DSpace Arşivi :: Ana Sayfa

İstanbul Gelişim Üniversitesi Kurumsal Açık Erişim Arşivi

DSpace@Gelişim, İstanbul Gelişim Üniversitesi tarafından doğrudan ve dolaylı olarak yayınlanan; kitap, makale, tez, bildiri, rapor, araştırma verisi gibi tüm akademik kaynakları uluslararası standartlarda dijital ortamda depolar, Üniversitenin akademik performansını izlemeye aracılık eder, kaynakları uzun süreli saklar ve yayınların etkisini artırmak için telif haklarına uygun olarak Açık Erişime sunar.

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Metal-Foam Skeleton Effects on PCM Thermal Storage for Greenhouse Microclimate Control: A Numerical Study
(ELSEVIER, RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS, 2026) Gouga, Samir; Chibani, Atef; Mesmoudi, Kamel; Rashid, Farhan Lafta; Alaofi, Zaki Mrzog; Alawaideh, Yazen M.; Alayed, Tasneem; Kezzar, Mohamed; Sarı, Mohamed Rafik; Mahariq, Ibrahim; Al-Zuheiri, Aya M.; Popa, Ioan-Lucian
This study investigates passive thermal management strategies for a south-facing greenhouse operating under hot-arid diurnal conditions by integrating phase change materials (PCMs) into the north wall and enhancing their thermal response using open-cell metal foams. A 2D transient CFD microclimate model is developed in ANSYS Fluent. The impact of metal-foam (MF) type is examined by comparing copper, aluminum, and steel foams at 20 PPI and high porosity (ε = 0.93). Results show that inserting MF improves heat spreading inside the PCM modules, leading to smoother temperature fields, reduced local overheating during charging, and more effective latent-heat utilization. Among the tested foams, copper provides the strongest stabilization, typically lowering PCM daytime peak or plateau temperatures by about 5–7 K relative to the nofoam case, followed by aluminum (about 3–5 K) and steel (about 2–3 K). Foam enhancement also accelerates melting, increasing the maximum daily liquid fraction from about 0.22 (no foam) to about 0.35 (copper), 0.33 (aluminum), and 0.29 (steel). The improved thermal coupling modifies buoyancy forcing and airflow patterns, reducing stagnant zones and promoting more uniform circulation. These findings provide material-level guidance for designing MF–PCM storage modules to improve greenhouse temperature buffering with minimal energy input.
Parameter Identification for Proton Exchange Membrane Fuel Cell Using an Enhanced Puma Optimizer
(Multidisciplinary Digital Publishing Institute (MDPI), 2026) Rai, Nawal; Kanouni, Badreddine; Laib, Abdelbaset; Necaibia, Salah; Al Dawsari, Saleh; Yahya, Khalid
Proton exchange membrane fuel cells (PEMFCs) represent a promising renewable energy technology that converts chemical energy from hydrogen and oxygen into electrical energy. Accurate mathematical modeling and precise parameter identification are essential for optimizing PEMFC performance and control. This study proposes a novel hybrid meta-heuristic algorithm, the mutated puma optimizer (Mu-PO), which integrates a mutation operator from differential evolution to enhance the exploration and exploitation capabilities of the conventional puma optimizer, enabling it to escape local minima and reach global optima in fewer iterations. A sum of squared error (SSE)-based objective function is formulated to minimize the discrepancy between estimated and experimental voltages. The proposed method identifies seven unknown parameters for three commercial PEMFC models (250 W, SR-12, and NedStack PS6), achieving SSE values of 0.6419, 1.0566, and 2.0791, respectively. Notably, Mu-PO attains these low SSE values in fewer than 50 iterations for all models, demonstrating rapid convergence. Comparative analysis using statistical indicators (minimum, mean, maximum, and standard deviation of SSE) confirms that Mu-PO outperforms well-established optimization algorithms in terms of convergence speed, stability, and accuracy. Furthermore, validation under dynamic operating conditions, including variations in pressure and temperature, demonstrates consistent and reliable parameter identification, highlighting the robustness and practical applicability of the proposed approach for PEMFC modeling and optimization.
Qualitative Dynamics and Solitary Wave Phenomena in a New Extended Boussinesq-Type KdV Equation
(Elsevier B.V., 2026) Şenol, Mehmet; Kopçasız, Bahadır; Türk, Hatice Ceyda; Emadifar, Homan; Ahmed, Karim K.
In the present study, the extended Boussinesq-type Korteweg-de Vries (KdV) equation is solved by the KumarMalik (KM) method to yield new families of analytical traveling-wave solutions. The model has been transformed into ordinary differential equations through suitable traveling-wave transformations, and the ODE is solved to obtain explicit solutions as Jacobi elliptic, hyperbolic, trigonometric, and exponential functions. The dynamical characteristics of these solutions are further examined via bifurcation analysis, sensitivity to initial conditions, and phase-portraits. In addition, the chaotic dynamics of the system are explored using bifurcation diagrams, Poincaré maps, and time series representations. The results demonstrate multifaceted dynamical effects, including periodic, quasi-periodic, and chaotic transitions. The study provides valuable information on the nonlinear complex processes of extended Boussinesq-type KdV models and may serve as a basis for additional theoretical and applied research.
