Experimental Thermal Performance Characterization of Flat Grooved Heat Pipes

ABSTRACT

The thermal characterization of aluminum flat grooved heat pipes is performed experimentally for different groove dimensions. Three heat pipes with groove widths of 0.2?mm, 0.4?mm, and 1.5?mm are used in the experiments. The effect of the amount of the working fluid is extensively studied for each groove width. The results reveal that, although all three succeed in dissipating the heat input through the phase change of the working fluid by continuous evaporation and condensation, the effectiveness of the heat transfer increases with reduced groove width. Furthermore, it is observed that there exists an optimum operating point, where the temperature difference between the heating and cooling sections is at a minimum, and the magnitude of this temperature difference is a strong function of the groove width. To the best of the authors’ knowledge, the combined effects of groove dimensions and the amount of the working fluid, from fully flooded to dry, is reported for the first time for aluminum flat grooved heat pipes.     

Adoption of Technology-Mediated Distance Education: A longitudinal analysis

We investigated the factors that facilitated the fast adoption and utilization of Technology-Mediated Distance Education (TMDE) among higher education institutions. Our analysis was based on a rich data set on the utilization of TMDE between the 1997–1998 and 2000–2001 academic years. The analysis showed that size, public/private status, and location significantly predicted its actual adoption. Being in an urban location negatively affected enrollment in the courses at the undergraduate but not at the graduate level. While the intent to adopt TMDE correlated significantly with actual adoption, many schools that were not interested in TMDE in 1997–1998 adopted it by 2000–2001. Interestingly, late adopters utilized certain technologies as frequently as early adopters, such as synchronous Internet-based instruction and the use of CD-ROMs.     

Assessment of the energy and exergy utilization efficiencies in the Turkish agricultural sector

This study deals with evaluating the energy and exergy utilization efficiencies in the Turkish agricultural sector over a 12‐year period from 1990 to 2001. In the energy and exergy analyses, two main energy sources, namely fuels and electricity, are taken into consideration, while the sectoral energy and exergy efficiencies are compared for this period. These main energy sources include diesel for tractors and other vehicles, and electricity for pumps. Overall energy utilization efficiencies are obtained to vary between 29.1 and 41.1%, while overall exergy utilization efficiencies are found to range from 27.9 to 37.4% in the analysed years, respectively. It may be concluded that the present technique proposed here may be used as a useful tool in analysing and evaluating the energy and exergy utilization efficiencies, identifying energy efficiency and/or energy conservation opportunities and dictating the energy strategies of countries. Copyright © 2005 John Wiley & Sons, Ltd.     

Forecasting based on sectoral energy consumption of GHGs in Turkey and mitigation policies

Recently, global warming and its effects have become one of the most important themes in the world. Under the Kyoto Protocol, the EU has agreed to an 8% reduction in its greenhouse gas (GHG) emissions by 2008–2012. The GHG emissions (total GHG, CO₂, CO, SO₂, NO₂, E (emissions of non-methane volatile organic compounds)) covered by the Protocol are weighted by their global warming potentials (GWPs) and aggregated to give total emissions in CO₂ equivalents. The main subject in this study is to obtain equations by the artificial neural network (ANN) approach to predict the GHGs of Turkey using sectoral energy consumption. The equations obtained are used to determine the future level of the GHG and to take measures to control the share of sectors in total emission. According to ANN results, the maximum mean absolute percentage error (MAPE) was found as 0.147151, 0.066716, 0.181901, 0.105146, 0.124684, and 0.158157 for GHG, SO₂, NO₂, CO, E, and CO₂, respectively, for the training data with Levenberg–Marquardt (LM) algorithm by 8 neurons. R² values are obtained very close to 1. Also, this study proposes mitigation policies for GHGs.     

Medical image segmentation with transform and moment based features and incremental supervised neural network

In this study, a novel incremental supervised neural network (ISNN) is proposed for the segmentation of medical images. Performance of the ISNN is investigated for tissue segmentation in medical images obtained from various imaging modalities. Two feature extraction methods based on transform and moments are comparatively investigated to segment the tissues in medical images. Two-dimensional (2D) continuous wavelet transform (CWT) and the moments of the gray-level histogram (MGH) are computed in order to form the feature vectors of ultrasound (US) bladder and phantom images, X-ray computerized tomography (CT) and magnetic resonance (MR) head images. In the 2D-CWT method, feature vectors are formed by the intensity of one pixel of each wavelet-plane of different energy bands. The MGH represents the tissues within the sub-windows by using the spatial variation of image intensities. In this study, the ISNN and Grow and Learn (GAL) network are employed for the segmentation task. It is observed that the ISNN has significantly eliminated the disadvantages of the GAL network in the segmentation of the medical images.     

Aeroelastic tailoring using lamination parameters

The aim of the present work is to passively reduce the induced drag of the rear wing of a Formula One car at high velocity through aeroelastic tailoring. The angle-of-attack of the rear wing is fixed and is determined by the required downforce needed to get around a turn. As a result, at higher velocity, the amount of downforce and related induced drag increases. The maximum speed on a straight part is thus reduced due to the increase in induced drag. A fibre reinforced composite torsion box with extension-shear coupled upper and lower skins is used leading to bending-torsion coupling. Three-dimensional static aeroelastic analysis is performed loosely coupling the Finite Element code Nastran and the Computational Fluid Dynamics panel code VSAERO using ModelCenter. A wing representative of Formula One rear wings is optimised for minimum induced drag using a response surface methodology. Results indicate that a substantial induced drag reduction is achievable while maintaining the desired downforce during low speed turns.     

Vic-dioximes: A new class of sensitive materials for chemical gas sensing

Vic-dioximes, a class of organic chemical compounds, are proposed and characterized for the first time as sensitive materials for volatile organic compound sensing with sorption based chemical gas sensors. Their peculiar sensing properties described in this work originate in the oxime functional group which is a powerful H bond donor interacting strongly but reversibly with H bond acceptors. These specific interactions result in a high preferential enrichment of analyte molecules with H bonding acceptor capabilities in the sensitive material. Accordingly, sensitivity and selectivity for these compounds of vic-dioxime based sensors are high. The advantageous sensing properties are demonstrated in this work with quartz crystal microbalance sensors using 11 selected volatile organic compounds and a set of vic-dioximes varied in their substituents. Vic-dioximes with short alkylthiol substituents were found highly sensitive to such H bond acceptors as organic amines, alcohols, and esters with partition coefficients up to 26,000. At the same time they showed low affinity for aromatic compounds and chlorocarbons. Vic-dioximes are considered powerful sensing materials and interesting for practical use in chemical gas sensor arrays.     

Routing table updating by using intelligent agents

In a network, one of the important problems is making an efficient routing decision. Many studies have been carried out on making a decision and several routing algorithms have been developed. In a network environment, every node has a routing table and these routing tables are used for making routing decisions. Nowadays, intelligent agents are used to make routing decisions. Intelligent agents have been inspired by social insects such as ants. One of the intelligent agent types is self a cloning ant. In this study, a self cloning ant colony approach is used. Self cloning ants are a new synthetic ant type. This ant assesses the situation and multiplies through cloning or destroying itself. It is done by making a routing decision and finding the optimal path. This study explains routing table updating by using the self cloning ant colony approach. In a real net, this approach has been used and routing tables have been created and updated for every node.