Browsing by Author "Acikgoz, O."

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Machine Learning Approach for Multi-Parameter Performance Estimations of EC Fan Coil Units Using Heating Tentative Database
(Elsevier Ltd, 2026) Uguz, B.; Çolak, A.B.; Karakoyun, Y.; Gemici, Z.; Acikgoz, O.; Dalkılıç, A.S.
Fan coils (FCs) are widely utilized, yet little is known about their performance under different operating conditions. A segment of a comprehensive experimental dataset with 1727 data points is used to develop and train four artificial neural network (ANN) architectures to computationally estimate the heat output and available power of a ceiling-mounted FC. The tests have been done via a specifically devised AMCA 210 test apparatus, under interior air and heat exchanger (HEX) fluid conditions recommended by EUROVENT. Utilizing six given inputs, the 1st ANN estimated the airflow rate and fan power. With five dissimilar input parameters, the exit temperature of air as well as the heating capacity was forecasted. Considering five separate inputs, the 3rd ANN assessed the pressure drops at the water side pertaining to the HEX. Depending on eight diverse inputs, the air exit temperature and power of the fan alongside total heating capacity were estimated. In the network models of 10 neurons in the hidden layer, the Levenberg-Marquardt training method has been utilized. Considering the 1st ANN, the deviation that pertained to the air flow rate was found to be −0.59%, as the deviations relevant to the air outlet temperature and heating capacity in the 2nd ANN were detected to be 0.001% and 0.03%, respectively. Additionally, the 3rd ANN resulted in a deviant value of −0.07%, referring to the fluid pressure loss. The 4th ANN has also brought about deviations of −0.005%, −0.13%, and + 0.09%, referring to exit air temperature, heating capacity, and fan power, respectively. © 2024
A New Hybrid Cfd Approach To Study the Impact of Forced Convection on Radiant Cooled Wall With Baseboard Diffuser Including Various Vane Angles
(Elsevier Masson s.r.l., 2025) Caliskan Temiz, M.; Bacak, A.; Camci, M.; Karakoyun, Y.; Acikgoz, O.; Dalkilic, A.S.
The current work examines the effect of forced convection on thermal comfort in a space, including radiant wall cooling and an innovative floor-level diffuser system. It examines the impact of various vane angles on thermal comfort in room air conditioning at 15°, 30°, 45°, 60°, and 75°, and employs experimental data to confirm a hybrid 3D computational fluid dynamics (CFD) model. A new floor-level diffuser system delivers air at temperatures between 18 °C and 22 °C, with supply air velocities of 5 m/s and 10 m/s measured at the exit side of diffuser while the supply water temperature is kept constant at 14 °C. In the hybrid 3D solution, experimentally derived convective heat transfer coefficients (CHTCs) for forced airflow are utilized. This is accomplished by merging a k-ω model with a hydronic radiant panel system that incorporates forced convection. The analysis examines temperature and velocity distributions, CHTCs on the radiant-cooled wall, and the PMV-PPD components. Results indicate that at a supply air velocity of 5 m/s, thermal comfort parameters do not satisfy PMV and PPD indices, except in proximity to the diffuser. Nevertheless, elevating the supply air velocity to 10 m/s ensures thermal comfort across the space, with the exception of regions next to the cooled wall surfaces. The examination of several vane angles indicated that a 45° angle yields the most advantageous thermal comfort conditions, irrespective of air velocity. The CHTC adjacent to the radiant wall is roughly 6 W/m2K at a velocity of 5 m/s and rises to 8 W/m2K at 10 m/s. The temperature disparity between the head and ankle regions at 5 m/s adheres to the 3 °C tolerance established by international standards. The study determines that a 45° vane angle ensures best thermal comfort, and the devised numerical method yields significant insights for the construction of analogous indoor settings. © 2025 Elsevier Masson SAS