Experiments offer incredible value to science, but results must always come with an uncertainty quantification to be meaningful. This requires grappling with sources of uncertainty and how to reduce them. In wind energy, field experiments are sometimes conducted with a control and treatment. In this scenario uncertainty due to bias errors can often be neglected as they impact both control and treatment approximately equally. However, uncertainty due to random errors propagates such that the uncertainty in the difference between the control and treatment is always larger than the random uncertainty in the individual measurements if the sources are uncorrelated. As random uncertainties are usually reduced with additional measurements, there is a need to know the minimum duration of an experiment required to reach acceptable levels of uncertainty. We present a general method to simulate a proposed experiment, calculate uncertainties, and determine both the measurement duration and the experiment duration required to produce statistically significant and converged results. The method is then demonstrated as a case study with a virtual experiment that uses real-world wind resource data and several simulated tip extensions to parameterize results by the expected difference in power. With the method demonstrated herein, experiments can be better planned by accounting for specific details such as controller switching schedules, wind statistics, and postprocess binning procedures such that their impacts on uncertainty can be predicted and the measurement duration needed to achieve statistically significant and converged results can be determined before the experiment.
In this work, thermogravimetric analysis (TGA) was performed on samples of a carbon fiber epoxy composite, a glass fiber epoxy composite, and a mixed carbon fiber/glass fiber epoxy composite, as well on each constituent material (polymer epoxy, carbon fibers and glass fibers). TGA was conducted for heating rates from 1-20 C/min with purified purge gases of nitrogen and dry air. For the fiberglass composite, we find that ~70% of the material remains after heating in air to 1200 C. For the carbon fiber epoxy composite, we observe greater mass loss as the carbon fibers can oxidize, leaving little material by the end of the test. The mixed composite, which has a 2:1 ratio of glass fibers to carbon fibers, experienced a total mass loss between the two other composites. By determining the relationship between the thermal decomposition of a composite material and its constituent materials, we can predict the fire behavior of novel composites during the material design phase.
The novel Hydromine harvests energy from flowing water with no external moving parts, resulting in a robust system with minimal environmental impact. Here two deployment scenarios are considered: an offshore floating platform configuration to capture energy from relatively steady ocean currents at megawatt-scale, and a river-based system at kilowatt-scale mounted on a pylon. Hydrodynamic and techno-economic models are developed. The hydrodynamic models are used to maximize the efficiency of the power conversion. The techno-economic models optimize the system size and layout and ultimately seek to minimize the levelized-cost-of-electricity produced. Parametric and sensitivity analyses are performed on the models to optimize performance and reduce costs.
The novel Hydromine harvests energy from flowing water with no external moving parts, resulting in a robust system with minimal environmental impact. Here two deployment scenarios are considered: an offshore floating platform configuration to capture energy from relatively steady ocean currents at megawatt-scale, and a river-based system at kilowatt-scale mounted on a pylon. Hydrodynamic and techno-economic models are developed. The hydrodynamic models are used to maximize the efficiency of the power conversion. The techno-economic models optimize the system size and layout and ultimately seek to minimize the levelized-cost-of-electricity produced. Parametric and sensitivity analyses are performed on the models to optimize performance and reduce costs.
This paper describes the methodology of designing a replacement blade tip and winglet for a wind turbine blade to demonstrate the potential of additive-manufacturing for wind energy. The team will later field-demonstrate this additive-manufactured, system-integrated tip (AMSIT) on a wind turbine. The blade tip aims to reduce the cost of wind energy by improving aerodynamic performance and reliability, while reducing transportation costs. This paper focuses on the design and modeling of a winglet for increased power production while maintaining acceptable structural loads of the original Vestas V27 blade design. A free-wake vortex model, WindDVE, was used for the winglet design analysis. A summary of the aerodynamic design process is presented along with a case study of a specific design.
