## 6 Discussion  
This study presents a hybrid optimization framework that integrates a Genetic Algorithm (GA), State-Space Network (SSN) modeling, and Computational Fluid Dynamics (CFD) simulation to address the degradation of oxidative stability in emergency diesel fuel used in nuclear power facilities. The proposed method enabled the automatic identification of efficient diesel conditioning pathways through GA-guided structural configuration searches, validated by physically grounded CFD results. The optimization process successfully reduced conditioning time by 5.38%--from 33,609 s to 31,800 s--while ensuring that the antioxidant volume fraction reached the required 0.0698% at all critical monitoring points. The CFD analysis provided insights into the dynamic evolution of mixing efficiency and additive distribution. Notably, under the optimized design, the system exhibited non-symmetric inlet-outlet configurations, which would have been excluded under symmetry-based modeling assumptions. The SSN configuration also offered a modular abstraction for describing multi-port flow pathways, clarifying the distinct roles of the tank nozzles. For example, nozzles 1 and 2 functioned as outlets for untreated diesel, while nozzle 3 served as the key inlet for antioxidant-enriched flow. These results not only demonstrate the technical viability of the proposed framework but also its practical value in improving diesel storage management.  
However, it is important to note that this study is based on a single case study. While this provides valuable insights into the diesel conditioning process, future research will benefit from testing additional scenarios, including different tank geometries, inlet/outlet configurations, and operating conditions. This would allow for the generalization of the  
Figure 9: Time-dependent antioxidant volume fraction at monitoring points.  
findings and provide a more comprehensive understanding of the optimization framework's robustness across different contexts.  
Due to stringent safety and regulatory restrictions, in situ experimental validation in actual nuclear facilities was not feasible. However, the use of measured physical properties and equipment-rated flow data helped ensure the realism and credibility of the simulation inputs. Future work should explore the applicability of this method in dynamic scenarios, multicomponent conditioning schemes, or integration with additional processes such as filtration or dewatering. Furthermore, a formal sensitivity analysis on key parameters (e.g., inlet velocity, antioxidant concentration) will be conducted in subsequent studies to evaluate the robustness of the optimization framework and its response to varying operational conditions.  
Finally, we recognize the importance of real-time applicability and computational efficiency. Although the current framework requires several hours of computation for each simulation, future work will focus on developing reduced-order models (ROMs) or surrogate models to improve real-time optimization and feedback capabilities. These models could significantly accelerate the process and enable real-time adjustment for operational optimization in real-world applications.