Development of a servo-hydraulic earthquake actuator for the Cambridge Turner beam centrifuge

Dynamic centrifuge modelling has been carried out at Cambridge since the late 1970s. Over this period, three different mechanical earthquake actuators were developed. In this paper the development of a new servo-hydraulic earthquake actuator is described. The basic design principles are explained along with the need to carry out these designs to match the existing services and systems of the 35 year old Turner beam centrifuge at Cambridge. In addition, some of the features of the Turner beam centrifuge are exploited in the design of this new earthquake actuator. The paper also explains the mechanical fabrication of the actuator and the control systems that were developed in order to generate real earthquake motions. Finally, the performance of this new servo-hydraulic earthquake actuator is presented and assessed based on a wide range of earthquake input motions.

Absence of branches from xylan in Arabidopsis gux mutants reveals potential for simplification of lignocellulosic biomass.

As one of the most abundant polysaccharides on Earth, xylan will provide more than a third of the sugars for lignocellulosic biofuel production when using grass or hardwood feedstocks. Xylan is characterized by a linear β(1,4)-linked backbone of xylosyl residues substituted by glucuronic acid, 4-O-methylglucuronic acid or arabinose, depending on plant species and cell types. The biological role of these decorations is unclear, but they have a major influence on the properties of the polysaccharide. Despite the recent isolation of several mutants with reduced backbone, the mechanisms of xylan synthesis and substitution are unclear. We identified two Golgi-localized putative glycosyltransferases, GlucUronic acid substitution of Xylan (GUX)-1 and GUX2 that are required for the addition of both glucuronic acid and 4-O-methylglucuronic acid branches to xylan in Arabidopsis stem cell walls. The gux1 gux2 double mutants show loss of xylan glucuronyltransferase activity and lack almost all detectable xylan substitution. Unexpectedly, they show no change in xylan backbone quantity, indicating that backbone synthesis and substitution can be uncoupled. Although the stems are weakened, the xylem vessels are not collapsed, and the plants grow to normal size. The xylan in these plants shows improved extractability from the cell wall, is composed of a single monosaccharide, and requires fewer enzymes for complete hydrolysis. These findings have implications for our understanding of the synthesis and function of xylan in plants. The results also demonstrate the potential for manipulating and simplifying the structure of xylan to improve the properties of lignocellulose for bioenergy and other uses.

Innovative capabilities, entrepreneurial process and the dynamic growth of university spin-outs: A process-oriented study with evidence from China

This research aims to develop a conceptual framework in order to enquire into the dynamic growth process of University Spin-outs (hereafter referred to as USOs) in China, attempting to understand the capabilities configuration that are necessary for the dynamic growth. Based on the extant literature and empirical cases, this study attempts to address the question how do USOs in China build and configure the innovative capabilities to cope with the dynamic growth. This paper aims to contribute to the existing literature by providing a theoretical discussion of the USOs' dynamic entrepreneurial process, by investigating the interconnections between innovation problem-solving and the required configuration of innovative capabilities in four growth phases. Further, it presents a particular interest on the impact to the USOs' entrepreneurial innovation process by the integrative capabilities, in terms of knowledge integration, alliance, venture finance and venture governance. To date, studies that have investigated the dynamic development process of USOs in China and have recognized the heterogeneity of USOs in terms of capabilities that are required for rapid growth still remain sparse. Addressing this research gap will be of great interest to entrepreneurs, policy makers, and venture investors. ©2009 IEEE.

Gaussian process change point models

We combine Bayesian online change point detection with Gaussian processes to create a nonparametric time series model which can handle change points. The model can be used to locate change points in an online manner; and, unlike other Bayesian online change point detection algorithms, is applicable when temporal correlations in a regime are expected. We show three variations on how to apply Gaussian processes in the change point context, each with their own advantages. We present methods to reduce the computational burden of these models and demonstrate it on several real world data sets. Copyright 2010 by the author(s)/owner(s).

Model based learning of sigma points in unscented Kalman filtering

The unscented Kalman filter (UKF) is a widely used method in control and time series applications. The UKF suffers from arbitrary parameters necessary for a step known as sigma point placement, causing it to perform poorly in nonlinear problems. We show how to treat sigma point placement in a UKF as a learning problem in a model based view. We demonstrate that learning to place the sigma points correctly from data can make sigma point collapse much less likely. Learning can result in a significant increase in predictive performance over default settings of the parameters in the UKF and other filters designed to avoid the problems of the UKF, such as the GP-ADF. At the same time, we maintain a lower computational complexity than the other methods. We call our method UKF-L. ©2010 IEEE.

Demodulation as probabilistic inference

Demodulation is an ill-posed problem whenever both carrier and envelope signals are broadband and unknown. Here, we approach this problem using the methods of probabilistic inference. The new approach, called Probabilistic Amplitude Demodulation (PAD), is computationally challenging but improves on existing methods in a number of ways. By contrast to previous approaches to demodulation, it satisfies five key desiderata: PAD has soft constraints because it is probabilistic; PAD is able to automatically adjust to the signal because it learns parameters; PAD is user-steerable because the solution can be shaped by user-specific prior information; PAD is robust to broad-band noise because this is modeled explicitly; and PAD's solution is self-consistent, empirically satisfying a Carrier Identity property. Furthermore, the probabilistic view naturally encompasses noise and uncertainty, allowing PAD to cope with missing data and return error bars on carrier and envelope estimates. Finally, we show that when PAD is applied to a bandpass-filtered signal, the stop-band energy of the inferred carrier is minimal, making PAD well-suited to sub-band demodulation. © 2006 IEEE.

