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Öğe Experimental study on passive flow control of circular cylinder via perforated splitter plate(Techno-Press, 2021) Sahin, Serdar; Durhasan, Tahir; Pinar, Engin; Akilli, HuseyinPresent experimental investigation aims to reduce the shedding of vortex in the near wake region of a circular cylinder using a perforated splitter plate. Perforated plates were placed in the wake region of the cylinder and aligned with the streamwise direction. The length of the plates was equal to the diameter of the cylinder. Different plate porosities and locations were examined and obtained results were compared to the baseline cylinder. Flow measurements downstream of the cylinder were performed in a water channel by employing a particle image velocimetry technique (PIV) at a Reynolds number of Re=5×103. It is observed that the effect of the porosity on the flow characteristics of the cylinder depends on the location of the plate. The strength of shear layers and flow fluctuations in the near wake region of the cylinder are considerably diminished by the perforated splitter plate. It is found that the porosity of ?=0.3 is the most effective control element for gap ratio of G/D=0.5. On the other hand, proper gap ratio is determined as G/D=2 for porosity of ?=0.7. It is concluded in the present study that the perforated splitter plate could be used as alternative passive flow control technique in order to reduce vortex shedding of the cylinder. Copyright © 2021 Techno-Press, Ltd.Öğe FLOW CONTROL OF A CIRCULAR CYLINDER BY PERMEABLE SPLITTER PLATE WITH DIFFERENT POROSITIES AND ANGLE VALUES(Turkish Soc Thermal Sciences Technology, 2024) Sahin, Serdar; Durhasan, Tahir; Pinar, Engin; Akilli, HuseyinFlow control of bluff bodies has been studied extensively to eliminate adverse effects of wake flow such as vibration and acoustic noise or resonance. The circular cylinder has been studied as the bluff body since it is basic geometry and has been used in engineering applications such as heat exchanger tubes, power transmission lines, chimney stacks, bridges, radio telescopes, power lines, offshore drilling rigs etc. In this study, a permeable splitter plate was located at various downstream locations to control the wake flow of the cylinder. All experiments were carried out in a large-scale closed-loop water channel in the Fluid Mechanics Laboratory at Cukurova University. PIV was used to measure the instantaneous velocity vector field in the wake region of the cylinder at Reynolds number Re=5000, which is based on the cylinder diameter, D. Four different splitter plate angle values (0 =0 degrees; 15 degrees; 30 degrees; 45 degrees), three different porosity values (epsilon=0.30; 0.50; 0.70) were investigated. The porosity (epsilon) of the separator plates is defined as the ratio of the total hole area to the plate surface area. All lengths are nondimensionalized by dividing by the cylinder diameter and shown with the * index. The splitter plate length kept to constant during the experiment as ls*=1. The distance between the leading edge of the splitter plate and the cylinder (lg*) is variable due to the rotation of the separator plate at certain angles in the flow direction. To overcome this, the distance between the splitter plate rotation axis and the cylinder was taken as a parameter and shown with the **. The gap between splitter plate midpoint and cylinder (lg**) kept to constant during the experiments as lg**=1.5. When the plates are rotated, the cross-section parallel to the flow decreases, which increases the interaction between the boundary layers. Since the permeable separator plates prevent the interaction of the boundary layers formed in the flow around the cylinder, the effect of the permeable separator plates increases in the downstream regions where the interaction of the boundary layers increases. Thus, the fluctuations are reduced, and a more stabilized trail flow occurs downstream of the cylinder. It was observed that the vortex formation was delayed with the increase of the separator plate angle. In this study, the effect of the separator plate angle and the effect of the plate permeability were clearly observed.