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Three-level editorial improvements: TACTICAL (sentence-level): - Applied Gopen's issue-point and topic-stress positioning throughout - Improved verb choice and sentence clarity - Tightened passive constructions to active voice - Enhanced topic strings for better paragraph coherence OPERATIONAL (paragraph-level): - Strengthened transitions between subsections - Improved flow within complex technical sections - Made mode classification rationale more explicit - Enhanced coherence in verification methodology STRATEGIC (document-level): - Made Heilmeier Catechism alignment explicit in section transitions - Added structured mapping of sections to Heilmeier questions in Sec 1 - Strengthened summary sections to reinforce question-answer structure - Improved subsection headings to signal content and purpose Changes preserve all technical content while significantly improving clarity, flow, and argument structure.
85 lines
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85 lines
6.0 KiB
TeX
\section{Broader Impacts}
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\textbf{Who cares? 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. However, nuclear power
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economics at this scale demand careful attention to operating costs.
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The U.S. Energy Information Administration's Annual Energy Outlook
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2022 projects advanced nuclear power entering service in 2027 will 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}. Nuclear power supplying this demand would
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generate total annual costs exceeding \$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 will 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. For large reactors producing 1,000+ MW, staffing costs spread across substantial output. Small modular reactors producing 50-300 MW face the same staffing requirements with far lower output, making the per-megawatt cost prohibitive. These staffing requirements drive the economic challenge
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that threatens SMR deployment for datacenter applications. 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. A fundamental shift from direct operator
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control to supervisory monitoring becomes possible, 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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This section answered three Heilmeier questions:
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\textbf{Who cares?} The nuclear industry, datacenter operators, and anyone facing high operating costs from staffing-intensive safety-critical control.
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\textbf{Why now?} AI infrastructure demands have made nuclear economics urgent, and formal methods tools have matured to the point where compositional hybrid verification is achievable.
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\textbf{What difference will it make?} Enabling autonomous control with mathematical safety guarantees addresses a \$21--28 billion annual cost barrier while establishing a generalizable framework for safety-critical autonomous systems.
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Section 8 addresses the final Heilmeier question—how long will it take?—presenting a structured 24-month research plan with milestones tied to Technology Readiness Level advancement.
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