International Journal of Engineering
Volume:25 Issue: 4, Dec 2012

A major problem in an isolated DC/DC converters operating at high switching frequencies is the attendant switching losses in the semiconductor devices. This can be reduced by introducing either zero-voltage switching (ZVS) or zero-current switching (ZCS) of the semiconductor switches. This paper deals with the simulation, design, fabrication and experimental evaluation of a novel soft-switching full bridge transformer isolated step up/down dc-dc converter. The output voltage of the converter can be set to be higher or lower than the DC input voltage, depending on the selected duty ratio, representing width modulation. Galvanic isolation between the source and load is also achieved in this configuration. The configuration achieves soft switching of all the semiconductor devices in the power circuit, resulting in higher overall efficiency. The system was extended for covering closed loop operation, wherein for a range of values for the set voltage, the ability of the system to maintain the same output voltage the context of variable DC input voltage and/or variable load is verified

In this paper the Space Vector Control Scheme is implemented for a Wind Energy System using Three Level Impedance Source Inverter (ZSI). The wind energy system uses a Self Excited Induction generator (SEIG) which is the most emerging application in the field of Wind Energy Conversion System (WECS). The proposed system is modelled with a generator-side Diode Bridge Rectifier and a Stand-Alone side ZSI. This employs as a bridge between the generator and the stand-alone application. The model has been simulated through MATLAB/SIMULINK using the components for Diode Bridge, Impedance Network and Induction Motor Load and the SEIG is modelled analytically. The control strategy for the proposed topology is developed from Space Vector Modulation (SVM) and Z-source Network operation principles. The maximum power from the wind turbine generator and the power is transferred to the stand alone system is achieved by adjusting the shoot-through duty cycles of the Z-source network. The performances of the SEIG based WECS has been analysed and the results are tested.

Recognizing genes with distinctive expression levels can help in prevention, diagnosis and treatment of the diseases at the genomic level. In this paper, fast Global k-means (fast GKM) is developed for clustering the gene expression datasets. Fast GKM is a significant improvement of the k-means clustering method. It is an incremental clustering method which starts with one cluster. Iteratively new clusters are added. In each epoch all data points are checked for the k-th cluster center. Therefore a near global solution is obtained. In the gene expression clustering problem, since genes with significant differential expression levels, across the output class labels, are important for the accurate classification of samples, a fuzzy entropy measure is used to adjust the fast GKM for the gene expression data clustering application. To demonstrate the usefulness of the proposed method, three published microarray datasets are used: Leukemia, Prostate, and Colon. Classification results are found robust and accurate using three public classification K-NN, SVM, and Naïve Bayesian.

This paper considers the Tchebychev distance for a facility location problem with a probabilistic line barrier in the plane. In particular, we develop a mixed-integer nonlinear programming (MINLP) model for this problem that minimizes the total Tchebychev distance between a new facility and the existing facilities. A numerical example is solved to show the validity of the developed model. Because of difficulty in solving this problem while increasing the number of existing facilities, we propose and design an efficient meta-heuristic algorithm, namely differential evolution (DE), for the given problem. Finally, the associated results are compared with the exact solution and lower bound for the different-sized problems.

This paper provides a closed-form optimal solution to the multi-objective model of the fair allocation of gains obtained by cooperation among all players. The optimality of the proposed solution is first proved. Then, the properties of the proposed solution are investigated. At the end, a numerical example in inventory control environment is given to demonstrate the application and to compare the results with the ones of an existing method.

The combustion processes and emission formation in pre and main chambers of a Lister 8.1 IDI diesel are simulated with the Computational Fluid Dynamics (CFD) code. The model includes spray atomization, mixture formation and distribution and subsequently the combustion processes and emissions formation modeling are carried out with considering of flow configurations in two chambers. A part load (50% load) and a full load simulation of engine are carried out. Also, the amount of mass burning rate of fuel, temperature, heat losses and emissions formation in pre and main chamber are presented with more details. The simulation results, such as the mean in-cylinder pressure and exhaust emissions are compared with the measured values and show good agreement. This work demonstrates that multidimensional modeling can be used at complex chamber geometry to gain more insight into the flow field, combustion process and emissions formation. The simulation results show that the CFD combustion simulation tool works quite correctly for the predicting combustion process and emission formation in Lister 8.1 IDI diesel engine.

