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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Computational Analysis of Time-Fractional Sobolev Equations Using A Hybrid Methodology With The Strang Splitting Technique
(WORLD SCIENTIFIC PUBL CO PTE LTD, 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE, 2026) Ahmad, Imtiaz; Jan, Rashid; Razak, Normy Norfiza Abdul; Alkhawar, Hisham Mohammad; Khan, Aziz; Abdeljawad, Thabet
The time-fractional Sobolev equations in two and three dimensions are investigated for numerical solutions using a relatively new computational methodology. The Caputo derivative is used for the time-fractional part of the problem, which is subsequently coupled with a splitting technique. For the spatial derivatives, a meshless collocation method based on Fibonacci and Lucas polynomials is utilized. These Lucas and Fibonacci polynomials are non-orthogonal and eliminate the need for interval transformations, facilitating the efficient approximation of higher-order derivatives for unknown functions. To validate the accuracy of the proposed method, various error norms are applied across both regular and irregular domains. Additionally, the results obtained using the proposed method are also compared with the exact solution and other numerical methods documented in recent studies.
Deconvoluting Grain and Grain Boundary Responses in Ag-Substituted Co-Ni-Zn-Cu Nanospinel Ferrites: A Comprehensive Impedance Spectroscopy Analysis
(SPRINGER HEIDELBERG, TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY, 2026) Mihmanlı, Ahmet; Almessiere, M. A.; Ünal, Bayram; Baykal, Abdülhadi; Korkmaz Demir, A.; Gondal, M. A.; Mojtahedi, E.; Shirsath, S. E.
Co0.25Ni0.25Zn0.25Cu0.25AgxFe2-xO4 (0.00 ≤ x ≤ 0.10) nanospinel ferrites (Ag → CoNiZnCu (x ≤ 0.10) NSFs) have been synthesized via a one-pot sol–gel method. XRD analysis was applied to prove the phase formation for each product. The morphologies were confirmed via SEM/TEM. This study introduced a detailed analysis of the electrical and dielectric properties of ion-substituted spinel ferrites. AC/DC conductivity and complex impedance spectroscopy were both used to understand how substitution of Ag+ affects the charge-transport properties of these (NSFs). The unsubstituted NSF (x 0.00) had very high resistivity (G range) and was found to be mainly affected by one process of relaxation that occurred due to high resistive grain boundaries. After substitution with Ag, the DC conductivity increased dramatically by several orders of magnitude, which correlated with a massive reduction in resistance of the grain boundaries. The overall electrostatic and dielectric properties of these NSFs changed drastically with Ag+ ion substitution as well; this change was due to the presence of the effect known as colossal permittivity ( > 104), which is explained by the Maxwell–Wagner theory of interfacial polarization. All types of analysis (using both the complementary impedance and electric modulus formalisms, including Nyquist plots) proved that by separating the different electrical responses from the grains and grain boundaries of the substituted NSFs, these materials are heterophase-natured. Therefore, it can be concluded that Ag + ion substitution is a very effective way to tune the properties of grain boundaries, resulting in a measurable difference in the total electrical/dielectric response of the NSFs.
Prediction of experimental condensation heat transfer characteristics of helically coiled tube-in-shell type heat exchangers by Artificial Neural Network and XGBoost
(SAGE PUBLICATIONS LTD, 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND, 2026) Önal, Büşra Selenay; Çolak, Andaç Batur; Dalkılıç, Ahmet Selim
This study addresses the inherent limitations of empirical correlations in accurately predicting condensation heat transfer and pressure drop within helical tubes, a crucial area given their superior thermal performance compared to straight configurations. A novel methodology was presented utilizing advanced machine learning algorithms, specifically ANN and XGBoost, to develop broad applicable and robust predictive models. A comprehensive dataset comprising 369 experimental entries from diverse literature sources, incorporating various pipe types and refrigerants, was structured with seven input and two output parameters. This dataset was judiciously split, with 70% allocated for model training and 30% for testing, randomly. Unlike other studies, the whole data of smooth straight, two types of smooth helical, dimpled helical, and microfinned helical tubes were used together to achieve the most difficult task of predicting the outputs. The results affirm the high predictive power of the developed models. The ANNs achieved a MSE of 0.00415 and a correlation coefficient of 0.99075, indicating strong agreement with actual values. Concurrently, XGBoost demonstrated exceptional performance, yielding a correlation coefficient value of 0.92 for the average condensation heat transfer coefficient and 0.86 for the average friction pressure drop, both with MSE values of 0.00. Further analysis revealed that enthalpy of vaporization and pipe length are the most influential parameters for heat transfer, while mass flux and enthalpy of vaporization predominantly govern friction pressure drop. This research provides highly accurate, data-driven tools that significantly advance the design and optimization of specific helical heat exchangers investigated in present work.
