DFIG Machine Design for Maximizing Power Output Based on Surrogate Optimization Algorithm

This paper presents a surrogate-model based optimization of a doubly-fed induction generator (DFIG) machine winding design for maximizing power yield. Based on site-specific wind profile data and the machine’s previous operational performance, the DFIG’s stator and rotor windings are optimized to match the maximum efficiency with operating conditions for rewinding purposes. The particle swarm optimization (PSO)-based surrogate optimization techniques are used in conjunction with the finite element method (FEM) to optimize the machine design utilizing the limited available information for the site-specific wind profile and generator operating conditions. A response surface method in the surrogate model is developed to formulate the design objectives and constraints. Besides, the machine tests and efficiency calculations follow IEEE standard 112-B. Numerical and experimental results validate the effectiveness of the proposed technologies.

Transition Between Open and Closed Wake in 3D Linear Aerospike Nozzles

Linear aerospike nozzles are envisaged as a possible device able to improve launcher engine performance. One of the most interesting properties of these nozzles is the possibility of a good integration with the vehicle. Tb improve the knowledge of the flow-field and performance of aerospike nozzles, they are studied numerically, with particular attention to the differences between the basic two-dimensional nozzle, usually considered in the design phase, and the more realistic three-dimensional nozzle. The study considers different plug lengths and ambient pressures to assess the role of the linear plug side truncation on the base pressure behavior. Numerical tests are carried out at supersonic flight Mach number. Copyright © 2005 by M. Geron and R. Paciorri.F. Nasuti, F. Sabetta, E. Martelli.

Conceptual Design and Workspace Analysis of an Exechon-inspired Parallel Kinematic Machine

Inspired by the commercial application of the Exechon machine, this paper proposed a novel parallel kinematic machine (PKM) named Exe-Variant. By exchanging the sequence of kinematic pairs in each limb of the Exechon machine, the Exe-Variant PKM claims an arrangement of 2UPR/1SPR topology and consists of two identical UPR limbs and one SPR limb. The inverse kinematics of the 2UPR/1SPR parallel mechanism was firstly analyzed based on which a conceptual design of the Exe-Variant was carried out. Then an algorithm of reachable workspace searching for the Exe-Variant and the Exchon was proposed. Finally, the workspaces of two example systems of the Exechon and the Exe-Variant with approximate dimensions were numerically simulated and compared. The comparison shows that the Exe-Variant possesses a competitive workspace with the Exechon machine, indicating it can be used as a promising reconfigurable module in a hybrid 5-DOF machine tool system.

Kinematic Analysis and Dimensional Synthesis of Exechon Parallel Kinematic Machine for Large Volume Machining

A parallel kinematic machine (PKM) topology can only give its best performance when its geometrical parameters are optimized. In this paper, dimensional synthesis of a newly developed PKM is presented for the first time. An optimization method is developed with the objective to maximize both workspace volume and global dexterity of the PKM. Results show that the method can effectively identify design parameter changes under different weighted objectives. The PKM with optimized dimensions has a large workspace to footprint ratio and a large well-conditioned workspace, hence justifies its suitability for large volume machining.

Coordinated Design of Phase Measurement Unit and Power System Stabilisers in Large Scale Power System

This paper presents a case-study of a PMU application with PSS support in a real large scale Chinese power system to suppress inter-area oscillations. The paper uses PMU measured feedback signals from a PSS input signal for dynamic torque analysis (DTA). In the paper, a mathematical model of multi-machine power system is described, followed by formation of the residue and DTA indices. Simulations of the model are used with a large-scale power system model to demonstrate the role of PSS and the equivalence of DTA residue indices.

