Araştırmacılar(28)

• Acar, M.T.
• Acımert, Özlem Baran
• Asl, Hojjat Gahramanzade
• Aslan Çakir, Mevra
• Bayrak, Özgü
• Bilgil, Keremhan
• Bozkurt, Y.B.
• Celen, Ali
• Çam, Salim
• Çomakli, O.
• Demir, Fatih
• Demirtaş, Salih
• Deveci, Hamza Arda
• Dogan, Ahmet
• Ganjehsarabi, Hadi
• Gelengeç, Z.
• Hojjat Ghahramanzadeh, A.S.L.
• Kalkan, Orhan
• Kara, Levent
• Kaya, Mehmet
• Kılınç, Cuma
• Köseoğlu, Burak
• Küçükgöze, Osman Mert
• Orhan, Osman
• Özel, Adnan
• Seyhan, Aslıhan Kurnuç
• Sonsuz Sert, Tugay
• Tandiroglu, A.

• ArticleYayıncıda açık erişim2026/r/ebyu/19214
  The Effect of Fin Parameters on Cooling of Lithium-ion Batteries: Numerical Analysis and Optimization

  Kalkan, Orhan; Demir, Fatih

  Lithium-ion batteries are frequently preferred for electric vehicles and diverse applications due to their high energy density, high specific energy, and superior efficiency. Despite these advantages, they generate substantial heat during charge and discharge at high C-rates, which can lead to thermal runaway. Thus, an appropriate Battery Thermal Management System is necessary to prolong their lifespan and maintain performance and capacity. This study considers a passive cooling technique for cooling a Lithium-ion battery using fins. The effect of fin geometric parameters on battery thermal management was numerically investigated, and optimal values were determined through multiple optimizations, thereby contributing to the literature. For this purpose, analyses were obtained for varying diameters (20 mm-36 mm), thicknesses (0.5 mm-2 mm), and numbers (3-9) of cylindrical fins placed on a cylindrical battery at a high discharge rate of 5C. In the results, battery surface temperatures, battery temperature difference, and mass were evaluated in detail. Next, the maximum and average battery temperatures, the maximum and minimum temperature differences for homogeneity, and mass were selected as objective functions for multiple optimizations. The I-optimal algorithm based on the Response Surface Method was used to create a Design of Experiment. Optimization parameters and levels were determined by numerical analysis. The mathematical models derived for the selected performance parameters (maximum temperature (Tmax), average temperature (Tavg), maximum and minimum temperature difference (∆T) for homogeneity, and mass were tested using ANOVA, with prediction accuracies of 0.9923%, 0.9866%, 0.9381%, and 0.9314%, respectively. The results indicate that increasing the fin diameter (from 20 to 36 mm) decreases Tmax by 3.4% and Tavg by 3.7%, but increases mass by 12.2% and ∆T by 5 K to 11 K. Moreover, as fin thickness increases from 0.5 mm to 2 mm, Tmax and Tavg decrease by 2.2%, but increases mass by 21% and ∆T by 9.8 K to 10.8 K. Furthermore, as the number of fins increases from 3 to 9, Tmax and ∆T decrease by 6.0% and 36.3%. Increasing the number of fins is more effective at increasing homogeneity than increasing their diameter and thickness. Optimization results show that with a fin diameter of 32.4 mm, a thickness of 1.35 mm, and 9 fins, the battery temperature can be maintained below 349 K under these optimal conditions.

