Yazarlar
- Akkurt, Nevzat(24)
- Kahraman, Gökhan(17)
- Sabanci, Serdal(11)
- Tuğan, Volkan(9)
- Işık, Erdem(8)
- Xu, Qian(8)
- Xiong, Yaxuan(7)
- Yucesan, Melih(7)
- Goksu, Ali(6)
- Demir, Yakup(5)
- Gul, Muhammet(5)
- Aghakhani, S.(4)
- Cevik, Mutlu(4)
- Icier, Filiz(4)
- Liu, Lin(4)
- Mahmoud, Mustafa Z.(4)
- Taşgın, Yahya(4)
- Yang, Gang(4)
- Yardımcı, Uğurcan(4)
- Younis, Obai(4)
- Yüzüak, E.(4)
- Yüzüak, G. Durak(4)
- Yüzüak, Gizem Durak(4)
- Çevik, Mutlu(4)
- Özdemir, Osman(4)
- Chen, Xingli(3)
- Ding, Yulong(3)
- Du, Yanping(3)
- Erdem, Necla Seval(3)
- Gunay, Mihriban(3)
- Göçmen, Elifcan(3)
- Hussein, Ahmed Kadhim(3)
- Karakaya, Hakan(3)
- Konurhan, Zekeriya(3)
- Qiang, Yujie(3)
- Sabancı, Serdal(3)
- Wang, Zhaoxiao(3)
- Yilmaz, Mehmet(3)
- Yüzüak, Ercüment(3)
- Zhang, Hui(3)
Yayınlar
- MakaleYalnız künye2026Investigation of flow design and deflector plate effect in air cooled battery pack at different discharge rates
Tuğan, Volkan; Yardımcı, Uğurcan
In this study, the effect of different flow designs and the use of deflector plates on the thermal performance of an air cooled battery pack was investigated using the CFD method. Model A, which has a conventional single inlet flow structures, was compared with Model B, which employs dual inlets and deflector plates. In both models, 24 cylindrical 18650 lithium-ion battery cells were used, and the inlet and outlet surface areas of the battery pack were designed to be equal. Analyses performed at 3C and 4C discharge rates showed that the dual inlet flow structure and deflector plates significantly improved the cooling efficiency of the battery pack. At a 3C discharge rate, the maximum battery temperature and maximum temperature difference obtained with Model B were 1.87 K and 1.08 K lower, respectively, compared to Model A. At a 4C discharge rate, this improvement became even more pronounced, with the maximum battery temperature reduced by 2.49 K and the maximum temperature difference decreased by 1.44 K. Examination of the temperature contours revealed that, in Model A, heat accumulation occurred particularly in the rear regions, whereas Model B achieved a more homogeneous temperature distribution.
- MakaleAçık Erişim2026Innovative System Design and Numerical Investigation Based on Air Cooling and Baffle Utilization in the Thermal Management of Lithium Ion Batteries
Tuğan, Volkan; Yardımcı, Uğurcan
Effective management of temperature in battery packs, which are used in a wide range of applications from portable devices to electric vehicles, plays a critical role in terms of both efficiency and safety. In this study, a new air cooling thermal management system was designed to improve the thermal management of lithium ion battery packs and was comparatively examined with a conventional system. In the developed design, front and rear air inlets along with a top outlet were implemented, and baffles were added to achieve a more homogeneous distribution of airflow between the cells. The conventional system, on the other hand, was modeled based on the principle of front inlet and rear outlet without baffles. These two designs were compared through CFD analyses in terms of maximum battery temperature and maximum temperature difference. In the analyses, three different inlet air temperatures of 299.15 K, 294.15 K, and 289.15 K were investigated. The airflow velocity at the entrance of the battery pack was set to 0.5 m/s. The analysis results showed that the developed design reduced the maximum battery temperature and maximum temperature difference by 1.82-3.38 K and 2.48-4.29 K, respectively, compared to the conventional design.
- MakaleYalnız künye2026Scopus SJRQ1Influence on the thermoelectric characteristics of the growth parameters of BiSeTe thin films
Yüzüak, G. Durak; Yüzüak, E.
