Improved analytical model for predicting the magnetic field distribution in brushless permanent-magnet machines

A general analytical technique predicts the magnetic field distribution in brushless permanent magnet machines equipped with surface-mounted magnets. It accounts for the effects of both the magnets and the stator windings. The technique is based on two-dimensional models in polar coordinates and solves the governing Laplacian/quasi-Poissonian field equations in the airgap/magnet regions without any assumption regarding the relative recoil permeability of the magnets. The analysis works for both internal and external rotor motor topologies, and either radial or parallel magnetized magnets, as well as for overlapping and nonoverlapping stator windings. The paper validates results of the analytical models by finite-element analyses, for both slotless and slotted motor

Analytical modeling of open-Circuit air-Gap field distributions in multisegment and multilayer interior permanent-magnet machines

We present a simple lumped magnetic circuit model for interior permanent-magnet (IPM) machines with multisegment and multilayer permanent magnets. We derived analytically the open-circuit air-gap field distribution, average air-gap flux density, and leakage fluxes. To verify the developed models and analytical method, we adopted finite-element analysis (FEA). We show that for prototype machines, the errors between the FEA and analytically predicted results are $≪$1% for multisegment IPM machines and $≪$ 2% for multilayer IPM machines. By utilizing the developed lumped magnetic circuit models, the IPM machines can be optimized for maximum fundamental and minimum total harmonic distortion of the air-gap flux density distribution

Acoustic noise radiated by PWM-controlled induction machine drives

This paper investigates the acoustic noise radiated from two nominally identical induction motors when fed from sinusoidal, and asymmetric regular sampling subharmonic and space-vector pulsewidth modulation (PWM) converters. The theory for analyzing the noise spectrum is developed further to account for the interaction between the motor and the drive. It is shown that manufacturing tolerances can result in significant differences in the noise level emitted from nominally identical motors, and that mechanical resonances can result in extremely high noise emissions. Such resonances can be induced by stator and rotor slot air-gap field harmonics due to the fundamental component of current, and by the interaction between the airgap field harmonics produced by the fundamental and the PWM harmonic currents. The significance of the effect of PWM strategy on the noise is closely related to the mechanical resonance with vibration mode order zero, while the PWM strategy will be critical only if the dominant cause of the emitted noise is the interaction of the fundamental air-gap field and PWM harmonic

Time Varying Dimension Models

Time varying parameter (TVP) models have enjoyed an increasing popularity in empirical macroeconomics. However, TVP models are parameter-rich and risk over-fi?tting unless the dimension of the model is small. Motivated by this worry, this paper proposes several Time Varying dimension (TVD) models where the dimension of the model can change over time, allowing for the model to automatically choose a more parsimonious TVP representation, or to switch between different parsimonious representations. Our TVD models all fall in the category of dynamic mixture models. We discuss the properties of these models and present methods for Bayesian inference. An application involving US inflation forecasting illustrates and compares the different TVD models. We ?find our TVD approaches exhibit better forecasting performance than several standard benchmarks and shrink towards parsimonious speci?cations.

Miniature mobile sensor platforms for condition monitoring of structures

In this paper, a wireless, multisensor inspection system for nondestructive evaluation (NDE) of materials is described. The sensor configuration enables two inspection modes-magnetic (flux leakage and eddy current) and noncontact ultrasound. Each is designed to function in a complementary manner, maximizing the potential for detection of both surface and internal defects. Particular emphasis is placed on the generic architecture of a novel, intelligent sensor platform, and its positioning on the structure under test. The sensor units are capable of wireless communication with a remote host computer, which controls manipulation and data interpretation. Results are presented in the form of automatic scans with different NDE sensors in a series of experiments on thin plate structures. To highlight the advantage of utilizing multiple inspection modalities, data fusion approaches are employed to combine data collected by complementary sensor systems. Fusion of data is shown to demonstrate the potential for improved inspection reliability

