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TACTICAL (sentence-level): - Improved issue-point positioning and topic-stress - Shortened compound sentences for clarity - Strengthened verb choices (active over passive) - Enhanced topic strings for better flow OPERATIONAL (paragraph/section): - Added transition sentences between subsections - Improved coherence within sections - Clarified connections between ideas - Enhanced paragraph-to-paragraph flow STRATEGIC (document-level): - Tightened Heilmeier question answers in each section - Strengthened 'Who cares? Why now?' in Broader Impacts - Improved global coherence across sections - Ensured section summaries clearly answer assigned questions
64 lines
7.0 KiB
TeX
64 lines
7.0 KiB
TeX
\section{Broader Impacts}
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\textbf{Who cares? Why now? What difference will it make?} These three Heilmeier questions connect technical methodology to economic and societal impact. Three stakeholder groups face the same economic constraint: the nuclear industry, datacenter operators, and clean energy advocates. All confront 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 economic challenge. Recent interest in powering artificial intelligence infrastructure has renewed focus on small modular reactors (SMRs), particularly for hyperscale datacenters requiring hundreds of megawatts of continuous power. Deploying SMRs at datacenter sites minimizes transmission losses and eliminates emissions. However, nuclear power economics at this scale demand careful attention to operating costs.
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The U.S. Energy Information Administration's Annual Energy Outlook 2022 projects advanced nuclear power entering service in 2027 will cost \$88.24 per megawatt-hour~\cite{eia_lcoe_2022}. Datacenter electricity demand is projected to reach 1,050 terawatt-hours annually by 2030~\cite{eesi_datacenter_2024}. Nuclear power supplying this demand would generate total annual costs exceeding \$92 billion. Operations and maintenance represents a substantial component: the EIA estimates that fixed O\&M costs alone account for \$16.15 per megawatt-hour, with additional variable O\&M costs embedded in fuel and operating expenses~\cite{eia_lcoe_2022}. Combined, O\&M-related costs represent approximately 23--30\% of total levelized cost, translating to \$21--28 billion annually for projected datacenter demand.
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\textbf{What difference will it make?} This research directly addresses the \$21--28 billion annual O\&M cost challenge. High-assurance autonomous control makes small modular reactors economically viable for datacenter power while maintaining nuclear safety standards.
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Current nuclear operations require full control room staffing for each 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, which makes per-megawatt costs prohibitive. These staffing requirements drive the economic challenge 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. 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. By 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 establishes impact by answering three critical Heilmeier questions:
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\textbf{Who cares?} Three stakeholder groups face the same constraint. The nuclear industry faces an economic crisis for small modular reactors due to per-megawatt staffing costs. Datacenter operators need hundreds of megawatts of continuous clean power for AI infrastructure. Clean energy advocates need nuclear power to be economically competitive. All three groups need autonomous control with safety guarantees.
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\textbf{Why now?} Two forces converge to make this research urgent. First, exponentially growing AI infrastructure demands have created immediate need for economical nuclear power at datacenter scale. Projections show datacenter electricity demand reaching 1,050 terawatt-hours annually by 2030. Second, formal methods tools have matured to where compositional hybrid verification has become computationally achievable—what was theoretically possible but practically intractable a decade ago is now feasible.
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\textbf{What difference will it make?} This research addresses a \$21--28 billion annual cost barrier by enabling autonomous control with mathematical safety guarantees. Beyond immediate economic impact, the methodology establishes a generalizable framework for safety-critical autonomous systems across critical infrastructure.
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The complete research plan now spans technical approach, success metrics, risk mitigation, and broader impact. One final Heilmeier question remains: \textbf{How long will it take?} Section 8 provides a structured 24-month research plan progressing through milestones tied to Technology Readiness Level advancement, demonstrating the proposed work is achievable within a doctoral timeline.
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