This study systematically investigates the influence of magnetron sputtering parameters on the structural, electronic, and thermoelectric properties of 100 nm thick Bi2SexTe3-x thin films deposited on Si(100). The initial parameters for applied film deposition are used to set the x value. The films are fabricated with sputtering powers ranging from 30 to 100 W and pressures from 3 to 15 mTorr, with films grown under optimal conditions selected for post-deposition annealing at 523 K. A comprehensive characterization suite, including X-ray diffraction (XRD), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), atomic force microscopy (AFM), and Kelvin probe force microscopy (KPFM), is used to establish direct correlations between processing, structure, and performance. XRD analysis reveals that increasing the sputtering pressure from 3 to 15 mTorr was critical for enhancing film crystallinity and promoting a strong (015) preferred orientation, while AFM showed a corresponding increase in average grain size from similar to 46 to similar to 60 nm via heat treatment (HT). EDS shows that films grown at 10 mTorr were almost certainly stoichiometric, but increasing the power to 15 mTorr caused tellurium deficiency. The films exhibited a power factor ranging from 2.5 to 2.8 mW m(- 1) K- 2 at 40 degrees C. HT improved the Seebeck coefficient from -115 mu V K- 1 to -140 mu V K- 1 and decreased the electrical resistivity by over 25 % through defect reduction and enhanced crystalline order. KPFM supported these findings, directly linking the crystallographic texture to the surface electronic structure and enhanced Seebeck effect.
AtıfWoS2OpenAlex2
İncele - MakaleAçık Erişim2026Scopus SJRQ1Upcycling Agricultural Biomass into Functional Bio-Oils through Pyrolysis: Toward Sustainable Rejuvenation of Asphalt Binder
Furtana Yalcin, Beyza; Yilmaz, Mehmet; Yalçin, Erkut; Vural Kok, Baha
This study investigates the feasibility of utilizing bio-oils derived from agricultural waste materials (such as straw, olive pomace, and pine cones) as rejuvenating additives in reclaimed asphalt pavement (RAP) containing bituminous mixtures, offering a sustainable and environmentally friendly approach in asphalt technology. The primary objective of the study is to enhance the physical and mechanical properties of aged binders using ecofriendly alternatives, thereby developing high-performance asphalt mixtures without harming the environment. The bio-oils, produced through pyrolysis, were evaluated in comparison with two commercial rejuvenators. Key performance criteria, including Marshall stability, moisture damage resistance (TSR), indirect tensile stiffness modulus (ITSM), and fatigue life, were tested. The results indicate that natural rejuvenators not only soften the aged binder but also effectively balance the flexibility, strength, and deformation resistance of the mixture. Notably, straw oil exhibited superior performance in terms of both high stability and extended fatigue life, while olive pomace oil demonstrated remarkable success in moisture resistance. Although commercial additives were effective to a certain extent, they failed to achieve the same level of structural compatibility and sustainability provided by natural bio-oils. In this regard, the study provides strong evidence that agricultural biomass waste can serve as a value-added solution in asphalt pavements, in alignment with the principles of the circular economy. In conclusion revealed that mixtures containing 75% RAP rejuvenated with straw oil achieved 11% higher Marshall stability and up to 14.5 times longer fatigue life compared to virgin mixtures, while olive pomace oil-based mixtures reached a TSR value of 81.5%, demonstrating superior moisture resistance.
- MakaleAçık Erişim2026Piezoelectric energy harvesters in bridge structures: Evaluation of optimal placement and energy harvesting
Sen, Bugra; Polat, Alper; Kapicioglu, Abdullah; Esen, Hikmet
In the present study, energy is harvested from vibrations in bridge structures caused by vehicle passages using piezoelectric energy harvesters (PEHs). In order to maximize the energy harvested, it was aimed to specify the optimal locations of PEHs. In the study, energy harvesting from vibrations in bridge structures was achieved using three different vehicle loads and three different piezoelectric materials. The project of the Ku & scedil;saray & imath;-2 bridge, located in Ku & scedil;saray & imath; village in Baskil, Elaz & imath;& gbreve;, was modelled in the ANSYS program using real measurements. The bridge was analyzed for modal, harmonic, and piezoelectric energy harvesting, respectively. The type with the highest energy production was determined using three different piezoelectric materials (PZT-4, PZT-5A, and PZT-8) for a total of 9 PEHs, and a total of 13 different analyses were performed under three different vehicle loads (60 kN, 90 kN, and 120 kN) to compare the results. The maximum total power obtained from all nine PEHs was 1.484 & micro;W under the 120 kN load with PZT-5A. The maximum power obtained from a single PEH (PEH-1) was 0.49 & micro;W. This power achieved will be adequate for wireless sensor systems used for monitoring the structural health of bridges, and considering that energy harvesting through vibration is independent of meteorological elements, unlike energy sources such as solar and wind, the environmental damage caused by waste batteries in existing systems will be prevented with the dissemination of these systems. With the development of these systems, the energy needs of services such as lighting and traffic signs can be supplied. The results provide an important foundation for the development of vibration-based energy harvesting systems in bridge structures. Thus, the study stands out as an innovative step toward sustainable energy generation in structural engineering.