Öğe Forecasting near-surface air temperature via SARIMA and LSTM: A regional time-series study(Pergamon-Elsevier Science Ltd, 2025) Aksoy, Muhammed M.; Mowla, Najmul; Bilgili, Mehmet; Pinar, Engin; Durhasan, Tahir; Asadi, DavoodAccurate modeling of near-surface air temperature (AT) trends is critical for assessing global and regional climate risks, particularly in light of the intensifying warming signals observed across the northern hemisphere and the tropics. This study proposes a robust and computationally efficient framework for forecasting near-surface AT across the global, the northern hemisphere, the southern hemisphere, and the tropics using two complementary time-series modeling techniques: seasonal autoregressive integrated moving average (SARIMA) and long short-term memory (LSTM) networks. The models are trained to capture both structured seasonal patterns and nonlinear temporal dynamics by leveraging the ERA5 reanalysis dataset (1970-2024) and incorporating preprocessing steps such as detrending and Z-score normalization. SARIMA consistently outperformed LSTM across most domains, particularly in the global region, achieving lower RMSE (0.0967 degrees C) and higher correlation (R = 0.9975), reflecting its superior capacity for linear and seasonal signal extraction. Quantitatively, SARIMA demonstrates 5%-10% lower RMSE and slightly higher correlation than LSTM across all domains, underscoring the statistical significance of its performance advantage. Projected near-surface AT anomalies by 2050 reveal a marked warming trend, with the SARIMA model estimating a global anomaly of +1.078 degrees C and a northern hemisphere anomaly of +1.474 degrees C, closely aligning with IPCC-reported trajectories and exceeding CMIP5 RCP4.5 projections. The findings underscore SARIMA's reliability for short-to mid-term near-surface AT forecasting and LSTM's potential for future hybrid modeling schemes. This work fills a critical methodological gap by integrating statistical rigor with scalable deep learning, offering enhanced fidelity for regional climate adaptation planning.Öğe Future forecast of global mean surface temperature using machine learning and conventional time series methods(Springer Wien, 2025) Durhasan, Tahir; Pinar, Engin; Uluocak, Ihsan; Bilgili, MehmetOne of the most important indicators of climate change and, consequently, global warming is the rise in the average surface temperature of the entire world. As observed in this light, forecasting the global mean surface temperature is an essential issue that must be addressed to develop adaptation measures for climate change. Many models have been developed to forecast the temperature of the air; however, these models often concentrate on local areas or use a restricted amount of station data. In this study, seasonal autoregressive integrated moving average (SARIMA), long-short-term memory (LSTM), and gated recurrent unit (GRU) models are used to predict global mean surface temperature (GMST) data. The data sets used in the study are GISTEMP and HardCRUT datasets and consist of land surface air temperature and water surface temperature. An evaluation of the performance of the models is carried out using various error measures to guarantee a high level of prediction accuracy. All models' results indicate that the yearly GMST value increase relative to 1961-1990 will be between 0.94 oC and 1.45 oC in 2050. In addition, the yearly GMST value, measured as approximately 14.8-15.00 oC in 2022, will be between 15.15 oC and 15.43 oC in 2050, according to the obtained models.Öğe Global monthly sea surface temperature forecasting using the SARIMA, LSTM, and GRU models(Springer Heidelberg, 2025) Bilgili, Mehmet; Pinar, Engin; Durhasan, TahirGlobal warming has become one of the world's most pressing problems in recent years, accompanied by rising sea surface temperature (SST). The SST time series data are an essential component in balancing the energy at the planet's surface. It is of the utmost importance to forecast future SSTs to assist us in better comprehending the climate dynamics and identifying catastrophic circumstances in advance based on historical observations received from earth observation systems. In this sense, monitoring and forecasting SST has become vital for better understanding future climate trends. In this regard, this study proposes a gated recurrent units (GRUs) model, a long short-term memory (LSTM) neural network technique, and a seasonal auto-regressive integrated moving average (SARIMA) statistical model to predict global monthly SST data. According to the findings from the testing procedure, the MAPE values were 0.1377% for the SARIMA model, 0.1374% for the LSTM model, and 0.1390% for the GRU model. All models were found to have MAE, RMSE, and R values within the ranges of 0.0250-0.0253 oC, 0.032-0.0323 oC, and 0.9772-0.9775, respectively. The results of the proposed SARIMA, LSTM, and GRU models showed that they could accurately and satisfactorily predict the global monthly SST time series.