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The authors observed that the turbine equipped with the lift-drag composite blade provided higher-energy performances than the turbine equipped with the NACA blades based on lift force. Tidal turbines generate energy from tidal currents. Contrarily to wind turbines, which provide energy over a long period of time depending on the wind conditions, tidal stream turbines can provide energy for four periods per day. Moreover, three types of turbines were considered: a Savonius-shaped turbine based on drag force, a NACA hydrofoil turbine based on lift force, and a thin-walled foil turbine based on lift and drag forces. [Extracted from the article]

The following study investigates the effectiveness of a deep learning-based method for predicting the flow field and flow-driven rotation of a vertical-axis hydrokinetic turbine operating in previously unseen free-stream velocities. A Convolutional Neural Network (CNN) is trained and tested using the solutions of five two-dimensional (2-D), foil-resolved Unsteady Reynolds-Averaged Navier-Stokes (URANS) simulations, with free-stream velocities of 1.0, 1.5, 2.0, 2.5, and 3.0 m/s. Based on the boundary conditions of free-stream velocity and rotor position, the flow fields of x-velocity, y-velocity, pressure, and turbulent viscosity are inferred, in addition to the angular velocity of the rotor. Three trained CNN models are developed to evaluate the effects of (1) the dimensions of the training data, and (2) the number of simulations used as training cases. Reducing data dimensions was found to diminish mean relative error in predictions of velocity and turbulent viscosity, while increasing it in predictions of pressure and angular velocity. Increasing the number of training cases from two to three was found to reduce relative error for all predicted unknowns. With the best achieved CNN model, the variables of x-velocity, y-velocity, pressure, turbulent viscosity, and angular velocity were inferred with mean relative errors of 6.93%, 9.82%, 10.7%, 7.48%, and 0.817%, respectively. [ABSTRACT FROM AUTHOR]

Wind turbine power curve modeling is an important tool in turbine performance monitoring and power forecasting. There are several statistical techniques to fit the empirical power curve of a wind turbine, which can be classified into parametric and nonparametric methods. In this paper, we study four of these methods to estimate the wind turbine power curve. Polynomial regression is studied as the benchmark parametric model, and issues associated with this technique are discussed. We then introduce the locally weighted polynomial regression method, and show its advantages over the polynomial regression. Also, the spline regression method is examined to achieve more flexibility for fitting the power curve. Finally, we develop a penalized spline regression model to address the issues of choosing the number and location of knots in the spline regression. The performance of the presented methods is evaluated using two simulated data sets as well as an actual operational power data of a wind farm in North America. [ABSTRACT FROM PUBLISHER]

The paper presents a study of the profile of the load imposed on a power system by grid-charging of the onboard battery pack of electric and plug-in hybrid vehicles. The study uses a large database of field-recorded driving cycles stamped with parking times and locations to predict realistic driving habits of drivers in an urban setting. A fuzzy-logic inference system is designed to emulate the decision-making process of a driver when deciding to charge the vehicle's battery. The charging load is then estimated on an hourly basis for a number of electric and plug-in hybrid vehicles with different storage capacities. Level-1 and level-2 charging regimes as well as two scenarios for charging, namely charging at home and charging at home and work, are considered. The load profile is presented as an hourly probability of charging for each vehicle type. The results demonstrate how penetration of plug-in hybrid and electric vehicles affects the load on a utility network. [ABSTRACT FROM PUBLISHER]

Present-day urban vehicle usage data recorded on a per second basis over a one-year period using GPS devices installed in 76 representative vehicles in the city of Winnipeg, Canada, allow predicting the electric load profiles onto the grid as a function of time for future plug-in electric vehicles. For each parking occurrence, load profile predictions properly take into account important factors, including actual state-of-charge of the battery, parking duration, parking type, and vehicle powertrain. Thus, the deterministic simulations capture the time history of vehicle driving and parking patterns using an equivalent 10 000 urban driving and parking days for the city of Winnipeg. These deterministic results are then compared to stochastic methods that differ in their treatment of how they model vehicle driving and charging habits. The new stochastic method introduced in this study more accurately captures the relationship of vehicle departure, arrival, and travel time compared to two previously used stochastic methods. It outperforms previous stochastic methods, having the lowest error at 3.4% when compared to the deterministic method for an electric sedan with a 24-kWhr battery pack. For regions where vehicle usage data is not available to predict plug-in electric vehicle load, the proposed stochastic method is recommended. In addition, using a combination of home, work, and commercial changing locales, and Level 1 versus Level 2 charging rates, deterministic simulations for urban run-out-of-charge events vary by less than 4% for seven charging scenarios selected. Using the vehicle usage data, charging scenarios simulated have no significant effect on urban run-out-of-charge events when the battery size for the electric sedan is increased. These results contribute towards utilities achieve a more optimal cost balance between: 1) charging infrastructure; 2) power transmission upgrades; 3) vehicle battery size; and 4) the addition of new renewable generation to address new electric vehicle loads for addressing energy drivers. [ABSTRACT FROM PUBLISHER]

