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At least 127 records · Page 7

High- and Mid-Fidelity Modeling Comparison for a Floating Marine Turbine System: Preprint

There is a lack of suitable numerical tools, particularly open-source tools, that can be used for designing and optimizing marine turbine systems. The National Renewable Energy Laboratory (NREL) has added features to their widely used mid-fidelity wind turbine modeling code, OpenFAST, to enable modeling of axial flow marine turbines. This necessitated the addition of several physical effects relevant to marine turbines that can be neglected for wind turbines. These include buoyant loads, added mass and inertia loads, wave-current superposition, and changes to the coordinate systems. This updated version of OpenFAST allows for the modeling of both fixed and floating marine turbine systems at a speed comparable to real time. While efficient for long simulations, large sets of load cases, and design studies, mid-fidelity codes cannot capture all of the potentially important physical phenomenon impacting marine turbine systems. High-fidelity computational fluid dynamics (CFD) simulations can capture more flow effects with fewer assumptions and provide detailed body pressure mapping and flow-field information. It is important to compare predictions between mid-fidelity and high-fidelity codes, both to verify the models and to understand the limitations. A floating marine turbine system designed by NREL was modeled both with OpenFAST and with the commercial CFD code, STARCCM+. The CFD model used a 3-D unsteady Reynolds-averaged Navier-Stokes solver for a volume-of-fluid numerical wave and current tank. The blade-resolved simulations used the sliding-interface technique for the spinning rotor and an overset grid to accommodate the rigid-body motion of the floating system. The mooring system was modelled with a custom coupling of the CFD solver with the open-source code, MoorDyn. This improves upon the existing quasi-static catenary solver in STARCCM+, which lacks seabed contact or line-to-line connections. Spatial and temporal convergence studies were conducted. The simulation results for a combined current and wave condition are compared between OpenFAST and CFD, highlighting the capabilities of the mid-fidelity code and identifying the areas where a high-fidelity approach is needed.

CFD↗

Floating Photovoltaic System Cost Benchmark: Q1 2021 Installations on Artificial Water Bodies

In this report, we conduct a bottom-up analysis of the installed costs for FPV systems deployed on artificial water bodies under average site conditions (wind load of about 40 m/s, snow load of 20 psf, water depth of 50 m, water level variation of 10 m, and swell height of 1 m). We estimate an installed system cost premium of $\$0.26$/WDC (25%) for 10-MWDC fixed-tilt FPV systems, compared with ground-mounted, fixed-tilt PV installed over bare ground. Higher structural costs related to the floats and anchoring system are the largest contributors to this premium. Currently, site investigation costs are also higher for FPV because of the additional effort needed to plan and design these more complex installations. Our preliminary sensitivity analysis shows that the installed cost of FPV systems is most sensitive to float costs, wind and snow loading, and module efficiency. FPV deployment is in an early stage, and additional experience, best practice development, and new configurations and technologies could help reduce the costs of FPV systems over time.

14 SOLAR ENERGY↗

A review of modelling techniques for floating offshore wind turbines

Modelling floating offshore wind turbines (FOWTs) is challenging due to the strong coupling between the aerodynamics of the turbine and the hydrodynamics of the floating platform. Physical testing at scale is faced with the additional challenge of the scaling mismatch between Froude number and Reynolds number due to working in the two fluid domains, air and water. In the drive for cost-reduction of floating wind energy, designers may be seeking to move towards high-fidelity numerical modelling as a substitute for physical testing. However, the numerical engineering tools typically used for FOWT modelling are considered as mid-fidelity to low-fidelity tools, and currently lack the level of accuracy required to do so. Furthermore, there is a lack of operational FOWT data available for further development and validation. High-fidelity tools, such as CFD, have greater accuracy but are cumbersome tools and still require validation. Physical scale model testing therefore continues to play an essential role in the development of FOWTs both as a source of validation data for numerical models and as an important development step along the path to commercialization of all platform concepts. The aim of this paper is to provide an overview of both numerical modelling and physical FOWT scale model testing approaches and to provide guidance on the selection of the most appropriate approach (or combination of approaches). The current state-of-the-art will be discussed along with current research trends and areas for further investigation.

