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Öğe Comparison of experimental thermal conductivity of water-based Al2O3–Cu hybrid nanofluid with theoretical models and artificial neural network output(Springer Science and Business Media B.V., 2024) Çolak, Andaç Batur; Bayrak, MustafaThe research aimed to experimentally test the thermal conductivity of five distinct Al2O3–Cu/water hybrid nanofluids. These nanofluids were generated at volumetric concentrations of 0.0125, 0.025, 0.05, 0.1, and 0.2. The measurements were conducted within a temperature range of 10–65 °C. The primary objective of this research is to tackle the insufficient empirical data on hybrid nanofluids and establish a dependable artificial neural network model for forecasting their thermal conductivity. A multilayer perceptron feed forward back propagation artificial neural network has been created using the acquired experimental thermal conductivity data. The experimental thermal conductivity data have been compared with four commonly used mathematical correlations and the outputs of an artificial neural network. The findings demonstrated that the constructed artificial neural network accurately forecasted the thermal conductivity of the Al2O3–Cu/water hybrid nanofluid, with an average deviation of just 0.4%. Nevertheless, Maxwell’s mathematical correlation proved to be the most accurate model in predicting the experimental findings, with an average error margin of just 0.08%. © Akadémiai Kiadó, Budapest, Hungary 2024.Öğe Path planning based on unmanned aerial vehicle performance with segmented cellular genetic algorithm(Gazi Universitesi, 2024) Gezer, Ahmet; Turan, Önder; Baklacıoğlu, TolgaAn important part of UAV technological development consists of improvements in the scope of path planning. Different choices can be made in path planning according to operational priorities, it may be preferred to reach the destination as fast as possible or to increase the airtime by compromising speed. For every speed and altitude that the UAV can fly; fuel data of cruise, climb and descent phases are used in the path planning algorithm. Thus, economical and airtime-maximizing paths could be produced on the basis of performance characteristics compatible with the kinematic constraints customized for the UAV. In this study, Cellular (cGA) and Segmented Cellular Genetic Algorithm (scGA) are proposed. The novel overprotective algorithm which has a fixed initial population and segmented chromosome structure achieves a high convergence speed to optimal solution and can generate paths which have 5.2 times higher fitness value on average compared with a conventional Genetic Algorithm (GA). It has been seen that scGA improves the initial population in terms of the best solutions 1.9 times and the general population 5.8 times better compared with GA.Öğe Comparison of MPPT algorithms under different environmental conditions for solar powered high altitude platforms(American Institute of Aeronautics and Astronautics Inc, AIAA, 2023) Küçükkör, Özge; Karakoç, Tahir Hikmet; Durak, UmutEnergy management is one of the most crucial issues for the solar powered High Altitude Platforms (HAPs). In UAVs with solar panels, sufficient power shall be provided to run the equipment on the vehicle and charge the battery during long flights. To provide this generated power to all these components in the most precise and efficient way, Maximum Power Point Tracking (MPPT) is an excellent option. There are some critical circumstances that affect the performance of the MPPT in the aircraft. Flight conditions affect the power that can be acquired from the photovoltaic (PV) panel on the UAV. In this article, irradiance values which are one of the input parameters of the PV panel are calculated by taking the altitude at 25 km and latitude at a 45° as flight conditions of the HAP as the reference. Under these conditions, the average irradiance values at 12:00 P.M. are calculated for each month of the year and efficiencies for different MPPT algorithms under these conditions has been discussed. © 2023, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.Öğe Analyzing the effects of different MPPT algorithms on lithium-ion battery degradation(American Institute of Aeronautics and Astronautics Inc, AIAA, 2023) Cinoğlu, Bahadır; Küçükkör, Özge; Karakoç, Hikmet; Durak, UmutFor a continuous flight, providing required power at the optimum level and maintaining battery health all the time is a critical issue that needs to be handle delicately. Since the efficiency of the maximum power is provided by the PV panel is directly related to the Maximum Power Point Tracking (MPPT) algorithms, each of these algorithms may also have an effect on the State of Charge (SOC) and State of Health (SOH) parameters of the battery. In this study, an MPPT model is simulated in order to determine how different algorithm’s efficiency effect the battery health.