Effects of Boundary Layer Ingestion on the Aero-Acoustics of Transonic Fan Rotors

The use of boundary-layer-ingesting, embedded propulsion systems can result in inlet flow distortions where the interaction of the boundary layer vorticity and the inlet lip causes horseshoe vortex formation and the ingestion of streamwise vortices into the inlet. A previously-developed body-force-based fan modeling approach was used to assess the change in fan rotor shock noise generation and propagation in a boundary-layer-ingesting, serpentine inlet. This approach is employed here in a parametric study to assess the effects of inlet geometry parameters (offset-to-diameter ratio and downstream-to-upstream area ratio) on flow distortion and rotor shock noise. Mechanisms related to the vortical inlet structures were found to govern changes in the rotor shock noise generation and propagation. The vortex whose circulation is in the opposite direction to the fan rotation (counter-swirling vortex) increases incidence angles on the fan blades near the tip, enhancing noise generation. The vortex with circulation in the direction of fan rotation (co-swirling vortex) creates a region of subsonic relative flow near the blade tip radius which decreases the sound power propagated to the far-field. The parametric study revealed that the overall sound power level at the fan leading edge is set by the ingested streamwise circulation, and that for inlet designs in which the streamwise vortices are displaced away from the duct wall, the sound power at the upstream inlet plane increased by as much as 9 dB. By comparing the far-field noise results obtained to those for a conventional inlet, it is deduced that the changes in rotor shock noise are predominantly due to the ingestion of streamwise vorticity.

Effects of Boundary Layer Ingestion on the Aero-Acoustics of Transonic Fan Rotors

The use of boundary-layer-ingesting, embedded propulsion systems can result in inlet flow distortions where the interaction of the boundary layer vorticity and the inlet lip causes horseshoe vortex formation and the ingestion of streamwise vortices into the inlet. A previously-developed body-force-based fan modeling approach was used to assess the change in fan rotor shock noise generation and propagation in a boundary-layer-ingesting, serpentine inlet. This approach is employed here in a parametric study to assess the effects of inlet geometry parameters (offset-to-diameter ratio and downstream-to-upstream area ratio) on flow distortion and rotor shock noise. Mechanisms related to the vortical inlet structures were found to govern changes in the rotor shock noise generation and propagation. The vortex whose circulation is in the opposite direction to the fan rotation (counter-swirling vortex) increases incidence angles on the fan blades near the tip, enhancing noise generation. The vortex with circulation in the direction of fan rotation (co-swirling vortex) creates a region of subsonic relative flow near the blade tip radius which decreases the sound power propagated to the far-field. The parametric study revealed that the overall sound power level at the fan leading edge is set by the ingested streamwise circulation, and that for inlet designs in which the streamwise vortices are displaced away from the duct wall, the sound power at the upstream inlet plane increased by as much as 9 dB. By comparing the far-field noise results obtained to those for a conventional inlet, it is deduced that the changes in rotor shock noise are predominantly due to the ingestion of streamwise vorticity.

Predicting the acoustics of squeak and rattle

This paper discusses the development of a computationally efficient numerical method for predicting the acoustics of rattle events upfront in the design cycle. The method combines Finite Elements, Boundary Elements and SEA and enables the loudness of a large number of rattle events to be efficiently predicted across a broad frequency range. A low frequency random vibro-acoustic model is used in conjunction with various closed form analytical expressions in order to quickly predict impact probabilities and locations. An existing method has been extended to estimate the statistics of the contact forces across a broad frequency range. Finally, broadband acoustic radiation is predicted using standard low, mid and high frequency vibro-acoustic methods and used to estimate impact loudness. The approach is discussed and a number of validation examples are presented.

Image deconvolution using a Gaussian scale mixtures model to approximate the wavelet sparseness constraint

This paper proposes to use an extended Gaussian Scale Mixtures (GSM) model instead of the conventional ℓ1 norm to approximate the sparseness constraint in the wavelet domain. We combine this new constraint with subband-dependent minimization to formulate an iterative algorithm on two shift-invariant wavelet transforms, the Shannon wavelet transform and dual-tree complex wavelet transform (DTCWT). This extented GSM model introduces spatially varying information into the deconvolution process and thus enables the algorithm to achieve better results with fewer iterations in our experiments. ©2009 IEEE.

Language model combination and adaptation using weighted finite state transducers

In speech recognition systems language model (LMs) are often constructed by training and combining multiple n-gram models. They can be either used to represent different genres or tasks found in diverse text sources, or capture stochastic properties of different linguistic symbol sequences, for example, syllables and words. Unsupervised LM adaptation may also be used to further improve robustness to varying styles or tasks. When using these techniques, extensive software changes are often required. In this paper an alternative and more general approach based on weighted finite state transducers (WFSTs) is investigated for LM combination and adaptation. As it is entirely based on well-defined WFST operations, minimum change to decoding tools is needed. A wide range of LM combination configurations can be flexibly supported. An efficient on-the-fly WFST decoding algorithm is also proposed. Significant error rate gains of 7.3% relative were obtained on a state-of-the-art broadcast audio recognition task using a history dependently adapted multi-level LM modelling both syllable and word sequences. ©2010 IEEE.

Multi-channel signal separation

The separation of independent sources from mixed observed data is a fundamental and challenging problem. In many practical situations, observations may be modelled as linear mixtures of a number of source signals, i.e. a linear multi-input multi-output system. A typical example is speech recordings made in an acoustic environment in the presence of background noise and/or competing speakers. Other examples include EEG signals, passive sonar applications and cross-talk in data communications. In this paper, we propose iterative algorithms to solve the n × n linear time invariant system under two different constraints. Some existing solutions for 2 × 2 systems are reviewed and compared.

Reversible jump sampler for autoregressive time series

We use reversible jump Markov chain Monte Carlo (MCMC) methods to address the problem of model order uncertainty in autoregressive (AR) time series within a Bayesian framework. Efficient model jumping is achieved by proposing model space moves from the full conditional density for the AR parameters, which is obtained analytically. This is compared with an alternative method, for which the moves are cheaper to compute, in which proposals are made only for new parameters in each move. Results are presented for both synthetic and audio time series.

Fixed-lag blind equalization and sequence estimation in digital communications systems using sequential importance sampling

We present methods for fixed-lag smoothing using Sequential Importance sampling (SIS) on a discrete non-linear, non-Gaussian state space system with unknown parameters. Our particular application is in the field of digital communication systems. Each input data point is taken from a finite set of symbols. We represent transmission media as a fixed filter with a finite impulse response (FIR), hence a discrete state-space system is formed. Conventional Markov chain Monte Carlo (MCMC) techniques such as the Gibbs sampler are unsuitable for this task because they can only perform processing on a batch of data. Data arrives sequentially, so it would seem sensible to process it in this way. In addition, many communication systems are interactive, so there is a maximum level of latency that can be tolerated before a symbol is decoded. We will demonstrate this method by simulation and compare its performance to existing techniques.