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.

Design of a model-based feedback controller for active sorting and synchronization of droplets in a microfluidic loop

Abstract: The transport of confined droplets in fluidic networks can lead to complex spatiotemporal dynamics, precluding full control of the position of droplets in the network. Here, we report the design of a model-based feedback controller that can actively regulate droplet positions in a network. We specifically consider droplet dynamics in a microfluidic loop where a main channel splits into two and recombines. Consistent with previous studies, we find that without active control, the dynamics of droplets in the loop can range from periodic to chaotic behaviors. However, by implementing the model-based feedback controller, we show that the droplets can be made to sort alternately into the branches of the loop as well as to synchronize the times at which pairs of droplets exit the loop. In particular, our computations demonstrate that the controller is capable of executing remarkable droplet sort-synchronization tasks in the otherwise chaotic dynamics in the loop. The design of our controller incorporates a hydrodynamic network model, that is, capable of predicting droplet positions and subsequently delivering an actuation to the branches in the loop through elastomeric valves. Efficacy of the controller is discussed in terms of actuation characteristics and constraints imposed by elastomeric valves. The model-based feedback controller framework presented in this study is likely to promote the development of lab-on-chip technologies in which droplet manipulation tasks are executed with active control. VC 2011 American Institute of Chemical Engineers AIChE J, 58: 2120– 2130, 2012

Interfacial contact resistance in polymer electrolyte membrane fuel cells: Recent developments and challenges

Abstract: Interfacial contact resistance in fuel cells is one of the primary challenges that needs to be overcome in the commercialization of fuel cells. This paper summarizes the governing parameters of the resistance such as surface morphology, contact pressure at the interface, electrical conductivities and corrosion resistance of gas diffusion layer and bipolar plate. Also, researchers’ contributions in modifying the cell components and clamping techniques are discussed in detail. Gas diffusion layer is found to be a crucial factor to control the contact resistance and hence its modification techniques are discussed. Moreover, surface of bipolar plate is prone to oxidation in acidic environment of the fuel cell. Different materials, anticorrosive coatings as well as surface modification techniques for the bipolar plate are therefore discussed in detail. Effect of clamping pressure on distribution of contact pressure is highlighted and various clamping techniques suggested by researchers are summarized. Effect of gasket selection on contact pressure distribution is considerable and therefore included in the paper. The review would be helpful for researchers in understanding the importance, reasons of existence and factors influencing the contact resistance at the interface of gas diffusion layer and bipolar plate.

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.