Browsing by Author "Dalkilic, Ahmet Selim"

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A Comparison of Heating and Cooling Systems Having Radiant and Ventilation Systems Regarding Thermal Comfort
(Springer, 2024) Rahmanparast, Amir; Bacak, Aykut; Camci, Muhammet; Karakoyun, Yakup; Acikgoz, Ozgen; Dalkilic, Ahmet Selim
Thermal comfort is crucial for indoor environmental quality, impacting occupant well-being and intellectual productivity. Despite the widespread use of HVAC technologies in residential and commercial buildings, there is growing awareness of thermal comfort, leading to more studies on this issue. According to international publication indexes nearly 60% of publications belongs to the categories of construction building technology, energy fuels, and civil engineering. It should also be noted that 40% of world energy consumption pertains to construction sector. In this context, radiant cooling and heating systems come forward with their low exergy destruction rates pointing out the potential to be energy-efficient due to their higher and lower operation temperatures. Displacement ventilation, with its low heating and cooling capacity, has not gained widespread preference. However, the increasing consciousness of global warming and energy efficiency, along with the fear of airborne virus contamination, views stand-alone or hybrid applications of radiant heating/cooling and displacement ventilation as potential future solutions. This review study investigates the impact of radiant heating/cooling and ventilation types, mixing, and displacement on thermal comfort performance, focusing on factors affecting thermal comfort in trending radiant cooling and heating applications like radiant walls, ceilings, and floors. The study emphasizes the importance of considering occupant preferences, building characteristics, and energy efficiency when choosing the most suitable heating and cooling systems for different indoor environments. Stand-alone and hybrid applications of radiant heating/cooling and displacement systems can enhance thermal comfort performance, with the exception of specific cases requiring a high thermal load or ventilation rate.
A Comprehensive Method for Exploratory Data Analysis and Preprocessing the Ashrae Database for Machine Learning
(Pergamon-elsevier Science Ltd, 2025) Rahmanparast, Amir; Milani, Muhammed; Camci, Muhammet; Karakoyun, Yakup; Acikgoz, Ozgen; Dalkilic, Ahmet Selim
Thermal comfort prediction is crucial for building energy efficiency and occupant comfort. ML methods are commonly used to predict thermal comfort. This research presents a comprehensive process for exploring and preprocessing the ASHRAE Database, providing a substantial dataset comprising 107,583 records of thermal comfort observations to create ML algorithms that can estimate Fanger's PMV. With the most detailed cleaning and preprocessing stages in the literature, which included the imputation of missing values and the management of outliers, the final dataset is reduced to 55,443 records for the analyses. For practical applications and indoor comfort assessments, its estimation offers significant advantages due to its speed, ease of use, and costeffectiveness. This study aimed to investigate which parameters are important in Fanger's PMV model and which subset of variables is best for variable selection using different feature selection and analysis methods. The Ta and Tr had a high correlation value of 0.92, indicating a robust link between these two variables. The study employed Feature importance, the SelectKBest, SHAP, P-box, and PDP analyses, which showed consistency and suggested condensing the first six elements into three, and also was validated with the Chinese Database with 41,977 entries. The study targeted three parameters: Ta, clo, and M, using less expensive and simple measurement devices. To evaluate the accuracy of the research performance, RF and SVM models were created based on these three parameters. The results indicated that they have the accuracies of 85% and 70%, respectively, which are far better than the conventional models.
Improving Pressure Drop Predictions for R134a Evaporation in Corrugated Vertical Tubes Using a Machine Learning Technique Trained With the Levenberg-Marquardt Method
(Springer, 2024) Colak, Andac Batur; Bacak, Aykut; Karakoyun, Yakup; Koca, Aliihsan; Dalkilic, Ahmet Selim
The present investigation utilized a machine learning structure to ascertain the pressure drop in vertically positioned, corrugated copper tubes during the evaporation process of R134a. The evaporator was a counter-flow heat exchanger, in which R134a flowed in the inner corrugated tube and hot water flowed in the smooth annulus. Different evaporation mass fluxes (195-406 kg m-2 s-1) and heat fluxes (10.16-66.61 kW m-2) were used with artificial neural networks at different corrugation depths. A multilayer perceptron artificial neural network model with 13 neurons in the hidden layer was proposed. Tan-Sig and Purelin transfer functions were used in the network model developed with the Levenberg-Marquardt training algorithm. The dataset, which consisted of 252 data points, related to the evaporation process, was divided into training (70%), validation (15%), and testing (15%) groups in an arbitrary manner. The artificial neural network model has been demonstrated to effectively forecast the pressure drop that occurs during evaporation. The mean squared error was computed for the Delta P values observed during the evaporation processes, yielding a value of 1.96E-03. The artificial neural network exhibited a high correlation coefficient value of 0.94479. The estimation fluctuations exhibited a range of +/- 10%, whereas the experimental and anticipated Delta P data demonstrated a divergence of +/- 10.3%.
Machine Learning Application With Bayesian Regularization for Predicting Pressure Drop in R134a's Annular Evaporation and Condensation
(Springer Heidelberg, 2025) Colak, Andac Batur; Bacak, Aykut; Karakoyun, Yakup; Koca, Aliihsan; Dalkilic, Ahmet Selim
The study of condensation and evaporation in plain pipes is a significant area of engineering and scientific inquiry, as it has relevance for enhancing and developing various industrial procedures. This study utilized a Bayesian artificial intelligence method to determine the pressure drop in a vertically oriented plain copper pipe during R134a's annular condensation and vaporization. The heat exchanger uses R134a and water in the tube and annulus sides, in turn. The tube has an inner diameter of 8 and a length of 500 mm. The training sets for artificial neural networks comprise R134a mass fluxes within the interval of 260-515 kg/m2s for in-tube condensation and 200-405 kg/m2s for evaporation. The obtained data on pressure drop during condensation and evaporation experiments were utilized in artificial neural network analysis using a differential pressure transducer in the test section. The study randomly divided 368 and 50 data points into training (85%) and testing (15%) sets for condensation and evaporation. The Bayesian method, primarily applied on this subject, effectively forecasts pressure drop during experimental condensation and evaporation. Regarding margin of deviation analyses, the models' condensation and evaporation conditions display variations of approximately +/- 7.5 and +/- 8.9%, respectively. The artificial neural network training technique was optimized to achieve minimal mean squared error values of 1.4211E-06 after 32 iterations for condensation and 1.3511E-06 after 40 iterations for evaporation. The predicted pressure drop data has a deviation of +/- 10% with the experimental one for both condensation and evaporation. The artificial neural network model produced R-values of 0.99903 and 0.98451 for condensation and evaporation, correspondingly.