- MakaleYalnız künye2026Scopus SJRQ1Compositionally segmented thermoelectric BiSbTe thin films on Cr/Si(100)
Yüzüak, G. Durak; Yüzüak, E.
Compositionally segmented thermoelectric (CS-TE) structures improve thermal energy conversion efficiency across large thermal gradients. However, their increased design complexity complicates performance modeling and optimization. In this study, we fabricate CS-TE BiSbTe thin films on Cr-buffered Si(100) substrates. By adopting a multidisciplinary approach, we aim to broaden the technological applicability and strategic relevance of advanced TE systems. We systematically analyze the influence of deposition pressures (5, 7, and 10 mTorr) on TE performance. Variations in working pressure alter the Bi/Sb atomic ratio in the films, while the Te content remains stable. This enables the controlled formation of CS structures. At 323 K, the power factor (PF) of TE thin films reaches 0.7, 2.4, and 7.3 mW/mK2 under 5, 7, and 10 mTorr deposition conditions, respectively. The Cr-buffered layer significantly improves the electrical conductivity of CS-TE BiSbTe films. We further engineer a p-type single-leg device integrating nanostructured Bi0.35Sb1.65Te3, Bi0.37Sb1.63Te3, and Bi0.4Sb1.60Te3 layers. These materials are optimized to achieve peak TE PF values across a wide temperature range (320 to 500 K) while ensuring robust interfacial adhesion. The CS-TE device generates 3 mW/cm2, showing a 15% improvement over single-composition samples at a 100 K temperature difference.
AtıfScopus3WoS3OpenAlex2
İncele - MakaleAçık Erişim2026Scopus SJRQ1Geospatial evaluation of river basin small hydropower potential using AHP
Korkmaz, Meral
This study develops an integrated Geographic Information Systems (GIS) and Analytic Hierarchy Process (AHP) framework to accurately identify small hydropower plants sites that support sustainable water–energy planning. The approach is applied to the Peri River Basin in Eastern Türkiye by combining multisource remote sensing datasets with a semi-automated narrow-valley detection technique to enhance the spatial precision of site selection. Nine hydrological and physiographic criteria including stream order, rainfall, slope, geology, drainage density, elevation, land use land cover, Stream Power Index SPI and Topographic Wetness Index TWI were weighted using the AHP method to generate a five-class suitability map for small hydropower plants development. A major contribution of this study is the explicit integration of valley morphology into the conventional GIS–AHP workflow, which significantly improves the identification of hydropower favorable river segments. Results indicate that approximately 3% of the basin exhibits very high suitability, while 46% is moderately suitable for run-of-river small hydropower plants development. Model validation was performed by overlaying the suitability map with the locations of existing dams/hydropower facilities, showing that current facilities are mainly located in high-suitability zones. Separately, the workflow identified 13 newly proposed high-suitability candidate dam sites (P1–P13), selected from screened suitable dam points using narrow-valley detection criteria. By minimizing ecological disturbance and aligning with watershed-based management principles, the proposed GIS-supported multicriteria method provides a transferable and cost-effective decision-support tool for small-hydropower planning in rural and mountainous basins, offering practical contributions to sustainable resource management and the water–energy nexus.
