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Abstract: This paper presents a meanline model to predict the performance parameters of a turbocharger turbine under steady state conditions. The turbine was developed at Imperial College and the design was based on a commercial nozzleless unit that was modified into a variable geometry single-entry turbine. The wide range of tests data from the Imperial College Turbocharger Group dynamometer enabled the evaluation of the model in the areas of the turbine map where currently no previous comparison had been made in the literature. This facility is designed to allow testing over a wide range of velocity ratios (0.3–1.1) previously unavailable with conventional test stands. The nozzleless turbine model was validated against experimental results spanning an equivalent speed range of 27.9 and 53.8rev/s√K while for the nozzled case the model was validated against one single speed (43.0rev/s√K) and three different vane angle settings (40°, 60° and 70°). The results of the model simulation showed that the performance can be predicted with excellent accuracy for different turbine speeds and vane angles. Based on the model prediction, a breakdown aerodynamic loss was performed. [Copyright &y& Elsevier]

Summary: This paper compares two Monte Carlo sequential data assimilation methods based on the Kalman filter, for estimating the effect of measurements on simulations of state error variance made by a one-dimensional hydrodynamic model. The first method used an ensemble Kalman filter (EnKF) to update state estimates, which were then used as initial conditions for further simulations. The second method used an ensemble transform Kalman filter (ETKF) to quickly estimate the effect of measurement error covariance on forecast error covariance without the need to re-run the simulation model. The ETKF gave an unbiased estimate of EnKF analysed error variance, although differences in the treatment of measurement errors meant the results were not identical. Estimates of forecast error variance could also be made, but their accuracy deteriorated as the time from measurements increased due in part to model non-linearity and the decreasing signal variance. The motivation behind the study was to assess the ability of the ETKF to target possible measurements, as part of an adaptive sampling framework, before they are assimilated by an EnKF-based forecasting model on the River Crouch, Essex, UK. The ETKF was found to be a useful tool for quickly estimating the error covariance expected after assimilating measurements into the hydrodynamic model. It, thus, provided a means of quantifying the ‘usefulness’ (in terms of error variance) of possible sampling schemes. [Copyright &y& Elsevier]