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Functional Requirements for the WEIS Toolset to Enable Controls Co-Design of Floating Offshore Wind Turbines

The goal of the new Wind Energy with Integrated Servo-control (WEIS) toolset under development is to provide the offshore wind industry and research communities with an open-source, user-friendly, flexible tool to enable true controls co-design (CCD) of the physical design of a floating offshore wind turbine together with the controller. WEIS will use a multifidelity library of models built on the foundations of WISDEM® and OpenFAST formerly known as FAST). This paper presents the WEIS development plan, including the functional requirements of WEIS (including improvements to WISDEM and OpenFAST) together with associated rationale for their establishment and a qualitative description of the modeling approaches that will be implemented to address these functional requirements. The development of WEIS is a project under the Aerodynamic Turbines Lighter and Afloat with Nautical Technologies and Integrated Servo-control program funded by the U.S. Department of Energy Advanced Research Projects Agency – Energy.

27 ARPA - Advanced Research Projects Agency-Energy↗

Functional Requirements for the WEIS Toolset to Enable Controls Co-Design of Floating Offshore Wind Turbines: Preprint

The goal of the new Wind Energy with Integrated Servo-control (WEIS) toolset under development is to provide the offshore wind industry and research communities with an open-source, user-friendly, and flexible tool to enable true Controls Co-Design (CCD) of the physical design of a floating offshore wind turbine (FOWT) together with the controller. WEIS will utilize a multifidelity library of models built upon the foundations of WISDEM and OpenFAST (formerly known as FAST). This paper presents the WEIS development plan, including the functional requirements of WEIS (including improvements to WISDEM and OpenFAST) together with associated rationale for their establishment, and a qualitative description of the modeling approaches that will be implemented to address these functional requirements. The development of WEIS is a project under the Aerodynamic Turbines, Lighter and Afloat, with Nautical Technologies and Integrated Servo-control (ATLANTIS) program funded by the U.S. Department of Energy (DOE) Advanced Research Projects Agency - Energy (ARPA-E).

27 ARPA - Advanced Research Projects Agency-Energy↗

RAFT (Response Amplitudes of Floating Turbines) [SWR-22-40]

RAFT is a Python code for frequency-domain analysis of floating wind turbines. RAFT works in the frequency domain to provide efficient computations of a floating wind system's response spectra accounting for platform hydrodynamics, quasi-static mooring reactions, rotor aerodynamics, and linearized control. It can be used to calculate response amplitude operators (RAO's), power spectral densities, mean/static properties, and response metrics based on mean and root-mean-square values of each output. RAFT serves as the Level 1 model in the WEIS (Wind Energy with Integrated Servo-control) toolkit, and features an OpenMDAO wrapper.

Hall, Matthew↗

Processed Lab Data for Neural Network-Based Shear Stress Level Prediction

Machine learning can be used to predict fault properties such as shear stress, friction, and time to failure using continuous records of fault zone acoustic emissions. The files are extracted features and labels from lab data (experiment p4679). The features are extracted with a non-overlapping window from the original acoustic data. The first column is the time of the window. The second and third columns are the mean and the variance of the acoustic data in this window, respectively. The 4th-11th column is the the power spectrum density ranging from low to high frequency. And the last column is the corresponding label (shear stress level). The name of the file means which driving velocity the sequence is generated from. Data were generated from laboratory friction experiments conducted with a biaxial shear apparatus. Experiments were conducted in the double direct shear configuration in which two fault zones are sheared between three rigid forcing blocks. Our samples consisted of two 5-mm-thick layers of simulated fault gouge with a nominal contact area of 10 by 10 cm^2. Gouge material consisted of soda-lime glass beads with initial particle size between 105 and 149 micrometers. Prior to shearing, we impose a constant fault normal stress of 2 MPa using a servo-controlled load-feedback mechanism and allow the sample to compact. Once the sample has reached a constant layer thickness, the central block is driven down at constant rate of 10 micrometers per second. In tandem, we collect an AE signal continuously at 4 MHz from a piezoceramic sensor embedded in a steel forcing block about 22 mm from the gouge layer The data from this experiment can be used with the deep learning algorithm to train it for future fault property prediction.

