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Öğe Effect of turbulence modeling on hydrodynamics of a turbulent contact absorber(Elsevier Science Sa, 2020) Ullah, Atta; Amanat, Amna; Imran, Muhammad; Gillani, Syed Shah Jehan; Kılıç, Mustafa; Khan, AfrasyabA computational fluid dynamics (CFD) study is conducted to find a suitable two equation turbulence model for accurate prediction of hydrodynamics of an inhouse turbulence contact absorber (TCA) at high gas and liquid velocities. Based on the multi-fluid Eulerian approach, hydrodynamics of TCA is simulated by incorporating three turbulence models i.e. standard k-epsilon model, RNG k-epsilon model and SST k-omega model in ANSYS Fluent (R). The solid phase stresses were closed by using the kinetic theory of granular flows (KTGF). TCA hydrodynamics parameters; expanded bed height and bed pressure drop were used to compare the results of this study with experimental data and also with earlier numerical study published with laminar viscous model. It was found that the RNG k-epsilon model predicted the bed height and pressure drop better than its counterparts. To accurately find the effects of secondary phase turbulence, two RNG k-epsilon model options i-e. per phase and dispersed were also evaluated. The results show that the per phase option of RNG k-epsilon model produced the expanded bed height and pressure drop in close agreement with available experimental data at similar operating conditions.Öğe Evaluation of Thermal Hydraulic Characteristics of a Two Phase Superhydrophobic Microfluidic Device using Volume of Fluid Method(Taylor & Francis Inc, 2022) Aziz, Umair; Nadeem, Muhammad; Xin, Feng; Kılıç, Mustafa; Ullah, AttaThe slug flow in a superhydrophobic microchannel with a T-junction was studied computationally. The continuous phase passed through the main channel while the dispersed phase(1) through the side channel. The volume of fluid (VOF) was employed to track the interface to study the dynamics of slug flow. First, a mesh independence study was carried out to select the optimum mesh by comparison of CFD results with experimental data. The developed model of microchannel was used to study slug flow heat transfer enhancement for micro cooling of electronic chips. The constant heat flux was applied on the walls of the microchannels and the axial wall temperature profile was noted. Upon quantification of heat transfer augmentation in terms of wall temperature reduction, Nusselt number and heat transfer coefficient enhancement, it was noted that slug flow performed much better vis-a-vis single-phase flows at similar conditions.Öğe Numerical investigation of effect of different parameter on heat transfer for a crossflow heat exchanger by using nanofluids(Yildiz Technical University, 2021) Kılıç, Mustafa; Ullah, AttaThe heat loads on electronic systems of an unmanned air vehicle are a significant problem. So enhancing heat transfer is a critical key to solve these thermal problems. This study is focused on increasing heat transfer rate in a crossflow heat exchanger by using nanofluids numerically. Effects of different Reynolds number of hot fluid (Re = 6000, 8000, 10000, 12000), different inlet velocity (Vair,inlet = 30, 45, 60, 90 m/s) of cooling fluid, temperature of cooling air at different altitude (Tair,inlet = 15, 10, 4, ?17°C) and different types of nanofluids (Cu-H2O, CuO-H2O, TiO2-H2O, H2O) on heat transfer were studied numerically. Realizable k-? turbulence model of ANSYS FLUENT computational fluid dynamics code was used for numerical analysis. It was obtained that increasing Reynolds number from Re = 6000 to 12000 causes an increase of 44.65% on average Nusselt Number. Increasing inlet velocity of cooling air from 30 m/s to 90 m/s causes an increase of 6.96% on average Nusselt number. Increasing or decreasing air inlet temperature at different attitude does not cause any significant change on average Nusselt number. Using Cu-H2O nanofluid, which shows the best performance, causes an increase of 6.63% on average Nusselt number according H2O. Numerical results were also compared with experimental results at literature. It was obtained that numerical model can represent experimental results in a good level. © 2021. Yıldız Technical University. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).