AtıfScopus2WoS2OpenAlex4İncele - MakaleAçık Erişim2026Scopus SJRQ1Multi-Task NILM with Anomaly Detection Using a Hybrid CNN–BilSTM–Transformer Model
Gunay, Mihriban; Demir, Yakup; Zhilevski, Marin
Non-Intrusive Load Monitoring (NILM) enables estimation of the energy use of individual appliances in smart buildings from a single aggregate meter. In practice, however, this task is not straightforward. Signals from different appliances can overlap, and the measured data may also include distortions such as spikes, drops, and noise. To address these issues, this study presents a multi-task triple-hybrid deep learning framework that handles appliance classification and anomaly detection together. The model brings together 1D-CNN, BiLSTM, and Transformer Attention so that local patterns, temporal dependencies, and wider contextual information can be learned within the same structure. It also uses a dual-output design to classify appliance categories and detect anomaly types simultaneously. Experiments were carried out on Building 1 of the UK-DALE dataset with four appliances: kettle, microwave, washer dryer, and fridge freezer. For the anomaly task, synthetic disturbances were added to segmented signal windows and grouped as normal, spike, drop, and noise. To check how well the proposed framework handled different scenarios, it was tested on both the UK-DALE and REDD datasets. Looking at the main UK-DALE results, the model correctly identified appliances 99.48% of the time and spotted anomalies with 98.80% accuracy. A secondary test on the REDD dataset yielded an 86.44% classification score. This proves the architecture can adjust to completely new power grid environments without losing its edge. On top of that, when pitted against standard benchmark models like Seq2Point, this triple-hybrid design clearly does a better job of mapping out complex signal changes. As a result, it yields much stronger anomaly detection metrics.
AtıfOpenAlex1İncele - MakaleYalnız künye2026Scopus SJRQ2An Innovative Thermal Management Approach Enhanced by Thermoelectric Stations and Turbulators for Lithium-Ion Batteries
Tuğan, Volkan; Yardımcı, Uğurcan
The design of an effective battery thermal management system is critical to ensure the safe, efficient, and long-lasting operation of lithium-ion batteries. Excessive heat generation and temperature non-uniformity among cells in high energy density battery systems lead to performance degradation, accelerated aging, and an increased risk of thermal runaway. Although liquid-cooled systems and thermoelectric module-assisted solutions have been extensively investigated separately in the literature, studies that holistically evaluate multiple thermoelectric stations combined with an innovative flow channel design and turbulator integration remain limited. This gap constitutes a significant research need, particularly in terms of improving temperature uniformity and reducing maximum cell temperature. In this study, to address this research gap, a novel liquid-cooled battery thermal management system integrating thermoelectric coolers and turbulators within the flow channel is proposed. The thermal performance of the proposed system was systematically analyzed using computational fluid dynamics under three different configurations (conventional system, thermoelectric-assisted system, and turbulator-integrated thermoelectric-assisted system) and four different mass flow rates (0.0008, 0.0013, 0.0018, and 0.0023 kg/s). The results indicate that, at a mass flow rate of 0.0023 kg/s, the integration of thermoelectric coolers and turbulators reduces the maximum battery temperature by 8.95 K and the maximum temperature difference by 6.83 K. At the highest mass flow rate, the proposed system achieves a maximum battery temperature of 306 K and a maximum temperature difference of 4.5 K, ensuring both effective cooling performance and high temperature uniformity. Overall, the results in terms of cooling performance and temperature uniformity demonstrate that the combination of thermoelectric modules and turbulators offers significant potential for advanced battery thermal management system applications and offers an effective solution for high-energy-density systems.
- MakaleAçık Erişim2026Scopus SJRQ1Real-time photovoltaic voltage data-driven dynamic green s-box generation framework for smart grid security
Aydın, Yılmaz; Garipcan, Ali Murat; Özkaynak, Fatih
In this study, a dynamic substitution box (s-box) generation method is proposed using real-time voltage data obtained from photovoltaic (PV) panels as an environmentally sensitive entropy source. These data, affected by environmental conditions such as solar radiation and temperature, exhibit irregular and unpredictable temporal behavior, offering strong potential for cryptographic randomness generation. In this context, the 0–1 test, Kolmogorov Entropy (KE), scale-index analysis, and the National Institute of Standards and Technology (NIST) SP 800-22 test suite are applied to PV voltage data collected at 1-minute intervals, experimentally confirming their chaotic dynamics and high-entropy characteristics. Following appropriate preprocessing and nonlinear mathematical transformations, the validated data are utilized to generate 8-bit s-box structures. The cryptographic strength of the generated s-boxes is quantitatively evaluated using standard security metrics, including nonlinearity (NL), Differential Probability (DP), Linear Probability (LP), Strict Avalanche Criterion (SAC), and Bit Independence Criterion (BIC). The findings reveal that PV data can serve as a functional resource not only for energy monitoring but also for randomness-based cryptographic construction. This approach enables lightweight cryptographic solutions for resource-constrained edge devices requiring <1 KB of memory and supports context-aware data security in renewable energy infrastructures.