17 WIND ENERGY↗

An adaptive model-free robotic force control strategy for hydrodynamic real-time hybrid simulation of floating offshore wind turbines

Real-time hybrid simulation (RTHS) - a cyber-physical testing approach - promises to enhance the simulation fidelity of the model-scale experiments used to prototype floating offshore wind turbines (FOWTs). In hydrodynamic RTHS (hydro-RTHS), actuators emulate aerodynamic forces on model-scale FOWT specimens subjected to physical waves in a hydrodynamic laboratory. Robotic arms are promising candidates for actuation in hydro-RTHS due to their compact multi-degree-of-freedom (DOF) capabilities. Unlike classical RTHS for seismic applications, which typically relies on displacement control, hydro-RTHS requires 6-DOF force control on newly designed floating prototypes in a model-scale setting, which presents significant challenges, including modeling uncertainties, directional asymmetry, configuration drift, bandwidth limitations, and time-varying delays. To mitigate these constraints without extensive pre-test calibration, this study proposes an adaptive model-free robotic force control strategy that combines task-space explicit force control with a secondary joint-space pose-keeping task. The Adaptive Feedforward Compensator (AFC) is integrated into the force control loop to compensate for time-varying delay. Experimental testing was conducted using a Franka Emika Panda robotic arm with a 1:50 scale FOWT specimen under operational wind and wave conditions. Results demonstrate stable and consistent 6-DOF force tracking. Effective delay compensation was observed, with low-frequency delay reductions ranging from 71.4% to 91.8% and improvements in low-frequency surge force tracking of 25.0% to 52.1%. This study enhances robotic actuation performance in hydro-RTHS and introduces a force control strategy that supports reliable robotic operation in uncertain floating environments. Future work will explore disturbance-observer mechanisms to further enhance wave rejection capabilities under extreme wind and wave conditions.

17 WIND ENERGY↗

Finding inputs that trigger floating-point exceptions in heterogeneous computing via Bayesian optimization

Testing code for floating-point exceptions is crucial as exceptions can quickly propagate and produce unreliable numerical answers. The state-of-the-art to test for floating-point exceptions in heterogeneous systems is quite limited and solutions require the application’s source code, which precludes their use in accelerated libraries where the source is not publicly available. We present an approach to find inputs that trigger floating-point exceptions in black-box CPU or GPU functions, i.e., functions where the source code and information about input bounds are unavailable. Our approach is the first to use Bayesian optimization (BO) to identify such inputs and uses novel strategies to overcome the challenges that arise in applying BO to this problem. Here, we implement our approach in the XSCOPE framework and demonstrate it on 58 functions from the CUDA Math Library and 81 functions from the Intel Math Library. XSCOPE is able to identify inputs that trigger exceptions in about 73% of the tested functions.

97 MATHEMATICS AND COMPUTING↗

Integrated floating wind farm layout design and mooring system optimization to increase annual energy production

As we cluster wind turbines in wind farms to gain energy from sites with high wind speeds, wake losses occur within the wind farm. Wake loss is a term used to describe the lower energy production of a downwind turbine that is totally or partially in the wake of an upwind turbine. To decrease wake losses inside the wind farm, the wind farm’s layout is optimized. However, a variety of factors constrain the wind farm layout optimization, such as the size of the lease area relative to the number of turbines to be placed, or the shape of the lease area. Therefore, many wind farms end up with a regular grid layout, such as the Horns Rev 1 wind farm in the North Sea. The ability of a floating offshore wind turbine (FOWT) to change its position based on the wind direction and its mooring system design presents an opportunity to further decrease wake losses in floating wind farms. In this work, we integrate the design of the FOWT mooring systems with the floating wind farm layout design with the goal of increasing the farm’s annual energy production. We use the Horns Rev 1 wind farm as a case study to demonstrate our method. The results show that allowing the FOWT to relocate can decrease wake losses up to 18%. Moreover, the newly developed mooring systems are less stiff and therefore allow larger motion of the FOWT; hence, the material cost of the mooring system decreases by an estimated 17%.