Development of a daily duty cycle based on real-world driving behavior and parking times is a critical requirement in the optimal design of power-train components of a plug-in vehicle. Standard driving cycles cannot completely emulate the real-world power demand of a vehicle and its downtimes in particular. To address these shortcomings, a large database of one year of measured data collected from a fleet of 76 cars in the city of Winnipeg, MB, Canada, is obtained and is then used to develop a new duty cycle. This paper describes a methodology for statistical analysis of the fleet data, including while a vehicle is parked. Due to the intrinsic differences in vehicle usage profiles during weekdays and weekends, two 24-h duty cycles with suitable windows of opportunity for charging are developed for weekday and weekend driving patterns. The uniqueness of the proposed statistical methodology and the resulting duty cycles contribute to addressing the present shortcomings of standard driving cycles. [ABSTRACT FROM PUBLISHER]

This paper develops a simulation-based framework for optimal sizing of the additional energy storage required to retrofit a hybrid electric vehicle (HEV) to a plug-in hybrid electric vehicle (PHEV). Simulations are conducted on a vehicular model developed for a midsize sedan (Toyota Prius) using a new weekly vehicle-usage profile constructed for average driving and most probable parking times based on the data collected in the city of Winnipeg (Canada). Three battery technologies that are commercially available for electric vehicle propulsion are used in the simulations to determine the optimal sizing of the battery storage, given the constraints on the volume of the battery pack for lowest cost. Overnight-charging and opportunity-charging scenarios are also implemented in the simulation, and their impact on the optimal sizing is discussed. [ABSTRACT FROM AUTHOR]

The retrofit conversion of currently available hybrid electric vehicles (HEVs) to plug-in HEVs (PHEVs) is studied in this paper through experiments and simulations using the powertrain system analysis toolkit (PSAT). First, a rule-based fuzzy controller of the battery energy-management unit is developed to simulate different energy-management policies. Second, by modifying the energy-control strategy, the model of the conversion PHEV (C-PHEV) is verified with experiments. Finally, the C-PHEV model is used to simulate different battery energy-management control strategies. The results show improvement in fuel economy, whereas the energy-management controller discharges the power through the plug-in battery pack only when the state of charge of the base vehicle battery is close to its minimum value. This method keeps the advantage of driving in electric mode using a combination of two batteries and optimizing the use of regenerative braking capabilities, which is the main advantage of HEVs. It is also found that increasing the power threshold of the internal combustion engine (ICE) improves the performance of C-PHEV. Increasing the ICE power threshold increases the engine efficiency by running the engine in its efficient points. It also drives the vehicle in electric mode in higher power demands. [ABSTRACT FROM AUTHOR]

A Renewable Energy Vehicle Simulator (REVS) that enables to simulate renewable energy vehicles using combination of propulsion system and fuels by adapting library modules to suit particular applications has been developed The simulation software predicts the energy use of the vehicle, taking into account the duty cycle and driver habits. Library modules have been developed to simulate the Plug-In Hybrid Electric Vehicle (PHEV) architecture as this platform offers energy scenario for cars and buses allowing combination of energy sources that include renewable electricity and renewable biofuels. This paper will discuss the methodology for designing system level vehicles using the REVS package. A series parallel HEV with a conventional ICE and power split transmission system has been designed using the simulation package. A fuzzy controller has been developed to simulate the driver and to command the acceleration and brake pedal. Another fuzzy controller has been employed to manage the power flow in hybrid vehicle. The energy management strategy has been applied through a rule-based fuzzy approach. Several rules are used to determine, based on the power demand value, how much power to get from electric motor if the power demand is positive. Simulation results of the vehicle will be presented for high and low capacity battery in various driving schedules. [ABSTRACT FROM AUTHOR]

