Investigation of self excited and acoustically forced combustion instabilities with laser diagnostics

Combustion oscillations in gas turbines can result in serious damage. One method used to predict such oscillations is to analyze the combustor acoustics using a simple linear model. Such a model requires a flame transfer function to describe the response of the heat release to flow perturbations inside the combustor. This paper reports on the application of Planar Laser Induced Fluorescence (PLIF) of OH radicals to analyze the response of a lean premixed flame to oncoming flow perturbations. Both self-excited oscillations and low amplitude forced oscillations at various frequencies are investigated in an atmospheric pressure model combustor rig. In order to visualize fluctuations of local fuel distribution, acetone-PLIF was also applied in non-reacting and acoustically forced flows at oscillation frequencies of 200 Hz and 510 Hz, respectively. OH-PLIF images were acquired over a range of operating parameters. The results presented in this paper originate from data sets acquired at fixed phase angles during the oscillation cycle. Comparative experiments in self excited and forced acoustic oscillations show that the flame and the combustion intensity develop similarly throughout the pressure cycle in both cases. Although the peak fluorescence intensities differ between self excited and the forced instabilities, there is a clear correspondence in the observed frequency and phase information from the two cases. This result encourages a comparison of the OH-PLIF and the acetone-PLIF results. Quantitative measurements of the equivalence ratio in specific areas of the measurement plane offer insight on the complex phenomena coupling acoustic perturbations, i.e. flow velocity fluctuations, to fluctuations in fuel distribution and combustion intensity, ultimately resulting in self excited combustion oscillations.

Distributed maximum likelihood for simultaneous self-localization and tracking in sensor networks

We show that the sensor self-localization problem can be cast as a static parameter estimation problem for Hidden Markov Models and we implement fully decentralized versions of the Recursive Maximum Likelihood and on-line Expectation-Maximization algorithms to localize the sensor network simultaneously with target tracking. For linear Gaussian models, our algorithms can be implemented exactly using a distributed version of the Kalman filter and a novel message passing algorithm. The latter allows each node to compute the local derivatives of the likelihood or the sufficient statistics needed for Expectation-Maximization. In the non-linear case, a solution based on local linearization in the spirit of the Extended Kalman Filter is proposed. In numerical examples we demonstrate that the developed algorithms are able to learn the localization parameters. © 2012 IEEE.

Self-stabilization and behavioral diversity of embodied adaptive locomotion

Locomotion is of fundamental importance in understanding adaptive behavior. In this paper we present two case studies of robot locomotion that demonstrate how higher level of behavioral diversity can be achieved while observing the principle of cheap design. More precisely, it is shown that, by exploiting the dynamics of the system-environment interaction, very simple controllers can be designed which is essential to achieve rapid locomotion. Special consideration must be given to the choice of body materials. We conclude with some speculation about the importance of locomotion for understanding cognition. © Springer-Verlag Berlin Heidelberg 2004.

Step synchronization and third person speed perception in virtual environment locomotion simulators

Human locomotion is known to be influenced by observation of another person's gait. For example, athletes often synchronize their step in long distance races. However, how interaction with a virtual runner affects the gait of a real runner has not been studied. We investigated this by creating an illusion of running behind a virtual model (VM) using a treadmill and large screen virtual environment showing a video of a VM. We looked at step synchronization between the real and virtual runner and at the role of the step frequency (SF) in the real runner's perception of VM speed. We found that subjects match VM SF when asked to match VM speed with their own (Figure 1). This indicates step synchronization may be a strategy of speed matching or speed perception. Subjects chose higher speeds when VMSF was higher (though VM was 12km/h in all videos). This effect was more pronounced when the speed estimate was rated verbally while standing still. (Figure 2). This may due to correlated physical activity affecting the perception of VM speed [Jacobs et al. 2005]; or step synchronization altering the subjects' perception of self speed [Durgin et al. 2007]. Our findings indicate that third person activity in a collaborative virtual locomotive environment can have a pronounced effect on an observer's gait activity and their perceptual judgments of the activity of others: the SF of others (virtual or real) can potentially influence one's perception of self speed and lead to changes in speed and SF. A better understanding of the underlying mechanisms would support the design of more compelling virtual trainers and may be instructive for competitive athletics in the real world. © 2009 ACM.