Application of scalar dissipation rate modelling to industrial burners in partially premixed regimes

The objective of this paper is to test various available turbulent burning velocity models on an experimental version of Siemens small scale combustor using the commercial CFD code. Failure of burning velocity model with different expressions for turbulent burning velocity is observed with an unphysical flame flashback into the swirler. Eddy Dissipation Model/Finite Rate Chemistry is found to over-predict mean temperature and species concentrations. Solving for reaction progress equation with its variance using scalar dissipation rate modelling produced reasonably good agreement with the available experimental data. Two different turbulence models Shear Stress Transport (SST) and Scale Adaptive Simulation (SAS) SST are tested and results from transient SST simulations are observed to be predicting well. SAS-SST is found to under-predict with temperature and species distribution.

Measurements of triggering and transient growth in a model lean-premixed gas turbine combustor

Instability triggering and transient growth of thermoacoustic oscillations were experimentally investigated in combination with linear/nonlinear flame transfer function (FTF) methodology in a model lean-premixed gas turbine combustor operated with CH 4 and air at atmospheric pressure. A fully premixed flame with 10kW thermal power and an equivalence ratio of 0.60 was chosen for detailed characterization of the nonlinear transient behaviors. Flame transfer functions were experimentally determined by simultaneous measurements of inlet velocity fluctuations and heat release rate oscillations using a constant temperature anemometer and OH */CH * chemiluminescence emissions, respectively. The phase-resolved variation of the local flame structure at a limit cycle was measured by planar laser-induced fluorescence of OH. Simultaneous measurements of inlet velocity, OH */CH * emission, and acoustic pressure were performed to investigate the temporal evolution of the system from a stable to a limit cycle operation. This measurement allows us to describe an unsteady instability triggering event in terms of several distinct stages: (i) initiation of a small perturbation, (ii) exponential amplification, (iii) saturation, (iv) nonlinear evolution of the perturbations towards a new unstable periodic state, (v) quasi-steady low-amplitude periodic oscillation, and (vi) fully-developed high-amplitude limit cycle oscillation. Phase-plane portraits of instantaneous inlet velocity and heat release rate clearly show the presence of two different attractors. Depending on its initial position in phase space at infinitesimally small amplitude, the system evolves towards either a high-amplitude oscillatory state or a low-amplitude oscillatory state. This transient phenomenon was analyzed using frequency- and amplitude-dependent damping mechanisms, and compared to subcritical and supercritical bifurcation theories. The results presented in this paper experimentally demonstrate the hypothesis proposed by Preetham et al. based on analytical and computational solutions of the nonlinear G-equation [J. Propul. Power 24 (2008) 1390-1402]. Good quantitative agreement was obtained between measurements and predictions in terms of the conditions for the onset of triggering and the amplitude of triggered combustion instabilities. © 2011 The Combustion Institute.

Application of scalar dissipation rate model to siemens DLE combustors

The standard design process for the Siemens Industrial Turbomachinery, Lincoln, Dry Low Emissions combustion systems has adopted the Eddy Dissipation Model with Finite Rate Chemistry for reacting computational fluid dynamics simulations. The major drawbacks of this model have been the over-prediction of temperature and lack of species data limiting the applicability of the model. A novel combustion model referred to as the Scalar Dissipation Rate Model has been developed recently based on a flamelet type assumption. Previous attempts to adopt the flamelet philosophy with alternative closure models have failed, with the prediction of unphysical phenomenon. The Scalar Dissipation Rate Model (SDRM) was developed from a physical understanding of scalar dissipation rate, signifying the rate of mixing of hot and cold fluids at scales relevant to sustain combustion, in flames and was validated using direct numerical simulations data and experimental measurements. This paper reports on the first industrial application of the SDRM to SITL DLE combustion system. Previous applications have considered ideally premixed laboratory scale flames. The industrial application differs significantly in the complexity of the geometry, unmixedness and operating pressures. The model was implemented into ANSYS-CFX using their inbuilt command language. Simulations were run transiently using Scale Adaptive Simulation turbulence model, which switches between Large Eddy Simulation and Unsteady Reynolds Averaged Navier Stokes using a blending function. The model was validated in a research SITL DLE combustion system prior to being applied to the actual industrial geometry at real operating conditions. This system consists of the SGT-100 burner with a glass square-sectioned combustor allowing for detailed diagnostics. This paper shows the successful validation of the SDRM against time averaged temperature and velocity within measurement errors. The successful validation allowed application of the SDRM to the SGT-100 twin shaft at the relevant full load conditions. Limited validation data was available due to the complexity of measurement in the real geometry. Comparison of surface temperatures and combustor exit temperature profiles showed an improvement compared to EDM/FRC model. Furthermore, no unphysical phenomena were predicted. This paper presents the successful application of the SDRM to the industrial combustion system. The model shows a marked improvement in the prediction of temperature over the EDM/FRC model previously used. This is of significant importance in the future applications of combustion CFD for understanding of hardware mechanical integrity, combustion emissions and dynamics of the flame. Copyright © 2012 by ASME.

