Characterization of concrete materials using non-destructive wave-propagation testing techniques

Non-destructive testing (NDT) of concrete members has been widely used for characterisation of material and assessment of functional structures without impairing their functions and performances. This thesis focuses on addressing critical challenges related to the practical implementation of NDT techniques based on wave-propagation approaches for characterisation of concrete members used in civil infrastructures. Specially, this research aims to achieve three interdependent objectives related to developing NDT techniques with piezoceramic-based transducers: monitoring of very early-age concrete hydration process, detection, and monitoring of cracking in concrete members of different complexity under loading. The concept of piezoceramic-based Smart Aggregate (SA) transducers is central to this research. Embedded SA transducers with an active sensing method have shown great potential for characterisation of construction materials such as concrete and concrete-steel composites. Based on the developed SA based approaches, an active sensing approach with appropriate arrangement of SAs in and on concrete members, and analysis of the received signal using the power spectral density, total received power and damage indexes is developed and applied in this thesis. To confirm its applicability for characterisation of very early-age concrete, a systematic investigation is performed into concrete specimens with different values of water-to-cement ratio due to slightly different initial water amounts, and different separation distances between the embedded SAs. For the detection and monitoring of cracking in concrete members under loading the mounted SA based approach is proposed and applied. It is shown that NDT systems, based on this approach, provide detection and monitoring of cracking in a variety of concrete members under loading, including relatively simple concrete beams and reinforced concrete beams under bending, and reinforced concrete slab as a part of a complex composite member under cyclic loading. Comparisons are provided between the proposed system and conventional load cell and strain gauge systems with each tested member

Design and Development of Real-Time Optimal Energy Management System for Hybrid Electric Vehicles

This paper describes a strategy to develop an energy management system (EMS) for a charge-sustaining power-split hybrid electric vehicle. This kind of hybrid electric vehicles (HEVs) benefit from the advantages of both parallel and series architecture. However, it gets relatively more complicated to manage power flow between the battery and the engine optimally. The applied strategy in this paper is based on nonlinear model predictive control approach. First of all, an appropriate control-oriented model which was accurate enough and simple was derived. Towards utilization of this controller in real-time, the problem was solved off-line for a vast area of reference signals and initial conditions and stored the computed manipulated variables inside look-up tables. Look-up tables take a little amount of memory. Also, the computational load dramatically decreased, because to find required manipulated variables the controller just needed a simple interpolation between tables

Assessment Of Nonlinear Static (Pushover) Procedures For Seismic Evaluation Of Reinforced Concrete Structures

In general, earthquake is one of the most serious natural disasters that mankind has ever suffered since the first day of civilization. Hence, the seismic performance of structures subjected to earthquake always becomes critical issues. This thesis presents the assessment of current nonlinear static procedures using nonlinear time history procedure. The selected pushover procedures in this research are consisting of Coefficient Method, Capacity Spectrum Method and Modal Pushover Method. Since plastic hinge length is an effective parameter in pushover analysis, this study discusses different plastic hinge lengths. These lengths are calculated for both default and user-defined cases. In this context, 2, 5, 8 and 12 storey frame were selected to represent the real low, medium and high rise regular reinforcement concrete structure. The results of the pushover analysis indicated that behaviour of the structures using modal pushover analysis method and coefficient method (under certain conditions) were more realistically than those analysed using capacity spectrum method. Moreover, the comparison of the results obtained from selected plastic hinge length reveals that, although the results of user-defined and default plastic hinge length in yielding state are almost similar, the results in ultimate state are significantly different. Therefore, it can be concluded that in this study proposed user-defined plastic hinge length shows better performance of hinge in analysis as compared to default plastic hinge length