- MakaleAçık Erişim2026Scopus SJRQ3Optimized bioleaching of spent FCC catalysts for the recovery of critical metals
Erust, Ceren; Altındaş, Mediha Demet Okudan
Spent fluid catalytic cracking (FCC) catalysts, generated in large volumes by petroleum refining, represent a promising secondary source of critical metals such as lanthanum, cerium, titanium, and vanadium. In this study, a biohydrometallurgical approach was applied to recover these metals using Acidithiobacillus ferrooxidans and Leptospirillum ferrooxidans. The spent FCC catalyst, supplied by petroleum refinery, was characterized through WD-XRF, XRD, SEM-EDS, and ICP-OES analyses, confirming significant concentrations of La2O3 (1.44%), TiO2 (1.43%), and CeO2 (0.23%). Bioleaching experiments were conducted under varying initial ferrous ion concentrations (1–9 g/L) and pulp densities (5–20% w/v) at 35°C over 14 days. Results revealed that the optimal conditions for maximum recovery occurred at 5 g/L Fe2+ and 10% pulp density, achieving leaching efficiencies of 95.5% for La, 90.5% for Ce, 92.5% for Ti, and 60.8% for V within seven days. Beyond this period, efficiency decreased due to jarosite formation and microbial activity constraints. The findings confirm that bioleaching is a cost-effective and sustainable route that aligns with circular economy principles by reducing reliance on virgin rare earth mining while mitigating the environmental impact of catalyst disposal.
- MakaleAçık Erişim2026Scopus SJRQ1Sustainable material pathways: Utilizing biomass-derived bio-oils from agricultural wastes to restore aged asphalt binder properties
Yalcin, Beyza Furtana; Yilmaz, Mehmet; Yalcin, Erkut; Kok, Baha Vural
This study explores the valorization of agricultural biomass wastes into high-value bio-based materials by evaluating the performance of pyrolysis-derived bio-oils obtained from straw, pine cone, and olive pomace for the rejuvenation of aged asphalt binders. The effectiveness of these biomass-derived bio-oils was comparatively assessed against two commercial rejuvenators to enhance binder reusability using renewable resources. The optimum rejuvenator dosages were determined through penetration, viscosity, and dynamic shear rheometer (DSR) tests, while the associated physical and chemical modifications were investigated using FTIR, SEM-EDS, and elemental analyses. Penetration and viscosity results indicated that all rejuvenators significantly reduced binder stiffness and improved fluidity. DSR analysis indicated that increasing rejuvenator content reduced G∗/sin δ values, reflecting a decrease in binder stiffness and a softening effect of the rejuvenators, which contributes to restoring the viscoelastic balance of the aged binder. FTIR results demonstrated that biomass-derived bio-oils, particularly those from pine cone and straw, effectively reduced carbonyl (C=O) and sulfoxide (S=O) functional groups, indicating strong chemical rejuvenation. SEM-EDS analyses showed that the heterogeneous and porous morphology of bio-oils promoted improved interaction with the asphalt binder. Based on the combined results of the three experimental approaches, the optimum rejuvenator dosages were identified as 25% for commercial products, 35% for pine cone and olive pomace bio-oils, and 45% for straw bio-oil. Overall, the findings highlight that biomass-derived bio-oils produced via pyrolysis offer a sustainable and performance-effective pathway for converting agricultural residues into value-added materials, supporting circular bioenergy and biomass utilization strategies.