Reduction Analysis and Solitary Wave Solutions of the (2+1)-D Kadomtsev-Petviashvili-Benjamin-Bona-Mahony Equation
(SPRINGER BASEL AG, PICASSOPLATZ 4, BASEL 4052, SWITZERLAND, 2026) Raza, Muhammad Zubair; Bin Iqbal, Muhammad Abdaal; Yousaf, Muhammad; Sadaf, Maasoomah; Akram, Ghazala; Rehman, Basit; Khan, Aziz; Abdeljawad, Thabet
In this study, the Lie symmetries of the (2 + 1)-D Kadomtsev-Petviashvili-BenjaminBona-Mahony equation, which is considerable extension of the KP equation with applications in water waves, fluid dynamics, nonlinear optics, and mathematical physics were investigated. This study has also focused on the exact solutions of this model. We systematically identify the infinitesimal generators and obtain symmetry reductions that convert the equation into lower-dimensional forms using Lie group analysis. The findings shed information on the solution space of the equation and demonstrate how particular symmetries affect its structure. Moreover, exact solutions describing wave propagation behavior are made possible by the simplified equations. The exp(−(ω))-expansion approach yields innovative traveling wave solutions after considerable investigation. Solving the nonlinear evolution equations using this analytical method yields rational, hyperbolic, and trigonometric functions. The research reveals new solutions to the suggested problem using the extremely effective proposed method. The stability of the system is explored by computing stability gains using a linearization technique, revealing solution behavior. 3D, 2D, and contour graphs illustrate the dynamics of the obtained solutions, enhancing our knowledge to suggested problem.
A novel biogas combustion-heat recovery for cooling/power co-production system considering a modified sCO₂ cycle and a generator-absorber-exchanger (GAX) cycle: Machine learning-driven optimization and economic study
(PERGAMON-ELSEVIER SCIENCE LTD, THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND, 2026) Zhang, Zhongkui; Yao, Ling; Alkhattabi, Khalid; Alkhatib, Omar J.; Dutta, Ashit Kumar; Albalawi, Hind; Ali, H. Elhosiny; Mahariq, Ibrahim
Biogas-driven combined cooling and power (CCP) systems face the challenge of simultaneously optimizing thermodynamic performance and financial viability under nonlinear design and operational constraints. This study proposes a novel biogas combustion–heat recovery configuration for CCP generation, evaluated through an integrated thermodynamic–financial framework and optimized using machine learning (ML)-driven softcomputing techniques. The system integrates a biogas combustion unit, a gas turbine, a modified supercritical CO₂ cycle, and a generator–absorber–exchanger (GAX) cycle. Thermodynamic analyses based on the first and second laws of thermodynamics are employed, while sustainability, financial, and environmental indicators are incorporated into the assessment. A hybrid optimization approach, combining ML with the genetic algorithm optimizer, is implemented to accelerate convergence and explore trade-offs among net present value (NPV), total unit product cost (TUPC), and sustainability index (SI). The optimized configuration achieves an NPV of 13.03 M $, an SI of 1.765, and a TUPC of 26.5 $/GJ. Besides, the system demonstrates an energy efficiency of 62.75%, an exergy efficiency of 43.32%, and a payback period of 3.79 years, confirming technical robustness and economic viability. Overall, ML-driven soft computing enables resilient, investment-ready CCP strategies, offering a scalable plan that aligns biogas utilization with sustainability, efficiency, and competitiveness.