Diesel fuel stored for long periods in emergency diesel tanks at nuclear power plants is susceptible to deterioration, potentially compromising the operational efficiency and reliability of emergency diesel units and posing risks to nuclear power plant safety. To maintain fuel quality, periodic filtration and blending, with additives when necessary, are required. Accordingly, this study proposes an efficient and accurate optimization method for emergency diesel conditioning that integrates computational fluid dynamics (CFD) simulations in FLUENT with a genetic algorithm implemented in MATLAB. The conditioning process is first simulated and analyzed using CFD, after which the genetic algorithm performs global optimization to determine a conditioning scheme that enables the monitoring points to meet the blending requirements in the shortest possible time. The proposed model improves the efficiency and accuracy of the conditioning process while reducing conditioning time, thereby supporting the operational reliability and performance of emergency diesel units.

Glass fiber (GF) reinforced nylon 6 (PA6) composites were prepared with a twin-screw extruder. The effects of GF mass fraction on the mechanical properties, thermal properties, density, water absorption and processability of PA6/GF composites were systematically investigated. The results show that the impact strength, tensile strength, flexural strength, flexural modulus, density and thermal deformation temperature of the composites increased with the increase of glass fiber content, while the water absorption and melt flow rate decreased with the increase of glass fiber mass fraction. The morphology showed that the GF was effectively wrapped and dispersed in the PA6 matrix when the GF mass fraction was increased up to 30%.