Flux observer algorithms for direct torque control of brushless doubly-fed reluctance machines

Direct Torque Control (DTC) has been extensively researched and applied to most AC machines during the last two decades. Its first application to the Brushless Doubly-Fed Reluctance Machine (BDFRM), a promising cost-effective candidate for drive and generator systems with limited variable speed ranges (such as large pumps or wind turbines), has only been reported a few years ago. However, the original DTC scheme has experienced flux estimation problems and compromised performance under the maximum torque per inverter ampere (MTPIA) conditions. This deficiency at low current and torque levels may be overcome and much higher accuracy achieved by alternative estimation approaches discussed in this paper using Kalman Filter (KF) and/or Sliding Mode Observer (SMO). Computer simulations accounting for real-time constraints (e.g. measurement noise, transducer DC offset etc.) have produced realistic results similar to those one would expect from an experimental setup

Coupling of Length Scales and Atomistic Simulation of MEMS Resonators

We present simulations of the dynamic and temperature dependent behavior of Micro-Electro-Mechanical Systems (MEMS) by utilizing recently developed parallel codes which enable a coupling of length scales. The novel techniques used in this simulation accurately model the behavior of the mechanical components of MEMS down to the atomic scale. We study the vibrational behavior of one class of MEMS devices: micron-scale resonators made of silicon and quartz. The algorithmic and computational avenue applied here represents a significant departure from the usual finite element approach based on continuum elastic theory. The approach is to use an atomistic simulation in regions of significantly anharmonic forces and large surface area to volume ratios or where internal friction due to defects is anticipated. Peripheral regions of MEMS which are well-described by continuum elastic theory are simulated using finite elements for efficiency. Thus, in central regions of the device, the motion of millions of individual atoms is simulated, while the relatively large peripheral regions are modeled with finite elements. The two techniques run concurrently and mesh seamlessly, passing information back and forth. This coupling of length scales gives a natural domain decomposition, so that the code runs on multiprocessor workstations and supercomputers. We present novel simulations of the vibrational behavior of micron-scale silicon and quartz oscillators. Our results are contrasted with the predictions of continuum elastic theory as a function of size, and the failure of the continuum techniques is clear in the limit of small sizes. We also extract the Q value for the resonators and study the corresponding dissipative processes.Comment: 10 pages, 10 figures, to be published in the proceedings of DTM '99; LaTeX with spie.sty, bibtex with spiebib.bst and psfi

A non-hybrid method for the PDF equations of turbulent flows on unstructured grids

In probability density function (PDF) methods of turbulent flows, the joint PDF of several flow variables is computed by numerically integrating a system of stochastic differential equations for Lagrangian particles. A set of parallel algorithms is proposed to provide an efficient solution of the PDF transport equation, modeling the joint PDF of turbulent velocity, frequency and concentration of a passive scalar in geometrically complex configurations. An unstructured Eulerian grid is employed to extract Eulerian statistics, to solve for quantities represented at fixed locations of the domain (e.g. the mean pressure) and to track particles. All three aspects regarding the grid make use of the finite element method (FEM) employing the simplest linear FEM shape functions. To model the small-scale mixing of the transported scalar, the interaction by exchange with the conditional mean model is adopted. An adaptive algorithm that computes the velocity-conditioned scalar mean is proposed that homogenizes the statistical error over the sample space with no assumption on the shape of the underlying velocity PDF. Compared to other hybrid particle-in-cell approaches for the PDF equations, the current methodology is consistent without the need for consistency conditions. The algorithm is tested by computing the dispersion of passive scalars released from concentrated sources in two different turbulent flows: the fully developed turbulent channel flow and a street canyon (or cavity) flow. Algorithmic details on estimating conditional and unconditional statistics, particle tracking and particle-number control are presented in detail. Relevant aspects of performance and parallelism on cache-based shared memory machines are discussed.Comment: Accepted in Journal of Computational Physics, Feb. 20, 200

Connected component identification and cluster update on GPU

Cluster identification tasks occur in a multitude of contexts in physics and engineering such as, for instance, cluster algorithms for simulating spin models, percolation simulations, segmentation problems in image processing, or network analysis. While it has been shown that graphics processing units (GPUs) can result in speedups of two to three orders of magnitude as compared to serial codes on CPUs for the case of local and thus naturally parallelized problems such as single-spin flip update simulations of spin models, the situation is considerably more complicated for the non-local problem of cluster or connected component identification. I discuss the suitability of different approaches of parallelization of cluster labeling and cluster update algorithms for calculations on GPU and compare to the performance of serial implementations.Comment: 15 pages, 14 figures, one table, submitted to PR

Systolic and Hyper-Systolic Algorithms for the Gravitational N-Body Problem, with an Application to Brownian Motion

A systolic algorithm rhythmically computes and passes data through a network of processors. We investigate the performance of systolic algorithms for implementing the gravitational N-body problem on distributed-memory computers. Systolic algorithms minimize memory requirements by distributing the particles between processors. We show that the performance of systolic routines can be greatly enhanced by the use of non-blocking communication, which allows particle coordinates to be communicated at the same time that force calculations are being carried out. Hyper-systolic algorithms reduce the communication complexity at the expense of increased memory demands. As an example of an application requiring large N, we use the systolic algorithm to carry out direct-summation simulations using 10^6 particles of the Brownian motion of the supermassive black hole at the center of the Milky Way galaxy. We predict a 3D random velocity of 0.4 km/s for the black hole.Comment: 33 pages, 10 postscript figure