WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8653
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Article Prediction of Noise Generated by Rod-Airfoil Configuration: an Investigation Based on Experiments and Machine Learning(Sage Publications Ltd, 2024) Kocak, Eyup; Ayli, EceThis study investigated the effects of various parameters on the SPL (Sound Pressure Level) levels of rod-airfoil configurations. An experimental study was performed to investigate the effects of the rod parameters, such as the configuration of the rod, the distance between the rod and the airfoil, the diameter effect of the rod, and the geometry of the rod, on the performance of the rod-airfoil configuration. An Artificial Neural Network (ANN) model was then developed and applied to accurately predict the SPL of rod-airfoil configurations. The results of the study revealed that the Levenberg-Marquardt (LM) algorithm with 2 hidden neurons produced the best performance in predicting the SPL level, with a training R-squared value of 0.9998 and a testing R-squared value of 0.998715. The findings also indicated that increasing rod diameter increases sound pressure level while reducing gap width increases SPL levels and decreases frequency values. This method offers a more precise and effective technique to forecast the SPL levels of rod-airfoil designs, allowing designers to enhance their creations and lower noise levels. The findings of this study can also be utilized to direct future research in this area and offer important information for a better understanding of the mechanism of rod-airfoil noise creation. To the best of the authors' knowledge, this is the first study to look into rod-airfoil design predictions made using machine learning approaches.Article Citation - WoS: 5Citation - Scopus: 6Prediction of the Heat Transfer Performance of Twisted Tape Inserts by Using Artificial Neural Networks(Korean Soc Mechanical Engineers, 2022) Kocak, Eyup; Ayli, EceA numerical study is undertaken to investigate the effect of twisted tape inserts on heat transfer. Twisted tapes with various aspect ratios and single, double, and triple inserts are placed inside a tube for Reynolds numbers ranging from 8000 to 12000. Numerical results show that the tube with a twisted tape and different numbers of tape is more effective than the smooth tube in terms of thermo-hydraulic performance. The highest heat transfer is achieved with the triple insert, with the highest turning number and an increment of 15 %. Then, an artificial neural network (ANN) model with a three-layer feedforward neural network is adopted to obtain the Nusselt number on the basis of four inputs for a heated tube with a twisted insert. Several configurations of the neural network are examined to optimize the number of neurons and to identify the most appropriate training algorithm. Finally, the best model is determined with one hidden layer and thirteen neurons in the layer. Bayesian regulation is chosen as the training algorithm. With the optimized algorithm, excellent precision for measuring the output is provided, with R2 = 0.97043. In addition, the optimized ANN architecture is applied to similar studies in the literature to predict the heat transfer performance of twisted tapes. The developed ANN architecture can predict the heat transfer enhancement performance of similar problems with R2 values higher than 0.93.Article Citation - WoS: 8Citation - Scopus: 8Supervised Learning Method for Prediction of Heat Transfer Characteristics of Nanofluids(Korean Soc Mechanical Engineers, 2023) Kocak, Eyup; Ayli, EceThis study focuses on the alication and investigation of the predictive ability of artificial intelligence in the numerical modelling of nanofluid flows. Numerical and experimental methods are powerful tools from an accuracy point of view, but they are also time- and cost-consuming methods. Therefore, using soft-computing techniques can improve such CFD drawbacks by patterning the CFD data. After obtaining the aropriate ANN and ANFIS architecture using the CFD data, many new data can be created without requiring numerical and experimental methods. In the scope of this research, the FCM-ANFIS and ANN methods are used to predict the thermal behaviour of the turbulent flow in a heated pipe with several nanoparticles. A parametric CFD study is carried out for water-TiO2, water-CuO, and water-SiO2 nanofluid through a pipe. The Reynolds number is varied between 7000 and 15000, and the nanofluid concentration is varied between 0.25 % and 4 %. The effects of using nanofluid on local values of Nusselt number and shear stress distribution were investigated. Numerical results indicate that with the increasing nanoparticle volume fraction of nanofluid, the average Nusselt number increases, but the required pumping power also increases. The obtained soft computing results demonstrate that the FCM clustering ANFIS has given better results both in training and testing when it is compared to the ANN architecture with an R-2 of 0.9983. Regarding this, the FCM-ANFIS is an excellent candidate for calculating the Nusselt number in heat transfer problems.Article Citation - WoS: 4Citation - Scopus: 4Performance Optimization of Finned Surfaces Based on the Experimental and Numerical Study(Asme, 2023) Ayli, Ece; Kocak, Eyup; Turkoglu, HasmetThis paper presents the findings of numerical and experimental investigations into the forced convection heat transfer from horizontal surfaces with straight rectangular fins at Reynolds numbers ranging from 23,600 to 150,000. A test setup was constructed to measure the heat transfer rate from a horizontal surface with a constant number of fins, fin width, and fin length under different flow conditions. Two-dimensional numerical analyses were performed to observe the heat transfer and flow behavior using a computer program developed based on the openfoam platform. The code developed was verified by comparing the numerical results with the experimental results. The effect of geometrical parameters on heat transfer coefficient and Nusselt number was investigated for different fin height and width