Raghunathan Rengasamy

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.

Recently we proposed a simple, computationally inexpensive multi-agent simulation strategy to understand drop movement in a microchannel with Hele-Shaw flow geometry (2D). This has helped us understand the collective behavior of the drops in these systems. However, transitioning these systems to practice would require that strategies are developed to combat the inherent fluctuations that are a part of any physical system. In the present work we utilize the simulation strategy for understanding the impact of design choices in minimizing the effect of uncertainties, and the sensitivity of the device behavior to operational variables that can potentially be used as controlled variables. We study a control metric, time of contact, which is defined as the time spent by a drop in the microchannel when its distance from its neighboring drops is less than some critical value. By simplifying the model further we are able to analyze the contact time dynamics as a function of geometric parameters and operating conditions analytically. We also carry out the complete numerical simulation of our models to understand active control of drop clusters.

Lithium ion batteries are one of the most commercially used batteries. Though they are widely used in mobile phones, laptops and other consumer electronics, concerns related to their safety and efficient operation still persists. One of the major issues in Li-ion batteries is the capacity degradation with aging due to various mechanisms such as solid electrolyte interphase (SEI) formation and dissolution, thermal runaway, and Li-plating. In this work, we describe a capacity fade minimizing model predictive control approach for identification and realization of optimal charge-discharge cycles for Li-ion batteries. Optimum charging profiles are obtained such that the reduction in charge carrying capacity with cycling is minimized, while still obtaining required charging. We expect the proposed strategy to improve battery capacity and prolong lifespan. Examples that demonstrate the significance of the proposed framework by comparing battery performance with and without the presence of controller are discussed. Extensions to this work in terms of addressing various battery failure mechanisms, on-line identification of failure mechanisms, and designs for on-line implementation in real battery systems are also outlined.

Energy storage (batteries) and conversion devices (fuel cells) operate based on electrochemical principles. Electrochemical impedance spectroscopy (EIS) is an important experimental technique that can be used to optimize the performance of these devices at the design stage. Further, EIS can also be used for non-invasive, in-situ diagnostics of device performance, while operational. While EIS is a powerful technique, the instrumentation required for implementation can be bulky and the time for analysis could also become unacceptable, particularly for online applications. The proposed work is an investigation and development of a rapid impedance measurement technology using large bandwidth and short duration diagnostic signals. The key concept behind the approach is the use of a particular definition of instantaneous frequency in tandem with chirp signals for testing. The instantaneous frequency definition allows one-to-one time frequency mapping and the chirp signal incorporates a wide bandwidth of frequencies. As a result, a novel impedance plot qualitatively, and largely quantitatively, matching the corresponding EIS plot is generated. Consequently, any diagnostic approach based on EIS can potentially be realized using the proposed approach. The impedance plots are generated in a very short time making the approach amenable for online applications. In-silico validation of the proposed method on few equivalent circuits of electrochemical systems is presented in this work; future work will include experimental validation of the technique on real electrochemical systems.

A two-dimensional concentrated emulsion exhibits spontaneous rapid destabilization through an avalanche of coalescence events which propagate through the assembly stochastically. We propose a deterministic model to explain the average dynamics of the avalanching process. The dynamics of the avalanche phenomenon is studied as a function of a composite parameter, the decay time ratio, which characterizes the ratio of the propensity of coalescence to cease propagation to that of propagation. When this ratio is small, the avalanche grows autocatalytically to destabilize the emulsion. Using a scaling analysis, we unravel the relation between a local characteristic of the system and a global system wide effect. The anisotropic nature of local coalescence results in a system size dependent transition from nonautocatalytic to autocatalytic behavior. By incorporating uncertainty into the parameters in the model, several possible realizations of the coalescence avalanche are generated. The results are compared with the Monte Carlo simulations to derive insights into how the uncertainty propagates in the system.

