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    Design and Characterization of Y2O3/Pr2O3-Enriched Inconel-718 Alloys for Nuclear Applications
    (Taylor & Francis Inc, 2026) Albayrak, Muhammet Gokhan; Guler, Omer; Guler, Seval Hale; Evin, Ertan; Almisned, Ghada; Sen Baykal, Duygu; Tekin, Huseyin Ozan
    This study explores the physical, structural, and radiation shielding enhancements in Inconel 718 superalloys reinforced with 1 wt% Y2O3 and varying Pr2O3 contents from 0 to 10 wt%, respectively. X-ray diffraction analysis confirmed preservation of the face-centered cubic structure, with increasing Pr2O3 inducing peak broadening and partial amorphization. The scanning electron microscopy/energy-dispersive X-ray spectroscopy results verified the homogeneous dispersion of oxides without agglomeration. Gamma-ray shielding parameters, including the mass attenuation coefficient (MAC), half-value layer, and effective atomic number, significantly improved with higher Pr2O3 content, particularly at low to mid photon energies.The 718Y-10PO sample exhibited the lowest transmission factors and the highest MAC values across all the tested energies. Buildup factors decreased in the Pr-rich samples, confirming reduced photon scattering. Notably, the fast neutron removal cross section for 718Y-10PO as 0.15521 cm-1 exceeded the benchmark materials like graphite and B4C. These findings establish the 718Y-10PO alloy as a promising candidate for advanced nuclear shielding applications, combining structural integrity with promising gamma and neutron attenuation capabilities.
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    Measurement-based assessment and optimization of electric bus energy consumption under thermal variability using long-term field data
    (Elsevier Sci Ltd, 2026) Ekici, Yunus Emre; Karadag, Teoman; Akdag, Ozan; Aydin, Ahmet Arif; Tekin, Huseyin Ozan
    This study investigates the effect of ambient temperature on the energy consumption of overhead battery-hybrid electric buses, also known as trolleybuses. The analysis uses real field data. The data were collected from 22 hybrid buses over 24 months. Each bus was monitored during daily passenger service. The onboard black-box units recorded the main operating signals at 1 Hz. This allowed second-by-second changes in speed, braking, passenger load, and energy use to be examined under real route conditions. The study is not based on standard driving cycles or simulation data. It uses measured data from daily bus operation. The dataset includes energy consumption, regenerative braking, vehicle speed, passenger load, road gradient, auxiliary power demand, and ambient temperature. Before the analysis, missing, corrupted, and inconsistent records were removed. This step was used to improve the reliability of the measurement dataset. In this study, outside temperature is not treated as a direct electric motor parameter. It is considered a factor that affects total energy use through battery behavior and heating, ventilation, and air conditioning demand. Different prediction methods were tested on the processed data. These methods include Decision Trees, Ensemble Learning, Gaussian Process Regression, Support Vector Machines, and Three-Layer Neural Networks. The Three-Layer Neural Network optimized with the Modified Tunicate Swarm Algorithm achieved the best predictive performance. According to the analysis results, the model aimed to provide a practical basis for seasonal energy forecasting and route-based fleet planning by evaluating the sensitivity of energy consumption over a wide temperature range.