Celal Bayar University

About: Celal Bayar University is a education organization based out in Magnesia ad Sipylum, Turkey. It is known for research contribution in the topics: Population & Heat transfer. The organization has 2960 authors who have published 6024 publications receiving 100646 citations.

Measurements of indirect CP asymmetries in D0 → K- K+ and D0 → π- π+ decays.

Abstract: A study of indirect CP violation in D0 mesons through the determination of the parameter AΓ is presented using a data sample of pp collisions, corresponding to an integrated luminosity of 1.0 fb−1, collected with the LHCb detector and recorded at the center-of-mass energy of 7 TeV at the LHC. The parameter AΓ is the asymmetry of the effective lifetimes measured in decays of D0 and D¯​0 mesons to the CP eigenstates K−K+ and π−π+. Fits to the data sample yield AΓ(KK)=(−0.35±0.62±0.12)×10−3 and AΓ(ππ)=(0.33±1.06±0.14)×10−3, where the first uncertainties are statistical and the second systematic. The results represent the world’s best measurements of these quantities. They show no difference in AΓ between the two final states and no indication of CP violation.

Entropy generation for pipe flow of a third grade fluid with Vogel model viscosity

Abstract: The flow of fluid–solid mixtures in a pipe can be treated as non-Newtonian fluids of third grade. Depending upon the fluid viscosity, entropy generation in the flow system varies. In the present study, flow of third grade fluid in a pipe is considered. The Vogel model is introduced to account for the temperature-dependent viscosity. Entropy generation due to fluid friction and heat transfer in the flow system is formulated. The influence of viscosity parameters A and B on the entropy generation number is investigated. It is found that increasing viscosity parameter A reduces the entropy generation number and opposite is true for increasing viscosity parameter B .

Numerical Study and POD-Based Prediction of Natural Convection in a Ferrofluids–Filled Triangular Cavity with Generalized Neural Networks

Abstract: The effects of a magnetic dipole source on the natural convection of ferrofluids in a triangular cavity are studied. A partial heater is added to the left vertical wall of the cavity while the right vertical wall is kept at the constant temperature. A magnetic dipole source is placed outside the cavity close to the heater. The governing equations of a coupled multi-physics system are solved with a commercial solver using the finite element method. Computations are performed for different ranges of parameters: Rayleigh number (104 ≤ Ra ≤ 106 ), strength of the magnetic dipole (0 ≤ γ ≤ 8), horizontal and vertical location of the magnetic dipole (−2.5H ≤ a ≤ −0.5H, 0.2H ≤ b ≤ 0.8H). It is observed that the interaction between natural convection and ferrofluid convection under the influence of magnetic dipole affects the flow and thermal field in such the triangular enclosure. The external magnetic field acts in such a way to decrease local heat transfer in some locations and increase it in others for certai...

Estimation of the Mixed Convection Heat Transfer of a Rotating Cylinder in a Vented Cavity Subjected to Nanofluid by Using Generalized Neural Networks

Abstract: In this study, numerical investigation of mixed convection in a square cavity with ventilation ports filled with nanofluids in the presence of an adiabatic rotating cylinder is conducted. The governing equations are solved with a commercial finite element code (COMSOL). The effects of Grashof number (Gr = 103 to Gr = 105), Reynolds number (Re = 50 to Re = 300), nanoparticle volume fraction (φ = 0 to φ = 0.05), and cylinder rotation angle (Ω = −5 to Ω = 5) on the flow and thermal fields are numerically studied for a range of different parameter sets. The generalized neural network (GRNN) is used to predict the thermal performance of the system. It is observed that the heat transfer increases almost linearly with increasing the nanoparticle volume fraction. The increasing rotation angle in the clockwise direction generally enhances the heat transfer. Moreover, the validation results with artificial neural networks show that generalized neural nets show better performance compared to radial basis and feed-fo...