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autonomy with provable correctness guarantees. Demonstrating this approach in
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nuclear power---one of the most regulated and safety-critical domains---will
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establish both the technical feasibility and regulatory pathway for broader
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adoption across critical infrastructure.
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