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• ArticleYalnız künye2026/r/ebyu/11344
  Experimental and optimization study of co-axial impinging jet parameters on thermal behavior at impingement plates with and without fins

  Demir, Fatih; Turgut, Oguz; Çolak, Andaç Batur; Abdulkarim, Ali Hussein

  Impingement jets are one of the promising cooling techniques in thermal management. This study investigates the thermal effects of impinging co-axial jets on different operating parameters. This objective is achieved by experimental and machine learning in the cases of confined and unconfined jets with different nozzle-to-plate distances at constant flow rate ratio and different total flow rates. The study is innovative in that it specifically examines the effect of total flow rate variation of coaxial impinging jet on thermal management at constant flow rate ratio for impingement plates with and without fins, in confined and unconfined jet cases. Experiments and machine learning are conducted for dimensionless nozzle-to-plate distances of H/D = 0.8, 1.6, 2.4, 3.2, a flat impingement plate, and impingement plates with fins of 2 and 6 mm length at Q = 60 and 80 LPM total flow rates and flow rate ratio Q* = 0.5. While the experimental results analyze stagnation, average Nusselt numbers, and heat transfer uniformity, machine learning provides highly accurate predictions and enables thermal performance optimization. The results reveal that while fins and a rise in total flow rate increase heat transfer, they reduce heat transfer uniformity. This is the opposite for H/D≥1.6. Heat transfer on a 2 mm fin-length plate improves by 26% and 22% at 80 and 60 LPM, respectively, compared to a flat plate. On the other hand, the heat transfer deviation on the flat plate is 2.16% and 1.79% for 80 and 60 LPM, while it is 9.05% and 7.33% for a 2 mm fin-length plate. Further, as H/D = 0.8 increases to 3.2, the heat transfer deviation for the flat plate at 60 LPM decreases from 2.62% to 1.79%, while for the 2 mm fin-length plate, it decreases from 9.10% to 7.33%, respectively. Finless plates and low total flow rates are recommended for applications requiring uniform heat transfer, while high heat transfer applications benefit from finned plates and high total flow rates. Finally, machine learning predicts the results with high accuracy.

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• ArticleYalnız künye2026/r/ebyu/11236
  Effects of Flow Rate Ratio and Swirl Number on Heat Transfer and Flow Field in Combined Swirl Impinging Jets

  Demir, Fatih; Turgut, Oguz

  Combined impingement jets are a promising cooling technique for enhancing heat transfer and uniformity. This study focuses on understanding the heat transfer and flow field of a combined impinging jet. It presents a novelty by addressing the compound effects of two important characteristics of a combined impingement jet: the swirl number and the flow rate ratio. To achieve this objective, combined impinging jets with axial and tangential velocities are produced using a combined nozzle that has circular and quadruple helical flow sections. The flow rate ratio is defined to examine the effects of the separate flow rates of the circular and quadruple helical sections of the combined nozzle, keeping the total flow rate constant at 75 L/min. The study is carried out for two different flow rate ratios (0.2 and 0.5) and a dimensionless nozzle-plate distance of 1. On the other hand, depending on the geometry of the combined nozzle, the swirl number is 0.0, 0.3, 0.6, 0.8, and 1.7. All parameters studied are investigated separately for confined and unconfined jet cases. The results demonstrate that the numerical model agrees with the experimental findings. It is observed that increasing the flow rate ratio from 0.2 to 0.5 results in a decrease in the Nusselt number of 28.4%–29.5% in the unconfined jet case and 28.7%–30.4% in the confined jet case, depending on the change in swirl number. Increasing the flow rate ratio improves heat transfer uniformity better than enhancing the swirl number. The swirl number becomes more effective on heat transfer and pressure with an increasing flow rate ratio. The confinement plate used to create the confined case reduces the drag ratio compared to the confined case. Finally, one advantage of the combined nozzle geometry is that the saddle-shaped formation, which negatively affects heat transfer uniformity, is not observed in the local Nusselt number distribution.

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• ArticleYalnız künye2026/r/ebyu/11235
  The influence of energy density in selective laser melting on TNT-coated Ti6Al4V: corrosion resistance and biocompatibility insights

  Çomaklı, O.; Acar, M.T.; Yazıcı, M.; Arslan, E.; Türkez, H.