17 WIND ENERGY↗

Design and Analysis of a Floating-Wind Shallow-Water Mooring System Featuring Polymer Springs

This paper presents a mooring system design featuring polymer springs for the VolturnUS-S 15-MW reference floating wind turbine in site conditions for the New York Bight at a 50-m water depth. Polymer springs have a nonlinear stress-strain curve that allows a stiffer response at low loads and a more flexible response at higher loads, potentially reducing peak mooring line tensions. The mooring dynamics model MoorDyn has been extended to model springs with nonlinear tension-strain curves from a user-inputted look-up table. This MoorDyn modeling advancement is verified against OrcaFlex simulation results. Using MoorDyn's updated capabilities, a spring-equipped catenary mooring system is designed for the 15-MW floating system, along with a baseline catenary mooring system that does not use polymer springs. The floating wind turbine simulator OpenFAST is used to simulate the mooring systems in design-driving load cases to show the effect of polymer springs on key dynamic behaviours. The results show that the spring-equipped design reduces peak tensions by up to 60%, whereas the turbine offsets stay within a maximum of 7.2 m, which is still a reasonable offset limit for cable considerations. The reduction in peak tensions results in a significant decrease in damage equivalent loads - on the order of 50% for upwind lines in fully loaded conditions. These results show that polymer springs can effectively reduce peak tensions and fatigue loads in mooring systems at shallow water depths.

floating offshore wind↗

Summary of Conclusions and Recommendations Drawn from the DeepCWind Scaled Floating Offshore Wind System Test Campaign: Preprint

The DeepCwind consortium is a group of universities, national labs, and companies funded under a research initiative by the U.S. Department of Energy (DOE) to support the research and development of floating offshore wind power. The two main objectives of the project are to better understand the complex dynamic behavior of floating offshore wind systems and to create experimental data for use in validating the tools used in modeling these systems. In support of these objectives, the DeepCwind consortium conducted a model test campaign in 2011 of three generic floating wind systems, a tension-leg platform (TLP), a spar-buoy (spar), and a semisubmersible (semi). Each of the three platforms was designed to support a 1/50th-scale model of a 5 MW wind turbine and was tested under a variety of wind/wave conditions. The focus of this paper is to summarize the work done by consortium members in analyzing the data obtained from the test campaign and its use for validating the offshore wind modeling tool, FAST.

17 WIND ENERGY↗

Comparison of Second-Order Loads on a Semisubmersible Floating Wind Turbine: Preprint

As offshore wind projects move to deeper waters, floating platforms become the most feasible solution for supporting the turbines. The oil and gas industry has gained experience with floating platforms that can be applied to offshore wind projects. This paper focuses on the analysis of second-order wave loading on semisubmersible platforms. Semisubmersibles, which are being chosen for different floating offshore wind concepts, are particularly prone to slow-drift motions. The slack catenary moorings usually result in large natural periods for surge and sway motions (more than 100 s), which are in the range of the second-order difference-frequency excitation force. Modeling these complex structures requires coupled design codes. Codes have been developed that include turbine aerodynamics, hydrodynamic forces on the platform, restoring forces from the mooring lines, flexibility of the turbine, and the influence of the turbine control system. In this paper two different codes are employed: FAST, which was developed by the National Renewable Energy Laboratory, and aNySIM, which was developed by the Maritime Research Institute Netherlands. The hydrodynamic loads are based on potential-flow theory, up to the second order. Hydrodynamic coefficients for wave excitation, radiation, and hydrostatic forces are obtained with two different panel codes, WAMIT (developed by the Massachusetts Institute of Technology) and DIFFRAC (developed by MARIN). The semisubmersible platform, developed for the International Energy Agency Wind Task 30 Offshore Code Comparison Collaboration Continuation project is used as a reference platform. Irregular waves are used to compare the behavior of this platform under slow-drift excitation loads. The results from this paper highlight the effects of these loads on semisubmersible-type platforms, which represent a promising solution for the commercial development of the offshore deepwater wind resource.