Real-time Optimal Energy Management System for Plug-in Hybrid Electric Vehicles

Air pollution and rising fuel costs are becoming increasingly important concerns for the transportation industry. Hybrid electric vehicles (HEVs) are seen as a solution to these problems as they off er lower emissions and better fuel economy compared to conventional internal combustion engine vehicles. A typical HEV powertrain consists of an internal combustion engine, an electric motor/generator, and a power storage device (usually a battery). Another type of HEV is the plug-in hybrid electric vehicle (PHEV), which is conceptually similar to the fully electric vehicle. The battery in a PHEV is designed to be fully charged using a conventional home electric plug or a charging station. As such, the vehicle can travel further in full-electric mode, which greatly improves the fuel economy of PHEVs compared to HEVs. In this study, an optimal energy management system (EMS) for a PHEV is designed to minimize fuel consumption by considering engine emissions reduction. This is achieved by using the model predictive control (MPC) approach. MPC is an optimal model-based approach that can accommodate the many constraints involved in the design of EMSs, and is suitable for real-time implementations. The design and real-time implementation of such a control approach involves control-oriented modeling, controller design (including high-level and low-level controllers), and control scheme performance evaluation. All of these issues will be addressed in this thesis. A control-relevant parameter estimation (CRPE) approach is used to make the control-oriented model more accurate. This improves the EMS performance, while maintaining its real-time implementation capability. To reduce the computational complexity, the standard MPC controller is replaced by its explicit form. The explicit model predictive controller (eMPC) achieves the same performance as the implicit MPC, but requires less computational effort, which leads to a fast and reliable implementation. The performance of the control scheme is evaluated through different stages of model-in-the-loop (MIL) simulations with an equation-based and validated high-fidelity simulation model of a PHEV powertrain. Finally, the CRPE-eMPC EMS is validated through a hardware-in-the-loop (HIL) test. HIL simulation shows that the proposed EMS can be implemented to a commercial control hardware in real time and results in promising fuel economy figures and emissions performance, while maintaining vehicle drivability

Using Adaptive Pole Placement Control Strategy for Active Steering Safety System

This paper studies the design of an adaptive control strategy to tune an active steering system for better drivability and maneuverability. In the first step, adaptive control strategy is applied to estimate the uncertain parameters on-line (e.g. cornering stiffness), then the estimated parameters are fed into the pole placement controller to generate corrective feedback gain to improve the steering system dynamic's characteristics. The simulations are evaluated for three types of road conditions (dry, wet, and icy), and the performance of the adaptive pole placement control (APPC) are compared with pole placement control (PPC) and a passive system. The results show that the APPC strategy significantly improves the yaw rate and side slip angle of a bicycle plant model

Design and evaluation of a real-time fuel-optimal control system for series hybrid electric vehicles

Razavian, R. S., Taghavipour, A., Azad, N. L., & McPhee, J. (2012). Design and evaluation of a real-time fuel-optimal control system for series hybrid electric vehicles. International Journal of Electric and Hybrid Vehicles, 4(3), 260. Final version published by Inderscience Publishers, and available at: https://doi.org/10.1504/IJEHV.2012.050501We propose a real-time optimal controller that will reduce fuel consumption in a series hybrid electric vehicle (HEV). This real-time drive cycle-independent controller is designed using a control-oriented model and Pontryagin's minimum principle for an off-line optimisation problem, and is shown to be optimal in real-time applications. Like other proposed controllers in the literature, this controller still requires some information about future driving conditions, but the amount of information is reduced. Although the controller design procedure explained here is based on a series HEV with NiMH battery as the electric energy storage, the same procedure can be used to find the supervisory controller for a series HEV with an ultra-capacitor. To evaluate the performance of the model-based controller, it is coupled to a high-fidelity series HEV model that includes physics-based component models and low-level controllers. The simulation results show that the simplified control-oriented model is accurate enough in predicting real vehicle behaviour, and final fuel consumption can be reduced using the model-based controller. Such a reduction in HEVs fuel consumption will significantly contribute to nationwide fuel saving.The authors would like to thank the Natural Sciences and Engineering Research Council (NSERC) of Canada, Toyota, and Maplesoft for their support of this research