ratios. Results showed that heat transfer can be increased by modifying the fin structure geometrical parameters. A correlation for Nusselt number was developed and presented for steady-state, turbulent flows over rectangular fin arrays, taking into account varying Prandtl number of fluids such as water liquid, water vapor, CO2, CH4, and air. The correlation developed predicts the Nusselt number with a relative root mean square error of 0.36%. This research provides valuable insights into the effects of varying Prandtl numbers on the efficiency of forced convection cooling and will help in the design and operation of cooling systems. This study is novel in its approach as it takes into account the effect of varying Prandtl numbers on the heat transfer coefficient and Nusselt number and provides a correlation for the same. It will serve as a valuable reference for engineers and designers while designing and operating cooling systems.Article Citation - WoS: 16Citation - Scopus: 17A Comparative Study of Multiple Regression and Machine Learning Techniques for Prediction of Nanofluid Heat Transfer(Asme, 2022) Ayli, Ece; Turkoglu, Hasmet; Kocak, EyupThe aim of this article is to introduce and discuss prediction power of the multiple regression technique, artificial neural network (ANN), and adaptive neuro-fuzzy interface system (ANFIS) methods for predicting the forced convection heat transfer characteristics of a turbulent nanofluid flow in a pipe. Water and Al2O3 mixture is used as the nanofluid. Utilizing fluent software, numerical computations were performed with volume fraction ranging between 0.3% and 5%, particle diameter ranging between 20 and 140 nm, and Reynolds number ranging between 7000 and 21,000. Based on the computationally obtained results, a correlation is developed for the Nusselt number using the multiple regression method. Also, based on the computational fluid dynamics results, different ANN architectures with different number of neurons in the hidden layers and several training algorithms (Levenberg-Marquardt, Bayesian regularization, scaled conjugate gradient) are tested to find the best ANN architecture. In addition, ANFIS is also used to predict the Nusselt number. In the ANFIS, number of clusters, exponential factor, and membership function (MF) type are optimized. The results obtained from multiple regression correlation, ANN, and ANFIS were compared. According to the obtained results, ANFIS is a powerful tool with a R-2 of 0.9987 for predictions.Article Citation - WoS: 2Numerical Investigation of Rod-Airfoil Configuration Aeroacoustic Characteristics Using Ffowcs-Williams Equations(Yildiz Technical Univ, 2021) Kocak, Eyup; Turkoglu, Hasmet; Ayli, EceThe rod-airfoil configuration is a fundamental study to understand sound generation processes and the acoustic phenomena in the application of turbines, fans, and airfoils. In the present research, the noise that is originated by the rod-airfoil configuration is examined using numerical methods which are Large Eddy Simulation (LES), and Reynolds Averaged Navier Stokes (RANS) models, coupled with an FFOWCS-WILLIAMS-HAWKINGS (FW-H) technique. For the RANS method, k-omega SST and Spalart Allmaras (S-A) turbulence models are utilized in order to investigate the capability of different models for the analysis of the aeroacoustic flow field. The ANSYS FLUENT solver is chosen to carry out the numerical simulations. The examined rod and chord diameter Reynolds numbers are 48000 and 480000, respectively and the Mach number is 0.2. Results are obtained for both in the near field and acoustic far-field. The obtained numerical results are verified with an experimental study from the literature, and the results of both approaches are compared with each other and the experiment. Comparisons are performed for mean velocity profiles in the rod and airfoil wakes, pressure spectra and power spectral density. The results obtained show that LES is preferable for this problem as it is capable of capturing the flow separation, reattachments, vortex street, and various length scales of turbulence. Although both RANS and LES methods provide a consistent flow field with experimental methods, the RANS approach overestimates the vortex shedding frequency and Strouhal number. The RANS model predicts the flow field well; however, it overestimates the noise spectra. The LES model predicts satisfactory acoustic spectra.Article Citation - WoS: 3Citation - Scopus: 5Optimization of Vortex Promoter Parameters To Enhance Heat Transfer Rate in Electronic Equipment(Asme, 2020) Ayli, Ece; Bayer, OzgurIn this paper, optimization of the location and the geometry of a vortex promoter located above in a finned surface in a channel with eight heat sources is investigated for a Reynolds number of 12,500 < Re < 27,700. Heat transfer rates and the corresponding Nusselt number distributions are studied both experimentally and numerically using different vortex promoter geometries (square, circular, and triangular) in different locations to illustrate the effect of vortex promoter on the fluid flow. Optimization study considered a range of following parameters: blockage ratio of 0.30<(y/C) < 0.45 and interpromoter distance ratio of 0.2277 <(x/L) < 0.3416. Results show that fins over which rectangular and circular promoters are integrated perform better in enhancing the heat transfer. According to the numerical and experimental results, higher blockage ratios cause significantly higher heat transfer coefficients. According to the observations, as the interpromoter distances increase, shedding gains strength, and more turbulence is created. All vortex promoters enhance heat transfer resulting in lower temperature values on the finned surface for different (y/C) and (x/L) values and Reynolds numbers. The use of promoters enhances the heat transfer, and the decrease in the maximum temperature values is recorded on the finned surface changing between 15% and 27%. The biggest decrease in maximum surface temperature value is 500 K-364 K and observed in circular promoter case with (y/C) = 0.43, (x/L) = 0.3416, and Reynolds numbers of 22,200.