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ArticleAçık Erişim2026/r/ebyu/19270
Kamu Malları Bağlamında Hidroelektrik Santraller
Gürbüz, Abdulsamet
Devlet kamu hizmetlerini yerine getirirken kamu kaynaklarını ve kamu mallarını kullanmaktadır. Ülkemizde kamu malları kapsamında değerlendirilen akarsular üzerinde hidroelektrik santrallerin inşası uzun yıllardır sürdürülüyor olsa da bu konu ancak 2000’li yıllardan itibaren kamuoyunun gündeminde belirgin bir yer edinmeye başlamıştır. Bunun bir nedeni daha önce devlet eliyle kurulan ve işletilen bu tesislerin 2000’li yılların başından itibaren özelleştirilmesidir. Bu yıllardan itibaren konuya ilişkin yasal değişikliklerin özel sektörün isteklerine göre gerçekleştirildiği, kurulan hidroelektrik santrallerin ise kurulma ve işletilme aşamalarında idare tarafından etkin bir denetime ve yaptırıma tabi kılınmadığı görülmektedir. Böylece santrallerin yapıldığı yerlerde yöre halkının menfaatini ve kamu yararını dışlayan uygulamalar söz konusu olmaktadır. Bu çalışmanın konusu hidroelektrik santrallerin yapımında özel şirketlerin istekleri ile kamunun beklentilerinin uyum içinde olup olmadığının araştırılmasıdır. Çalışmada, konuya ilişkin yaşanan sorunların çözülmesine ilişkin faydalı sonuçlara varılması amaçlanmaktadır.
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ArticleAçık 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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ArticleAçık Erişim2026/r/ebyu/19026
Heterogeneous Effects of Green Technology Innovations on Renewable Energy Supply Share: A Dynamic Panel Evidence
Balkar, Oğuz Emre; Özenoğlu, Yunus Emre; Alkan, Okan
This study examines the determinants of renewable energy efficiency (REE) in a balanced panel of 17 countries (1995–2021), focusing on green technology innovations (GTI), economic growth (EG), and energy taxation (ET). The analysis accounts for heterogeneity, common shocks, and dynamic adjustment by combining two-way fixed effects with dependence-robust and dynamic common correlated effects (CCE) estimators using Driscoll–Kraay inference. The results show strong persistence in REE and a robust positive association between GTI and REE in the preferred common-factor framework, while the average effects of EG and ET are weaker and model-sensitive. Dumitrescu–Hurlin panel causality tests indicate feedback between REE and EG and bidirectional linkages between REE and ET at the level, whereas innovation dynamics are asymmetric. Overall, the evidences support innovation-centered transition strategies and emphasizes evaluating policy instruments over multi-year horizons given strong persistence and cross-country heterogeneity.
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ArticleAçık Erişim2026/r/ebyu/18253
Machine Learning-Based Modeling and Performance Analysis of a Novel Battery Thermal Management System with a Ternary Periodic Minimal Surface Porous Structured Liquid Cooling Channel
Çolak, Andac Batur; Kilinç, Cuma
This article examines the fundamental requirements for effective thermal management of batteries, particularly in high-energy density applications such as electric vehicles and energy storage systems. A unique method is introduced that utilizes a triple periodical minimal surface pore structure in liquid cooling channels to improve heat dissipation and temperature homogeneity in battery packs. Experimental validation and optimization via artificial neural networks are used to evaluate and improve thermal performance, focusing on critical metrics such as maximum temperature and temperature difference within the battery system. Artificial neural network models demonstrated model reliability, exhibiting ideal prediction accuracy with low mean square error values of 1.29E-05 and 9.87E-06, and a correlation coefficient of 0.99999. AThis work builds upon previous attempts by combining an innovative structural design with machine learning, aiming to provide a robust and data-driven solution that aligns cooling efficiency with structural integrity. The novelty of the study lies in its dual experimental machine learning approach, which provides a foundation for improved predictive control in battery temperature management systems with high accuracy and minimal resource requirements.