  Ti6Al4V alloys were fabricated using a selective laser melting (SLM) method at different energy densities. Afterward, TiO2 nanotube (TNT) films were deposited on the surfaces of these substrates via anodization to investigate the effect of energy density on the resulting TNT structures. According to results, the high-energy–density SLM sample exhibited a relative density (RD) exceeding 98.8%, representing the highest RD among all the fabricated samples. Moreover, the smallest nanotube diameter and the greatest film thickness were obtained from the anodization of the substrate with the highest relative density, which was a result of being fabricated at the highest energy density. Electrochemical tests revealed significant improvements in corrosion resistance for TNT-coated samples compared to untreated Ti6Al4V. The TNT-coated Ti6Al4V sample fabricated at the highest SLM energy density (A2) exhibited the highest corrosion potential (Ecorr = −1.24 V) and the lowest corrosion current density (Icorr = 56 × 10⁻⁸ A/cm2), while untreated Ti6Al4V (A1) showed Ecorr = −1.74 V and Icorr = 303 × 10⁻⁷ A/cm2. Biological evaluations demonstrated that TiO₂ nanotube (TNT) coatings significantly enhanced the biocompatibility and antibacterial properties of Ti6Al4V surfaces, particularly for the A2 sample. Fluorescence staining, MTT assays, and nuclear staining confirmed that A2, which featured reduced nanotube diameter and increased length, supported superior osteoblast adhesion, proliferation, and viability, while also exhibiting the strongest antibacterial activity against E. coli. These improvements were attributed to the optimized nanotopography, which promoted cell interaction and inhibited bacterial adhesion.

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• ArticleYayıncıda açık erişim2026/r/ebyu/10908
  GMDH and NSGA-III based design optimization of bio-inspired cooling systems for prismatic LFP batteries

  Kalkan, Orhan

  This study presents a high-fidelity multi-objective optimization of bio-inspired cold plates (Ginkgo, Tilia, Tulip) for prismatic LFP battery thermal management. The coupled effects of mass flow rate (0.5–20 g s−1), inlet temperature (5–30°C), and glycol volume fraction (0–60%) on maximum battery temperature (Tmax), temperature non-uniformity (ΔT), and pressure drop (ΔP) are systematically examined under a 3C discharge rate. Numerical reliability is confirmed via GCI, with experimental validation showing a maximum error of 9.83%. To overcome the limitations of conventional regression, the Group Method of Data Handling (GMDH) is utilized to develop precise surrogate models, which are subsequently coupled with the Non-dominated Sorting Genetic Algorithm III (NSGA-III) for multi-objective optimization. The optimization identifies a robust, feasible Pareto front that strictly adheres to the safety constraints of 15°C ≤ Tmax≤35°C and ΔT≤5°C. Quantitative results reveal 10 candidate optimal designs, with Design 1 offering the highest efficiency at a minimum pressure drop of 1058.96 Pa, and Design 10 providing maximum thermal safety with a Tmax of 32.18°C. Furthermore, integrating freezing-point constraints enables climate-adaptive selection; designs with glycol fractions up to 58.43% ensure system integrity down to −49°C, albeit with a 58% increase in hydraulic penalty compared to the base optimal design.

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• ArticleYalnız künye2026/r/ebyu/10796
  Process-Based Control of Heat Input to Mitigate Anisotropy in CMT-Based Wire Arc Additive Manufacturing of AISI 316LSi Stainless Steel

  Kara, Levent; Yılmaz, Oğuzhan; Korkmaz, Habip Gökay; Kaş, Mustafa; Bol, Nevzat; Toros, Serkan

