\section{Broader Impacts}

\textbf{Heilmeier Questions: Who cares? Why now? What difference will it make?}

Sections 2--5 established the complete technical research plan. Section 2 answered what has been done and identified the limits of current practice. Section 3 answered what is new and why it will succeed. Section 4 answered how success will be measured through TRL advancement. Section 5 answered what could prevent success and provided mitigation strategies for each risk.

This section addresses the remaining Heilmeier questions by connecting technical methodology to economic and societal impact.

Three stakeholder groups converge on one economic constraint—high operating costs driven by staffing requirements. The nuclear industry faces uncompetitive per-megawatt costs for small modular reactors. Datacenter operators need hundreds of megawatts of continuous clean power for AI infrastructure. Clean energy advocates need nuclear power to be economically viable.

This research directly addresses a \$21--28 billion annual cost barrier by enabling economically viable small modular reactors for datacenter power and establishing a generalizable framework for safety-critical autonomous systems across critical infrastructure.

Why now? Exponentially growing AI infrastructure demands have transformed this longstanding challenge into an immediate crisis. This creates a market demanding solutions that did not exist before.

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. SMRs deployed at datacenter sites minimize transmission losses and eliminate emissions. At this scale, however, nuclear power economics demand careful attention to operating costs.

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.

\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.

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. This 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 specifications automates routine operational sequences that currently require constant human oversight. This enables a fundamental shift from direct operator control to supervisory monitoring. Operators oversee multiple autonomous reactors rather than manually controlling individual units.

The correct-by-construction methodology proves critical for this transition.
Traditional automation approaches cannot provide sufficient safety guarantees
for nuclear applications, where regulatory requirements and public safety
concerns demand the highest levels of assurance. By formally verifying both the
discrete mode-switching logic and the continuous control behavior, this research
produces controllers with mathematical proofs of correctness. These
guarantees enable automation to safely handle routine operations---startup
sequences, power level changes, and normal operational transitions---that
currently require human operators to follow written procedures. Operators will
remain in supervisory roles to handle off-normal conditions and provide
authorization for major operational changes, but the routine cognitive burden of
procedure execution shifts to provably correct automated systems that are much
cheaper to operate.

SMRs represent an ideal deployment target for this technology. Nuclear
Regulatory Commission certification requires extensive documentation of control
procedures, operational requirements, and safety analyses written in structured
natural language. As described in our approach, these regulatory documents can
be translated into temporal logic specifications using tools like FRET, then
synthesized into discrete switching logic using reactive synthesis tools, and
finally verified using reachability analysis and barrier certificates for the
continuous control modes. The infrastructure of requirements and specifications
already exists as part of the licensing process, creating a direct pathway from
existing regulatory documentation to formally verified autonomous controllers.

Beyond reducing operating costs for new reactors, this research will establish a
generalizable framework for autonomous control of safety-critical systems. The
methodology of translating operational procedures into formal specifications,
synthesizing discrete switching logic, and verifying continuous mode behavior
applies to any hybrid system with documented operational requirements. Potential
applications include chemical process control, aerospace systems, and autonomous
transportation, where similar economic and safety considerations favor increased
autonomy with provable correctness guarantees. Demonstrating this approach in
nuclear power---one of the most regulated and safety-critical domains---will
establish both the technical feasibility and regulatory pathway for broader
adoption across critical infrastructure.


This section answered three critical Heilmeier questions: Who cares? Why now? What difference will it make?

\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.

\textbf{Why now?} Two forces converge. First, exponentially growing AI infrastructure demands create 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 sufficiently to make compositional hybrid verification computationally achievable. What was theoretically possible but practically intractable a decade ago is now feasible. The problem is urgent. The tools exist.

\textbf{What difference will it make?} This research addresses a \$21--28 billion annual cost barrier. It enables autonomous control with mathematical safety guarantees. Beyond immediate economic impact, the methodology establishes a generalizable framework for safety-critical autonomous systems across critical infrastructure. Impact extends beyond nuclear power to any safety-critical system requiring provable correctness.

The complete research plan spans technical approach, success metrics, risk mitigation, and broader impact. One final Heilmeier question remains: 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.