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  Optimizing Maintenance Strategies in Power Generation: Embracing Predictive and Preventive Approaches
  In the high-stakes world of power generation, ensuring continuous operation and reducing downtime are central priorities. With the increasing complexity of power generation systems, maintenance practices are evolving to meet these demands more efficiently. Understanding the roles of Predictive Maintenance (PdM), Preventive Maintenance (PM), and Reactive Maintenance (Run-to-Failure) is crucial for maintenance professionals in the energy sector to make informed decisions about equipment management and long-term operational strategy.
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Using as a starting base the high-density spent-fuel storage racks to be put into the Chinshan and Kuo-shang nuclear power plants, a series of criticality analyses with various combinations of fuel assemblies and storage rack designs were performed using an AMPX-KENO/XSDRNPM computer code package. The calculated k_∞ value for the storage pools in the two subject plants using Boral (0.013 g/cm^{2 10}B) poisoned rack lattices and 3.2 wt% enriched fuel assemblies is 0.900 under conservative assumptions. Considering all the calculation biases and statistical and manufacturing uncertainties, the maximum k_∞ value is estimated to be 0.929 under normal storage conditions. Variation in water temperature and density or abnormal positioning of fuel assemblies will result only in a negative effect on value. The deviation of the calculated k_∞ values between the one-dimensional S_n XSDRNPM code and the KENO-IV code is within the normal Monte Carlo variations. Based on XSDRNPM calculations,K_∞ values and the associated uncertainties due to fuel and rack manufacturing tolerances are tabulated. These interpolations can be used for the estimation of the value for any particular fuel and rack combination based on the tabulated data.