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ArticleYalnız künye2026/r/ebyu/18238
Adaptive hybrid control algorithm for integrated wind-solar energy systems: Enhancing efficiency and minimizing environmental impact
Can, Erol
This study introduces a three-tier adaptive control strategy designed for hybrid wind - solar energy systems, with the goal of optimizing energy capture, stabilizing the DC bus voltage, and enhancing overall system performance under varying environmental conditions. The control framework incorporates a TSR-based Maximum Power Point Tracking (MPPT) method for the wind turbine, a temperature-sensitive Perturb-and-Observe (P&O) MPPT for the photovoltaic array, and a real-time energy coordination system that dynamically adjusts the power allocation between wind and solar sources based on their instantaneous availability. A conventional Proportional-Integral (PI) controller manages the DC bus voltage, minimizing fluctuations and ensuring seamless integration with energy storage or the grid. The proposed approach is adaptable to both off-grid and grid-connected microgrids and is computationally efficient for real-time application. Simulation results from four typical scenarios - including high wind/low solar, low wind/high solar, intermittent generation, and balanced generation - show system efficiencies consistently exceeding 92%, along with a substantial reduction in voltage ripple when compared to traditional MPPT techniques. These findings validate the effectiveness of the coordinated control strategy in maintaining power quality, system reliability, and stable energy output, offering a scalable and practical solution for integrating renewable energy into microgrid systems.
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ArticleYalnız künye2026/r/ebyu/18157
A new full insulated multi output DC-DC converter for photovoltaic energy regulation and multi-battery charging
Can, Erol
The integration of solar energy into electronic devices and battery-charging systems is highly valued due to its status as a clean, environmentally friendly power source. To satisfy energy demands while minimising environmental impact, it is essential to adopt efficient, sustainable technologies. This study introduces a multi-output (dual-inductor, single-capacitor) LLC DC-DC converter designed for battery charging applications, capable of independently charging multiple batteries while reducing input voltage fluctuations from photovoltaic (PV) sources. The proposed converter features independently operated coils for each output channel, preventing long-duration short circuits in the inductors. It achieves high-voltage conversion with reduced output voltage variation, offering greater safety and stability compared to conventional converter designs. A key advantage of this system is its ability to deliver different voltage levels across outputs, enabling flexible, simultaneous charging of various batteries and loads. Initially, circuit modelling and system definition are performed to illustrate these benefits. The dual-inductor, dual-output converter is then evaluated under varying temperatures and irradiance levels during the application phase. Results show reduced ripple levels and increased PV output voltage with lower oscillation while maintaining electrical isolation between batteries and loads.
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ArticleYalnız künye2026/r/ebyu/18035
A thermal-aware and self-adapting method for accurate state of charge estimation in lithium-ion batteries
Can, E. R. O. L.
Accurate state-of-charge (SOC) estimation remains a key challenge for lithium-ion batteries (LIBs) operating under varying thermal and load conditions, particularly in electric vehicles and grid-scale energy storage systems. Conventional model-based estimators such as EKF and UKF typically assume fixed parameters and neglect coupled electro-thermal dynamics, which degrades estimation accuracy under temperature fluctuations and aging effects. To address these limitations, this paper proposes a Thermal-Aware Hierarchical Self-Tuning Square Root Unscented Kalman Filter (TA-HST-SRUKF) for real-time SOC estimation. The proposed framework explicitly incorporates a lumped thermal model into the SOC estimation loop and employs a hierarchical self-tuning mechanism to adapt process and measurement noise covariances online. In addition, the square-root formulation enhances numerical stability and reduces computational burden, making the approach suitable for embedded battery management systems. Compared with conventional EKF- and UKF-based methods, the proposed TA-HST-SRUKF demonstrates improved robustness and accuracy across a wide temperature range and varying operating conditions. Simulation results confirm lower estimation errors and better adaptability without increasing computational complexity. The proposed method provides an effective and practical solution for highaccuracy SOC estimation in thermally dynamic and aging-prone battery applications.
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ArticleAçık Erişim2026/r/ebyu/17916
A New High Gain Single-Switch Quadratic Boost Converter with Low Voltage Stress
Ting, Naim Suleyman; Ipek, Asli Seyda
Recent progress in DC microgrids necessitates high gain DC-DC converters for effective energy conversion. In this regard, this paper introduces a new high gain single-switch quadratic boost converter (HGS2QBC). This converter, comprising a switch, two inductors, four capacitors, and five diodes, incorporates a voltage multiplier cell (VMC) that provides an 8-times voltage gain at a 0.5 duty cycle. The salient features of the proposed converter are its high voltage gain, having only one switch, low voltage stress across semiconductor devices, and non-pulsating input source current. In addition to its high voltage gain, one of the most significant added-value features of the proposed converter is the presence of a single switch in its structure and the fact that the voltage stress on this switch is half of the output voltage. Furthermore, a 100 W, 200 V, and 20 kHz laboratory prototype is constructed to assess practical viability, and the related experimental results are provided. Experimental analysis confirms the converter's effectiveness in managing voltage and current waveforms across its components, reducing stress on the semiconductor elements and minimizing voltage levels across the diodes. Such design optimizations not only provide superior performance but also offer cost advantages that make the converter economically viable.
