Marine turbines

The feasibility and optimization of small unmanned mobile marine hydrokinetic (MHK) energy platforms for harvesting marine current energy in coastal and tidal waters are examined. A case study of a platform based on the use of a free-surface waterwheel (FSWW) mounted on an autonomous unmanned surface vehicle (USV) was conducted. Such platforms can serve as recharging stations for aerial drones (UAVs), enabling extension of the UAVs’ autonomous operating time. An unmanned MHK platform potentially meets this need with sustainable power harvested from water currents. For the case study, six different waterwheel configurations were field-tested in the Intracoastal Waterway of South Florida in support of determining the configuration that produced the most power. Required technologies for unmanned operations of the MHK platform were developed and tested. The data from the field-testing were analyzed to develop an empirical relation between the wheel’s theoretical hydrokinetic power produced and the mechanical power harnessed by the MHK platform with various waterwheel configurations during field-testing. The field data was also used to determine the electrical power generated by the FSWW configurations during field-testing. The study has led to the development of standardized testing procedures. The empirical relation is used to examine predicted power production through scaling up different physical aspects of the waterwheel.

Modeling, implementation, field testing and control of a power takeoff (PTO) device equipped with a ball-type continuously variable transmission (B-CVT) for a small marine hydrokinetic (MHK) turbine deployed from a floating unmanned autonomous mobile catamaran platform is described. The turbine is a partially submerged multi-blade undershot waterwheel (USWW). A validated numerical torque model for the MHK turbine has been derived and a speed controller has been developed, implemented and tested in the field. The dependance of the power generated as a function of number and submergence level of turbine blades has been investigated and the number of blades that maximizes power production is determined. Bench and field testing in support of characterizing the power conversion capabilities of MHK turbine and PTO are described. Detailed results of the final torque and power coefficient models, the controls architecture, and the MHK turbine performance with varying numbers of blades are provided.

Ocean current turbines (OCT) convert the kinetic energy housed within the earth’s ocean currents into electricity. However, due to the harsh environmental conditions that these turbines operate in, their system performance naturally degrades over time. This degradation correlates to high operation and maintenance (O&M) costs, which necessitates the need for robust condition monitoring and fault detection (CMFD). Unfortunately, OCT operational data is not publicly available in large and/or diverse enough quantities to develop such frameworks. Therefore, from an industry-wide perspective, the technologies needed to harvest this energy source are still in their infancy.

Marine and hydrokinetic (MHK) energy systems, including marine current turbines and wave energy converters, could contribute significantly to reducing reliance on fossil fuels and improving energy security while accelerating progress in the blue economy. However, technologies to capture them are nascent in development due to several technical and economic challenges. For example, for capturing ocean flows, the fluid velocity is low but density is high, resulting in early boundary layer separation and high torque. This dissertation addresses critical challenges in modeling, optimization, and control co-design of MHK energy systems, with specific case studies of a variable buoyancy-controlled marine current turbine (MCT). Specifically, this dissertation presents (a) comprehensive dynamic modeling of the MCT, where data recorded by an acoustic Doppler current profiler will be used as the real ocean environment; (b) vertical path planning of the MCT, where the problem is formulated as a novel spatial-temporal optimization problem to maximize the total harvested power of the system in an uncertain oceanic environment; (c) control co-design of the MCT, where the physical device geometry and turbine path control are optimized simultaneously. In a nutshell, the contributions are summarized as follows:

This thesis describes a methodology for mechanical fault detection and diagnostics in an ocean turbine using vibration analysis and modeling. This methodology relies on the use of advanced methods for machine vibration analysis and health monitoring. Because of some issues encountered with traditional methods such as Fourier analysis for non stationary rotating machines, the use of more advanced methods such as Time-Frequency Analysis is required. The thesis also includes the development of two LabVIEW models. The first model combines the advanced methods for on-line condition monitoring. The second model performs the modal analysis to find the resonance frequencies of the subsystems of the turbine. The dynamic modeling of the turbine using Finite Element Analysis is used to estimate the baseline of vibration signals in sensors locations under normal operating conditions of the turbine. All this information is necessary to perform the vibration condition monitoring of the turbine.

This thesis is an approach of numerical optimization of thermal design of the ocean turbine developed by the Centre of Ocean Energy and Technology (COET). The technique used here is the integrated method of finite element analysis (FEA) of heat transfer, artificial neural network (ANN) and genetic algorithm (GA) for optimization purposes.

A computational tool has been developed by integrating National Renewable Energy Laboratory (NREL) codes, Sandia National Laboratories' NuMAD, and ANSYS to investigate a horizontal axis composite ocean current turbine. The study focused on the design, analysis, and life prediction of composite blade considering random ocean current, cyclic rotation, and hurricane-driven ocean current. A structural model for a horizontal axis FAU research OCT blade was developed. Following NREL codes were used: PreCom, BModes, ModeShape, AeroDyn and FAST. PreComp was used to compute section properties of the OCT blade. BModes and ModeShape calculated the mode shapes of the blade. Hydrodynamic loading on the OCT blade was calculated by modifying the inputs to AeroDyn and FAST. These codes were then used to obtain the dynamic response of the blade, including blade tip displacement, normal force (FN) and tangential force (FT), flap and edge bending moment distribution with respect to blade rotation.

This thesis proposes a sensor approach for quantifying the hydrodynamic performance of Ocean Current Turbines (OCT), and investigates the influence of sensor-specific noise and sampling rates on calculated turbine performance. Numerical models of the selected sensors are developed, and then utilized to add stochastic measurement error to numerically-generated, non-stochastic OCT data. Numerically-generated current velocity and turbine performance measurements are used to quantify the relative influence of sensor-specific error and sampling limitations on sensor measurements and calculated OCT performance results. The study shows that the addition of sensor error alters the variance and mean of OCT performance metric data by roughly 7.1% and 0.24%, respectively, for four evaluated operating conditions. It is shown that sensor error results in a mean, maximum and minimum performance metric to Signal to Noise Ration (SNR) of 48.6% and 6.2%, respectively.

Submerged turbines which harvest energy from ocean currents are an important potential energy resource, but their harsh and remote environment demands an automated system for machine condition monitoring and prognostic health monitoring (MCM/PHM). For building MCM/PHM models, vibration sensor data is among the most useful (because it can show abnormal behavior which has yet to cause damage) and the most challenging (because due to its waveform nature, frequency bands must be extracted from the signal). To perform the necessary analysis of the vibration signals, which may arrive rapidly in the form of data streams, we develop three new wavelet-based transforms (the Streaming Wavelet Transform, Short-Time Wavelet Packet Decomposition, and Streaming Wavelet Packet Decomposition) and propose modifications to the existing Short-TIme Wavelet Transform. ... The proposed algorithms also create and select frequency-band features which focus on the areas of the signal most important to MCM/PHM, producing only the information necessary for building models (or removing all unnecessary information) so models can run on less powerful hardware. Finally, we demonstrate models which can work in multiple environmental conditions. ... Our results show that many of the transforms give similar results in terms of performance, but their different properties as to time complexity, ability to operate in a fully streaming fashion, and number of generated features may make some more appropriate than others in particular applications, such as when streaming data or hardware limitations are extremely important (e.g., ocean turbine MCM/PHM).

This thesis describes a method to detect, localize and identify a faulty bearing in a rotating machine using narrow band envelope analysis across an array of accelerometers. This technique is developed as part of the machine monitoring system of an ocean turbine. A rudimentary mathematical model is introduced to provide an understanding of the physics governing the vibrations caused by a bearing with a raceway defect. This method is then used to detect a faulty bearing in two setups : on a lathe and in a dynamometer.