Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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

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Access type: Metadata onlyPublisher: search.filters.publisher.Yildiz Technical University

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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.; Geni̇Şoğlu,M.; Sofuoğlu,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. Copyright 2021, Yıldız Technical University.
  • Article
    Citation - Scopus: 1
    Performance Indices of Soft Computing Models To Predict the Heat Load of Buildings in Terms of Architectural Indicators
    (Yildiz Technical University, 2016) Turhan,C.; Kazanasmaz,T.; Akkurt,G.G.
    This study estimates the heat load of buildings in Izmir/Turkey by three soft computing (SC) methods; Artificial Neural Networks (ANNs), Fuzzy Logic (FL) and Adaptive Neuro-based Fuzzy Inference System (ANFIS) and compares their prediction indices. Obtaining knowledge about what the heat load of buildings would be in architectural design stage is necessary to forecast the building performance and take precautions against any possible failure. The best accuracy and prediction power of novel soft computing techniques would assist the practical way of this process. For this purpose, four inputs, namely, wall overall heat transfer coefficient, building area/ volume ratio, total external surface area and total window area/total external surface area ratio were employed in each model of this study. The predicted heat load is evaluated comparatively using simulation outputs. The ANN model estimated the heat load of the case apartments with a rate of 97.7% and the MAPE of 5.06%; while these ratios are 98.6% and 3.56% in Mamdani fuzzy inference systems (FL); 99.0% and 2.43% in ANFIS. When these values were compared, it was found that the ANFIS model has become the best learning technique among the others and can be applicable in building energy performance studies. © 2016. All Rights Reserved.