The complexity and associated uncertainties involved with atmospheric-turbine-wake interactions produce challenges for accurate wind farm predictions of generator power and other important quantities of interest (QoIs), even with state-of-the-art high-fidelity atmospheric and turbine models. A comprehensive computational study was undertaken with consideration of simulation methodology, parameter selection, and mesh refinement on atmospheric, turbine, and wake QoIs to identify capability gaps in the validation process. For neutral atmospheric boundary layer conditions, the massively parallel large eddy simulation (LES) code Nalu-Wind was used to produce high-fidelity computations for experimental validation using high-quality meteorological, turbine, and wake measurement data collected at the Department of Energy/Sandia National Laboratories Scaled Wind Farm Technology (SWiFT) facility located at Texas Tech University's National Wind Institute. The wake analysis showed the simulated lidar model implemented in Nalu-Wind was successful at capturing wake profile trends observed in the experimental lidar data.
Organic materials are an attractive choice for structural components due to their light weight and versatility. However, because they decompose at low temperatures relative to tradiational materials they pose a safety risk due to fire and loss of structural integrity. To quantify this risk, analysts use chemical kinetics models to describe the material pyrolysis and oxidation using thermogravimetric analysis. This process requires the calibration of many model parameters to closely match experimental data. Previous efforts in this field have largely been limited to finding a single best-fit set of parameters even though the experimental data may be very noisy. Furthermore the chemical kinetics models are often simplified representations of the true de- composition process. The simplification induces model-form errors that the fitting process cannot capture. In this work we propose a methodology for calibrating decomposition models to thermogravimetric analysis data that accounts for uncertainty in the model-form and experimental data simultaneously. The methodology is applied to the decomposition of a carbon fiber epoxy composite with a three-stage reaction network and Arrhenius kinetics. The results show a good overlap between the model predictions and thermogravimetric analysis data. Uncertainty bounds capture devia- tions of the model from the data. The calibrated parameter distributions are also presented. In conclusion, the distributions may be used in forward propagation of uncertainty in models that leverage this material.
The prevalent use of organic materials in manufacturing is a fire safety concern, and motivates the need for predictive thermal decomposition models. A critical component of predictive modeling is numerical inference of kinetic parameters from bench scale data. Currently, an active area of computational pyrolysis research focuses on identifying efficient, robust methods for optimization. This paper demonstrates that kinetic parameter calibration problems can successfully be solved using classical gradient-based optimization. We explore calibration examples that exhibit characteristics of concern: high nonlinearity, high dimensionality, complicated schemes, overlapping reactions, noisy data, and poor initial guesses. The examples demonstrate that a simple, non-invasive change to the problem formulation can simultaneously avoid local minima, avoid computation of derivative matrices, achieve a computational efficiency speedup of 10x, and make optimization robust to perturbations of parameter components. Techniques from the mathematical optimization and inverse problem communities are employed. By re-examining gradient-based algorithms, we highlight opportunities to develop kinetic parameter calibration methods that should outperform current methods.
Multivariate designs using three optimization procedures were performed on a low Reynolds number (order 100,000) turbine blade that maximized lift over drag. The turbine blade was created to interface to AeroMINE, a novel wind energy harvester that has no external moving parts. To speed up the optimization process, an interpolation-based procedure using the Proper Orthogonal Decomposition (POD) method was used. This method was used in two ways: by itself (POD-i) and as an initial guess to a full-order model (FOM) solution that is truncated before it reaches full convergence (POD-i with truncated FOM). To compare the result of these methods and their efficiency, optimization using a FOM was also conducted. It was found that there exists a trade off between efficiency and optimal result. The FOM found the highest L/D of 28.87 while POD-i found a L/D of 16.19 and POD-i with truncated FOM found a L/D of 19.11. Nonetheless, POD-i and POD-i with truncated FOM were 32,302 and 697 times faster than the FOM, respectively.
