Computational fluid dynamics phd thesis

Computational fluid dynamics phd thesis

Thesis Advisor: Daniel G. Cole, PhD, Associate Professor, Department of Mechanical Engineering and Materials Science ii. ON THE USE OF COMPUTATIONAL FLUID DYNAMICS FOR EVALUATION OF NONLINEAR HYDRODYNAMIC GALLOPING ENERGY HARVESTER PERFORMANCE Michael Kristufek, M.S. University of Pittsburgh, Nov 04,  · Correct computational fluid dynamics (CFD) terminology should be used to introduce CFD method correctly. Appropriate boundary condition is selected. Meshing scheme is explicitly explained. 3). Results (30%) "Computational Techniques for Turbulence Generated Noise", PhD thesis, Dept. of Thermo and Fluid Dynamics, Chalmers University of Technology, Göteborg,

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A thesis submitted in conformity with the requirements for the degree of Master of Applied Science Graduate Department of Mechanical and Industrial Engineering University of Toronto. Shi Miao Yu Master of Applied Science Graduate Department of Mechanical and Industrial Engineering University of Toronto Hydraulic pumping systems account for 8.

research has gone into improving pumping system efficiency. Pressure control devices, such as throttling. valves, are crucial to any large industrial or municipal hydraulic system.

However, they are inherently. a source of inefficiency as they achieve pressure reduction by forcing the flow through an orifice thereby. wasting all the mechanical energy. Specifically, this work focused on the design of a control system that can achieve both pressure regulation. and maximum power recovery. Several control methods were surveyed, and the final design presented.

made use of an Unscented Kalman filter, Fuzzy Logic Hill Climb Search method, and a low-level PID. controller in a cascade control structure. The controller was tested in simulation and experimentally. for set-point tracking and disturbance rejection, computational fluid dynamics phd thesis. Finally, the first demonstration of a small scale hydro. This work would not have been possible without the help and support of many people, computational fluid dynamics phd thesis.

First, I would like to express my sincere gratitude to my supervisor, Professor Amy Bilton, who has been a tremendous support during my studies. She was always very patient and generous with her time and made my time at WERL truly enjoyable. I would also like to thank my committee members, Professor Alison Olechowski, for her valuable feedback during the last stage of this thesis.

I want to extend my thanks to all my fellow lab members at WERL. I would like to thank Rowan Walsh and Dr. Hossein Hosseinimanesh for their guidance at the early stage of the project. I would also like to thank Francis Cedric Cruz, Austin McLean, Tarriq Purivatra, Johnny McGroarty, and Nitish Sarkar, for offering me wholehearted support during the physical testing of my work and insightful discussions during the downtimes, computational fluid dynamics phd thesis.

I would also like to thank Han Richard Hu and Jun Seo for their hard work and contribution during their work terms at WERL. Most of all, I would like to thank Youngmok Justin Ko for his help and collaboration during this project. I am very grateful that I get to work along side you, and computational fluid dynamics phd thesis project would not have been possible without your insights, prototyping expertise, and hard work.

It really has been a delightful experience working with all of you, and I will truly miss my time here. I would also like to acknowledge all the financial support that made this project possible.

Specifically, I would like to thank the Department of Mechanical and Industrial Engineering at the University of Toronto, the Natural Sciences and Engineering Research Council of Canada, and the Metcalfe Family Fellowship for Sustainable Energy for all their financial support, computational fluid dynamics phd thesis.

Lastly, I would like to thank my parents, friends, and partner, Computational fluid dynamics phd thesis Li, computational fluid dynamics phd thesis their continuous love and support throughout this work.

List of Figures vii. List of Tables ix. List of Symbols x. A Code Documentation 84 A. Bibliography EMDS systems are the single largest electrical end-use, consuming more than twice as much electricity as lighting, computational fluid dynamics phd thesis, computational fluid dynamics phd thesis next largest end-use.

The same report projected that bywithout comprehensive and effective energy-efficiency improvement measures, energy consumption from electric motors is expected to rise to computational fluid dynamics phd thesis, TWh per year and CO2 emissions to 8, Mt per year. These daunting computational fluid dynamics phd thesis are the aggregation of the energy consumed by different motors operating within a wide set of applications in every energy use sector.

The European Union also estimated that pumping systems has an energy savings potential of 42 billion kWh 1. This means that there is room for efficiency increase and a huge energy saving opportunity for pumping systems. To lower energy consumption, significant research has gone into increasing hydraulic systems effi- ciency. The primary focuses are increasing pump efficiency, proper sizing, computational fluid dynamics phd thesis, piping pressure drop, and mechanical transmission efficiency.

These valves regulate system pressure by dissipating mechanical energy through the action of springs, diaphragms, and orifices. For PRVs, energy is inevitably lost during the process of pressure reduction leading to system inefficiencies.

An often proposed alternative to PRVs is the use of variable frequency devices VFD to control pump outlet pressure. However, this solution is only viable for small systems with a single pressure requirement. For larger hydraulic systems, such as city water distribution networks, PRVs are unavoidable and necessary [5].

The considerable energy- saving opportunities and limited PRV alternatives, the Water and Energy Research Lab WERL at the University of Toronto proposed a novel pico-scale hydro turbine design that can simultaneously regulate pressure and recover energy. Commercial hydro turbines are mega infrastructures employed in water dams that can generate up.