AtıfScopus2WoS3OpenAlex3İncele - MakaleAçık Erişim2026Scopus SJRQ2Synergistic Effect of Cylindrical Fins and Phase Change Material on the Thermal and Economic Performance of a Double-Slope Solar Distillation Device Under Tunceli Climatic Conditions
Tuğan, Volkan; Işık, Erdem; İnallı, Mustafa
Global water scarcity driven by population growth, climate variability, and rising freshwater demand increases the need for more efficient solar desalination systems. In this study, a double-slope solar distillation device supported by a phase change material pool was developed to improve freshwater production. The phase change material layer was placed directly beneath the absorber plate to store excess thermal energy during peak solar radiation and release it during low-radiation periods. To strengthen thermal interaction between the absorber surface and the storage medium, cylindrical fins were embedded within the phase change material pool. The development of a double-slope solar distillation device integrated with a finned phase change material pool, and the simultaneous comparison of the thermal efficiency and economic analyses of single-slope and double-slope solar distillation devices under the same climatic conditions, are the prominent features of this study. Additionally, the performance of solar distillation devices has not previously been investigated under the climatic conditions of Tunceli, which further motivates this research. The developed system reached a peak freshwater productivity of 2459.2 g/m2/day, providing a 21.4% enhancement over the conventional configuration. In addition, the thermal efficiency increased from 17.7% in the conventional unit to 21.4% in the phase change material-assisted design. The economic analysis showed that the cost of distilled water was $0.097/kg for the conventional system and $0.105/kg for the proposed system, with the corresponding payback periods being approximately 11 and 12 months, respectively.
AtıfScopus2WoS2OpenAlex2İncele - MakaleYalnız künye2026Scopus SJRQ1Modeling stack voltage and remaining useful life of proton exchange membrane fuel cells using deep neural networks
Dogan, Gurkan; Imak, Andaç
Currently, various energy conversion systems are being developed to reduce environmental impacts. Proton exchange membrane fuel cells (PEMFCs), which stand out with their high efficiency characteristics, have emerged as an important alternative. However, these advantages, along with high costs and uncertainties regarding remaining useful life (RUL) estimation, restrict their further commercialization, especially in the automotive industry. This study presents a forecasting framework using a long short-term memory (LSTM) architecture enhanced with the Bahdanau attention mechanism for predicting RUL and long-term degradation trends. The model thus exhibits a performance that can more effectively capture dependencies in time series and adaptively weight significant time steps. Furthermore, the tree-structured parzen estimator (TPE) was chosen for the appropriate selection of hyperparameters in the developed forecasting framework. The validity, stability, and performance of the proposed model under different operating conditions were examined on the FC1 and FC2 datasets. Experimental studies have shown that the proposed method is the most successful among the best methods, achieving RE metric values of 1.628 × 10−3, 0.003 × 10−1, 99.16 × 10−2, and 0.044 × 10−1 respectively in estimating the best RMSE, MAPE, R2, and RUL.
- DüzeltmeAçık Erişim2026Scopus SJRQ1Correction: 2D MHD Mixed Convection in a Zigzag Trapezoidal Thermal Energy Storage System Using NEPCM (Nanomaterials, (2022), 12, 19, (3270), 10.3390/nano12193270)
Abderrahmane, Aissa; Younis, Obai; Al-Khaleel, Mohammad; Laidoudi, Houssem; Akkurt, Nevzat; Guedri, Kamel; Marzouki, Riadh
There were errors in the original publication [1]. In Abstract and Introduction, any reference to “heat transfer (HT) irreversibility,” “fluid friction (FF) irreversibility,” or “Bejan number” is erroneous and should be removed. Any reference to “wave number” should be corrected to “zigzag number,” which is the relevant parameter in the study. The corrected Abstract and the corrected sentence in the last paragraph of the Introduction appear below, respectively. In a magnetic field, two-dimensional (2D) mixed convection is investigated within a zigzagged trapezoidal chamber. The lower side of the trapezoidal chamber is irregular, in particular, a zigzagged wall with different zigzag numbers N. The fluid particles move in the room due to the motion of the upper wall, while the porosity-enthalpy approach represents the melting process. The thermal parameters of the fluid are enhanced by what is called a nano-encapsulated phase change material (NEPCM) consisting of polyurethane as the shell and a nonadecane as the core, while water is used as the base fluid. In order to treat the governing equations, the well-known Galerkin finite element method (GFEM) is applied. In addition, the heat transfer (HT) and the dynamic behavior of flow are treated in terms of average Nusselt number and contours of streamlines. The main results show that the melt band curve of heat capacity behaves almost parabolically at smaller values of Reynolds number (Re) and larger values of Hartmann number (Ha). Moreover, minimizing the zigzag number is better in order to obtain a higher heat transfer rate. In addition, a comparative study of the heat transfer and the dynamic behavior of flow in terms of average Nusselt number and contours of streamlines has been presented. In Section 2.1. Mathematical Modeling and Equations, Paragraph 3 and Paragraph 4, the term of porosity must be added to the momentum equations. The Equations (2), (3), (8) and (9) should be updated to the following: (Formula presented.) (Formula presented.) (Formula presented.) (Formula presented.) In Section 2.1. Mathematical Modeling and Equations, Paragraph 4, Paragraph 5 and Paragraph 9, there is no cylinder inside the cavity, and any reference to a “cylinder” or “diameter D” is incorrect and should be disregarded. The pressure in Equation (6), the Reynolds Number in Equation (11) and the definition for the average Nusselt number at the zigzag wall in Equation (16) should be updated to the following: (Formula presented.) (Formula presented.) (Formula presented.) The authors state that the scientific conclusions are unaffected. This correction was approved by the Academic Editor. The original publication has also been updated.
