MODELLING AND CONTROL OF COMBUSTION PHASING OF AN RCCI ENGINE

Reactivity controlled compression ignition (RCCI) is a novel combustion strategy introduced to achieve near-zero NOx and soot emissions while maintaining diesel-like efficiencies. Meanwhile, precise control of combustion phasing is a key in realization of high fuel conversion efficiency as well as meeting stringent emission standards. Model-based control of RCCI combustion phasing is a great tool for real-time control during transient operation of the engine, which requires a computationally efficient combustion model that encompasses factors such as, injection timings, fuel blend composition and reactivity. In this thesis, physics-based models are developed to predict the combustion metrics of an RCCI engine. A mean value control-oriented model (COM) of RCCI is then developed by combining the auto-ignition model, the burn duration model, and a Wiebe function to predict combustion phasing. Development of a model-based controller requires a dynamic model which can predict engine operation, i.e., estimation of combustion metrics, on a cycle-to-cycle basis. Hence, the mean-value model is extended to encompass the full-cycle engine operation by including the expansion and exhaust strokes. In addition, the dynamics stemming from the thermal coupling between cycles are accounted for, that results in a dynamic RCCI control-oriented model capable of predicting the transient operation of the engine. This model is then simplified and linearized in order to develop a linear observer-based feedback controller to control the combustion phasing using the premixed ratio (the ratio of the PFI fuel to the total fuel injected) of the gasoline/diesel fuel. The designed controller depicts an accurate tracking performance of the desired combustion phasing and successfully rejects external disturbances in engine operating conditions

Plug-and-play robust voltage control of DC microgrids

The purpose of this paper is to explore the applicability of linear time-invariant dynamical systems with polytopic uncertainty for modeling and control of islanded dc microgrids under plug-and-play (PnP) functionality of distributed generations (DGs). We develop a robust decentralized voltage control framework to ensure robust stability and reliable operation for islanded dc microgrids. The problem of voltage control of islanded dc microgrids with PnP operation of DGs is formulated as a convex optimization problem with structural constraints on some decision variables. The proposed control scheme offers several advantages including decentralized voltage control with no communication link, transient stability/performance, PnP capability, scalability of design, applicability to microgrids with general topology, and robustness to microgrid uncertainties. The effectiveness of the proposed control approach is evaluated through simulation studies carried out in MATLAB/SimPowerSystems Toolbox

The Role of General Causality Orientations on Self-Care Behaviors in Patients with Type 2 Diabetes

AbstractThe aim of this study was to assess the role of causality orientations in self-care behaviors in patients with type II diabetes. In this research, 60 Ss from Tabriz center of Sina hospital were selected through random sampling. Two questionnaires including General Causality orientations (GCOS), and Self-care behaviors (SDSCA) were used. Data were analyzed by Pearson's correlation coefficients, regression analysis and t-test. According to results self-care behaviors showed a significant positive relation with autonomy orientation (and a significant negative relation with impersonal orientation.. Additionally, males and females didn’t differ in self care behaviors and causality orientations

Nano-integrated Polymeric Suspended Microfluidic Platform for Ultra-Sensitive Bio-Molecular Recognition

The development of biosensors for the detection of biomolecules recognition is an extremely important problem in life science and clinical diagnostics. Many researchers and bio-scientists around the world are looking to present a simple and cost-effective ultra-sensitive platform for the bio-sensing applications in order to detect protein-protein interaction, DNA hybridization, and antigen-antibody interaction. Microcantilever (MC) transducer is a well-known sensing mechanism for biosensing application. In this method, a surface-stress will be induced through the bimolecular recognition on the cantilever surface, which results in the MC deflection. The magnitude of deflection is related to the number of (or concentration of) biomolecules of interest that were immobilized on the MC's surface during the biosensing protocole. However, one of the main drawbacks of cantilever biosensors is the high amount of analytes (proteins, DNA, polypeptides, etc.) that are required for sensing experiments as the cantilever must be submerged in the analytes. In addition, the cantilever bending read-out system (that can be optical or electrical) has to be designed sensitive enough to make it possible to measure deflection in the range of a few nanometers. In order to move toward ultra-sensitive biomolecular detection, one way is to increase the read-out resolution which makes the system more complex and expensive. Another solution is to fabricate the MC from materials different from silicon with less stiffness. In addition, in order to reduce analytes consumption, integration of microfluidic systems with microcantilevers would be beneficial. Through this integration, both the volume of analytes and the time of response are reduced. This thesis reports a promising method towards ultra-sensitive biosensing by integration of microfluidic system into a polymeric microcantilever. The fabricated platform is referred as “Polymeric Suspended Microfluidics". In order to immobilize biomolecule of interest for the biosensing application inside the microfluidic system, gold nanoparticles (AuNPs) are integrated into the buried microfluidics by two different methods. The thesis also presents a novel 3D micromixer in order to implement in-situ synthesis of AuNPs. The 3D micromixer has been fabricated and tested for characterizing mixing performance. The results of biosensing shows significant improvement in the sensitivity of the proposed platform compared with the common silicon based MC biosensor. The results show the proposed integrated sensing platform achieved a detection limit of 2ng/ml (100pM) toward the growth hormones biosensing (Ag-Ab interaction detection). The results demonstrate a proof-of-principal for successful polymeric cantilever fabrication towards the next generation of cantilever-based biosensing mechanism which has high potential to enable femtomolar (fM) biomolecular recognition detection

VEHICULAR TRAFFIC MODELLING, DATA ASSIMILATION, ESTIMATION AND SHORT TERM TRAVEL TIME PREDICTION

This dissertation deals with the problem of short term travel time prediction. Traffic dynamics models and traffic measurements are in particular the tools in approaching this problem. Effectively, a data-driven traffic modeling approach is adopted. Assimilating key traffic variables (flow, density, and speed) under standard continuum traffic flow models is fairly straight-forward. In current practice, travel time (space integral of pace or inverse of speed) is obtained through trajectory construction methods. However, the inverse problem of estimating speeds based on travel times is generally under-determined. In this dissertation, appropriate dynamic model and solution algorithms are proposed to jointly estimate speeds and travel times. This model essentially paves the way to assimilate travel time data with other traffic measurements. The proposed travel time prediction framework takes into account the fact that in reality neither traffic models nor measurements are flawless. Therefore, optimal state estimation methods to solve the resulting state-space model in real-time are proposed. Alternative optimality criterion such as minimization of the variance of estimate errors and minimization of the maximum (minmax) estimate errors are considered. Practical considerations such as occurrence of missing data, delayed (out of order) arrival of measurements and their impact on solution quality are addressed. Proposed models and algorithms are tested on datasets provided under NGSIM project

Fixed-order Controller Design for State Space Polytopic Systems by Convex Optimization

In this paper, a new method for fixed-order controller design of systems with polytopic uncertainty in their state space representation is proposed. The approach uses the strictly positive realness (SPRness) of some transfer functions, as a tool to decouple the controller parameters and the Lyapunov matrices and represent the stability conditions and the performance criteria by a set of linear matrix inequalities. The quality of this convex approximation depends on the choice of a central state matrix. It is shown that this central matrix can be computed from a set of initial fixed-order controllers computed for each vertex of the polytope. The stability of the closed-loop polytopic system is guaranteed by a linear parameter dependent Lyapunov matrix. The results are extended to fixed-order H infinity controller design for SISO systems