15 GEOTHERMAL ENERGY↗

Utah FORGE: Fault Shear Reactivation Experimental Data for Fluid Injection-Rate Controls on Seismic Moment

Included are experimental data recorded from shear experiments that specifically explore the link between fluid-injection rate and seismic moment resulting from shear reactivation of laboratory faults. Raw mechanical data from three experiments are included alongside corresponding MATLAB scripts that import and plot the data, as well as use it to calculate shear and normal stress. Experiments are performed on 2.5-3 inch long granitoid cores from the Utah FORGE EGS demonstration site, containing a single inclined fracture with small-scale roughness added to the fracture surface. The raw data included here were recorded from an aluminum triaxial pressure vessel (TEMCO) configured with three independent servo-controlled pumps, with distilled water used as the working fluid. The pumps control confining pressure, upstream pore pressure, and axial pressure, with each pump connected to a LabView interface to record applied pressures, cumulative injected water volumes, and pump flow rates. The downstream outlet from the fracture is closed to allow pressurization, which is measured by an external pressure transducer. A linear variable differential transformer (LVDT) attached to the axial piston measures axial displacement, from which we calculate shear displacement along the fracture. Additionally, P-wave transducers are used to record acoustic signatures, where acoustic emission events and maximum amplitudes are compared against seismic moment and shear slip velocity. Fluid injection rates range between 0.05 mL/min, 0.25 mL/min, and 0.75 mL/min for each experiment. Along-fault pressure distributions are progressively less uniform as injection rates increase, representing a switch from steady-state to transient conditions. Triggered shear displacement is used as a proxy for seismic moment and is indexed against cumulative injection volume and rate. Each experiment is performed under constant shear stress conditions, and the sample is fully saturated with DI water. Axial and confining stresses are applied to 3 MPa through pressure-stepping in 500 kPa increments. The pore pressure is held constant at 200 kPa prior to initiating the experiment, and initial axial displacement is recorded. The axial stress is then increased to initiate shear mobilization during the loading phase (run-in) until a peak steady state is achieved. The initial shear stress is reduced to approximately 80% of the peak shear stress by decreasing the axial stress, then held constant for the duration of each experiment.

15 GEOTHERMAL ENERGY↗

Computationally Efficient Control Co-Design Optimization Framework with Mixed-Fidelity Fluid and Structure Analysis: OpenTurbineCoDe (OTCD) - ARPA-E ATLANTIS Phase I Project (Final Technical Report)

The goal of the Department of Energy (DOE) Advanced Research Projects Agency-Energy (ARPA-E) Aerodynamic Turbines Lighter and Afloat with Nautical Technologies and Integrated Servo-control (ATLANTIS) Program is to develop new technologies for floating offshore wind turbines, or wind farms, using the discipline of control co-design (CCD.) In this context, our goal is to develop a computationally efficient optimization framework for design of floating offshore wind turbines. Our specific aim is to utilize high-fidelity structural, aerodynamic, aero-structural tools, and to derive control-oriented reduced- or low-order models directly from the high-fidelity tools. We proposed a mixed-fidelity modeling approach which means that we are also using low- and mid-fidelity tools when necessary. This research is conducted by a multidisciplinary team consisting of Rutgers University, University of Michigan, Brigham Young University, and the National Renewable Energy Laboratory (NREL). The computational framework, called OpenTurbineCoDe, is designed to integrate, where possible, traditional structural, aerodynamic, aeroelastic models (e.g., OpenFAST) and advanced control algorithms with higher fidelity simulation tools including Reynolds-averaged Navier–Stokes (RANS) solvers, and three-dimensional structural finite element solvers. All the high-fidelity tools used in this research provide numerically exact gradients to facilitate both efficient optimization and local linearization for control implementation.

17 WIND ENERGY↗

WEIS and FAST.Farm Advancements Beyond Wind Turbine Aeroelasticity

Presentation at the 7th Wind Energy Systems Engineering workshop from NREL principal engineer Jason Jonkman, Ph.D., on NREL's numerical tools Wind Energy with Integrated Servo-control (WEIS), which focuses on the integrated design of floating wind turbines, and FAST.Farm, a simulation tool for multi-turbine wind farms based on popular aero-servo-hydro-elastic solver OpenFAST. The presentation discusses recent advancements beyond wind turbine aero-elasticity.

17 WIND ENERGY↗