A Distributed Parameter Model for a Solid Oxide Fuel Cell: Simulating Realistic Operating Conditions

We present a detailed multiphysics model capable of simulating the dyn amic behavior of a solid oxide fuel cell (SOFC). This model includes a description of a ll the important physical and chemical processes in a fuel cell: fluid flow, mass and heat trans fer, electronic and ionic potential fields, as well as the chemical and electrochemical react ions. The resulting highly nonlinear, coupled system of differential equations is solved using a fi nite volume discretization. Our interest lies in simulating realistic operating conditions with the obj ective of high efficiency operation at high fuel utilization. While there are a number of studies in the literature that present multiphysics models for SOFCs, few have focused on simulat ing operating conditions that are necessary if SOFC systems are to realize their promise of h igh efficiency conversion of chemical energy to electrical energy. In this report we present s imulation results at operating conditions that approach the required ranges of power density an d overall efficiency. Our results include a) the temperature and composition profiles along a typical f uel cell in a SOFC stack, b) the dynamic response of the cell to step changes in the available inpu t variables. Since models such as the one presented here are fairly expensive computationa lly and cannot be directly used for online model predictive control, one generally looks to use simplifie d reduced order models for control. We briefly discuss the implications of our model results o n the validity of using reduced models for the control of SOFC stacks to show that avoid ing operating regions where well-known degradation modes are activated is non-trivial without u sing detailed multiphysics models

State Estimation in Solid Oxide Fuel Cell(SOFC)System

Solid oxide fuel cells (SOFCs) oer a clean, low pollution technology to electrochemically generate electricity at high eciency. An SOFC consists of a dense solid electrolyte and two porous electrodes in contact with an interconnect on either side. The control of an SOFC stack becomes important in order to ensure adequate and disturbance free electric power. As several controlled/constrained variables are not directly measured in a stack, state estimators can be used in order to study the dynamic behaviour of SOFC stacks as well as to design eective SOFC controllers. In this thesis, A zero dimensional model represented by a set of ordinary dierential equations is derived for dynamic modeling. The model consists of molar balances and an energy balance coupled with a simplied description of the fuel cell electrochemistry. The chemical species considered are H2 and H2O for fuel side (anode side) and O2 and N2 for air side (cathode side) and the electrochemical model accounts for ohmic, concentration and activation losses. Considering the estimation part, the state vector which is to be estimated consists of partial pressure of chemical species and temperature, with voltage as the measurement. Estimation of states for linear systems can be done by Kalman Filter. States of nonlinear systems can be estimated using Extended Kalman Filter(EKF), Unscented Kalman Filter (UKF). We choose UKF for non linear state estimation. UKF is a derivative free state estimator for non linear systems. This work investigates the use of non linear state estimator UKF to estimate the states of SOFC system. This method can be applied to estimate states in any type of fuel cells (PEMFC, AFC etc.) by very slight modications

INCOIS-Real time Automatic Weather Station(IRAWS) dataset - Quality control and significance of height correction

The INCOIS-Real time Automatic Weather Station(IRAWS) program was started in the year 2009 and was first installed onboard ORV Sagar Nidhi. Currently, there are 36 ships carrying IRAWS setup. Apart from storing one minute observations in the log onboard the ship, hourly averaged observations are reported through INSAT satellite communication. This report briefs about the hourly dataset of IRAWS and its quality control. In this report, QC results of SST and all meteorological parameters except radiation parameters is discussed. Specific quality check was applied to wind speed (WS) and sea surface temperature (SST) observations. The WS observations measured onboard few ships had a dimensional correction and SST was observed only on few ships. As SST observations are required to compute meteorological variables like DBT, RH, WS to standard height of 10 m, level-3 dataset of AVHRR SST was utilized in place of IRAWS SST wherever the data is found to be faulty. On similar terms bias correction could not be applied to IRAWS SST with the help of AVHRR SST as the error in SST observations are due to the failure of sensor. However all those IRAWS SST observations that passed the QC check were observed to be of high quality and have a correlation coefficient of 0.5 with AVHRR SST and is significant at 95% significant level. Apart from SST and radiation observations, all other parameters observations are found out to be of good quality with 70 to 90 QC pass percentage . Apart from the details of QC check, significance of representing climate variable at a homogeneous standard height is also shown in this repor