Oscillating hydrofoils were installed in a water tunnel as a surrogate model for a hydrokinetic cross-flow tidal turbine, enabling the study of the effect of flexible blades on the performance of those devices with high ecological potential. The study focuses on a single tip-speed ratio (equal to 2), the key non-dimensional parameter describing the operating point, and solidity (equal to 1.5), quantifying the robustness of the turbine shape. Both parameters are standard values for cross-flow tidal turbines. Those lead to highly dynamic characteristics in the flow field dominated by dynamic stall. The flow field is investigated at the blade level using high-speed particle image velocimetry measurements. Strong fluid–structure interactions lead to significant structural deformations and highly modified flow fields. The flexibility of the blades is shown to significantly reduce the duration of the periodic stall regime; this observation is achieved through systematic comparison of the flow field, with a quantitative evaluation of the degree of chaotic changes in the wake. In this manner, the study provides insights into the mechanisms of the passive flow control achieved through blade flexibility in cross-flow turbines. [ABSTRACT FROM AUTHOR]

Abstract Reducing the cost of hydrokinetic turbines is an important step in the commer-cialization of the technology. One of the costs of the implementation of hydrokinetic turbines is the selection of potential hydrokinetic sites. In this paper, it is proposed that site selection for a hydrokinetic turbine can be improved in river environments in Canada by the use of winter imagery that is available at little or no cost. This is of particular value to remote communities who power their community using diesel fuel, as some pay over $1.00 per kWh. It is shown that river sections that do not freeze during the winter have higher velocity magnitudes than sections that are covered with ice. The average velocity magnitude for open ice locations is found to be 1.09 m/s, with a standard deviation of 0.48 m/s. Ice covered locations were found to have an average velocity magnitude of 0.301 m/s. The relationship between river depth and velocity magnitude is shown to have a low correlation, with an R2 value of 0.185. [ABSTRACT FROM AUTHOR]

Abstract: We demonstrate a concept potentially suitable for detecting in-cloud ice and indication of ice accretion rate within a laboratory setting. The concept measures the change in capacitance and resistance due to ice accretion between two charged cylindrical probes. An ice sensor using this concept could be located on meteorological towers or nacelle of wind turbines for icing measurements. During the controlled icing experiments, ice builds up on the cylindrical probes: capacitance increases and resistance decreases. Capacitance correlates with the mass, thickness and the location of ice deposits; the type of icing—glaze versus rime—correlates well with resistance measurements. A 2-D electric field simulation around cylindrical probes demonstrates the sensitivity of this ice sensor as a function of the distance between the probes and the location of ice deposition around the probes. Capacitance measurements using different acrylic annuli machined to high tolerances and placed over each probe to simulate ice validates the electric field simulation. Experimental tests of the cylindrical probes in an icing wind tunnel facility under controlled rime and glaze ice conditions validate the concept. [Copyright &y& Elsevier]

Abstract: Reported are a series of river velocity measurements taken one diameter upstream of a 25 kW vertical axis river kinetic turbine deployed in the Winnipeg River at Pointe du Bois, located 150 km northeast of Winnipeg. A load-bank located on the river shore provides a resistive load for the turbine generator. An acoustic Doppler velocimeter measures velocity fluctuations at a sample rate of 200 Hz. Analysis of acoustics Doppler velocimeter data measured upstream of the turbine quantifies the variation in the mean and fluctuating components of river flow velocity. Results show that in this particular test site the river contains eddies with the same order of magnitude as the turbine diameter or greater. Measurements upstream of the operating turbine indicate reduction in the mean velocity with eddies breaking down into smaller sizes. These smaller eddies increase the turbulence intensity of the incoming flow and increase the high frequencies energy level in the power spectra. [Copyright &y& Elsevier]

• Vertical microstructure profiler is an accurate sensor for small vortices characterisation. • Aquaculture cages are responsible of turbulence generation and modification. • The anisotropy of vortices grows with the water velocity and along the production structures. • Wavelets are powerfull for both spatial and temporal turbulence analysis. Characterization of tidal flow variations and turbulence levels surrounding aquaculture infrastructures contribute to optimal fish production management and to a good environmental quality assessment. Such flow characterization was performed in Cherbourg Roadstead, France. An acoustic Doppler current profiler was used to measure flow velocities and a vertical microstructure profiler to assess turbulence levels and length scales in the vicinity of a commercial fish farm. Flow measurement results show that fish nets increase local turbulence level, impacting the flow and vorticity produced by the nets, contributing to more scouring of the ocean floor. A wavelet analysis allows to evaluate vortex sizes and where they are localized along the water column. Flow characterization shows these vortices are produced in the vicinity of fish nets and in their wake. Larger and more energetic turbulent structures are produced below aquaculture cages, which extend into the wake and onto the ocean floor. [ABSTRACT FROM AUTHOR]