In this study, large eddy simulations (LES) of offshore boundary layers near the Nantucket coast are performed using Nalu-Wind. The marine boundary layer conditions are chosen to match the predominantly unstable and neutral conditions measured by the Cape Wind platform. The appropriate domain, resolution, and boundary condition settings required for the LES are established through this work. Differences between stable and unstable cases are found in the wind speed profiles, averaged statistics, and wind spectra, and explained in terms of stratification effects. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525. SAND2020-5996C.
Doubrawa Moreira, Paula; Quon, Eliot; Martinez; Tossas, Luis (Tony) M.; Shaler, Kelsey; Debnath, Mithu; Hamilton, Nicholas; Herges, Thomas H.; Maniaci, David C.; Kelley, Christopher L.; Laros, James H.; Blaylock, Myra L.; Van Der Laan, Paul; Andersen, Soren J.; Krueger, Sonja; Cathelain, Marie; Schlez, Wolfgang; Jonkman, Jason; Branlard, Emmanuel; Steinfeld, Gerald; Schmidt, Sascha; Blondel, Frederic; Lukassen, Laura J.; Moriarty, Patrick
Previous research has revealed the need for a validation study that considers several wake quantities and code types so that decisions on the trade-off between accuracy and computational cost can be well informed and appropriate to the intended application. In addition to guiding code choice and setup, rigorous model validation exercises are needed to identify weaknesses and strengths of specific models and guide future improvements. Here, we consider 13 approaches to simulating wakes observed with a nacelle-mounted lidar at the Scaled Wind Technology Facility (SWiFT) under varying atmospheric conditions. We find that some of the main challenges in wind turbine wake modeling are related to simulating the inflow. In the neutral benchmark, model performance tracked as expected with model fidelity, with large-eddy simulations performing the best. In the more challenging stable case, steady-state Reynolds-averaged Navier–Stokes simulations were found to outperform other model alternatives because they provide the ability to more easily prescribe noncanonical inflows and their low cost allows for simulations to be repeated as needed. Dynamic measurements were only available for the unstable benchmark at a single downstream distance. These dynamic analyses revealed that differences in the performance of time-stepping models come largely from differences in wake meandering. This highlights the need for more validation exercises that take into account wake dynamics and are able to identify where these differences come from: mesh setup, inflow, turbulence models, or wake-meandering parameterizations. In addition to model validation findings, we summarize lessons learned and provide recommendations for future benchmark exercises.
Quiroz-Arita, Carlos; Blaylock, Myra L.; Gharagozloo, Patricia E.; Bark, David; Prasad Dasi, Lakshmi; Bradley, Thomas H.
Turbulent mixing in pilot-scale cultivation systems influences the productivity of photoautotrophic cultures. We studied turbulent mixing by applying particle image velocimetry and acoustic doppler velocimetry to pilot-scale, flat-panel photobioreactor, and open-channel raceway. Mixing energy inputs were varied from 0.1 to 2.1 W·m−3. The experimental results were used to quantify turbulence and to validate computational fluid dynamics models, from which Lagrangian representations of the fluid motion in these reactors were derived. The results of this investigation demonstrated that differences in mixing energy input do not significantly impact the structure of turbulence and the light/dark cycling frequencies experienced by photoautotrophic cells within the reactors. The experimental and computational results of our research demonstrated that well-mixed conditions exist in pilot-scale, flat-panel photobioreactors and open-channel raceways, even for relatively low mixing energy inputs.
Milestone Description: Enhance Nalu-Wind's actuator disc model through hardening, documenting, stress-testing, verifying, and validating. Existing workflows will be improved by reducing the data output stream, and by making the analysis capabilities more modular and generally better. These model capabilities are needed by other A2e areas, namely Wake Dynamics, AWAKEN, and VV&UQ.
The availability of repair garage infrastructure for hydrogen fuel cell vehicles is becoming increasingly important for future industry growth. Ventilation requirements for hydrogen fuel cell vehicles can affect both retrofitted and purpose-built repair garages and the costs associated with these requirements can be significant. A hazard and operability study (HAZOP) was performed to identify key risk-significant scenarios related to hydrogen vehicles in a repair garage. Detailed simulations and modeling were performed using appropriate computational tools to estimate the location, behavior, and severity of hydrogen release based on key HAZOP scenarios. This work compares current fire code requirements to an alternate ventilation strategy to further reduce potentially hazardous conditions. Overall, the amount of flammable mass of hydrogen at any one time in the simulation is low compared to the total mass of hydrogen released, due to the low flow rate of a low pressure release. It is shown that position, direction, and velocity of ventilation have a significant impact on the amount of instantaneous flammable mass in the domain.