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ArticleYalnız künye2026/r/ebyu/11320
Optimization of MXene/CNT free-standing composite electrodes for enhanced electrochemical performance in symmetric supercapacitors
Yesilbag, Yasar Ozkan; Salih, Ahmed Jalal Salih
The integration of one-dimensional (1D) and two-dimensional (2D) materials into three-dimensional (3D) structures shows great potential for improving energy storage. This study investigates the optimization of interlayer spacing in MXene structures by incorporating conductive materials. Free-standing Ti3C2Tx MXene and carbon nanotube (CNT) composite electrodes were fabricated via vacuum filtration, with CNT weight ratios of 10 %, 20 %, and 30 %. The MXene/CNT10 (MX1) composite exhibited superior electrochemical performance, with a specific capacitance of 254 F g⁻1 and remarkable durability, retaining 101.2 % of its initial capacitance after 10,000 cycles. A symmetric supercapacitor (SSC) using the MX1 electrode achieved a stable voltage window of 0–1.2 V, a specific capacitance of 70.1 F g⁻¹, and retained 85 % of its capacitance after 5000 cycles. With an energy density of 14.1 Wh kg⁻1 and a power density of 13.9 kW kg⁻1, MXene/CNT composites demonstrate significant potential for advanced energy storage applications.
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ArticleYalnız künye2026/r/ebyu/11252
Thermal and thermodynamic performance of a tube equipped with angled vane vortex generators: Cfd-based analysis of heat transfer, flow characteristics and irreversibility
Firat, Ilker
This study numerically investigates the first- and second-law thermodynamic performance of a circular tube equipped with novel angled vane vortex generators (AVVGs). The effects of vane angle (α = 10°, 20°, and 30°) and pitch ratio (PR = 5, 10, and 15) on heat transfer, friction loss (f), entropy generation (S˙gen′), exergy efficiency (ηEx), Bejan number (Be), and thermal performance factor (TPF) were analyzed using ANSYS Fluent 2021 with the SST k–ω turbulence model. Simulations were performed for water under turbulent flow conditions within a Reynolds number (Re) range of 12,770–20,824. The results demonstrated that AVVGs significantly enhanced convective heat transfer by intensifying flow mixing and disrupting the thermal boundary layer. The maximum Nusselt number (Nu) of 161.06 was achieved at PR = 5, α = 30°, and Re = 20,824, whereas the highest heat transfer enhancement ratio reached 1.37 compared with the plain tube. Although friction losses increased with decreasing pitch ratio and increasing vane angle, entropy generation due to heat transfer decreased considerably. The minimum total entropy generation (S'gen, tot = 0.1979 W m⁻1 K⁻1) occurred at PR = 5, α = 30°, and Re = 20,824. The highest thermal performance factor of 1.045 was obtained at PR = 15, α = 20°, and Re = 14,378, indicating the optimum thermohydraulic balance. Additionally, empirical correlations with prediction errors less than 7.2% were established for the Nusselt number, friction factor, and TPF. The proposed AVVG design demonstrates strong potential for improving the thermal and thermodynamic performance of compact heat exchanger systems.
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ArticleYalnız künye2026/r/ebyu/11071
A Cost-Effective Ultra-High-Gain Single-Switch DC–DC Converter with Direct Power Transfer for Low-Voltage PV Applications
Sahin, Yakup; Ozgenel, Mehmet Cihat; Ting, Naim Suleyman
This paper presents a novel cost-effective, nonisolated ultra-high-gain single-switch DC–DC converter (UHGS2C) employing a voltage lift (VL) cell and a direct power transfer (DPT) mechanism, specifically designed for low-voltage photovoltaic (PV) applications. The proposed topology achieves an 8× voltage gain at a duty cycle of 0.5 using only a single active switch, three inductors, three capacitors, and five diodes, thereby reducing component count and control complexity compared with conventional high-gain converters. Through the DPT mechanism, a portion of the input energy is transferred directly to the load during the switch-on interval, thereby reducing the energy processed by the magnetic elements and lowering both switch current stress and capacitor voltage ratings. The steady-state analysis confirms that the maximum capacitor voltage is limited to half of the output voltage (VCmax = VO/2), enabling the use of compact, low–voltage-rated capacitors that improve power density and reduce cost. Both continuous and discontinuous conduction modes (CCM/DCM) are analyzed in detail, and a systematic design procedure, along with a comprehensive power loss model, is provided. To validate the theoretical analysis, a 200-W experimental prototype operating at 25-V input, 200-V output, and 50-kHz switching frequency was implemented. The prototype achieves a peak efficiency of 96.4%, while the closed-loop PI control verifies stable line- and load-regulation performance. Comparative results demonstrate that the proposed UHGS2C provides a favorable trade-off among voltage gain, efficiency, component stress, and compactness, making it a strong candidate for renewable energy applications such as PV systems.
