Hakan F. Oztop

Bio: Hakan F. Oztop is an academic researcher from Fırat University. The author has contributed to research in topics: Heat transfer & Nanofluid. The author has an hindex of 61, co-authored 405 publications receiving 11187 citations. Previous affiliations of Hakan F. Oztop include King Abdulaziz University & University College of Engineering.

A review of melting and freezing processes of PCM/nano-PCM and their application in energy storage

Abstract: Phase change materials (PCMs) are capable of storing energy as latent energy by changing the phase and provide the stored energy when they are returned to their initial phase at a desired time. Due to the varying melting temperature of these materials, their application in air conditions of buildings, as well as the provision of hygienic hot water has received much attention, recently. This paper first provides a detailed illustration of phase change materials and their working principle, different types, and properties. Then a characteristic example of PCMs in solar energy storage and the design of PCMs are reviewed and analyzed. Next, this paper focuses on the heat transfer, melting and freezing processes of PCM/nano-PCMS including different models and experimental research on the natural convection and thermal energy storage. Finally, some challenges and suggestions are presented following the conclusion of this article. It is found that these materials generally improve system efficiency. Without using mechanical equipment, these materials are naturally adapted to the temperature fluctuations of the environment, leading to a reduction in energy consumption and subsequently energy management.

Corrugated conductive partition effects on MHD free convection of CNT-water nanofluid in a cavity

Abstract: In the present study, free convection in a cavity with a corrugated partition which have different fluids on different parts of the partition was numerically examined. In one of the domains carbon nanotube (CNT)-water nanofluid with an inclined uniform magnetic field is considered. A triangular wave form of conductive corrugated partition is used. The numerical simulation was performed with Galerkin weighted residual finite element method. Various values of pertinent parameters of current thermal configuration such as Rayleigh number (between 104 and 106), Hartmann number (between 0 and 50), magnetic inclination angle (between 0° and 90°), solid particle volume fraction (between 0 and 0.03), number of triangular waves (between 1 and 40), height of triangular waves (between 0.01H and 0.2H) and thermal conductivity ratio (between 0.1 and 100) and their influence on the hydro-thermal behavior were examined. It was observed that significant enhancements in the Nusselt number is obtained with CNTs. The average heat transfer decreases for higher values of Hartmann number but slightly varies as the value of magnetic inclination angle changes. As the number and height of the triangular waves increase, the average heat transfer reduce which are 32 % and 27 % for the highest values of number and height of triangular waves both for water and nanofluid. For forecasting the average heat transfer coefficient of the current thermal system, a novel method based on Proper Orthogonal Decomposition (POD) and Adaptive-Network-Based Fuzzy Inference System (ANFIS) is used which yields highly accurate results that are computationally inexpensive.

MHD mixed convection and entropy generation of nanofluid filled lid driven cavity under the influence of inclined magnetic fields imposed to its upper and lower diagonal triangular domains

Abstract: In this study, mixed convection of CuO–water nanofluid filled lid driven cavity having its upper and lower triangular domains under the influence of inclined magnetic fields is numerically investigated. The top horizontal wall of the cavity is moving with constant speed of u w with + x direction while no-slip boundary conditions are imposed on the other walls of the cavity. The top wall of the cavity is maintained at constant cold temperature of T c while the bottom wall is at hot temperature of T h and on the other walls of the cavity are assumed to be adiabatic. The governing equations are solved by using Galerkin weighted residual finite element formulation. Entropy generation is produced by using formulation and integrated with calculated velocities and temperatures. The numerical investigation is performed for a range of parameters: Richardson number (between 0.01 and 100), Hartmann number (between 0 and 50), inclination angle of magnetic field (between 0° and 90°) and solid volume fraction of the nanofluid (between 0 and 0.05). Different combinations of Hartmann numbers and inclination angles of the magnetic fields are imposed in the upper and lower triangular domains of the square cavity. It is observed that the local and averaged heat transfer deteriorates when the Richardson number, Hartmann number of the triangular domains increase. When the Hartmann number and magnetic angle of the upper triangle are increased, more deterioration of the averaged transfer is obtained when compared to lower triangular domain. Local and averaged heat transfer increase as the solid volume fraction of the nanoparticles increases and adding nanoparticles is more effective for the local enhancement of the heat transfer when the heat transfer rate is high and convection is not damped with lowering the Hartmann number. Second law analysis of the system for different combinations of flow parameters is also performed.

