A CFD framework for offshore and onshore wind farm simulation

We present a wind simulation framework for offshore and onshore wind farms. The simulation framework involves an automatic hybrid high-quality mesh generation process, a pre-processing to impose initial and boundary conditions, and a solver for the Reynolds Averaged Navier-Stokes (RANS) equations with two different turbulence models, a modified standard k-epsilon model and a realizable k-epsilon model in which we included the Coriolis effects. Wind turbines are modeled as actuator discs. The wind farm simulation framework has been implemented in Alya, an in-house High Performance Computing (HPC) multi-physics finite element parallel solver. An application example is shown for an onshore wind farm composed of 165 turbines.This work has been supported by the EU H2020 projects New European Wind Atlas ERA-NET PLUS (NEWA), High Performance Computing for Energy (HPC4E, grant agreement 689772), and the Energy oriented Centre of Excellence (EoCoE, grant agreement 676629). We also thank two anonymous reviewers for their constructive comments on the first version of the manuscript.Peer ReviewedPostprint (published version

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 204

This bibliography lists 140 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1980

The Consequences of Air Density Variations over Northeastern Scotland for Offshore Wind Energy Potential

Hywind-Scotland is a wind farm in Scotland that for many reasons is at the leading edge of technology and is located at a paradigmatic study area for offshore wind energy assessment. The objective of this paper is to compute the Capacity Factor ( CF ) changes and instantaneous power generation changes due to seasonal and hourly fluctuations in air density. For that reason, the novel ERA5 reanalysis is used as a source of temperature, pressure, and wind speed data. Seasonal results for winter show that CF values increase by 3% due to low temperatures and denser air, with economical profit consequences of tens of thousands (US$). Hourly results show variations of 7% in air density and of 26% in power generation via FAST simulations, emphasizing the need to include air density in short-term wind energy studying.This work was financially supported by the Spanish Government through the MINECO project CGL2016-76561-R, (MINECO/ERDF, UE) and the University of the Basque Country (UPV/EHU, GIU 17/002). ERA5 hindcast data were downloaded at no cost from the Copernicus Climate Data Store. All the calculations and plots were made using R: https://www.r-project.org

Inverse-Dynamics MPC via Nullspace Resolution

Optimal control (OC) using inverse dynamics provides numerical benefits such as coarse optimization, cheaper computation of derivatives, and a high convergence rate. However, in order to take advantage of these benefits in model predictive control (MPC) for legged robots, it is crucial to handle its large number of equality constraints efficiently. To accomplish this, we first (i) propose a novel approach to handle equality constraints based on nullspace parametrization. Our approach balances optimality, and both dynamics and equality-constraint feasibility appropriately, which increases the basin of attraction to good local minima. To do so, we then (ii) adapt our feasibility-driven search by incorporating a merit function. Furthermore, we introduce (iii) a condensed formulation of the inverse dynamics that considers arbitrary actuator models. We also develop (iv) a novel MPC based on inverse dynamics within a perception locomotion framework. Finally, we present (v) a theoretical comparison of optimal control with the forward and inverse dynamics, and evaluate both numerically. Our approach enables the first application of inverse-dynamics MPC on hardware, resulting in state-of-the-art dynamic climbing on the ANYmal robot. We benchmark it over a wide range of robotics problems and generate agile and complex maneuvers. We show the computational reduction of our nullspace resolution and condensed formulation (up to 47.3%). We provide evidence of the benefits of our approach by solving coarse optimization problems with a high convergence rate (up to 10 Hz of discretization). Our algorithm is publicly available inside CROCODDYL.Comment: 17 pages, 14 figures, under-revie

Motion Control of Hexapod Robot Using Model-Based Design

Six-legged robots, also referred to as hexapods, can have very complex locomotion patterns and provide the means of moving on terrain where wheeled robots might fail. This thesis demonstrates the approach of using Model-Based Design to create control of such a hexapod. The project comprises the whole range from choosing of hardware, creating CAD models, development in MATLAB/Simulink and code generation. By having a computer model of the robot, development of locomotion patterns can be done in a virtual environment before tested on the hardware. Leg movement is implemented as algorithms to determine leg movement order, swing trajectories, body height alteration and balancing. Feedback from the environment is implemented as a internal measurement unit that measures body angles using sensor fusion. The thesis has resulted in successful creation of a hexapod platform for locomotion development through Model-Based Design. Both a virtual hexapod in Sim-Mechanics and a hardware hexapod is created and code generation to the hardware from the development environment is fully supported. Results include successful implementation of hexapod movement and the walking algorithm has the ability to walk on a flat surface, rotate and alter the body height. Implementation also contains a successful balancing mode for the hexapod whereas it is able to keep the main body level while the floor angle is altered

Optimizing Process-Based Models to Predict Current and Future Soil Organic Carbon Stocks at High-Resolution

From hillslope to small catchment scales (\u3c 50 km2), soil carbon management and mitigation policies rely on estimates and projections of soil organic carbon (SOC) stocks. Here we apply a process-based modeling approach that parameterizes the MIcrobial-MIneral Carbon Stabilization (MIMICS) model with SOC measurements and remotely sensed environmental data from the Reynolds Creek Experimental Watershed in SW Idaho, USA. Calibrating model parameters reduced error between simulated and observed SOC stocks by 25%, relative to the initial parameter estimates and better captured local gradients in climate and productivity. The calibrated parameter ensemble was used to produce spatially continuous, high-resolution (10 m2) estimates of stocks and associated uncertainties of litter, microbial biomass, particulate, and protected SOC pools across the complex landscape. Subsequent projections of SOC response to idealized environmental disturbances illustrate the spatial complexity of potential SOC vulnerabilities across the watershed. Parametric uncertainty generated physicochemically protected soil C stocks that varied by a mean factor of 4.4 × across individual locations in the watershed and a − 14.9 to + 20.4% range in potential SOC stock response to idealized disturbances, illustrating the need for additional measurements of soil carbon fractions and their turnover time to improve confidence in the MIMICS simulations of SOC dynamics

Research Brief

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Model order selection in multi-baseline interferometric radar systems

Synthetic aperture radar interferometry (InSAR) is a powerful technique to derive three-dimensional terrain images. Interest is growing in exploiting the advanced multi-baseline mode of InSAR to solve layover effects from complex orography, which generate reception of unexpected multicomponent signals that degrade imagery of both terrain radar reflectivity and height. This work addresses a few problems related to the implementation into interferometric processing of nonlinear algorithms for estimating the number of signal components, including a system trade-off analysis. Performance of various eigenvalues-based information-theoretic criteria (ITC) algorithms is numerically investigated under some realistic conditions. In particular, speckle effects from surface and volume scattering are taken into account as multiplicative noise in the signal model. Robustness to leakage of signal power into the noise eigenvalues and operation with a small number of looks are investigated. The issue of baseline optimization for detection is also addressed. The use of diagonally loaded ITC methods is then proposed as a tool for robust operation in the presence of speckle decorrelation. Finally, case studies of a nonuniform array are studied and recommendations for a proper combination of ITC methods and system configuration are given