Power production of the turbines at the Department of Energy/Sandia National Laboratories Scaled Wind Farm Technology (SWiFT) facility located at the Texas Tech University’s National Wind Institute Research Center was measured experimentally and simulated for neutral atmospheric boundary layer operating conditions. Two V27 wind turbines were aligned in series with the dominant wind direction, and the upwind turbine was yawed to investigate the impact of wake steering on the downwind turbine. Two conditions were investigated, including that of the leading turbine operating alone and both turbines operating in series. The field measurements include meteorological evaluation tower (MET) data and light detection and ranging (lidar) data. Computations were performed by coupling large eddy simulations (LES) in the three-dimensional, transient code Nalu-Wind with engineering actuator line models of the turbines from OpenFAST. The simulations consist of a coarse precursor without the turbines to set up an atmospheric boundary layer inflow followed by a simulation with refinement near the turbines. Good agreement between simulations and field data are shown. These results demonstrate that Nalu-Wind holds the promise for the prediction of wind plant power and loads for a range of yaw conditions.
In its simplest implementation, patent-protected AeroMINE consists of two opposing foils, where a low-pressure zone is generated between them. The low pressure draws fluid through orifices in the foil surfaces from plenums inside the foils. The inner plenums are connected to ambient pressure. If an internal turbine-generator is placed in the path of the flow to the plenums, energy can be extracted. The fluid transports the energy through the plenums, and the turbine-generator can be located at ground level, inside a controlled environment for easy access and to avoid inclement weather conditions or harsh environments. This contained internal turbine-generator has the only moving parts in the system, isolated from people, birds and other wildlife. AeroMINEs could be used in distributed-wind energy settings, where the stationary foil pairs are located on warehouse rooftops, for example. Flow created by several such foil pairs could be combined to drive a common turbine-generator.
In its simplest implementation, patent-protected AeroMINE consists of two opposing foils, where a low-pressure zone is generated between them. The low pressure draws fluid through orifices in the foil surfaces from plenums inside the foils. The inner plenums are connected to ambient pressure. If an internal turbine-generator is placed in the path of the flow to the plenums, energy can be extracted. The fluid transports the energy through the plenums, and the turbine-generator can be located at ground level, inside a controlled environment for easy access and to avoid inclement weather conditions or harsh environments. This contained internal turbine-generator has the only moving parts in the system, isolated from people, birds and other wildlife. AeroMINEs could be used in distributed-wind energy settings, where the stationary foil pairs are located on warehouse rooftops, for example. Flow created by several such foil pairs could be combined to drive a common turbine-generator.
The proposed Aero-MINE technology will extract energy from wind without any exterior moving parts. Aero-MINEs can be integrated into buildings or function stand-alone, and are scalable. This gives them advantages similar to solar panels, but with the added benefit of operation in cloudy or dark conditions. Furthermore, compared to solar panels, Aero-MINEs can be manufactured at lower cost and with less environmental impact. Power generation is isolated internally by the pneumatic transmission of air and the outlet air-jet nozzles amplify the effectiveness. Multiple units can be connected to one centrally located electric generator. Aero-MINEs are ideal for the built-environment, with numerous possible configurations ranging from architectural integration to modular bolt-on products. Traditional wind turbines suffer from many fundamental challenges. The fast-moving blades produce significant aero-acoustic noise, visual disturbances, light-induced flickering and impose wildlife mortality risks. The conversion of massive mechanical torque to electricity is a challenge for gears, generators and power conversion electronics. In addition, the installation, operation and maintenance of wind turbines is required at significant height. Furthermore, wind farms are often in remote locations far from dense regions of electricity customers. These technical and logistical challenges add significantly to the cost of the electricity produced by utility-scale wind farms. In contrast, distributed wind energy eliminates many of the logistical challenges. However, solutions such as micro-turbines produce relatively small amounts of energy due to the reduction in swept area and still suffer from the motion-related disadvantages of utility-scale turbines. Aero-MINEs combine the best features of distributed generation, while eliminating the disadvantages.