Öğe Editorial of VSI IGEC 2023(Elsevier Ltd, 2024) Karakoç, Tahir Hikmet; Yu, Zhibin; Ekici, Selçuk; Zhao, Jian[No abstract available]Öğe Neural network and thermodynamic optimization of magnetized hybrid nanofluid dissipative radiative convective flow with energy activation(Taylor & Francis Inc, 2024) Ferdows, M.; Ahmed, Muktadir; Bhuiyan, Miraj Ahmed; Beg, O. Anwar; Çolak, Andaç Batur; Leonard, H. J.This article, motivated by hybrid magnetic coating manufacturing developments, utilizes a neural network-based computational program to study the dynamics of hybrid magnetic nanofluids with entropy generation. A new physico-chemo-mathematical model has been presented to simulate the hybrid magnetic nano-coating flow along a stretching surface to a porous medium with viscous heating. A Rosseland flux model is used for radiation heat transfer and Darcy's model for the isotropic porous medium. The stretching sheet is porous, and wall suction or injection are possible. A robust neural network has been deployed to optimize the physical parameters controlling the transport characteristics of hybrid nanofluids. Specifically, two hybrid nanoparticle combinations are addressed, namely graphite oxide (GO)-molybdenum disulfide (MoS2) and copper (Cu)-silicon dioxide (SiO2), both with engine oil as the base fluid. The dimensional boundary layer model is transformed via suitable scaling variables from a partial differential system into a dimensionless non-linear coupled ordinary differential system. The transformed boundary value problem is solved numerically with the BVP4C subroutine in the symbolic software MATLAB, which achieves exceptional accuracy. Validation with previous simpler studies is conducted and a good correlation is obtained. The neural network optimization analysis incorporates Bayesian regularization as the training algorithm. The Bejan entropy generation minimization (EGM) analysis shows that with increasing radiation parameter R-d, both entropy generation rate and Bejan number are increased. Furthermore, an elevation in Brinkman number Br leads to an upsurge in entropy generation rate and a downtrend in the Bejan number. The numerical solution of the boundary value problem reveals that with an increment in nanoparticle solid volume fraction phi(2), magnetic parameter M, inverse permeability parameter epsilon, surface injection parameter (s<0), Eckert number Ec and radiation parameter R-d and with a decrement in suction parameter (s>0) and Prandtl number Pr, there is a strong enhancement in temperature magnitude and thermal boundary layer thickness. With greater nanoparticle solid volume fraction phi(2), magnetic parameter M, inverse permeability parameter epsilon, suction parameter s and a reduction in thermal buoyancy parameter lambda, strong flow deceleration is induced, and momentum boundary layer thickness is increased. The skin friction coefficient is substantially boosted with lower values of magnetic parameter M, inverse permeability parameter epsilon, suction parameter s and higher values of thermal buoyancy parameter lambda. There is a significant decrement also computed in Nusselt number with a greater radiation parameter R-d. The simulations provide a good benchmark for future extensions that may consider non-Newtonian behavior.Öğe A comprehensive review of state-of-art FishBAC - fishbone active camber morphing wing surfaces-: A promising morphing method(Emerald Group Publishing Ltd, 2024) Özbek, Emre; Ekici, Selçuk; Karakoç, Tahir HikmetPurposeThe current research conducts a comprehensive review on FishBAC (fishbone active camber morphing wing surfaces) for researchers and scientists and sheds light on challenges and opportunities of FishBAC development. Design/methodology/approachThis is a review article and this study reviews previous research on FishBAC.FindingsThe current FishBAC applications could be upgraded into more efficient designs in materials, design and mechanisms with more perspectives involved. Then, this promising branch of morphing surface design could be integrated with rotor blades, unmanned aerial vehicle wings, general aviation aircraft surfaces and so on.Research limitations/implications. This is a review article.Practical implicationsThe contributions of the study are summarized as follows: to provide an overview of FishBAC research; to compare various approaches and trends in FishBAC designs; to address the research gap in the roadmap for FishBAC design; and to discuss the challenges and opportunities of FishBAC development. Originality/valueTo the best of the authors' knowledge, this is the first review on a promising morphing method and an alternative for conventional flaps and ailerons.Öğe Analyses of structural and electrical properties of aluminium doped ZnO-NPs by experimental and mathematical approaches(Elsevier, 2022) Mahmood, Arslan; Munir, Tariq; Fakhar-E-Alam, M.; Atif, Muhammad; Shahzad, Kaleem; Alimgeer, K. S.; Gia, Tuan Nguyen; Ahmad, Hijaz; Ahmad, ShafiqPure and aluminium doped ZnO-NPs were played the central role in every field of life due to extraordi-nary physical, chemical and electrical properties. The main objective of the present research was used to enhance the electrical conductivity and reduce the electrical resistivity of aluminium doped zinc oxide-NPs. Synthesis of pure and aluminium doped zinc oxide-NPs (Zn1-xAlxO) at x = 0, 2.5, 5, 7.5 and 10 wt% was carried out by co-precipitation method. The XRD results depicted that hexagonal wurtzite crystal structure and crystallite size in the range of 13-25 nm were calculated by using Debye-Scherrer's equa-tion. Likewise, the non-uniform, irregular and pore like surface morphology of the prepared NPs was evi-dent from SEM micrographs. Various functional groups (CH, CO, OH and ZnO) attached to the surface of aluminium doped zinc oxide-NPs were identified by FTIR analysis. The UV-VIS spectra also depicted a shift towards the blue region of the visible spectrum. In terms of electrical properties with the help of experimental and mathematical analyses of aluminum doped zinc oxide-NPs exhibited higher conductiv-ity (1.34 x 10(-6) to 1.43 x 10(-3) S/cm) and lower resistivity (5.46 x 10(5) to 6.99 x 10(2) Omega-cm). The present results suggest that the aluminum doped zinc oxide-NPs have been improved the structural and electrical properties which make it a good candidate for optoelectronic devices. (c) 2021 The Authors. Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
