Superconducting magnetic bearings for energy storage flywheels

We are investigating the use of flywheels for energy storage. Flywheel devices need to be of high efficiency and an important source of losses is the bearings. In addition, the requirement is for the devices to have long lifetimes with minimal or no maintenance. Conventional rolling element bearings can and have been used, but a non-contact bearing, such as a superconducting magnetic bearing, is expected to have a longer lifetime and lower losses. At Cambridge we have constructed a flywheel system. Designed to run in vacuum this incorporates a 40kg flywheel supported on superconducting magnetic bearings. The production device will be a 5kW device storing 5 kWh of retrievable energy at 50,000 rpm. The Cambridge system is being developed in parallel with a similar device supported on a conventional bearing. This will allow direct performance comparisons. Although superconducting bearings are increasingly well understood, of major importance are the cryogenics and special attention is being paid to methods of packaging and insulating the superconductors to cut down radiation losses. The work reported here is part of a three-year program of work supported by the EPSRC. © 1999 IEEE.

Bayesian parameter identification for vibro-acoustic models with nonparametric uncertainty

Vibration and acoustic analysis at higher frequencies faces two challenges: computing the response without using an excessive number of degrees of freedom, and quantifying its uncertainty due to small spatial variations in geometry, material properties and boundary conditions. Efficient models make use of the observation that when the response of a decoupled vibro-acoustic subsystem is sufficiently sensitive to uncertainty in such spatial variations, the local statistics of its natural frequencies and mode shapes saturate to universal probability distributions. This holds irrespective of the causes that underly these spatial variations and thus leads to a nonparametric description of uncertainty. This work deals with the identification of uncertain parameters in such models by using experimental data. One of the difficulties is that both experimental errors and modeling errors, due to the nonparametric uncertainty that is inherent to the model type, are present. This is tackled by employing a Bayesian inference strategy. The prior probability distribution of the uncertain parameters is constructed using the maximum entropy principle. The likelihood function that is subsequently computed takes the experimental information, the experimental errors and the modeling errors into account. The posterior probability distribution, which is computed with the Markov Chain Monte Carlo method, provides a full uncertainty quantification of the identified parameters, and indicates how well their uncertainty is reduced, with respect to the prior information, by the experimental data. © 2013 Taylor & Francis Group, London.

A species conserving genetic algorithm for multimodal function optimization

This paper introduces a new technique called species conservation for evolving parallel subpopulations. The technique is based on the concept of dividing the population into several species according to their similarity. Each of these species is built around a dominating individual called the species seed. Species seeds found in the current generation are saved (conserved) by moving them into the next generation. Our technique has proved to be very effective in finding multiple solutions of multimodal optimization problems. We demonstrate this by applying it to a set of test problems, including some problems known to be deceptive to genetic algorithms.