On the existence of equivalence class of RIP-compliant matrices

In Compressed Sensing (CS), the matrices that satisfy the Restricted Isometry Property (RIP) play an important role. But it is known that the RIP properties of a matrix Φ and its `weighted matrix' GΦ (G being a non-singular matrix) vary drastically in terms of RIP constant. In this paper, we consider the opposite question: Given a matrix Φ, can we find a non-singular matrix G such that GΦ has compliance with RIP? We show that, under some conditions, a class of non-singular matrices (G) exists such that GΦ has RIP-compliance with better RIP constant. We also provide a relationship between the Unique Representation Property (URP) and Restricted Isometry Property (RIP), and a direct relationship between RIP and sparsest solution of a linear system of equations

Comparative Study of Parsimonious NARX Models for Three Phase Separator

The three phase separator is an important unit in oil and gas production facilities to separate gas, water and condensate from the fluid (raw gas) generated from gas wells. A dynamic model of the three phase separator is essential for process optimization and control design. Since first principles modelling of the three phase separator is complex, a data based model (Wavelet Network based Nonlinear AutoRegressive eXogenous model (WN-NARX)) is used to capture the dynamics of the process. As most of the terms in the WN-NARX expansion are redundant, this identification problem is over parameterized. In order to handle this issue, a sparsity constraint on the parameter vector is considered and sparse estimation algorithms such as Orthogonal Matching Pursuit (OMP) and Least Angle Regression (LAR) are used for identification of the WA-NARX model. The application of these sparse estimation methods for identification of an industrial three phase separator process is demonstrated

Imaging using Synthetic Aperture Radar

Synthetic Aperture Radar (SAR) is a coherent radar system which utilizes the flight path of the platform to simulate an extremely large antenna aperture synthetically providing high resolution images of large terrain. The aim of this work is to obtain a computationally less costly imaging algorithm to synthesize a 2D and 3D images from corresponding SAR data. In order to achieve this we adopted a interpolation free frequency domain method called Range Stacking. This imaging method forms the target function at individual range bins within the radar range swath which are then stacked together to form final image, hence, it is called range stacking. The algorithm has been applied for 2D point targets as well as 2D terrain images for different squints, look angles and the results are presented. The results show that the range stacking algorithm can provide accurate reconstruction even for very high squint angles. Also a 3D imaging algorithm which is an extension of 2D range stacking algorithm is discussed along with mathematical formulation

Multivariable Polynomials for the Construction of Binary Sensing Matrices

In compressed sensing, the matrices that satisfy restricted isometry property (RIP) play an important role. But to date, very few results for designing such matrices are available. Of interest in several applications is a matrix whose elements are 0’s and 1’s (in short, 0, 1-matrix), excluding column normalization factors. Recently, DeVore (J Complex 23:918–925, 2007) has constructed deterministic 0, 1-matrices that obey sparse recovery properties such as RIP. The present work extends the ideas embedded in DeVore (J Complex 23:918–925, 2007) and shows that the 0, 1-matrices of different sizes can be constructed using multivariable homogeneous polynomials

Ocean fronts detection over the Bay of Bengal using changepoint algorithms - A non-parametric approach

Oceanic fronts are regions over the oceans where a significant change in the characteristics of the water masses is observed. Advanced Very High Resolution Radiometer (AVHRR) satellite imagery over the Bay of Bengal shows regions that are populated by frontal structures. Over the Bay of Bengal, some of the strongest gradients in temperature and salinity are observed. In recent years, there has been a tremendous growth in the availability of satellite imagery and the necessity of automated fast detection of the frontal features is needed for services like potential fishing zones over open oceans. In this article, an algorithm to infer oceanic fronts over the Bay of Bengal is described using changepoint analysis. The changepoint algorithm is combined in a novel way with a contextual median filter to detect frontal features in AVHRR imagery. The changepoint analysis is a non-parametric technique that does not put thresholds on the gradients of brightness temperatures of the satellite imagery. In the open oceans, the gradients of temperature and salinity are not sharp and changepoint analysis is found to be a useful complementary technique to the existing front detecting methods when combined with contextual median filters