Measurements of intake separation hysteresis in a model fan and nacelle RIG

A 1/20-scale, low speed model rig representing the fan and nacelle of a high bypass ratio jet engine has been tested under crosswind conditions. The flow conditions under which the intake flow separates and reattaches have been found to exhibit considerable hysteresis. This phenomenon has been examined by a careful test procedure in which the crosswind angle has been slowly increased and then decreased. Measurements of the hysteresis associated with separation and reattachment are presented for independent variations in stream-tube contraction ratio, ground clearance, fan operating point and Reynolds number. The results reveal that particular care must be taken to allow for any hysteresis when testing intakes under crosswind conditions. They also indicate that separation hysteresis is particularly sensitive to fan operating point and the position of the ground plane. These findings suggest that it is important for high Reynolds number intake tests and calculations to include a ground plane and a model of the downstream turbomachinery. © 2002 by the author(s).

Bayesian inference and learning in Gaussian process state-space models with Particle MCMC

State-space models are successfully used in many areas of science, engineering and economics to model time series and dynamical systems. We present a fully Bayesian approach to inference and learning (i.e. state estimation and system identification) in nonlinear nonparametric state-space models. We place a Gaussian process prior over the state transition dynamics, resulting in a flexible model able to capture complex dynamical phenomena. To enable efficient inference, we marginalize over the transition dynamics function and, instead, infer directly the joint smoothing distribution using specially tailored Particle Markov Chain Monte Carlo samplers. Once a sample from the smoothing distribution is computed, the state transition predictive distribution can be formulated analytically. Our approach preserves the full nonparametric expressivity of the model and can make use of sparse Gaussian processes to greatly reduce computational complexity.

Experimental study of the effects of temperature variation on droplet size and wetness fraction in a low-pressure model steam turbine

The three-stage low-pressure model steam turbine at the Institute of Thermal Turbomachinery and Machinery Laboratory (ITSM) was used to study the impact of three different steam inlet temperatures on the homogeneous condensation process and the resulting wetness topology. The droplet spectrum as well as the particle number concentration were measured in front of the last stage using an optical-pneumatic probe. At design load, condensation starts inside the stator of the second stage. A change in the steam inlet temperature is able to shift the location of condensation onset within the blade row up- or downstream and even into adjoining blade passages, which leads to significantly different local droplet sizes and wetness fractions due to different local expansion rates. The measured results are compared to steady three-dimensional computational fluid dynamics calculations. The predicted nucleation zones could be largely confirmed by the measurements. Although the trend of measured and calculated droplet size across the span is satisfactory, there are considerable differences between the measured and computed droplet spectrum and wetness fractions. © IMechE 2013 Reprints and permissions: sagepub.co.uk/ journalsPermissions.nav.