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ArticleYalnız künye2026/r/ebyu/10985
An adaptive hybrid energy management system for UAVs integrating fuzzy logic, solar forecasting and real-time control
Can, Erol
This study presents a hybrid energy management system (EMS) for unmanned aerial vehicles (UAVs), integrating flexible photovoltaic (PV) panels with a high-capacity lithium-polymer (Li-Po) battery to enhance flight endurance and energy resilience. The proposed EMS features a multi-layered adaptive control architecture, combining fuzzy logic-based decision-making, short-term solar irradiance prediction using machine learning and closed-loop proportional-integral-derivative (PID) motor control. This architecture dynamically allocates power based on real-time solar irradiance, battery state of charge (SOC) and propulsion load. The approach aims to minimise battery stress while ensuring stable UAV operation under variable environmental conditions. Simulation results indicate that, without PV support, the UAV battery depletes in approximately 28 min. However, incorporating an 80 W PV panel extends the endurance to 37 min, and a 160 W effective PV configuration increases the flight time to over 50 min, while maintaining the SOC above 20%. This corresponds to a 79% improvement in endurance and nearly a 50% reduction in net battery energy consumption. The EMS effectively mitigates deep discharge cycles, improving battery health and operational lifespan. These findings demonstrate that the proposed hybrid EMS provides a scalable, adaptive and computationally efficient solution for balancing renewable and stored energy. It outperforms conventional static or single-source control strategies, particularly under fluctuating solar conditions.
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ArticleAçık Erişim2026/r/ebyu/10917
Compact and Cost-Efficient Dual-Buck NPC Inverter with Dead-Time Elimination and Shoot-Through Suppression
Sahin, Yakup; Blaabjerg, Frede; Hatas, Hasan; Ting, Naim Suleyman; Bakan, Ahmet Faruk; Ahmed, Md Rishad
Dead-time insertion is the conventional method for preventing shoot-through faults in neutral-point-clamped inverters (NPCIs), but it inevitably degrades output-voltage quality, lowers efficiency, and increases control complexity due to dead-time compensation. This article presents a novel transformer-based dual-buck NPCI (T-DB-NPCI) cell that inherently solves this problem at the topology level. The proposed structure not only eliminates the need for dead-time during normal switching transitions but also provides robust hardware suppression of destructive, long-duration shoot-through faults. The proposed cell is designed to safely withstand forced shoot-through faults of up to 2 μs, ensuring high reliability without requiring complex detection algorithms. The topology features a remarkably compact structure comprising only a single high-frequency transformer and one auxiliary diode, enabling all semiconductor devices to operate at only half the DC-link voltage. Furthermore, the streamlined structure allows for the use of standard sinusoidal pulse width modulation (SPWM), significantly reducing control complexity. Comprehensive results from a 425-W prototype achieving 97.4% peak experimental efficiency confirm excellent performance. A comparative analysis reveals an 8% improvement in power density (683 W/kg) and a 6% cost advantage over its closest counterpart. The T-DB-NPCI topology emerges as a highly promising solution where reliability and simplicity are critical.
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ArticleAçık 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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Conference ObjectAçık Erişim2026/r/ebyu/10816
Neutronic and thermal-hydraulic performance analysis of a novel thorium-based fuel in dual-cooled annular assemblies for SMR applications
Rifai, Hanan; Kabach, Ouadie; Uzun, Sinem; Genç, Yasin; Uzun, Bedrettin; Amsil, Hamid; Chakir, El Mahjoub; Sadoune, Zouhair; Didi, A.; Baatout, S.