  Additive manufacturing (AM) technologies are prone to microstructural and mechanical anisotropy, driven primarily by directional solidification and the non-uniform thermal history associated with their layer-by-layer fabrication. This problem is more pronounced in Wire Arc Additive Manufacturing (WAAM), where elevated heat input and large molten pools increase thermal gradients and favor columnar grain development accompanied by δ-ferrite segregation in stainless steels. To address these issues, post-processing strategies such as inter-pass rolling, hammer peening, and heat treatment are frequently implemented. Nevertheless, regardless of being partially effective, these methods will add more processing steps that will result in an increase of time and costs. In this context, the present study seeks solutions to commonly reported limitations in the WAAM fabrication of AISI 316LSi stainless steel, which is widely used in industrial applications, by varying the wire feed speed (WFS) to regulate heat input with the aim of improving microstructural characteristics and mechanical performance. Accordingly 3 different heat input was attained by changing WFS in the system of CMT based WAAM method. Based on this approach, subsequent microstructural analysis revealed changes in δ-ferrite morphology from skeletal δ-ferrite to more refined lathy and globular forms across the investigated conditions. Within the examined processing parameters, samples corresponding to higher heat input levels, associated with increased wire feed speed (WFS), showed reduced anisotropy in grain orientation. Furthermore, samples produced under these conditions exhibited yield strength values of approximately 390 MPa, ultimate tensile strength values of approximately 620 MPa, and enhanced impact toughness, accompanied by a reduction in anisotropy. Overall, the findings indicate that adjustment of heat input during WAAM provides a viable process-engineering route for microstructural refinement and anisotropy reduction in AISI 316LSi stainless steel.

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• ArticleYayıncıda açık erişim2026/r/ebyu/10760
  Enhanced Surface Properties and In Vitro Corrosion Tests of Ti─Nb─Sn Coating on CoCrMo Alloy for Biomedical Applications

  Çakır, Mevra Aslan

  Ti–Nb–Sn thin films were deposited onto CoCrMo alloy substrates by magnetron sputtering to enhance surface properties for biomedical applications. Structural and compositional analyses revealed a dense, crack-free, columnar coating with a sharp interface. Potentiodynamic polarization tests in PBS demonstrated a significant improvement in corrosion resistance, with approximately 70% reduction in corrosion current density and more than 10-fold increase in polarization resistance compared to the uncoated alloy. Wear tests under dry and PBS conditions showed that the Ti─Nb─Sn coating reduced the wear rate by more than five times, indicating enhanced wear performance. In vitro cytotoxicity assessments using MTT and LDH assays confirmed high cell viability, indicating non-cytotoxic behavior. Overall, the Ti─Nb─Sn coating offers an effective surface modification strategy for CoCrMo-based biomedical implants.

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• ArticleYayıncıda açık erişim2026/r/ebyu/10670
  Machine Learning-Based Performance Analysis of Solar Thermal Storage Tanks with Fin-Configured Phase Change Materials

  Çolak, Andaç Batur; Kılınç, Cuma

  Solar thermal energy storage systems play a vital role in bridging the gap between intermittent solar availability and continuous energy demand; however, their efficiency is severely constrained by the inherently low thermal conductivity of phase change materials. Integrating physical heat transfer enhancements, such as radial fins, offers a practical solution, but evaluating these non-linear thermal dynamics across diverse design configurations typically incurs heavy computational costs. To address this challenge, this research investigates an artificial intelligence-based predictive framework capable of accurately modeling complex phase change dynamics in fin-configured storage tanks. Utilizing high-fidelity 2D Computational Fluid Dynamics simulation data of a stainless-steel double-tube storage tank filled with RT-50 paraffin wax across 10, 20, and 29 fin configurations, a Multi-Layer Perceptron Artificial Neural Network trained with the Bayesian Regularization algorithm was developed. The model predicts liquid fraction, latent heat distribution, and buoyancy-driven natural convection (Reynolds number) based on fin count and time. The optimal architecture, featuring 30 hidden neurons, achieved exceptional predictive precision, yielding a coefficient of determination of 0.99999, along with individual Mean Squared Error values of 1.29 × 10−3 for liquid fraction, 5.73 × 10−1 for latent heat distribution, and 2.64 × 10−5 for Reynolds number, with average prediction deviation rates consistently below 0.5%. These results demonstrate that high-precision surrogate modeling can effectively replace computationally intensive numerical simulations, offering significant practical implications for the real-time thermal monitoring, rapid design optimization, and intelligent control of advanced solar energy storage technologies.