Elaboration and Characterization of Composite Material Based on Epoxy Resin and Cynara Scolymus Fibers: Weibull Statistics Analysis
(ELSEVIER, RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS, 2026) Alqurashi, Hazzaa F.; Hafez, Ramy M.; Talbi, Nabil; Kezzar, Mohamed; Rashid, Farhan Lafta; Abdalrahem, Mushtaq K.; Sarı, Mohamed Rafik; Khan, Ilyas; Mahariq, Ibrahim
The research will fill the increasing demand of sustainable composite materials by designing and characterizing an epoxy based biocomposite that is reinforced with natural fibers that are derived through the Cynara scolumus (artichoke stem) agricultural waste. The study methodically examines how fiber reinforcement influences the performance of the composite under a controlled extraction of the fibers, and also the unidirectional laminates of the composite were produced with single, double, and triple ply. The fibers were methodologically described in physical properties with the help of water absorption kinetics based on the Peleg equation, and mechanical performance with tensile tests and impact tests according to the ASTM standards, including SEM and EDX tests. The most important findings include the fact that the mechanical properties are greatly increased with the number of fiber plies: single-ply composite reached the ultimate tensile strength of about 15 MPa, double-ply composite tensile strength was 32 MPa, and the triple-ply composite tensile strength was 60 MPa with better strain at break (about 2.2%). The resistance to impact also improved with the number of ply and the adhesion of fibers to the matrix was also confirmed by SEM with a small number of voids and EDX gave a fiber composition of 56.49% carbon and 34.05% oxygen. The paper presents the new application of the Cynara scolymus fibers, which are an untested agricultural waste, in epoxy composites, and is the first use of Weibull statistical analysis to describe the consistency and reliability of these fibers as a sustainable reinforcement. The paper concludes that Cynara scolymus fibers are an effective, renewable reinforcement, with a very good balance of low density, better specific strength, and acceptable moisture uptake, thus contributing to the valorization of agricultural residues to support the eco-friendly structural and semi-structural composites.
A Hybrid Shuffled Frog Leaping–Shuffled Complex Evolution Algorithm for Photovoltaic Parameter Identification
(Multidisciplinary Digital Publishing Institute (MDPI), 2026) Faris, Hajer; Mahmood, Musaria Karim; Rai, Nawal; Al Dawsari, Saleh; Yahya, Khalid
Accurate identification of photovoltaic (PV) cell and module parameters remains a fundamental yet challenging task, particularly as model complexity increases from five to nine unknown parameters. In this study, the parameter extraction problem is rigorously formulated as a nonlinear optimization task and addressed using a novel hybrid metaheuristic algorithm, termed the Shuffled Frog Leaping–Shuffled Complex Evolution (SFL-SCE) method. The proposed approach synergistically integrates the population-based social learning mechanism of the Shuffled Frog Leaping Algorithm (SFL) with the robust global search and refinement capabilities of Shuffled Complex Evolution (SCE), thereby achieving an effective balance between exploration and exploitation. The SFL-SCE algorithm minimizes the root-mean-square error (RMSE) between measured and simulated current–voltage characteristics and is systematically applied to three widely used PV technologies: the RTC-France silicon solar cell, the polycrystalline Photowatt-PWP201 module, and the monocrystalline STM6-40/36 module. For each device, parameter identification is performed under one-diode, two-diode, and three-diode modelling frameworks, encompassing increasing levels of physical fidelity and computational complexity. Experimental data are employed throughout to ensure practical relevance and robustness. The performance of the proposed algorithm is comprehensively evaluated against its constituent algorithms (SFLA and SCE) as well as several state-of-the-art hybrid optimization techniques reported in the literature. Comparative results demonstrate that SFL-SCE consistently achieves superior accuracy, enhanced reliability, and faster convergence, as evidenced by lower minimum, mean, and maximum RMSE values, reduced standard deviation, and improved convergence behavior across all test cases. These findings confirm the effectiveness of the proposed hybridization strategy and establish SFL-SCE as a powerful and reliable tool for high-precision PV model parameter identification.