Modelling Breaks and Clusters in the Steady States of Macroeconomic Variables

Macroeconomists working with multivariate models typically face uncertainty over which (if any) of their variables have long run steady states which are subject to breaks. Furthermore, the nature of the break process is often unknown. In this paper, we draw on methods from the Bayesian clustering literature to develop an econometric methodology which: i) finds groups of variables which have the same number of breaks; and ii) determines the nature of the break process within each group. We present an application involving a fiv-variate steady-state VAR

Angiotensin-converting enzyme (ACE) inhibition in type 2, diabetic patients – interaction with ACE insertion/deletion polymorphism

Angiotensin-converting enzyme (ACE) insertion(I)/deletion (D) polymorphism may modify the effect of inhibition of the renin–angiotensin–aldosterone system (RAAS) on survival and cardiorenal outcomes in type 2, diabetes. A consecutive cohort of 2089 Chinese type 2 diabetic patients with mean (±standard deviation) age of 59.7±13.1 years were genotyped for this polymorphism by polymerase chain reaction method and were followed prospectively for a median period of 44.6 (interquartile range: 23.7, 57.5) months. Clinical outcomes, including all-cause mortality, cardiovascular and renal end points, were examined. The frequency for I allele was 67.1 and 32.9% for D allele, with observed genotype frequencies of 45.8, 42.6, and 11.6% for 3, DI and DD, respectively. ACE DD polymorphism was an independent predictor for renal end point with hazard ratio (HR) (95% confidence interval) of 1.72 (1.16, 2.56), but not for cardiovascular end point or mortality. After controlling for confounding factors, including ACE I/D genotype, the usage of RAAS inhibitors was associated with reduced risk of mortality (HR 0.34 (0.23, 0.50)) and renal end point (HR 0.55 (0.40, 0.75)). On subgroup analysis, the beneficial effects on survival (II vs DI vs DD: HR 0.29 (0.16, 0.51) vs 0.25 (0.14, 0.46) vs 1.33 (0.41, 4.31)) and renoprotection (II vs DI vs DD: 0.52 (0.30, 0.90) vs 0.43 (0.25, 0.72) vs 0.95 (0.43, 2.12)) were most evident in II and DI carriers. In conclusion, inhibition of RAAS was associated with reduced risk of mortality and occurrence of renal end point in Chinese type 2 diabetic patients. These benefits were most evident among II and DI carriers

Large Bayesian VARMAs

Vector Autoregressive Moving Average (VARMA) models have many theoretical properties which should make them popular among empirical macroeconomists. However, they are rarely used in practice due to over-parameterization concerns, difficulties in ensuring identification and computational challenges. With the growing interest in multivariate time series models of high dimension, these problems with VARMAs become even more acute, accounting for the dominance of VARs in this field. In this paper, we develop a Bayesian approach for inference in VARMAs which surmounts these problems. It jointly ensures identification and parsimony in the context of an efficient Markov chain Monte Carlo (MCMC) algorithm. We use this approach in a macroeconomic application involving up to twelve dependent variables. We find our algorithm to work successfully and provide insights beyond those provided by VARs

Analytical and experimental bearing capacities of system scaffolds

We investigated the structural behavior and bearing capacity of system scaffolds. The research showed that the critical load of a system scaffold structure without diagonal braces is similar to that of a door-shaped steel scaffold structure. Joint stiffness between vertical props in system scaffolds can be defined based on a comparison between analytical and experimental results. When the number of scaffold stories increases, the critical loads of system scaffolds decrease. Diagonal braces markedly enhance the critical load of system scaffolds. The coupling joint position between vertical props should be kept away from story-to-story joints to prevent a reduction in critical loads. The critical load of a system scaffold decreases as the quantity of extended vertical props at the bottom of the structure increases. A large Christmas tree set up by system scaffolds under various loads was used as an example for analysis and to check the design of system scaffolds