An advanced multi-configuration stator well cooling test facility

Optimisation of cooling systems within gas turbine engines is of great interest to engine manufacturers seeking gains in performance, efficiency and component life. The effectiveness of coolant delivery is governed by complex flows within the stator wells and the interaction of main annulus and cooling air in the vicinity of the rim seals. This paper reports the development of a test facility which allows the interaction of cooling air and main gas paths to be measured at conditions representative of those found in modern gas turbine engines. The test facility features a two stage turbine with an overall pressure ratio of approximately 2.6:1. Hot air is supplied to the main annulus using a Rolls-Royce Dart compressor driven by an aero-derivative engine plant. Cooling air can be delivered to the stator wells at multiple locations and at a range of flow rates which cover bulk ingestion through to bulk egress. The facility has been designed with adaptable geometry to enable rapid changes of cooling air path configuration. The coolant delivery system allows swift and accurate changes to the flow settings such that thermal transients may be performed. Particular attention has been focused on obtaining high accuracy data, using a radio telemetry system, as well as thorough through-calibration practices. Temperature measurements can now be made on both rotating and stationary discs with a long term uncertainty in the region of 0.3 K. A gas concentration measurement system has also been developed to obtain direct measurement of re-ingestion and rim seal exchange flows. High resolution displacement sensors have been installed in order to measure hot running geometry. This paper documents the commissioning of a test facility which is unique in terms of rapid configuration changes, non-dimensional engine matching and the instrumentation density and resolution. Example data for each of the measurement systems is presented. This includes the effect of coolant flow rate on the metal temperatures within the upstream cavity of the turbine stator well, the axial displacement of the rotor assembly during a commissioning test, and the effect of coolant flow rate on mixing in the downstream cavity of the stator well. Copyright © 2010 by ASME.

Model based learning of sigma points in unscented Kalman filtering

The unscented Kalman filter (UKF) is a widely used method in control and time series applications. The UKF suffers from arbitrary parameters necessary for sigma point placement, potentially causing it to perform poorly in nonlinear problems. We show how to treat sigma point placement in a UKF as a learning problem in a model based view. We demonstrate that learning to place the sigma points correctly from data can make sigma point collapse much less likely. Learning can result in a significant increase in predictive performance over default settings of the parameters in the UKF and other filters designed to avoid the problems of the UKF, such as the GP-ADF. At the same time, we maintain a lower computational complexity than the other methods. We call our method UKF-L. © 2011 Elsevier B.V.

Assessment of computational fluid dynamics and experimental data for shock boundary-layer interactions

A workshop on the computational fluid dynamics (CFD) prediction of shock boundary-layer interactions (SBLIs) was held at the 48th AIAA Aerospace Sciences Meeting. As part of the workshop, numerous CFD analysts submitted solutions to four experimentally measured SBLIs. This paper describes the assessment of the CFD predictions. The assessment includes an uncertainty analysis of the experimental data, the definition of an error metric, and the application of that metric to the CFD solutions. The CFD solutions provided very similar levels of error and, in general, it was difficult to discern clear trends in the data. For the Reynolds-averaged Navier-Stokes (RANS) methods, the choice of turbulence model appeared to be the largest factor in solution accuracy. Scale-resolving methods, such as large-eddy simulation (LES), hybrid RANS/LES, and direct numerical simulation, produced error levels similar to RANS methods but provided superior predictions of normal stresses. Copyright © 2012 by Daniella E. Raveh and Michael Iovnovich.

Physical modelling of flexible retaining walls under seismic actions

A series of dynamic centrifuge tests on reduced scale models of flexible retaining structures were conducted on the Turner beam centrifuge at the Schofield Centre of the University of Cambridge. The paper illustrates the main results of the experimental work in terms of observed amplifications of ground motion and mobilised shear stiffness and damping ratio for all tests. The experimental results for one test on a pair of cantilevered walls in dense sand are also presented in terms of measured bending moments and horizontal displacements of the walls during (maximum values) and at the end of (residual values) each seismic event. Finally, the experimental data are discussed in the light of the results obtained from dynamic numerical analyses of the behaviour of cantilevered walls under real seismic actions. © 2010 Taylor & Francis Group, London.

Probabilistic amplitude and frequency demodulation

A number of recent scientific and engineering problems require signals to be decomposed into a product of a slowly varying positive envelope and a quickly varying carrier whose instantaneous frequency also varies slowly over time. Although signal processing provides algorithms for so-called amplitude-and frequency-demodulation (AFD), there are well known problems with all of the existing methods. Motivated by the fact that AFD is ill-posed, we approach the problem using probabilistic inference. The new approach, called probabilistic amplitude and frequency demodulation (PAFD), models instantaneous frequency using an auto-regressive generalization of the von Mises distribution, and the envelopes using Gaussian auto-regressive dynamics with a positivity constraint. A novel form of expectation propagation is used for inference. We demonstrate that although PAFD is computationally demanding, it outperforms previous approaches on synthetic and real signals in clean, noisy and missing data settings.