Uncertainty is present in all wind energy problems of interest, but quantifying its impact for wind energy research, design and analysis applications often requires the collection of large ensembles of numerical simulations. These predictions require a range of model fidelity as predictive models, that include the interaction of atmospheric and wind turbine wake physics, can require weeks or months to solve on institutional high-performance computing systems. The need for these extremely expensive numerical simulations extends the computational resource requirements usually associated with uncertainty quantification analysis. To alleviate the computational burden, we propose here to adopt several Multilevel-Multifidelity sampling strategies that we compare for a realistic test case. A demonstration study was completed using simulations of a V27 turbine at Sandia National Laboratories’ SWiFT facility in a neutral atmospheric boundary layer. The flow was simulated with three models of disparate fidelity. OpenFAST with TurbSim was used stand-alone as the most computationally-efficient, lower-fidelity model. The computational fluid dynamics code Nalu-Wind was used for large eddy simulations with both medium-fidelity actuator disk and high-fidelity actuator line models, with various mesh resolutions. In an uncertainty quantification study, we considered five different turbine properties as random parameters: yaw offset, generator torque constant, collective blade pitch, gearbox efficiency and blade mass. For all quantities of interest, the Multilevel-Multifidelity estimators demonstrated greater efficiency compared to standard and multilevel Monte Carlo estimators.
Proceedings of the 6th European Conference on Computational Mechanics: Solids, Structures and Coupled Problems, ECCM 2018 and 7th European Conference on Computational Fluid Dynamics, ECFD 2018
Wind energy is stochastic in nature; the prediction of aerodynamic quantities and loads relevant to wind energy applications involves modeling the interaction of a range of physics over many scales for many different cases. These predictions require a range of model fidelity, as predictive models that include the interaction of atmospheric and wind turbine wake physics can take weeks to solve on institutional high performance computing systems. In order to quantify the uncertainty in predictions of wind energy quantities with multiple models, researchers at Sandia National Laboratories have applied Multilevel-Multifidelity methods. A demonstration study was completed using simulations of a NREL 5MW rotor in an atmospheric boundary layer with wake interaction. The flow was simulated with two models of disparate fidelity; an actuator line wind plant large-eddy scale model, Nalu, using several mesh resolutions in combination with a lower fidelity model, OpenFAST. Uncertainties in the flow conditions and actuator forces were propagated through the model using Monte Carlo sampling to estimate the velocity defect in the wake and forces on the rotor. Coarse-mesh simulations were leveraged along with the lower-fidelity flow model to reduce the variance of the estimator, and the resulting Multilevel-Multifidelity strategy demonstrated a substantial improvement in estimator efficiency compared to the standard Monte Carlo method.
The Alternative Fuels Risk Assessment Models (A1tRAM) toolkit combines Quantitative Risk Assessment (QRA) with simulations of unignited dispersion, ignited turbulent diffusion flames, and indoor accumulation with delayed ignition of fuels. The models of the physical phenomena need to be validated for each of the fuels in the toolkit. This report shows the validation for methane which is being used as a surrogate for natural gas. For the unignited dispersion model, seven previously published experiments from credible sources were used to validate. The validation looked at gas concentrations with respect to the distance from the release point. Four of these were underexpanded jets (i.e. release velocity equal to or greater than local speed of sound) and the other three subsonic releases. The methane plume model in AltRAM matched both varieties well, with higher accuracy for the underexpanded releases. For the jet flame model, we compared the heat flux and thermal radiation data reported from five separate turbulent jet flame experiments to the quantities calculated by A1tRAM. Four of the five datasets were for underexpanded diffusion jets flames. While the results still match well enough to give a good estimate of what is occurring, the error is higher than what was seen with the plume model. For the underexpanded flames A1tRAM provided reasonable approximations, which would lead to conservative risk assessments. Some modeling errors can be attributed to environmental effects (i.e. wind) since most large scale flame experiments are conducted outdoors. A1tRAM has been shown to be a reasonably accurate tool for calculating the concentration or flame properties of natural gas releases. Improvements could still be made for the plume of subsonic releases and radiative heat fluxes to reduce the conservative nature of these predictions. These models can provide valuable information for the risk assessment of natural gas infrastructure.