Raghunathan Rengaswamy

Bio: Raghunathan Rengaswamy is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Proton exchange membrane fuel cell & Fault detection and isolation. The author has an hindex of 39, co-authored 210 publications receiving 9632 citations. Previous affiliations of Raghunathan Rengaswamy include Indian Institute of Technology Bombay & Bosch.

Coalescence of drops in a 2D microchannel: critical transitions to autocatalytic behaviour

Abstract: A single coalescence event in a 2D concentrated emulsion in a microchannel can trigger an avalanche of similar events that can destabilize the entire assembly of drops. The sensitive dependence of the process on numerous parameters makes the propagation dynamics appear probabilistic. In this article, a stochastic simulation framework is proposed to understand this collective behavior in a system employing a large number of drops. We discover that the coalescence propagation dynamics exhibit a critical behavior where two outcomes are favored: no avalanche and large avalanches. Our analysis reveals that this behavior is a result of the inherent autocatalytic nature of the process. The effect of the aspect ratio of the drop assembly on the propagation dynamics is studied. We generate a parametric plot that shows the region of the parameter space where the propagation, averaged over the ensemble, is autocatalytic: where the possibility of near destabilization of the drop assembly appears.

Issues in modeling stiction in process control valves

Abstract: Stiction has been reported as the most commonly occurring nonlinearity in control valves. In the literature, mechanistic and data based models have been proposed to characterize stiction. In this paper, the available models are critically analyzed. The complexities associated with modeling stiction are highlighted. It is shown through experiments on industrial valves that in the presence of static and dynamic friction, the valve behavior is dependent on the rate of the valve input. An approach to model this rate dependent valve behavior - which is not considered in existing data driven models - is proposed.

Data driven approach for performance assessment of linear and nonlinear Kalman filters

Abstract: A new technique is developed for assessing the performance of linear and nonlinear Kalman filter based state estimators. The proposed metric will indicate the performance of these state estimators which will be primarily influenced by: (i) difference between the model dynamics and process dynamics and, (ii) various approximations of the nonlinear plant dynamics used in nonlinear Kalman filters. Currently, there exists no such quantification method to analyze the performance of linear and nonlinear Kalman filters, a key requirement for improvement and a practical benchmark for comparison of these state estimation algorithms. The proposed technique uses the generalized Hurst exponent of the prediction errors (difference in measured output and a posteriori estimates) obtained from the state estimators to quantify the performance. This technique could be implemented on-line as it requires only plant operating data and the predicted outputs (from the linear and nonlinear Kalman filters) to assess the performance. Several simulation studies demonstrate the applicability of the proposed performance metric to both linear and non-linear Kalman filters.

Multiple root cause analysis of linear oscillatory closed-loop single-input single-output (SISO) systems

Abstract: In general, oscillatory variables indicate poor performance of control loops. Therefore, diagnosis of the causes for oscillations in control loops is vital for maintaining the product quality within desired limits. In a linear closed-loop SISO system, oscillations can occur due to one or more of the following reasons: (i) poor controller tuning, (ii) control valve stiction and, (iii) external oscillatory disturbances. Several offline data-driven methods have been developed to address the diagnosis problem by focusing on only one of the causes for oscillations. In this work, an algorithm for identification of multiple root causes for oscillations in closed-loop systems is presented. The proposed approach comprises of: (i) Hammerstein based stiction detection algorithm, (ii) amplitude based discrimination algorithm using Hilbert Huang (HH) spectrum for identification of controller and disturbance caused oscillations and, (iii) an algorithm for analyzing the model obtained from Hammerstein approach. A decision algorithm based on the information obtained from the above three components is used for determination of multiple causes for oscillations in linear SISO systems.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

The Ensemble Kalman Filter: Theoretical formulation and practical implementation

Abstract: The purpose of this paper is to provide a comprehensive presentation and interpretation of the Ensemble Kalman Filter (EnKF) and its numerical implementation. The EnKF has a large user group, and numerous publications have discussed applications and theoretical aspects of it. This paper reviews the important results from these studies and also presents new ideas and alternative interpretations which further explain the success of the EnKF. In addition to providing the theoretical framework needed for using the EnKF, there is also a focus on the algorithmic formulation and optimal numerical implementation. A program listing is given for some of the key subroutines. The paper also touches upon specific issues such as the use of nonlinear measurements, in situ profiles of temperature and salinity, and data which are available with high frequency in time. An ensemble based optimal interpolation (EnOI) scheme is presented as a cost-effective approach which may serve as an alternative to the EnKF in some applications. A fairly extensive discussion is devoted to the use of time correlated model errors and the estimation of model bias.