- MakaleYayıncıda açık erişim2026Scopus SJRQ1Explainable AI-based deep learning architectures for hydrogen prediction in biomass syngas and photocatalysis processes: Towards a sustainable and efficient energy economy
Imak, Andaç
This study, focusing on sustainable energy production and particularly the hydrogen (H2) economy, presents an advanced artificial intelligence (AI) framework for accurately predicting H2 production from sucrose photocatalysis catalyzed by LaFeO3 (GLFO) and the H2 concentration in syngas derived from biomass conversion. The research was conducted along two main directions: (i) deep learning (DL) architectures comprising nine specially designed recurrent neural network-based models and (ii) six conventional machine learning (ML) algorithms. For both H2 production systems, the best-performing models were identified and further interpreted using explainable AI (XAI) based on SHapley Additive exPlanations (SHAP). Among all investigated algorithms, a hybrid One-dimensional Convolutional Neural Network–Bidirectional Long Short-Term Memory–Bidirectional Gated Recurrent Unit (1D-CNN–BiLSTM–BiGRU) model demonstrated the highest predictive performance. Using 10-fold cross-validation, the proposed model achieved outstanding accuracy, with an R2 value of 0.9987 ± 0.0011 for photocatalytic H2 production prediction and 0.9980 ± 0.0006 for syngas H2 concentration prediction. These results highlight the potential of the proposed framework as a precise, reliable, and practical methodological approach for modeling and optimizing complex thermochemical energy systems.
- MakaleYalnız künye2026Scopus SJRQ1Engineering high-performance BiSeTe ultra-thin film on flexible FEP
Yüzüak, G. Durak; Yüzüak, E.
Flexible thermoelectric (TE) devices hold great promise for powering wearable electronics by harvesting the temperature difference between human skin and the environment. Conventional Bi2Te3, although efficient, suffers from rigidity and limited suitability for flexible applications, while organic alternatives typically lack sufficient TE performance. Here, we demonstrate an n-type BiSeTe thin film deposited on a flexible fluorinated ethylene propylene (FEP) substrate, incorporating a Cr buffer layer, achieving a room-temperature power factor of ∼3.9 μW.cm−1.K−2, which is within the range reported for flexible thin-film TE devices on polymer substrates. The film exhibits excellent mechanical resilience, retaining 90 % of its initial conductivity after 5000 bending cycles, and shows high thermal stability over 50 heating–cooling cycles with deviations below 1.5 %. A four-leg n-type prototype generates 10 mV and 70 nW at ΔT = 40 K, demonstrating its potential for powering low-energy wearable sensors. This work presents a scalable inorganic thin-film TE platform that effectively balances mechanical flexibility, device stability, and practical energy conversion performance for wearable and localized power generation applications.
