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.

Degradation of high temperature PEM fuel cells and the impact on electrical performance

Abstract: This paper presents the effect different degradation mechanisms have on the electrical performance of a high temperature PEM fuel cell. The changes to the fuel cell composition and internal chemistry is related the drop in electrical performance. In particular, three main effects will be discussed. This includes the impact of carbon support and catalyst degradation, loss of phosphoric acid in the membrane and CO poisoning of the catalyst layer.

Integrated bio-electrochemical model for a micro photosynthetic power cell

Abstract: A simple first-principles mathematical model is developed to predict the performance of a micro photosynthetic power cell ($\mu$PSC), an electrochemical device which generates electricity by harnessing electrons from photosynthesis in the presence of light. A lumped parameter approach is used to develop a model in which the electrochemical kinetic rate constants and diffusion effects are lumped into a single characteristic rate constant $K$. A non-parametric estimation of $K$ for the $\mu$PSC is performed by minimizing the sum square errors (SSE) between the experimental and model predicted current and voltages. The developed model is validated by comparing the model predicted $v-i$ characteristics with experimental data not used in the parameter estimation. Sensitivity analysis of the design parameters and the operational parameters reveal interesting insights for performance enhancement. Analysis of the model also suggests that there are two different operating regimes that are observed in this $\mu$PSC. This modeling approach can be used in other designs of $\mu$PSCs for performance enhancement studies.

Isothermal isobaric reactive flash problem

Abstract: In this article, we present some results for isothermal isobaric reactive separation process problems. We also demonstrate that, even in isothermal isobaric reactive separation processes, which are probably the least nonlinear of all reactive separation processes, we get nonlinear phenomenon such as Hopf bifurcations. While it has been shown that Hopf bifurcations are impossible in isothermal continuous stirred tank reactor (CSTR) problems involving the methyl tert-butyl ether (MTBE) and tert-amyl methyl ether (TAME) reactions, and also in nonreactive flash problems, we demonstrate in this paper that isothermal reactive flash processes involving both MTBE and TAME mixtures exhibit Hopf bifurcations. This shows that instabilities and oscillations can occur even in isothermal reactive separation systems and are not necessarily due to multiple stages. Additionally, we show that the Rachford−Rice procedure can be extended to reactive systems.

A systems engineering perspective on electrochemical energy technologies and a framework for application driven choice of technology

Abstract: Electrochemical energy, being a safe and clean renewable energy source, is gaining considerable attention in recent years. The electrochemical energy technologies available are numerous and the choice of technology is usually guided by heuristics. The paper presents a critical review and comparison of three competing electrochemical energy technologies (secondary batteries, fuel cells and flow batteries) based on various characterization criteria like energy & power densities, cost, weight, efficiency, controllability, reliability etc. A rational framework is proposed to characterize the technology best suited for a particular application to achieve the best energy utilization. The idea is posed as a constrained multi objective optimization of design, followed by choice based on gravimetric and volumetric cost comparison of the three systems under consideration. For various combinations of characterization criteria, optimal choice of technology for an extensive scope of power and energy is found using the proposed framework and presented as area plots for easy understanding. This framework is then used to examine the prospects and directions of improvement for emerging technologies. Discussions are presented using the example of Vanadium Redox Flow Battery (VRFB).

On the role of hydrodynamic interactions in the engineered-assembly of droplet ensembles

Abstract: Droplets, as they flow inside a microchannel, interact hydrodynamically to result in spatio-temporal patterns. The nature of the interaction decides the type of collective behaviour observed. In this context, we study the application of droplet microfluidics in the area of complex-shape particle synthesis. We show how the dynamics of droplet motion, the steady-state characteristics, the short and long-range hydrodynamics, the dependence on inlet conditions etc. are all related to the features that characterize a device like the functionality (producing many shapes) and robustness (insensitivity to fluctuations). Two primary operating regimes are identified, one where long-range interactions are dominant and the other where they are short-range. In the former, the shapes formed by droplets are steady-state solutions to the governing equations, while in the latter they are a function of how the droplets enter the channel (frequency of entry). We show that identifying the inlet conditions for producing a particle of the desired shape requires the use of a systematic approach to design which involves solving an optimization problem (using genetic algorithms) to identify the optimal operating strategy. With the knowledge of the hydrodynamics between the droplets, we demonstrate how one can reduce the complexity of the design process. We also discuss the control strategies required if one were to realize the identified operating strategy experimentally.

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.