Introduction 2. to MW of power with runners up to 10 m in diameter. The pico-scale turbine presented in this work is only 10 cm in diameter and has a rated mechanical power output of W. The turbine was designed to be easily installed into existing piping due to its small size and inline configuration.

The work presented in this thesis follows from previous work on the turbine by Rowan Walsh [6] and Yongmok Ko [7]. The pico-scale turbine was first designed for a self-powered water disinfection device.

In this initial application, the turbine had stationary guide vanes and was optimized for a single operating point. In his work, Walsh streamlined the design process and optimization process of pico-scale sized turbines. As a final missing piece to the proof of concept, computational fluid dynamics phd thesis, the work presented in this thesis will detail the control system design of the pressure regulating pico-scale turbine.

Recovering power while achieving pressure reduction is not a completely new idea. For example, in steam systems, it is common to employ a steam turbine to supply low-pressure steam instead of a PRV wherever it is economical to do so [8], computational fluid dynamics phd thesis. In hydraulic systems, however, this practice is less common.

The only device largely used inside pipes are Pump-As-Turbine PAT systems in water distribution networks [9] [10]. However, computational fluid dynamics phd thesis, a major drawback of the PAT system is that it lacks a control device able to change its characteristic curve along with the desired discharge. Other proposed pressure reducing turbines includes the positive displacement turbine for heated water pipes in Ref.

In all the proposed turbines, there is generally a lack of control system due to their small size. Hydraulic system conduits, such as water distribution pipes, are often characterized by large variability in flow rate and pressure drop.

This variability often discourages the industry from installing energy recovering devices such as the PAT systems. In general, there is little to no literature on control system design and analysis for small scale hydro turbines. This literature gap and the potential for major energy-savings were the main motivators for this work.

In other words, computational fluid dynamics phd thesis, the turbine needs to adjust itself to keep the outlet pressure constant while upstream pressure and flow rate can vary depending on other processes in the fluid network. In addition, a secondary objective of the control system is to maximize energy recovery.

This means that the turbine needs to operate at maximum efficiency at all times, and with changing pressure and flow rates, the turbine would need to find and navigate to new optimal points.

This objective is often referred to as optimal computational fluid dynamics phd thesis control in literature. This thesis outlines the design of a control system that can achieve both pressure regulation and optimal tracking. The main contributions of this thesis are as follow:. The model took a novel approach for non-linear dynamics modeling.

Where most turbine used look up tables or polynomial fitting, the work in this thesis used a Gaussian Process Regression model to map turbine performances.

Chapter 1. Introduction 3. In this work, parameter identification was formulated as a non-linear optimiza- tion problem. To computational fluid dynamics phd thesis this problem, global heuristic search algorithms, namely Genetic Algorithm and Particle Swarm Optimization, were used to find the best parameters that minimized the error between the simulated and the actual system.

The final identified model was used for all subsequent controller testing. The proposed strategies range from a model-based approach to pure feedback control. The proposed methods were tested both in simulation and experimentally.

Two different control approaches, a model-based and a feedback-based controller were proposed and tested in simulation and experimentally. To prioritize pressure regulation, the outer loop optimal tracking controller was run at a slower rate than the lower level pressure controller, computational fluid dynamics phd thesis.

The final controller design was tested experimentally and resulted in the first demonstration of combined pressure control and optimal tracking of a pico-scale turbine. Chapter 3 presents the physical pico-scale turbine and experimental set-up.

Following the physical system, Chapter 4 presents the modeling and the parameter identification methods used to create a turbine simulator for controller testing. Chapter 5 then presents the different control strategies for both pressure regulation and optimal tracking. Chapter 2. Both pressure control valves PCVs and pressure regulating valves PRVs are commonly used devices in fluids networks. A PCV is an electromechanical device that opens and closes in response to pressure measurements, while PRVs are a fully mechanical alternative to PCVs and provide a similar downstream pressure regulation at a lower cost.

Both of them will be referred to as pressure regulating devices PRDs in this work. An example diagram of a commercial PRD with a pilot valve is shown in Fig. From the figure, it can be seen that the valve aims to maintain constant downstream pressure, while the upstream pressure and flow rates fluctuate. The pressure stays constant due to the force of the spring pushing against the incoming flow closing and opening the orifice accordingly, computational fluid dynamics phd thesis.

Computational Fluid Dynamics Research at the Department of Aeronautics

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Computational Fluid Dynamics | Custom PHD Thesis

Students Computational Fluid Dynamics Phd Thesis are often pressed for time when juggling multiple responsibilities such as babysitting, part-time jobs, and even chores around the house. You may even have other homework assignments that need more attention /10() It is entirely up to you which package you choose, whether it Phd Dissertation Computational Fluid Dynamics is the cheapest one or the most expensive one, our quality of work will not depend on the Phd Dissertation Computational Fluid Dynamics package. We provide top-notch quality to every client, irrespective of the amount they pay to us/10() Computational Fluids Dynamics Model A detailed Computational Fluids Dynamics (CFD) model was developed and validated in previous work in the lab. The work in this thesis will use CFD modelling to predict turbine performance at different guide vane gaps to test controller robustness in