A coupled neutronic and thermal-hydraulic assessment is performed for a dual-cooled annular fuel concept applied to light-water SMR conditions. A mixed thorium-based oxide, (Th-233U-235U)O2, is evaluated against conventional UO2 and solid-pin benchmarks while sweeping the inner moderator radius (0.05-0.43165 cm) at constant fuel volume. The thorium annular cases sustain an infinite multiplication factor (kinf) of 1.002-1.003 at EOC and achieve 3-batch discharge burnups of 166.3-173.5 GWd/t, compared with 142.4 GWd/t for solid UO2. Radial peaking remains low (maximum PPF ≈ 1.056, well below the 1.65 Westinghouse-types PWR design limit). All configurations exhibit negative FTC and MTC, with stronger negative feedback at larger inner radii. Thermal-hydraulic results show smooth coolant heating and robust DNBR margins (minimum ∼ 2.3) with negligible sensitivity to inner radius. These outcomes support the feasibility of thorium-based annular fuel for SMR deployment with improved fuel utilization and preserved safety margins.
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Review ArticleAçık Erişim2026/r/ebyu/10806
A review of photovoltaic plants and battery energy storage systems in terms of power systems stability
Çavdar, Bora; Karaca, Eda; Okumuş, Hatice; Nuroğlu, Fatih Mehmet
Nowadays, the rapid increase in PV plants in power systems has reached a point that may negatively impact power system stability. Battery storage systems have also begun to be integrated into power systems to mitigate the negative effects of non-rotating plants such as PV plants. The expected increase in PV penetration levels and BESS size must be analyzed jointly to address stability issues in power systems. It is evident that existing reviews on the hybrid integration of PV and BESS are limited. This paper aims to contribute to future developments by reviewing the most recent status and potential outcomes of studies related to the impact of PV plants and battery storage system (BESS) units on power system stability. The reviewed studies have been classified to provide valuable insights into the impact of PV plants and BESS units on power system stability. Each classification focuses on the studies in terms of the stability analysis type, the PV/BESS unit combination, the analysis method, the structure of the power system, the PV penetration level, the size of the BESS, whether frequency support is provided, and the use of simulation programs. In the classification of stability, frequency stability and rotor angle (for transients) stability are the most discussed topics. In the review for the units, it was found that studies on the stability of PV power plants were frequently addressed. In addition, studies on BESS and PV-BESS configurations have increased over time. Almost all stability studies focus on time-domain analysis. In addition, modal analysis, which is widely used in small-signal stability studies, is the second most frequently used analysis method in studies. In the studies, the effect of a three-phase short circuit fault on the stability of the system is usually analyzed. Apart from this fault, the implementation of a fault in the load is among the popular cases considered. In the power system classification used, either large or small power systems are usually considered. It is rare for an impact to be considered in both a small and a large power system. Different penetration levels and sizes of PV and BESS units, respectively, are usually analyzed separately in terms of stability. The number of papers investigating the effect of both PV and BESS together on the stability of a power system is quite rare. As a result of the classification on frequency support, it is seen that research on frequency support of PV power plants is handled from similar perspectives, although it has gained popularity recently. When the simulation programs used, which represent the last classification group, are examined, it is observed that DigSilent PowerFactory and MATLAB are generally preferred.
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ArticleYalnız künye2026/r/ebyu/10752
Prediction of cutting forces in turning Ti6Al4V in different cooling environments using a multiple signal data fusion approach
Akdulum, Aslan; Sİyambaş, Yusuf; Çakiroğlu, Ramazan; Uzun, Gültekİn
This study investigates the relationship between cutting forces and real-time energy consumption signals during the turning of titanium alloys, as well as the impact of various cooling methods on these forces. A hybrid approach combining energy signal analysis and forward regression modeling was applied to model the cutting forces. This work aims to model and predict cutting forces using individual and multiple signal data fusion approaches using data obtained from energy signals, such as reactive power, power factor, apparent power, and active power. A random forest regressor was preferred for model set-up, and the Scopt Optimizer was preferred for fine-tuning the hyperparameters. Six methods were used to score the features based on their importance level. The results showed that multi-energy signal data fusion outperforms the models based on single signals in predicting cutting forces. This method offers a new approach for optimizing metrics, such as the main cutting force (Fc) and feed force (Ff). The MAPE value of 4.91% obtained in the modeling estimation of the Ff emphasizes the power of the proposed method. The presented framework has the potential to increase processing performance while reducing energy consumption. The findings highlight the effectiveness of combining energy signals and machine learning in developing sustainable manufacturing processes. The proposed model can be adapted to different processing scenarios and provide a basis for industrial applications.
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ArticleAçık 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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ArticleAçık 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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