Rain igestion in axial flow compressors at part speed

New experimental work is reported on the effects of water ingestion on the performance of an axial flow compressor. The background to the work is the effect that heavy rain has on an aeroengine compressor when operating in a "descent idle" mode, i.e., when the compressor is operating at part speed and when the aeromechanical effects of water ingestion are more important than the thermodynamic effects. Most of our existing knowledge in this field comes from whole engine tests. The current work provides the first known results from direct measurements on a stand-alone compressor. The influence of droplet size on path trajectory is considered both computationally and experimentally to show that most rain droplets will collide with the first row of rotor blades. The water on the blades is then centrifuged toward the casing where the normal airflow patterns in the vicinity of the rotor tips are disrupted. The result of this disruption is a reduction in compressor delivery pressure and an increase in the torque required to keep the compressor speed constant. Both effects reduce the efficiency of the machine. The behavior of the water in the blade rows is examined in detail, and simple models are proposed to explain the loss of pressure rise and the increase in torque. The measurements were obtained in a low speed compressor, making it possible to study the mechanical (increase in torque) and aerodynamic (reduction in pressure rise) effects of water ingestion without the added complication of thermodynamic effects. Copyright © 2008 by ASME.

Sequential decision making in general state space models

Many problems in control and signal processing can be formulated as sequential decision problems for general state space models. However, except for some simple models one cannot obtain analytical solutions and has to resort to approximation. In this thesis, we have investigated problems where Sequential Monte Carlo (SMC) methods can be combined with a gradient based search to provide solutions to online optimisation problems. We summarise the main contributions of the thesis as follows. Chapter 4 focuses on solving the sensor scheduling problem when cast as a controlled Hidden Markov Model. We consider the case in which the state, observation and action spaces are continuous. This general case is important as it is the natural framework for many applications. In sensor scheduling, our aim is to minimise the variance of the estimation error of the hidden state with respect to the action sequence. We present a novel SMC method that uses a stochastic gradient algorithm to find optimal actions. This is in contrast to existing works in the literature that only solve approximations to the original problem. In Chapter 5 we presented how an SMC can be used to solve a risk sensitive control problem. We adopt the use of the Feynman-Kac representation of a controlled Markov chain flow and exploit the properties of the logarithmic Lyapunov exponent, which lead to a policy gradient solution for the parameterised problem. The resulting SMC algorithm follows a similar structure with the Recursive Maximum Likelihood(RML) algorithm for online parameter estimation. In Chapters 6, 7 and 8, dynamic Graphical models were combined with with state space models for the purpose of online decentralised inference. We have concentrated more on the distributed parameter estimation problem using two Maximum Likelihood techniques, namely Recursive Maximum Likelihood (RML) and Expectation Maximization (EM). The resulting algorithms can be interpreted as an extension of the Belief Propagation (BP) algorithm to compute likelihood gradients. In order to design an SMC algorithm, in Chapter 8 uses a nonparametric approximations for Belief Propagation. The algorithms were successfully applied to solve the sensor localisation problem for sensor networks of small and medium size.

Making the most of production

This report from the IfM's Centre for Economics and Policy investigates the importance of production to companies and seeks to understand the long-term implications of moving production abroad or outsourcing production to other companies.