In this study, by using a multi-objective optimization technique, the optimal design points of forced convective heat transfer in tubular arrangements were predicted upon the size, pitch and geometric configurations of a tube bank. In this way, the main concern of the study is focused on calculating the most favorable geometric characters which may gain to a maximum heat exchange as well as a minimum pressure loss. Gathering the required wide range of set of design information, a numerical simulation of various configurations of the elliptic tubular arrangements was performed by using the FLUENT software. Afterwards, the group method of data handling (GMDH)-type neural networks and the evolutionary algorithms (EAs) were used to model the effects of design parameters; horizontal diameter of ellipse (a), vertical diameter of ellipse (b), transverse pitch (Sn), and longitudinal pitch (Sp), on pressure loss (ΔP) and, the temperature difference (ΔT) to achieve a meta- model through a prediction procedure by using evolved GMDH neural networks and finally, the model was used to gain the multi-objective Pareto-curves to depict the optimal design zones.

The use of cutting fluid in manufacturing industries has now become more problematic due to environmental pollution and health related problems of employees. Also the minimization of cutting fluid leads to the saving of lubricant cost and cleaning time of machine, tool and work-piece. The concept of minimum Quantity Lubrication (MQL) has come in to practice since a decade ago in order to overcome the disadvantages of flood cooling. This experimental investigation deals with the effects of MQL parameters during turning for the minimization of cutting zone temperature by considering surface roughness as constraint. The selected MQL parameters are varied through four levels. The maximum temperature values during machining in all the test conditions as per L16 orthogonal array are recorded. The best levels of selected MQL parameters for the minimization of cutting zone temperature were identified by using Taguchi’s Design of Experiments. A validation experiment is conducted with the identified best levels of parameters and the corresponding cutting zone temperature is recorded. This analysis further inter-relates the performances of Particle Swarm Optimization (PSO), Simulated Annealing Algorithm (SAA) and Differential Evolution (DE) for the minimization of cutting zone temperature. The results obtained from DE are comparatively better than that of the results obtained from other techniques.

Due to the development of science and technology, the computer has become a useful tool for supporting engineering activities in product design. Many computer aided tools such as CAD/CAM, product data management (PDM), product life cycle assessment (PLA), etc., have been popularly used in industry for reducing product development lead-time and increasing total product quality. However, the numerical model of product created on these tools can only represent nominal information of the product. It is not able to deal with various kinds of multi-physical effect during the whole product life cycle, especially in manufacturing/assembly and product usage stage. In addition, most of performance simulations of the product are carried out by using this nominal model. It can make the quality of the product designed not to meet fully the requirements of customers and users. Thus, we propose in this paper a global methodology that allows integrating the multi-physical effect throughout the product life cycle into the performance simulation.

This paper studies a new approach to analyze the large deflection behavior of prismatic and non-prismatic beams of non-homogenous material under combined load and multiple boundary conditions. The mathematical formulation has been derived which led to a set of six first-order ordinary differential equations. The geometric nonlinearity was solved numerically using the multiple shooting method combined with a quadratic programming technique. This solution technique was also performed to compare the result with existing solutions of less complex cases in the literature. The method may be applied to the design of compliant mechanisms, nonlinear springs or any related subject

The Hilbert-Huang transform (HHT) is a powerful method for nonlinear and non-stationary vibrations analysis. This approach consists of two basic parts of empirical mode decomposition (EMD) and Hilbert spectral analysis (HSA). To achieve the reliable results, Bedrosian and Nuttall theorems should be satisfied. Otherwise, the phase and amplitude functions are mixed together and consequently, the confidence of resultant frequencies are reduced. To prevent such an event, various methods as improved Hilbert-Huang transforms have been proposed. Yet, another method is introduced in this paper that has a high ability to identify the mechanical defects easily. According to this method, the signal is decomposed to its intrinsic mode functions (IMFs) and then each of the IMF is analyzed by fast Fourier transform (FFT). As the proposed method, which is called EMD-FFT, used, the mechanical defects of an electro motor have been detected in Kerman combined power plant. While, the classical FFT method is unable to detect all the defects and the HHT, due to the mixing phase and amplitude functions, does not provide accurate results.

Tidal energy is the most foreseeable form of renewable energy. Tidal energy can be harnessed by tidal barrage, tidal fence and tidal current technologies. Present efforts are focused on diffuser augmented tidal turbines that exploit the kinetic energy of the tidal currents. The power output by a tidal turbine is directly proportional to the cube of velocity of incoming fluid flow. Thus, even a minor increase in velocity considerably increases the power output. The diffuser helps accelerate the incoming fluid flow. Hence, the efficiency of the turbine is substantially increased by using a diffuser. Many research groups are investing considerable time and financial resources in this emerging domain. Limited results are available for diffuser augmented tidal turbines due to their emerging nature, large and costly research and development setup, startup cost and proprietary issues. The purpose of this paper is to study NACA 0015 airfoil for diffuser design for tidal current turbines. Numerical simulation of the diffuser is carried out to check the velocity and mass flow rate at throat. The drag, for various NACA 0015 diffuser models, is also calculated on the diffuser.