Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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  • Article
    Machine Learning in Flow Boiling: Predicting Bubble Lift-Off Diameter Despite Data Limitations
    (Yildiz Technical University, 2025) Tabrizi, Atta Heydarpour; Mohammadpourfard, Mousa; Mohammadpourfard, Mostafa
    This study concentrates on applying machine learning techniques to flow boiling in order to predict the bubble lift-off diameter. This prediction is critical because the diameter plays a key role in understanding boiling dynamics and calculating heat transfer rates. Additionally, accurately predicting this diameter is essential for optimizing thermal systems and enhancing energy efficiency. By evaluating the performance of three different machine learning algorithms: M5 tree, multilinear regression, and random forest, we aimed to assess their effectiveness in providing reliable predictions even with limited experimental data. This research is essential as it demonstrates the potential of machine learning to enhance predictive accuracy in scenarios where obtaining extensive datasets is challenging. Our findings show that these machine-learning techniques are effective for accurate predictions. The results show that the coefficient of determination ranged from 0.64 to 0.94, indicating how well the models fit the data. The root mean square error was between 0.009 and 0.02, and the mean absolute error ranged from 0.0004 to 0.02. Based on the findings, it can be inferred that the machine learning algorithms used in this study are reliable for predicting bubble lift-off diameter. This reliability also extends to other experimental parameters, suggesting that these techniques can be effectively applied in various contexts with limited data. This study demonstrates the potential of machine learning to predict experimental parameters and advances previous research by identifying key factors that influence bubble lift-off diameter. © 2025 Elsevier B.V., All rights reserved.
  • Article
    Occurrence of Bromide and Bromate in Chlorinated Indoor Swimming Pools, And Associated Health Risks
    (Yildiz Technical University, 2023) Dumanoğlu, Y.; Genisoglu, M.; Sofuoglu, S.C.
    Swimming is a physical activity that is accessible to people of all ages in all seasons. However, continuous organic and inorganic precursor load and disinfectant dosing make pool water chemistry much more complex than other disinfected waters. Carcinogenic bromate compound is one of the hundreds of disinfection by-products in pool water. The occurrence of bromate in pool waters depends on the precursor content of filling water, the disinfection process, operating parameters, and the purity of disinfectants. While the average filling water bromide concentrations of University Campus indoor swimming pool in Gülbahçe –Urla (SP1) and Buca public indoor swimming pool (SP2) were determined to be 182 μg/L and 11.0 μg/L, respectively, the average bromate concentrations of SP1 and SP2 were 59.4 μg/L and 68.3 μg/L. Estimated chronic-toxic health risks of accidental ingestion of pool water during swimming (between 10-3 and 10-1) were lower than the threshold level (‘1’). Although the carcinogenic risks in central tendency scenario (<10-6) indicate negligible risks for swimmers, worst case scenario indicates carcinogenic risks (medians were ranged from 1.61×10-6 to 9.42×10-6) for highly exposed specific swimmer groups. Bromate accumulation in swimming pools needs attention for mitigating the health risks for swimmers. © 2021, Yıldız Technical University.