MHD natural convection and entropy generation of ferrofluid in an open trapezoidal cavity partially filled with a porous medium

Abstract: In this study, natural convection combined with entropy generation of Fe3O4–water nanofluid within an open trapezoidal cavity filled with a porous layer and a ferrofluid layer under the effect of uniform inclined magnetic field is numerically analyzed. Porous layer is located on the bottom wall and heated from the left inclined wall. Bottom wall, right and left tilted walls of the cavity are adiabatic except for the active part along the left inclined wall where hot temperature Th is constant, upper open boundary is kept at constant cold temperature Tc. Governing equations with corresponding boundary conditions formulated in dimensionless stream function and vorticity using Brinkman-extended Darcy model for porous layer have been solved numerically using finite difference method. Numerical analysis has been carried out for a wide range of Hartmann number, magnetic field inclination angle, height of the porous layer and nanoparticles volume fraction. It has been found that an increase in Hartmann number leads to a growth of oscillations amplitude for average Nusselt number and average entropy generation. At the same time inclination angle α = π/2 illustrates unstable behavior of heat and fluid flow.

Conjugate natural convection in a cavity with a conductive partition and filled with different nanofluids on different sides of the partition

Abstract: In this study, conjugate natural convection–conduction heat transfer in an inclined partitioned cavity filled with different nanofluids (Al 2 O 3 –water and CuO–water) on different sides of the partition is numerically investigated by using finite element method. The left and right vertical walls of the square enclosure are maintained at constant hot and cold temperatures while other wall enclosures are assumed adiabatic. Different combinations of solid nanoparticle volume fractions are imposed in the left and right half cavities. Numerical simulations are performed for different values of Grashof numbers (between 10 3 and 10 6 ), inclination angles of the cavity (between 0 o and 275 o ), partition locations (between 0.15 and 0.75), thermal conductivity ratio (between 0.01 and 10) and solid volume fraction of the nanofluids of the two cavities (between 0 and 0.04). The averaged heat transfer enhances with Grashof number and solid particle volume fraction. It is also observed that adding nanoparticles with low thermal conductivity on the right cavity is effective for the heat transfer enhancement as compared to adding nanoparticles with high thermal conductivity. As the thermal conductivity ratio of the partition increases, the averaged heat transfer rate enhances and the highest value of the thermal conductivity ratio of 10, 14.11% of averaged heat transfer enhancement is obtained when both cavities are filled with nanofluids at the highest value of nanoparticle volume fractions.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Introduction to the Finite Element Method

Abstract: This chapter introduces the finite element method (FEM) as a tool for solution of classical electromagnetic problems. Although we discuss the main points in the application of the finite element method to electromagnetic design, including formulation and implementation, those who seek deeper understanding of the finite element method should consult some of the works listed in the bibliography section.

Numerical Heat Transfer And Fluid Flow

Abstract: Thank you for reading numerical heat transfer and fluid flow. Maybe you have knowledge that, people have search numerous times for their favorite books like this numerical heat transfer and fluid flow, but end up in infectious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they cope with some malicious virus inside their computer. numerical heat transfer and fluid flow is available in our digital library an online access to it is set as public so you can get it instantly. Our books collection spans in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the numerical heat transfer and fluid flow is universally compatible with any devices to read.

A Benchmark Study on the Thermal Conductivity of Nanofluids

Abstract: This article reports on the International Nanofluid Property Benchmark Exercise, or INPBE, in which the thermal conductivity of identical samples of colloidally stable dispersions of nanoparticles or “nanofluids,” was measured by over 30 organizations worldwide, using a variety of experimental approaches, including the transient hot wire method, steady-state methods, and optical methods. The nanofluids tested in the exercise were comprised of aqueous and nonaqueous basefluids, metal and metal oxide particles, near-spherical and elongated particles, at low and high particle concentrations. The data analysis reveals that the data from most organizations lie within a relatively narrow band (±10% or less) about the sample average with only few outliers. The thermal conductivity of the nanofluids was found to increase with particle concentration and aspect ratio, as expected from classical theory. There are (small) systematic differences in the absolute values of the nanofluid thermal conductivity among the various experimental approaches; however, such differences tend to disappear when the data are normalized to the measured thermal conductivity of the basefluid. The effective medium theory developed for dispersed particles by Maxwell in 1881 and recently generalized by Nan et al. [J. Appl. Phys. 81, 6692 (1997)], was found to be in good agreement with the experimental data, suggesting that no anomalous enhancement of thermal conductivity was achieved in the nanofluids tested in this exercise.

Convection Heat Transfer

Abstract: Geometry: shape, size, aspect ratio and orientation Flow Type: forced, natural, laminar, turbulent, internal, external Boundary: isothermal (Tw = constant) or isoflux (q̇w = constant) Fluid Type: viscous oil, water, gases or liquid metals Properties: all properties determined at film temperature Tf = (Tw + T∞)/2 Note: ρ and ν ∝ 1/Patm ⇒ see Q12-40 density: ρ ((kg/m) specific heat: Cp (J/kg ·K) dynamic viscosity: μ, (N · s/m) kinematic viscosity: ν ≡ μ/ρ (m/s) thermal conductivity: k, (W/m ·K) thermal diffusivity: α, ≡ k/(ρ · Cp) (m/s) Prandtl number: Pr, ≡ ν/α (−−) volumetric compressibility: β, (1/K)