Models and algorithms for tracking of maneuvering objects using variable rate particle filters

Standard algorithms in tracking and other state-space models assume identical and synchronous sampling rates for the state and measurement processes. However, real trajectories of objects are typically characterized by prolonged smooth sections, with sharp, but infrequent, changes. Thus, a more parsimonious representation of a target trajectory may be obtained by direct modeling of maneuver times in the state process, independently from the observation times. This is achieved by assuming the state arrival times to follow a random process, typically specified as Markovian, so that state points may be allocated along the trajectory according to the degree of variation observed. The resulting variable dimension state inference problem is solved by developing an efficient variable rate particle filtering algorithm to recursively update the posterior distribution of the state sequence as new data becomes available. The methodology is quite general and can be applied across many models where dynamic model uncertainty occurs on-line. Specific models are proposed for the dynamics of a moving object under internal forcing, expressed in terms of the intrinsic dynamics of the object. The performance of the algorithms with these dynamical models is demonstrated on several challenging maneuvering target tracking problems in clutter. © 2006 IEEE.

Discriminative language model adaptation for Mandarin broadcast speech transcription and translation

This paper investigates unsupervised test-time adaptation of language models (LM) using discriminative methods for a Mandarin broadcast speech transcription and translation task. A standard approach to adapt interpolated language models to is to optimize the component weights by minimizing the perplexity on supervision data. This is a widely made approximation for language modeling in automatic speech recognition (ASR) systems. For speech translation tasks, it is unclear whether a strong correlation still exists between perplexity and various forms of error cost functions in recognition and translation stages. The proposed minimum Bayes risk (MBR) based approach provides a flexible framework for unsupervised LM adaptation. It generalizes to a variety of forms of recognition and translation error metrics. LM adaptation is performed at the audio document level using either the character error rate (CER), or translation edit rate (TER) as the cost function. An efficient parameter estimation scheme using the extended Baum-Welch (EBW) algorithm is proposed. Experimental results on a state-of-the-art speech recognition and translation system are presented. The MBR adapted language models gave the best recognition and translation performance and reduced the TER score by up to 0.54% absolute. © 2007 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.

Language model cross adaptation for LVCSR system combination

State-of-the-art large vocabulary continuous speech recognition (LVCSR) systems often combine outputs from multiple subsystems developed at different sites. Cross system adaptation can be used as an alternative to direct hypothesis level combination schemes such as ROVER. In normal cross adaptation it is assumed that useful diversity among systems exists only at acoustic level. However, complimentary features among complex LVCSR systems also manifest themselves in other layers of modelling hierarchy, e.g., subword and word level. It is thus interesting to also cross adapt language models (LM) to capture them. In this paper cross adaptation of multi-level LMs modelling both syllable and word sequences was investigated to improve LVCSR system combination. Significant error rate gains up to 6.7% rel. were obtained over ROVER and acoustic model only cross adaptation when combining 13 Chinese LVCSR subsystems used in the 2010 DARPA GALE evaluation. © 2010 ISCA.