SLOW (Simplified Low-Order Wind turbine) is a software specifically developed for conceptual, (pre-)FEED numerical design purposes of Floating Offshore Wind Turbines
- Nonlinear coupled aero-hydro-servo-elastic time-domain loads, suitable for Ultimate Limit State (ULS) and Fatigue Limit State (FLS) calculations according to IEC and DNV standards
- SLOW is fast: 1hr simulation completes in under 1 minute (fastest alternative on the market is 20min)
- Linearization for resonance avoidance (Campbell diagram) and matrices for linear model-based controller design
Step by step
Pre-FEED FOWT design for ULS and FLS
Tower
Mooring lines
Platform watch circle
Aero-Hydro-Servo-Elastic Response Amplitude Operator (RAO)
Annual Energy Production (AEP)
Extensive sensitivity studies and design optimization
Controller design
Real-time digital twin
Online simulation, fed by sensor data for load prediction and uncertainty estimate
State-observer calculating the best estimate of loads, merging sensor and model information; Accurate data source for fatigue and Condition Monitoring [3]
Our Approach
- Structural dynamics: 2D and 3D rigid- or flexible-body motion, aligned or misaligned wind and waves. Platform rigid-body motion, elastic tower deformation, rigid rotor
- Aerodynamics: Actuator-disk model of the lumped rotor,
wind misalignment by cosine-portion of aerodynamic forces - Hydrodynamics: Linear potential flow hydrodynamics, with
simplified radiation model, augmented by viscous drag from
Morison’s equation - Mooring dynamics: Quasi-static or dynamic nonlinear model
- Controller dynamics: Blade pitch, generator torque
- Integrator: Runge-Kutta 4th order
- Architecture: Portable executable, text-based input files
(yaml format)
Extensions
- Dynamic mooring model for mooring fatigue assessment
- Blade structural dynamics model (as in OrcaFlex, OpenFAST, Bladed) to resolve blade loads and Three-Times-Per-Revolution (3p) tower loads
- Blade-Element Momentum theory (as in OrcaFlex, OpenFAST, Bladed) to analyze misaligned inflow and wind shear influence
- Elastic floater beam model for a better approximation of global tower natural frequency and the possibility to extract internal floater loads for hull design
Usage
- User guide: Manual with easy-to-use tutorials
- Controller: Bladed-style Dynamic Link Library (DLL)
- Batch runs: Easy parallelization by user due to portable executable without installation (MS Windows)
- Postprocessing: Files readable by PyDatView
- Structural design: Output signals for
- Hydrostatic design (trim in operation/idling)
- Mooring design (fairlead tensions, watch circle)
- Tower design (tower-base section loads)
- Controller design (rotor speed, generator torque, blade pitch angle, el. power, shaft loads)
References and Track Record
- SLOW has been used in numerous FOWT design and controller design projects
- SLOW has been extensively compared against OpenFAST, i.e. [1]
- SLOW has been validated against two scaled experiments [2,3]
[1] Lemmer, F., Yu, W., Luhmann, B., Schlipf, D., & Cheng, P. W. (2020). Multibody modeling for concept-level floating offshore wind turbine design. Multibody System Dynamics, 49(2), 203–236. https://doi.org/10.1007/s11044-020-09729-x
[2] Lemmer, F., Yu, W., Cheng, P. W., Pegalajar-Jurado, A., Borg, M., Mikkelsen, R., & Bredmose, H. (2018). The TripleSpar campaign: Validation of a reduced-order simulation model for floating wind turbines. Proceedings of the ASME 37th International Conference on Ocean, Offshore and Arctic Engineering. https://doi.org/10.1115/OMAE2018-78119
[3] Lemmer, F., Lehmann, K., Raach, S., Al, M., Skandali, D., Schlipf, D., … Cheng, P. W. (2021). Assessment of a state-feedback controller and observer in a Floating Wind scaled experiment. Proceedings of the Wind Energy Science Conference. https://doi.org/10.5281/zenodo.5004916
[4] Lemmer, F., Yu, W., Schlipf, D., & Cheng, P. W. (2020). Robust gain scheduling baseline controller for floating offshore wind turbines. Wind Energy, 23(1). https://doi.org/10.1002/we.2408
[5] Lemmer, F., Schlipf, D., & Cheng, P. W. (2016). Control design methods for floating wind turbines for optimal disturbance rejection. Journal of Physics: Conference Series, 753. https://doi.org/10.18419/opus-8906
[6] Schlipf, D., Lemmer, F., & Raach, S. (2020). Multi-variable feedforward control for floating wind turbines using lidar. Proceedings of the 18th International Offshore and Polar Engineering Conference. https://doi.org/10.18419/opus-11067
[7] Schlipf, D., Sandner, F., Raach, S., Matha, D., & Cheng, P. W. (2013). Nonlinear model predictive control of floating wind turbines. Proceedings of the 23rd International Ocean and Polar Engineering Conference, 440–447. https://doi.org/10.18419/opus-3908