In power networks, where multiple fuel cell stacks are employed in a series-parallel configuration to deliver the required power, optimal sharing of the power demand between different stacks is an important problem. This is because the total current collectively produced by all the stacks is directly proportional to the fuel utilization, through stoichiometry. As a result, one would like to produce the required power while minimizing the total current produced. In this paper, an optimization formulation is proposed for this power distribution control problem. An algorithm that identifies the globally optimal solution for this problem is developed. Through an analysis of the KKT conditions, the solution to the optimization problem is decomposed into off-line and on-line computations. The on-line computations reduce to solving a nonlinear equation. For an application with a specific V–I function derived from data, we show that analytical solutions exist for on-line computations. We also discuss the wider applicability of the proposed approach for similar problems in other domains.

In this contribution we propose an active Fault Tolerant Control (FTC) strategy which enables the isolation and identification of valve stiction and valve blocking, in addition to the additive faults like sensor and actuator biases. The developed method is an extension of the original method proposed by Prakash et al. (2002). This method is based on the Kalman filter and is developed under the assumption that the monitored system is Linear Time Invariant (LTI). It has been shown to work well for additive faults such as sensor and actuator biases. Within this method the fault isolation and identification task is based on the Generalized Likelihood Ratio (GLR) test by which the most plausible fault type in a library of faults is selected following estimation of fault parameters. © 2010 Elsevier B.V.

The primary objective in solving optimal input design problems is to obtain maximally informative inputs to be used as perturbation signals in system identification experiments. In plant-friendly identification, the designer has to respect constraints on experiment time, input and output amplitudes or input move sizes. This work focuses on plant friendly input design with constraints on input move size and output power. We present a convex relaxation to the problem of designing an informative input subject to input move size and output power constraints. The problem is finitely parametrized using ideas from Tchebycheff systems and reformulated as a SemiDefinite Programme. © 2011 American Chemical Society.

Kalman filter and its variants have been used for state estimation of systems described by ordinary differential equation (ODE) models. While state and parameter estimation of ODE systems has been studied extensively, differential algebraic equation (DAE) systems have received much less attention. However, most realistic chemical engineering processes are modelled as DAE systems and hence state and parameter estimation of DAE systems is a significant problem. Becerra et al. (2001) proposed an extension of the extended kalman filter (EKF) for estimating the states of a system described by nonlinear differential-algebraic equations (DAE). One limitation of this approach is that it only utilizes measurements of the differential states, and is therefore not applicable to processes in which algebraic states are measured. In this paper, we address the state estimation of constrained nonlinear DAE systems. The novel aspects of this work are: (i) development of a modified EKF approach that can utilize measurements of both algebraic and differential states, (ii) development of a recursive approach for the inclusion of constraints, and (iii) development of approaches that utilize unscented sampling in state and parameter estimation of nonlinear DAE systems; this has not been attempted before. The utility of these estimators is demonstrated using electrochemical and reactive distillation processes. © 2010 Elsevier Ltd.

State estimation is an important problem in process operations. For linear dynamical systems, Kalman Filter (KF) results in optimal estimates. Chemical engineering problems are characterized by nonlinear models and constraints on the states. Nonlinearities in these models are handled effectively by the Extended Kalman Filter (EKF), whereas constraints pose more serious problems. Several constrained estimation problems where the EKF approach fails have been reported in the literature. To address this issue, receding horizon approaches such as the Moving Horizon Estimation (MHE) have been proposed. The MHE approach has been shown to provide the most reliable estimates in several example problems; albeit at a high computational price. Unlike the KF, the MHE formulation does not use an explicit predictor-corrector approach. In this paper, we study the following questions in nonlinear constrained state estimation: (i) can the EKF be extended to include a receding horizon in a simple intuitive fashion? (ii) are there any performance gains over an EKF due to a receding horizon? and, (iii) are there any computational gains over the standard MHE through such an extension? A Receding Nonlinear Kalman (RNK) Filter formulation is proposed to answer these questions. The RNK formulation follows a predictor-corrector approach and uses linearization of the state space model for covariance calculation much like the EKF approach. We demonstrate through examples that inclusion of a receding horizon improves performance over the standard EKF approach. We also discuss the computational properties of RNK in comparison with MHE. © 2011 Elsevier B.V.