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

Intelligent Integration of a Wind Farm to an Utility Power Network with Improved Voltage Stability

The increasing effect of wind energy generation will influence the dynamic behavior of power systems by interacting with conventional generation and loads. Due to the inherent characteristics of wind turbines, non-uniform power production causes variations in system voltage and frequency. Therefore, a wind farm requires high reactive power compensation. Flexible AC transmission systems (FACTS) devices such as SVCs inject reactive power into the system which helps in maintaining a better voltage profile. This paper presents the design of a linear and a nonlinear coordinating controller between a SVC and the wind farm inverter at the point of interconnection. The performances of the coordinating controllers are evaluated on the IEEE 12 bus FACTS benchmark power system where one of the generators is replaced by a wind farm supplying 300 MW. Results are presented to show that the voltage stability of the entire power system during small and large disturbances is improved

Optimization of Time-Course Experiments for Kinetic Model Discrimination

Systems biology relies heavily on the construction of quantitative models of biochemical networks. These models must have predictive power to help unveiling the underlying molecular mechanisms of cellular physiology, but it is also paramount that they are consistent with the data resulting from key experiments. Often, it is possible to find several models that describe the data equally well, but provide significantly different quantitative predictions regarding particular variables of the network. In those cases, one is faced with a problem of model discrimination, the procedure of rejecting inappropriate models from a set of candidates in order to elect one as the best model to use for prediction

Mesenchymal stem cells: from experiment to clinic

There is currently much interest in adult mesenchymal stem cells (MSCs) and their ability to differentiate into other cell types, and to partake in the anatomy and physiology of remote organs. It is now clear these cells may be purified from several organs in the body besides bone marrow. MSCs take part in wound healing by contributing to myofibroblast and possibly fibroblast populations, and may be involved in epithelial tissue regeneration in certain organs, although this remains more controversial. In this review, we examine the ability of MSCs to modulate liver, kidney, heart and intestinal repair, and we update their opposing qualities of being less immunogenic and therefore tolerated in a transplant situation, yet being able to contribute to xenograft models of human tumour formation in other contexts. However, such observations have not been replicated in the clinic. Recent studies showing the clinical safety of MSC in several pathologies are discussed. The possible opposing powers of MSC need careful understanding and control if their clinical potential is to be realised with long-term safety for patients

Lateral response of pile due to combined load under free and fixed conditions

Pile foundations are used to support both vertical and horizontal loads in many geotechnical projects, such as coastal and offshore engineering. In this project, the Finite Difference Method is proposed to solve the differential equation governing the lateral and axial pile response. Initially, the behaviour of the pile subjected to lateral load will be analysed. The effect of various parameters like pile head fixity, the cohesion of surrounding soil, pile diameter, and length of the pile on lateral pile response will be analysed. Finally with these conditions, the deflections profile of the pile subjected to both lateral and axial load is investigated. By using python code we can easily find out the increase in diameter of pile, cohesion of surrounding soil effect on pile head and effect of increase in combined load will be studied. The above stated parameters will be studied for combined loading also under the free and fixed head conditions

Identification of urinary proteins potentially associated with diabetic kidney disease

Diabetic nephropathy (DN) is the most common cause of chronic kidney disease. Although several parameters are used to evaluate renal damage, in many instances, there is no pathological change until damage is already advanced. Mass spectrometry-based proteomics is a novel tool to identify newer diagnostic markers. To identify urinary proteins associated with renal complications in diabetes, we collected urine samples from 10 type 2 diabetes patients each with normoalbuminuria, micro- and macro-albuminuria and compared their urinary proteome with that of 10 healthy individuals. Urinary proteins were concentrated, depleted of albumin and five other abundant plasma proteins and in-gel trypsin digested after prefractionation on sodium dodecyl sulfate polyacrylamide gel electrophoresis. The peptides were analyzed using a nanoflow reverse phase liquid chromatography system coupled to linear trap quadrupole-Orbitrap mass spectrometer. We identified large number of proteins in each group, of which many were exclusively present in individual patient groups. A total of 53 proteins were common in all patients but were absent in the controls. The majority of the proteins were functionally binding, biologically involved in metabolic processes, and showed enrichment of alternative complement and blood coagulation pathways. In addition to identifying reported proteins such as α2-HS-glycoprotein and Vitamin D binding protein, we detected novel proteins such as CD59, extracellular matrix protein 1 (ECM1), factor H, and myoglobin in the urine of macroalbuminuria patients. ECM1 and factor H are known to influence mesangial cell proliferation, and CD59 causes microvascular damage by influencing membrane attack complex deposition, suggestive their biological relevance to DN. Thus, we have developed a proteome database where various proteins exclusively present in the patients may be further investigated for their role as stage-specific markers and possible therapeutic targets