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• ArticleYalnız künye2026/r/ebyu/10657
  Comparative study of diverging and converging coaxial jet nozzles for thermal management of H-CPV modules

  Demir, Fatih; Kalkan, Orhan

  Jet impingement cooling is widely recognized as one of the most effective methods for achieving high heat transfer rates in compact systems, making it particularly suitable for cooling photovoltaic devices. This paper investigates the effects of newly designed coaxial nozzle configurations on the thermal management of a high-concentration photovoltaic system. Four different nozzle geometries, such as square, circular, hexagonal, and triangular, are considered, with constant outer cross-sectional areas, while the inner sections are designed to diverge or converge. Simulations are performed for Reynolds numbers of 2000–4000 and dimensionless coaxial nozzle-heat sink distances of 0.5–3. The results reveal that the coaxial nozzle enhances heat transfer performance compared to conventional nozzles. The diverged triangular coaxial nozzle achieves the best heat transfer at a Reynolds number of 4000, with a 19 % increase in the modified Nusselt number compared to the conventional triangular nozzle. The highest temperature uniformity (6.39 °C) is observed in the converged hexagonal coaxial nozzle, while the lowest maximum and average cell temperatures (46.1 °C and 44 °C) are achieved with the diverged triangular coaxial nozzle. The lowest pressure drop (1297 Pa) is found in a diverged hexagonal coaxial nozzle, corresponding to the lowest net electrical power (17.53 W) and cell efficiency (38.97 %). Despite the pressure drops, the diverged triangular coaxial nozzle achieves the highest electrical efficiency of 39.41 %, as its geometry promotes favorable power generation. The coaxial configuration increases the cell efficiency by up to 1.13 % and improves temperature uniformity by 23.3 %. Total exergy follows the same trend as electrical exergy, with the converged hexagonal coaxial nozzle exhibiting the highest total exergy efficiency of 38.35 %. The findings demonstrate that coaxial nozzles can significantly improve thermal uniformity and system efficiency in high-concentration photovoltaic cooling applications.

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• ArticleYayıncıda açık erişim2026/r/ebyu/10636
  Accelerating Sustainable Hydrogen Production: A Scalable Machine Learning Approach for Predictive Modeling and Performance Assessment of Proton Exchange Membrane Electrolyzers

  Çolak, Andaç Batur; Kılınç, Cuma

  This study investigates machine learning techniques for predicting the behavior of proton exchange membrane electrolyzers, which are vital for sustainable hydrogen production. This work addresses these challenges by integrating artificial neural networks to develop predictive models capable of capturing the performance of proton exchange membrane electrolyzers with high accuracy. This research utilizes a multi-layer perceptron network architecture, optimized through rigorous data preprocessing, parameter tuning, and error minimization strategies. The dataset used was based on published PEME numerical simulation datasets and encompasses key performance indicators, including stack voltage, water transport, and electrochemical reactions. The trained artificial neural networks models achieved mean squared error values of 3.66 × 10−5 and 9.75 × 10−6, with correlation coefficients of 0.99996 and 0.99958, demonstrating near-perfect predictive accuracy. A comparative benchmarking study against alternative regression algorithms revealed that the proposed MLP models significantly outperformed Gradient Boosting and Random Forest by several orders of magnitude, thereby establishing a higher level of persuasiveness and reliability for the developed framework. Average deviation rates of 0.11% and −0.01% further validated model reliability. The novelty of this work lies in its comprehensive approach, which goes beyond isolated metrics by addressing interactions across system parameters. This integrated framework enables enhanced prediction, control, and optimization of proton exchange membrane electrolyzer’s performance, setting a new benchmark for leveraging machine learning in hydrogen energy systems. These findings pave the way for scalable, cost-effective solutions to improve proton exchange membrane electrolyzers’ efficiency and operational reliability.

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