Nuclear power plants are highly complex systems that must integrate diverse engineering domains to produce electricity safely and efficiently. The system-level challenge addressed in this project is the optimization of a nuclear power plant design to achieve maximum efficiency and cost-effectiveness while meeting strict safety, thermal, regulatory, and environmental constraints.

The nuclear power plant is decomposed into three major interacting subsystems:

1. The Nuclear Reactor, which generates thermal energy via nuclear fission.

2. The Power Conversion System, which transforms thermal energy into usable electrical energy

3. The Cooling System, which dissipates waste heat to the environment.

These subsystems are physically coupled - e.g., the thermal output from the reactor becomes input to the turbine system and must be balanced by the cooling tower’s heat rejection capacity. We also chose the MAGNATEX® MAXP A85-10 industrial water pump to move coolant between our systems. The system optimization seeks to coordinate these components to minimize the cost per unit energy output while respecting engineering feasibility, regulatory limits, and safety constraints.

The optimization involves a set of tightly coupled decision variables, constraints, and performance outputs across subsystems:

Method:

• Perturb each decision variable from the optimized values

• Re-optimize or evaluate the change in fmin (net earnings per joule)

Most Sensitive Variables:

•  Fuel Enrichment (fuel): Increasing efuel beyond 0.03 significantly raises fuel cost and violates safety limits without meaningful thermal gain.

• Coolant Sprayer Nozzles (#): Limiting the number of nozzles directly impacts the size of the reactor. 

Constraint Most Binding: Reactor power constraint

• Many solutions approach the 5000 MW cap; increasing thermal output beyond this invokes a penalty, influencing all downstream systems (conversion and cooling).

Real-World Implications:

• Fuel enrichment must be tightly regulated for both cost and safety.

• Reactor power limits drive the design of conversion and cooling systems, emphasizing integrated regulatory compliance and system coordination.

Relevance:

• The final design provides feasible, cost-effective, and regulation-compliant performance while maximizing economic yield.

Model Fidelity:

• Each subsystem incorporates realistic physical laws (e.g., thermodynamics, fluid dynamics), and the system model preserves coupling and economic integration.

• Example: Cooling load is driven by waste heat, which is a direct function of reactor output and conversion efficiency — this link is respected throughout.

Software:

• MATLAB’s fmincon with the interior-point algorithm

• Used for its capability to handle high-dimensional, nonlinear, constrained problems

• Validated across 5 trials for consistency and robustness