Fracture strength of machined ceramic crowns as a function of tooth preparation design and the elastic modulus of the cement

Objectives: To determine, by means of static fracture testing the effect of the tooth preparation design and the elastic modulus of the cement on the structural integrity of the cemented machined ceramic crown-tooth complex. 
Methods: Human maxillary extracted premolar teeth were prepared for all-ceramic crowns using two preparation designs; a standard preparation in accordance with established protocols and a novel design with a flat occlusal design. All-ceramic feldspathic (Vita MK II) crowns were milled for all the preparations using a CAD/CAM system (CEREC-3). The machined all-ceramic crowns were resin bonded to the tooth structure using one of three cements with different elastic moduli: Super-Bond C&B, Rely X Unicem and Panavia F 2.0. The specimens were subjected to compressive force through a 4 mm diameter steel ball at a crosshead speed of 1 mm/min using a universal test machine (Loyds Instrument Model LRX.). The load at the fracture point was recorded for each specimen in Newtons (N). These values were compared to a control group of unprepared/unrestored teeth. 
Results: There was a significant difference between the control group, with higher fracture strength, and the cemented samples regardless of the occlusal design and the type of resin cement. There was no significant difference in mean fracture load between the two designs of occlusal preparation using Super-Bond C&B. For the Rely X Unicem and Panavia F 2.0 cements, the proposed preparation design with a flat occlusal morphology provides a system with increased fracture strength. 
Significance: The proposed novel flat design showed less dependency on the resin cement selection in relation to the fracture strength of the restored tooth. The choice of the cement resin, with respect to its modulus of elasticity, is more important in the anatomic design than in the flat design. © 2013 Academy of Dental Materials.

Parametric design optimization of hard turning of AISI 4340 steel (69 HRC)

Continuous research endeavors on hard turning (HT), both on machine tools and cutting tools, have made the previously reported daunting limits easily attainable in the modern scenario. This presents an opportunity for a systematic investigation on finding the current attainable limits of hard turning using a CNC turret lathe. Accordingly, this study aims to contribute to the existing literature by providing the latest experimental results of hard turning of AISI 4340 steel (69 HRC) using a CBN cutting tool. An orthogonal array was developed using a set of judiciously chosen cutting parameters. Subsequently, the longitudinal turning trials were carried out in accordance with a well-designed full factorial-based Taguchi matrix. The speculation indeed proved correct as a mirror finished optical quality machined surface (an average surface roughness value of 45 nm) was achieved by the conventional cutting method. Furthermore, Signal-to-noise (S/N) ratio analysis, Analysis of variance (ANOVA), and Multiple regression analysis were carried out on the experimental datasets to assert the dominance of each machining variable in dictating the machined surface roughness and to optimize the machining parameters. One of the key findings was that when feed rate during hard turning approaches very low (about 0.02mm/rev), it could alone be most significant (99.16%) parameter in influencing the machined surface roughness (Ra). This has, however also been shown that low feed rate results in high tool wear, so the selection of machining parameters for carrying out hard turning must be governed by a trade-off between the cost and quality considerations.

Machine learning for transient discovery in Pan-STARRS1 difference imaging

Efficient identification and follow-up of astronomical transients is hindered by the need for humans to manually select promising candidates from data streams that contain many false positives. These artefacts arise in the difference images that are produced by most major ground-based time-domain surveys with large format CCD cameras. This dependence on humans to reject bogus detections is unsustainable for next generation all-sky surveys and significant effort is now being invested to solve the problem computationally. In this paper, we explore a simple machine learning approach to real-bogus classification by constructing a training set from the image data of similar to 32 000 real astrophysical transients and bogus detections from the Pan-STARRS1 Medium Deep Survey. We derive our feature representation from the pixel intensity values of a 20 x 20 pixel stamp around the centre of the candidates. This differs from previous work in that it works directly on the pixels rather than catalogued domain knowledge for feature design or selection. Three machine learning algorithms are trained (artificial neural networks, support vector machines and random forests) and their performances are tested on a held-out subset of 25 per cent of the training data. We find the best results from the random forest classifier and demonstrate that by accepting a false positive rate of 1 per cent, the classifier initially suggests a missed detection rate of around 10 per cent. However, we also find that a combination of bright star variability, nuclear transients and uncertainty in human labelling means that our best estimate of the missed detection rate is approximately 6 per cent.