AtıfScopus2WoS2OpenAlex2İncele - MakaleAçık Erişim2026Scopus SJRQ1Analyzing Failures In Wind–Solar Hybrid Energy Systems Using a Fuzzy-Based BWM-MARCOS Approach: Challenges and Solutions
Başhan, Veysi; Yucesan, Melih; Demirel, Hakan; Gul, Muhammet
The reliability of hybrid renewable energy systems (HRES) depends heavily on the identification and management of potential failure modes. This study employs a fuzzy-based BWM-MARCOS approach to systematically analyze and prioritize failure modes within wind–solar hybrid systems. The model aims to prioritize the failures considering four important risk parameters: (1) severity of the failure on system, staff, and failure, (2) failure occurrence chance, (3) effort and ease of detecting the cause of the failure, and (4) economic impact of the failure. In this context, four key risk indicators were evaluated to rank failures, revealing that SP1 (cell damage), ESS1 (battery degradation), and WT11 (battery fire) are the most critical due to their potential impact on system operations. Sensitivity analysis confirmed the stability of these rankings under varying parameter weights. Additionally, cross-method validation using fuzzy TOPSIS, SAW, and MARCOS demonstrated high correlation coefficients, underscoring the reliability of the results. Tailored mitigation strategies, including advanced diagnostics, durable materials, and robust monitoring systems, are proposed to address these critical failures. While the current methodology applies to various HRES configurations, future research should incorporate real-world operational data and machine learning techniques to enhance predictive capabilities and dynamic risk management.
AtıfScopus7WoS7OpenAlex8İncele - MakaleYalnız künye2026Scopus SJRQ2A Parametric Study on a Thermoelectric-Integrated Battery Thermal Management System for Electric Vehicles: New Designs Utilizing Different Numbers of Thermoelectric Modules
Tuğan, Volkan
The safety, lifespan, and performance of battery systems in electric vehicles directly depend on the implementation of an effective thermal management strategy. In this study, a novel lithium-ion battery thermal management system integrated with a wavy cooling channel and different numbers of thermoelectric modules is proposed. Computational fluid dynamics-based numerical simulations were conducted at two ambient temperatures (298.15 K and 303.15 K) and three different thermoelectric temperatures (288.15 K, 293.15 K, and 298.15 K). Three configurations with four, six, and eight thermoelectric modules were analyzed and compared with the conventional case without thermoelectrics. The lowest maximum battery temperature was obtained with the design employing eight thermoelectric modules under the conditions of 298.15 K ambient temperature and 288.15 K thermoelectric temperature. It was found that integrating thermoelectrics into the battery thermal management system reduced the maximum battery temperature by up to 12.11 K and the maximum temperature difference by up to 5.83 K. Furthermore, maintaining the thermoelectric temperature approximately 5–10 K below the ambient temperature was identified as a critical design parameter to ensure both efficient cooling and thermal homogeneity. The use of different numbers of thermoelectric modules and the selection of appropriate operating temperatures were shown to play a decisive role in optimizing system performance.
- MakaleAçık Erişim2026Scopus SJRQ2Hybrid Supervised Classification and Deep Embedding–Based Profiling Framework for Electricity Consumption Analysis
Gunay, Mihriban; Yildirim, Ozal; Demir, Yakup; Zhilevski, Marin; Mikhov, Mikho; Yordanov, Nikolay
This study proposes a hybrid deep learning framework that integrates supervised classification and unsupervised profiling for electricity consumption analysis. In the supervised phase, a one-dimensional Convolutional Neural Network combined with Long Short-Term Memory (1D CNN–LSTM) architecture is developed to classify daily load patterns. The performance of the proposed model is compared with traditional machine learning and deep learning approaches, including Support Vector Machine (SVM), k-Nearest Neighbors (KNN), a standalone Long Short-Term Memory (LSTM) model, a Transformer-based model, and a standalone 1D CNN model. Experimental results on the Precon house dataset and the CU-BEMS dataset demonstrate that the proposed hybrid architecture outperforms the benchmark models, achieving classification accuracies of 87.59% and 86.40%, respectively. In the unsupervised phase, the trained CNN–LSTM encoder is utilized as a deep feature extractor. The resulting 32-dimensional latent embeddings are clustered using K-Means, Gaussian Mixture Model (GMM), Agglomerative, Spectral, and Ensemble methods. Clustering robustness is evaluated through bootstrap-based stability analysis using the Adjusted Rand Index (ARI) and the Normalized Mutual Information (NMI). The results demonstrate stable and interpretable electricity consumption profiles, particularly in the residential dataset, where near-perfect clustering stability is observed for K-Means. The proposed framework provides both improved classification performance and robust consumption profiling based on deep embedding, offering a practical tool for energy management.
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