Progress Process, Existing Barriers And The Presentation Of Solutions For Modification And Attraction Of Private Sector Investment In Developing Non-Governmental Schools In Iran

 The present study seeks to investigate the obstacles to the participation and expansion of private sector's activities in education. This paper has been prepared having following aims in the mind:  "how the existing status of nongovernmental schools in Iran", "identifying barriers to attracting private sector investment in school”™s Non-governmental organizations in the country "and" prioritizing appropriate strategies for attracting private sector investment in these schools ". This paper sums up research findings. The statistical population of this study is all the founders and managers of nongovernmental schools, these school's students, their parents, as well as all experts and pundits in this field. The statistical sample of the study have been selected based on random sampling in six provinces. The results of the study show that the human resources, space condition and facilities of non-governmental schools in the sample were appropriate. Non-governmental schools cover more than 9 percent of all the students of the country

Real-time predictive control strategy for a plug-in hybrid electric powertrain

The final publication is available at Elsevier via https://dx.doi.org/10.1016/j.mechatronics.2015.04.020 © 2015. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Model predictive control is a promising approach to exploit the potentials of modern concepts and to fulfill the automotive requirements. Since, it is able to handle constrained multi-input multi-output optimal control problems. However, when it comes to implementation, the MPC computational effort may cause a concern for real-time applications. To maintain the advantage of a predictive control approach and improve its implementation speed, we can solve the problem parametrically. In this paper, we design a power management strategy for a Toyota Prius plug-in hybrid powertrain (PHEV) using explicit model predictive control (eMPC) based on a new control-oriented model to improve the real-time implementation performance. By implementing the controller to a PHEV model through model and hardware-in-the-loop simulation, we get promising fuel economy as well as real-time simulation speed.NSERCToyotaMaplesoft Industrial Research Chair progra

Parameter estimation of an electrochemistry-based lithium-ion battery model

The final publication is available at Elsevier via http://doi.org/10.1016/j.jpowsour.2015.04.154" © 2015. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Parameters for an electrochemistry-based Lithium-ion battery model are estimated using the homotopy optimization approach. A high-fidelity model of the battery is presented based on chemical and electrical phenomena. Equations expressing the conservation of species and charge for the solid and electrolyte phases are combined with the kinetics of the electrodes to obtain a system of differential-algebraic equations (DAEs) governing the dynamic behavior of the battery. The presence of algebraic constraints in the governing dynamic equations makes the optimization problem challenging: a simulation is performed in each iteration of the optimization procedure to evaluate the objective function, and the initial conditions must be updated to satisfy the constraints as the parameter values change. The ε-embedding method is employed to convert the original DAEs into a singularly perturbed system of ordinary differential equations, which are then used to simulate the system efficiently. The proposed numerical procedure demonstrates excellent performance in the estimation of parameters for the Lithium-ion battery model, compared to direct methods that are either unstable or incapable of converging. The obtained results and estimated parameters demonstrate the efficacy of the proposed simulation approach and homotopy optimization procedure.The financial support of the NSERC/Toyota/Maplesoft Industrial Re-search Chair program is gratefully acknowledged

An optimal power management strategy for power split plug-in hybrid electric vehicles

Originally published by Inderscience: Taghavipour, A., Azad, N. L., & McPhee, J. (2012). An optimal power management strategy for power split plug-in hybrid electric vehicles. International Journal of Vehicle Design, 60(3/4), 286. doi:10.1504/ijvd.2012.050085Model Predictive Control (MPC) can be an interesting concept for designing a power management strategy for Hybrid Electric Vehicles (HEVs) according to its capability of online optimisation by receiving current information from the powertrain and handling hard constraints on such problems. In this paper, a power management strategy for a power split plug-in HEV is proposed using the concept of MPC to evaluate the effectiveness of this method on minimising the fuel consumption of those vehicles. Also, the results are compared with dynamic programming