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.

Multivariable optimization studies of cathode catalyst layer of a polymer electrolyte membrane fuel cell

Abstract: The amount of current generated in a polymer electrolyte membrane fuel cell (PEMFC) depends strongly on the local conditions in a cathode such as available oxygen, surface area available for the reactions, amount of ionomer, and amount of electro-catalyst. In the present work, design parameters of a cathode catalyst layer are optimized to achieve the maximum current density at a given operating voltage. The decision variables are chosen such that they can be realized experimentally. To understand the effect of the model fidelity on the decision variables, optimization is performed with a single phase model and a two-phase model with and without membrane. Other objective functions such as maximization of current generation per catalyst loading, minimization of catalyst layer cost per power and minimization of cell cost per power are also considered to study the effects of the objective functions on the decision variables.

Multivariate Control Loop Performance Assessment With Hurst Exponent and Mahalanobis Distance

Abstract: A novel data-driven technique for performance assessment of multivariate control loops that takes into account the interactions within the system is proposed. The technique merges the Hurst-exponent-based single-input single-output controller performance index with Mahalanobis distance to devise a multiple-input multiple-output (MIMO) controller performance index. The distinct advantage over the standard minimum variance index and novelty of the proposed approach lies in its ability to quantify the performance of MIMO controller without the knowledge of interactor matrix or system description, which leads to the technique being insensitive to model plant mismatch and easily applicable to nonlinear systems. Only closed-loop routine operating data are required. This new methodology is tested on benchmark systems from the literature and simulation results are presented. Comparison with minimum variance index-based techniques reveals excellent agreement in the trends of both approaches. The results establish the proposed approach as a promising tool for interactor-matrix-independent MIMO control loop performance assessment.

Plant Friendly Input Design: Convex Relaxation and Quality

Abstract: A common practice in a system identification exercise is to perturb the system of interest and use the resulting data to build a model. The problem of interest in this contribution is to synthesize an input signal that is maximally informative for generating good quality models while being “plant friendly,” i.e., least hostile to plant operation. In this contribution, limits on input move sizes are the plant friendly specifications. The resulting optimization problem is nonlinear and nonconvex. Hence, the original plant friendly input design problem is relaxed which results in a convex optimization problem. We formulate a SemiDefinite Programme using the theory of generalized Tchebysheff inequalities to derive tight bounds on the quality of relaxation. Simulations show that the relaxation results in more plant friendly input signals.

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.