1 dr. nikolas xiros naval arch. & marine eng. at the university of new orleans southeast...

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Dr. Nikolas Xiros

Naval Arch. & Marine Eng. at the University of New Orleans

Southeast Symposium on Contemporary Engineering TopicsNew Orleans, Louisiana – September 19, 2014

Ocean Current Power Harvesting by

Integrated Turbine Control

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CONTENTS

Introduction and research statement

Electric power plant modeling

Representation of a Catenary Riser’s Dominant Nonlinear Dynamics

Distributed autonomous swarm intelligence using Robust Probabilistic Control and Physicomimetics

Conclusions and discussion

Ocean Current Power Harvesting by Integrated Turbine ControlECCS 1308168 – Nikolas Xiros – University of New Orleans

I. Recent Outcomes & Accomplishments: 1) Data analysis of prototype turbine power, torque & rpm performance. Using the data series obtained by field trials and experimentation, a comprehensive computer simulation model including impeller hydrodynamics and mooring hydromechanics as well as electromechanical power takeoff system was developed. 2) Audit of commercial ocean current turbine concepts analysis. One such audit has been completed to determine major feature commonality. This is the first step to select an Ocean Current Turbine (OCT) arrangement which suits not only electromechanical and flight control methodology development of a single OCT, but also ensures consistency and relevance for the greatest number of commercially proposed concepts.3) A paper to be presented at DSCC 2014. The tentative title is “Power take-off Control of moored in-stream hydrokinetic turbines”. Beyond ASME Dynamic Systems and Control Conference, a publication is in preparation to be submitted to a prestigious journal of the field.OTHER COLLABORATING INSTITUTIONS ON THIS NSF GRANT- Florida Atlantic University – Dr. James VanZwieten- Virginia Tech – Dr. Cornel Sultan

NSF ENG/ECCS 2014 Award Highlight

III. Broader Impact: (a)Intellectual, Industrial and Societal: An outstanding obstacle to commercialization of Marine Hydrokinetic Energy (MHK) will be solved to accelerate the availability of ocean current generated energy around the world. The results of this effort will enable the U.S. Department of Energy to make more confident investment decisions, the international standards groups to establish standards more swiftly, and will assist regulatory framework development for the Bureau of Ocean Energy Management. A more diverse world-wide portfolio for energy generation, especially with sustainable renewables, will certainly benefit society at large. This project will also broaden the participation of underrepresented groups with science and engineering.(b)Educational: A successful program of developing K-12 educational materials coupled with teacher training has been integrated with engineering and research activities. This effort, like other initiatives, will be included. The lessons learned as a result of the research are incorporated into the curriculum development and researchers regularly participate in training and outreach. Also, graduate students are mentored by being integrated directly with project planning, management, and execution.

II. Basic Principles:

•Technical Two major areas of expertise are needed to successfully perform the work proposed: (1) modeling of physical oceanographic processes coupled with submerged body dynamics, and (2) modeling of electrical power generation coupled with advanced vehicle control. •Non-technical Harnessing ocean currents is an immature commercial sector. Although ambitious inventors have proposed numerous solutions, until the 21st century, there has not been an adequate available combination of critical technologies and simultaneous social need for alternative energy. Although there has been considerable investment in Europe and Asia during the past decade to develop tidal and wave conversion technologies, ocean current technology is under developed. Today, only a handful of companies have secured sufficient investment and proposed reasonable extraction concepts. All, however, are currently focused on demonstrating turbine efficiencies, environmentally benign installations, and positive economic value for single turbine scales. No pre-commercial scale prototypes have been tested in relevant environments and no significant effort has yet been dedicated to a major challenge that must be addressed before full commercial implementation – how will the proposed turbines position correctly in the water column, and how will the dynamics of a flexible mooring system affect power generation

Artist’s rendering of the Southeast National Marine Renewable Energy Center’s (Florida Atlantic University) small-scale turbine test berth and experimental turbine deployment configuration.

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Electric power plant modeling

SYSTEM OUTLINE

Direct-drive AC induction motor

Flexible propeller shaft with bearings and damping

Surface piercing propeller (SPP)

Lookup table for feed-forward control setting

Proportional-Integral-Differential (PID) feedback control

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Net 1

SPP modeling with neural nets

Net 2

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Net 1

SPP modeling with neural nets

Net 2

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Net 1

SPP torque-speed curve generated from neural nets

Net 2

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Shafting model as mechanical system

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Shafting model as Simulink module

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Matlab induction motor model to obtain speed-torque curves

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Speed torque curves of induction motor

50 HP, 3-phase, 60 Hz, 3600 rpm

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Speed-frequency table generation for induction motor

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Generated speed-frequency table with propeller shaft inertia

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CATENARY RISER’S NONLINEAR DYANMICS

Problem setup

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INTRODUCTION – GOVERNING EQUATIONS

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TEST CASE SPECIFICS & NUMERICAL SIMULATION SETUP

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NUMERICAL SIMULATION RESULTS

Frequency domain response for node 4, excitation frequency 1.0 rad/sec, excitation amplitude 0.5 m/sec.

Time domain response for node 35, excitation frequency 0.8 rad/sec, excitation amplitude 1.0 m/sec.

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COMPLEX SINGULAR VALUE DECOMPOSITION

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DATA-DRIVEN SINGULAR MODAL ANALYSIS

Riser’s singular modes for axial (a) and normal (b) motions. Natural frequencies (rad/sec) No 1 through 4: 0.1005, 0.1798, 0.2578, 0.3324

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MODAL POWER DISTRIBUTION MATRIX – CATENARY ID

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Autonomous swarm control paradigm: Mine detection system

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Vehicle level control

Route planning and navigation of the autonomous vehicles

Although, economic costs are more or less straightforward to define, mission accomplishment indices need in most cases some additional contemplation. On the other hand, a generic performance index that comes to mind when military operations, financial or strategic decisions are of interest is probabilities associated with various events. Returning to the mine cueing scenario and limiting our investigation here to a 2D rectangular area, without significant loss of generality, the following spatial function (map) is introduced for any point (x,y) in our 2D area

f(x,y) = 0 if mine is not present at (x,y); 1 if mine is present at (x,y).

Furthermore, we assume that f is time-invariant. Therefore, as time goes by any mine detection system covers an increasingly larger portion of the target area and assigns 0 or 1 to each one of the points ‘visited’.

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ROBUST PROBABILISTIC CONTROL

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However, for each decision made there is an associated probability of error pe(x,y; t). It is noted here that probability of error is time dependent because each point in the target area may be ‘revisited’ as many times needed and by any type of vehicles. Therefore, a reasonable setup of the mine detection problem is

minimize t0 for which it holds pe(x,y; t) < pe0 for t > t0 and all (x,y) in target area.

Solving the problem stated above requires the determination of various system parameters including but not limited to number of vehicles and vehicle types, vehicle speed, number of sensors and sensor types, level of cooperation, target area coverage scheme etc. Evidently, additional constraints will be introduced to the problem due to the physics and engineering involved as e.g. vehicle speed ceiling, coverage scheme limitations due to maneuverability limitations etc.

ROBUST PROBABILISTIC CONTROL

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ROBUST PROBABILISTIC CONTROL

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CONCLUSIONS & FUTURE RESEARCH

What has been done so far:

• Development of Robust Probabilistic Control Framework

• Development of power plant model

• Development of pressure vessel hydromechanics model

Future research:

• Motion control system for single turbine

• Power optimization of single system

• Implementation of Robust Probabilistic Control

Thank you for your attention – DISCUSSION