Öğe Preservation of fruits through drying-A comprehensive review of experiments and modeling approaches(Wiley, 2024) Kılıç, Mustafa; Sahin, Mahir; Hassan, Aitazaz; Ullah, AttaA significant part of the world's population still has problems in accessing food. The growing world population will exacerbate this issue in the future. Innovative studies conducted in this field play a crucial role in addressing the issue of drying and storage of foods. Atmospheric drying methods, such as rotary, tunnel, conveyor, cabinet, tower, and kiln dryers, offer advantages in relation to high production capacities, cost-effective initial setup, and economical operating conditions. However, concurrently, the weaknesses of these methods arise from factors such as drying duration, uneven moisture content, and space requirements. The solar dryer method is especially effective in dehydrating agricultural products, offering an energy-saving advantage compared to other methods. However, it is important to note that this approach, which involves exposing crops to direct sunlight, comes with several drawbacks affecting both food quality and health. In cases where the quality of highly valued foodstuffs is crucial, subatmospheric drying methods like vacuum, freeze, and microwave freeze are typically preferred. However, the primary drawback of this approach lies in its high operating costs, particularly in terms of energy consumption. Artificial neural networks (ANNs) can be used for predictive modeling, helping to forecast drying behavior and optimize process parameters in food drying applications especially nonlinear connections among variables. ANNs are adept at managing nonlinearities, offering a more precise depiction of the intricate interactions within food drying systems. This review examines articles from the last 5 years in the literature, synthesizing research conducted in food drying. The findings indicate a predominant interest among researchers in methodologies with lower environmental impact, prompting increased attention to studies addressing this aspect. There is a notable emphasis on the frequent exploration of energy-efficient systems. The ongoing research focuses on the development of methods utilizing ultrasonic, infrared radiation, and electrohydrodynamic techniques to achieve more effective, shorter-duration, energy-efficient drying processes with enhanced control over the final product. This study addresses global food access challenges, highlighting the impact of population growth. It explores atmospheric and subatmospheric drying methods, emphasizing the effectiveness of solar drying for energy savings. The review delves into artificial neural networks for predictive modeling in food drying, revealing a growing interest in environmentally friendly and energy-efficient drying techniques, including ultrasonic, infrared radiation, and electrohydrodynamic methods for improved control and shorter durations. imageÖğe Reliable prediction of thermophysical properties of nanofluids for enhanced heat transfer in process industry: a perspective on bridging the gap between experiments, CFD and machine learning(Springer, 2023) Ullah, Atta; Kılıç, Mustafa; Habib, Ghulam; Sahin, Mahir; Khalid, Rehan Zubair; Sanaullah, KhairuddinIn recent years, traditional fluids are frequently being replaced by efficient heat transfer fluids showing physical and thermal stability. One such category of fluids is called nanofluids, in which solid nanoparticles (metals or their oxides, nitrides and so on) are suspended in a base fluid resulting in enhanced heat transfer characteristics. These nanofluids are increasingly used in low to medium temperature applications toward intensification of process and power plants by reducing the overall size and heat losses. However, as compared to a pure fluid, prediction of thermal and physical properties of nanofluids is a challenge due to unavailability of a general model. These thermal and hydraulic characteristics are strongly dependent upon multiple factor including particle size, particle volume concentration, particle composition, particle shape, temperature, base fluid material, pH and shear rate. Keeping these challenges in mind and availability of modeling tools, we first summarize and comment on popular correlations available to predict thermal and physical properties of nanofluids. Then, a general approach for carrying out reliable computational fluid dynamics (CFD) simulations is presented. The limitation of a general correlation of physical properties for input into CFD code can be overcome by use of machine learning (ML) tools such as artificial neural networks (ANN) taking advantage of the huge databank of physical properties of nanofluids. The use of ML to compliment CFD for accurate and reliable simulation of systems employing nanofluids as working fluids is highlighted at the end as potential emerging areas of research. [GRAPHICS] .