Öğe Long-term projections of global, northern hemisphere, and arctic sea ice concentration using statistical and deep learning approaches(Pergamon-Elsevier Science Ltd, 2025) Bilgili, Mehmet; Pinar, Engin; Mowla, Md. Najmul; Durhasan, Tahir; Aksoy, Muhammed M.The accelerating decline in sea ice concentration (SIC) poses significant challenges for global climate regulation, maritime navigation, and arctic ecosystem stability. This study develops and evaluates two advanced time-series forecasting models, seasonal autoregressive integrated moving average (SARIMA) and long short-term memory (LSTM) networks, to project SIC trends through 2050 across three spatial domains: the globe, the northern hemisphere, and the arctic. Utilizing the ERA5 reanalysis dataset (1970-2024) from the European center for medium-range weather forecasts (ECMWF), the models capture seasonal cycles and complex temporal dependencies to enable robust long-term projections. Comparative analysis demonstrates that SARIMA effectively models periodic fluctuations, while LSTM excels at learning nonlinear dependencies inherent in SIC dynamics. Performance metrics, including mean absolute percentage error (MAPE), root mean square error (RMSE), and correlation coefficient (R), confirm the high accuracy of both models, with SARIMA showing superior capability in representing structured seasonal patterns. Projections indicate a persistent decline in SIC, with arctic concentrations decreasing from 55.60% in 2023 to approximately 46.84% by 2050, underscoring the pronounced effects of arctic amplification. These results provide valuable insights for climate modeling, arctic policy formulation, and the development of adaptive navigation strategies in a rapidly changing polar environment.Öğe Multimodal deep learning for estimating mean precipitable water vapor(Wiley, 2026) Mowla, Md. Najmul; Aksoy, Muhammed M.; Pinar, Engin; Bilgili, Mehmet; Durhasan, TahirPrecipitable water vapor (PWV) is a crucial atmospheric variable that influences weather systems, climate variability, and hydrological processes. Accurate PWV estimation is essential for improving numerical weather prediction, climate modeling, and remote-sensing applications. However, existing methods often rely on extensive meteorological inputs or computationally intensive architectures, limiting their applicability in data-sparse regions. This study introduces a novel hybrid framework, EMMA-NN-BiGRU-XGBoost, designed to forecast monthly mean PWV across Turkey using only four physically meaningful inputs: latitude, longitude, altitude, and seasonal indicators. The framework integrates an enhanced multimodal attention (EMMA) mechanism that disentangles spatial, altitudinal, and seasonal influences, improving interpretability and physical consistency. Bidirectional gated recurrent units (BiGRU) capture temporal dependencies, and XGBoost models nonlinear feature interactions within a weighted stacking ensemble. Hyperparameters are optimized via particle swarm optimization and Bayesian optimization, with particle swarm optimization demonstrating superior tuning efficiency. Extensive benchmarking against traditional machine-learning models, using grid search and random search with fivefold cross-validation, as well as deep-learning baselines, demonstrates significant improvements in predictive accuracy, achieving an root-mean-square error of and an of 0.92, representing a 15%-20% reduction in error compared with state-of-the-art methods. The model also exhibits robustness across diverse climatic zones in Turkey. Shapley additive explanations further elucidate feature importance, aligning model outputs with climatological principles. Beyond methodological advances, this work provides a scalable, interpretable, and data-efficient baseline for PWV forecasting, thereby facilitating enhanced climate diagnostics, hydrological risk assessments, and early warning systems, particularly in regions with limited meteorological observations.Öğe Time series analysis of sea surface temperature change in the coastal seas of Turkiye(Pergamon-Elsevier Science Ltd, 2024) Bilgili, Mehmet; Durhasan, Tahir; Pinar, EnginSea surface temperature (SST) is a crucial geophysical parameter in assessing heat exchange between the air and sea surface. Changes in SST and its accurate prediction play a pivotal role in explaining the global heat balance, determining atmospheric circulations, and constructing global climate models. This work aims to reveal a model for one-month-ahead forecasting of SST time series data along the Turkiye coasts, encompassing the Mediterranean, Aegean, Marmara, and Black Seas, and their long-term future forecast. A long short-term memory (LSTM) neural network and seasonal autoregressive integrated moving average (SARIMA) models are used for this purpose. The ECMWF ERA5 (0.5(o)x0.5 degrees) monthly SST dataset spanning the years 1970-2023 is used for model development. The results obtained from the LSTM and SARIMA models show that there will be an increasing trend in SSTs along these seacoasts until 2050. The SST measurements of 23.4 degrees C, 20.2 degrees C, 17.0 degrees C, and 16.6 degrees C recorded along the Mediterranean, Aegean, Marmara, and Black Seas in 2023 are expected to rise to 25.1 degrees C, 21.9 degrees C, 18.1 degrees C, and 18.8 degrees C, respectively, by 2050. These figures indicate an increase of 7.3%, 8.4%, 6.5%, and 13.3% in the SST values across these coastal seas over the next quarter century.
