4 resultados para Local linear
em Cambridge University Engineering Department Publications Database
Resumo:
In standard Gaussian Process regression input locations are assumed to be noise free. We present a simple yet effective GP model for training on input points corrupted by i.i.d. Gaussian noise. To make computations tractable we use a local linear expansion about each input point. This allows the input noise to be recast as output noise proportional to the squared gradient of the GP posterior mean. The input noise variances are inferred from the data as extra hyperparameters. They are trained alongside other hyperparameters by the usual method of maximisation of the marginal likelihood. Training uses an iterative scheme, which alternates between optimising the hyperparameters and calculating the posterior gradient. Analytic predictive moments can then be found for Gaussian distributed test points. We compare our model to others over a range of different regression problems and show that it improves over current methods.
Resumo:
Local measurements of the heat transfer coefficient and pressure coefficient were conducted on the tip and near tip region of a generic turbine blade in a five-blade linear cascade. Two tip clearance gaps were used: 1.6% and 2.8% chord. Data was obtained at a Reynolds number of 2.3 × 10 5 based on exit velocity and chord. Three different tip geometries were investigated: a flat (plain) tip, a suction-side squealer, and a cavity squealer. The experiments reveal that the flow through the plain gap is dominated by flow separation at the pressure-side edge and that the highest levels of heat transfer are located where the flow reattaches on the tip surface. High heat transfer is also measured at locations where the tip-leakage vortex has impinged onto the suction surface of the aerofoil. The experiments are supported by flow visualisation computed using the CFX CFD code which has provided insight into the fluid dynamics within the gap. The suction-side and cavity squealers are shown to reduce the heat transfer in the gap but high levels of heat transfer are associated with locations of impingement, identified using the flow visualisation and aerodynamic data. Film cooling is introduced on the plain tip at locations near the pressure-side edge within the separated region and a net heat flux reduction analysis is used to quantify the performance of the successful cooling design. copyright © 2005 by ASME.
Resumo:
In this paper, we tackle the problem of learning a linear regression model whose parameter is a fixed-rank matrix. We study the Riemannian manifold geometry of the set of fixed-rank matrices and develop efficient line-search algorithms. The proposed algorithms have many applications, scale to high-dimensional problems, enjoy local convergence properties and confer a geometric basis to recent contributions on learning fixed-rank matrices. Numerical experiments on benchmarks suggest that the proposed algorithms compete with the state-of-the-art, and that manifold optimization offers a versatile framework for the design of rank-constrained machine learning algorithms. Copyright 2011 by the author(s)/owner(s).
Resumo:
We consider the linear global stability of the boundary-layer flow over a rotating sphere. Our results suggest that a self-excited linear global mode can exist when the sphere rotates sufficiently fast, with properties fixed by the flow at latitudes between approximately 55°-65° from the pole (depending on the rotation rate). A neutral curve for global linear instabilities is presented with critical Reynolds number consistent with existing experimentally measured values for the appearance of turbulence. The existence of an unstable linear global mode is in contrast to the literature on the rotating disk, where it is expected that nonlinearity is required to prompt the transition to turbulence. Despite both being susceptible to local absolute instabilities, we conclude that the transition mechanism for the rotating-sphere flow may be different to that for the rotating disk. © 2014 Elsevier Masson SAS. All rights reserved.