NuScale and ORNL Apply AI to Multi-Reactor Fuel Strategy: What Managers Should Know
NuScale Power (NYSE: SMR) is partnering with Oak Ridge National Laboratory to apply an AI-enabled nuclear design framework to a 12-NuScale Power Module configuration. The goal is straightforward: explore smarter, cross-module fuel strategies that can cut costs and improve efficiency at a single site.
The U.S. Department of Energy's GAIN initiative awarded funding to ORNL for this collaboration, marking part of the first round of GAIN Vouchers for fiscal year 2026. For context on GAIN's role in accelerating nuclear innovation, see the DOE GAIN initiative.
Why this matters
Fuel management for a single reactor is a solved problem. NuScale's multi-module architecture shifts the playing field: up to 12 reactors can share one fuel pool and draw from more configuration options than a typical single-unit plant.
With AI scanning many scenarios fast, the team can test cross-module sharing strategies that may reduce total fuel spend, smooth reload cycles, and lift output consistency. Fewer surprises, tighter planning, and better use of existing fuel inventory are the wins to look for.
What's new in the approach
NuScale uses proven, off-the-shelf fuel assemblies. The difference here is system design: a shared pool and multiple modules create more decision points for how, where, and when to deploy fuel.
ORNL will contribute expertise in AI, machine learning, fuel management, and compute resources. Together, they'll run large scenario sets to find practical strategies that a single unit could never access on its own.
Leadership signal
NuScale's CEO, John Hopkins, underscored the intent: collaborate with ORNL, use advanced computational methods, and find ways to manage fuel more efficiently across modules while reducing costs for future operations. The message is clear-align engineering innovation with economic outcomes as demand for clean, reliable energy grows.
Implications for decision-makers
- Cost: Potential reduction in total fuel cost through smarter allocation and timing across modules.
- Operations: More flexible reload planning, fewer bottlenecks, and shorter outage durations.
- Finance: Possible improvements in LCOE via better fuel utilization and deferred purchases.
- Supply chain: Tighter forecasting and inventory turns from a shared pool strategy.
KPIs to track
- Fuel $/MWh (component of LCOE)
- Capacity factor and unplanned downtime
- Days between refueling events and outage duration
- Fuel pool inventory turns and holding costs
- Thermal margins and safety limits maintained across scenarios
- Model-to-plant variance in predicted vs. actual outcomes
Risk and governance checklist
- Model validation and verification (independent review of AI/ML methods)
- Regulatory engagement and documentation discipline
- Cybersecurity for model pipelines and plant data
- Quality assurance on data lineage, scenario assumptions, and change control
- Clear decision rights between plant ops, fuel procurement, and analytics teams
What to watch next
- Initial simulation results and any published methodologies
- Pilot applications at multi-module sites and measured fuel savings
- Feedback from DOE/GAIN milestones and regulatory interactions
- Procurement signals: revised reload schedules, inventory policies, or vendor terms
Practical next steps for managers
- Stand up a joint task group (operations, fuel engineering, finance) to define success metrics.
- Audit data readiness: sensor quality, historical fuel performance, and integration points.
- Define governance early: model risk management, approvals, and reporting cadence.
- Plan workforce readiness: cross-train engineers on AI-assisted planning tools.
If you're building team capability around AI in operations and planning, explore focused learning paths by role: AI courses by job.
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