Axial compressor response to inlet flow distortions by a CFD analysis

The usual approach to compressor design considers uniform inlet flow characteristics. Especially in aircraft applications, the inlet flow is quite often non uniform, and this can result in severe performance degradation. The magnitude of this phenomenon is amplified in military engines due to the complexity of inlet duct configurations and the extreme flight conditions. CFD simulation is an innovative and powerful tool for studying inlet distortions and can bring this inside the very early phases of the design process. This project attempts to study the effects of inlet flow distortions in an axial flow compressor trying to minimize the use computer resources and computational time. The first stage of a low bypass ratio compressor has been analyzed and its clean and distorted performance compared outlining the principal changes due to uneven flow distribution: drop in mass flow, increase in pressure and temperature ratios, decrease in surge margin. Three different studies have then been conducted to better understand the effects of the level, the type and the frequency of the distortion.

Statistical parametric speech synthesis based on speaker and language factorization

An increasingly common scenario in building speech synthesis and recognition systems is training on inhomogeneous data. This paper proposes a new framework for estimating hidden Markov models on data containing both multiple speakers and multiple languages. The proposed framework, speaker and language factorization, attempts to factorize speaker-/language-specific characteristics in the data and then model them using separate transforms. Language-specific factors in the data are represented by transforms based on cluster mean interpolation with cluster-dependent decision trees. Acoustic variations caused by speaker characteristics are handled by transforms based on constrained maximum-likelihood linear regression. Experimental results on statistical parametric speech synthesis show that the proposed framework enables data from multiple speakers in different languages to be used to: train a synthesis system; synthesize speech in a language using speaker characteristics estimated in a different language; and adapt to a new language. © 2012 IEEE.