17 WIND ENERGY↗

Investigation of Tank 241-AN-101 Floating Solids

Tank 241-AN-101 is the receiver tank for retrieval of several C-Farms waste tanks, including Tanks 241-C-102 and 241-C-111. Tank 241 C 111 received first-cycle decontamination waste from the bismuth phosphate process and Plutonium and Uranium Extraction cladding waste, as well as hydraulic fluid. Three grab samples, 1AN-16-01, 1AN-16-01A, and 1AN-16-01B, were collected at the surface of Tank 241-AN-101 on April 25, 2016, after Tank 241-C-111 retrieval was completed. Floating solids were observed in the three grab samples in the 11A hot cell after the samples were received at the 222-S Laboratory. Routine chemical analyses, solid phase characterization on the floating and settled solids, semivolatile organic analysis mainly on the aqueous phase for identification of degradation products of hydraulic fluids were performed. Investigation of the floating solids is reported.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Updated Oregon Floating Offshore Wind Cost Modeling [Slides]

The study provides heat maps showing updated estimates of the levelized cost of energy (LCOE) for floating offshore wind energy off the coast of Oregon. This project builds on a 2019 National Renewable Energy Laboratory (NREL) floating offshore wind power cost study in Oregon (Musial et al. 2019) and a recent NREL California cost analysis (Beiter et al. 2020). Floating wind power cost data, modeling methodology, and resource data are updated and LCOE is estimated through 2032 using 2019 as a reference year. Comparisons are made to the previous offshore wind energy cost studies (Musial et al. 2019; Beiter et al. 2020). The study does not prioritize specific sites or make judgments about marine spatial planning viability.

17 WIND ENERGY↗

The Costs and Feasibility of Floating Offshore Wind Energy in the O'ahu Region

The State of Hawai'i has set a target to achieve a 100% Renewable Portfolio Standard (RPS) by 2045, and is well suited to become the first state to achieve this goal due to its relatively small load, high electricity prices, heavy reliance on imported fossil fuels, and favorable conditions for wind and solar. The Bureau of Ocean Energy Management contracted NREL to conduct a cost and feasibility study to provide information to decision makers on Hawai'i about the viability of floating offshore wind to be a part of the 100% RPS. We used NREL's Offshore Regional Cost Analyzer (ORCA) spatial cost model to evaluate the Levelized Cost of Energy (LCOE) in the region surrounding O'ahu as this is the island with the highest energy demand. The ORCA results showed that LCOE could range from around $\$$83 MWh to $\$$194 MWh for commercial operation dates in 2019 but has the potential to decrease to $\$$48 MWh - $\$$109 MWh by 2032 due to maturing global supply chains, increasing turbine rating, and new technological innovations. These costs are expected to be competitive with global floating wind costs in the early 2030s. The strong wind resource, proximity to infrastructure on land, and benign metocean conditions can potentially compensate for the logistical complexities of installing projects in Hawai'i far from mainland supply chains if sufficient investments are made to develop ports, grid infrastructure, and workforce on O'ahu to support the construction and operation of offshore wind projects. In addition to the cost results, this report also discusses the likely technologies that would comprise floating wind projects near O'ahu, the existing infrastructure available to projects, unique conditions facing offshore wind in the region such as exposure to hurricanes and limits on allowable export cable capacity, newly developed wind resource data sets for the region, and local stakeholder perspectives on offshore wind.

17 WIND ENERGY↗

Considerations for Floating Wind Energy Development in the Gulf of Maine

This report summarizes the primary considerations for developing floating offshore wind energy in the Gulf of Maine based on the current knowledge of the staff at the National Renewable Energy Laboratory (NREL) as of June 2023. This work was performed as a supplement to information solicited by the Bureau of Ocean Energy Management (BOEM) in their Call for Information and Nominations announced in April 2023 (BOEM 2023b). The purpose of this report is to provide general information to the citizens of Maine, Massachusetts, and New Hampshire, and to inform decision makers and stakeholders about the unique challenges of developing floating offshore wind in the Gulf of Maine. This report intends to provide context to show the importance of the energy markets created by the states that floating offshore wind development in the Gulf of Maine will serve.

BOEM↗

Ultraflexible Smart FLoating Offshore Wind Turbine (USFLOWT) (Final Report) [Slides]

In this final report, we summarize activities and results from the Ultraflexible Smart Floating Offshore Wind Turbine (USFLOWT) project. The project involved co-design of a 10-MW floating offshore wind system, especially the substructure. This substructure was based on the SpiderFLOAT, an ultra-compliant, ultralight, and modular system with actuators for smart platform control that was promising for control co-design. The project’s overall goal was to unlock the floating offshore wind market based on conditions on the North Atlantic coast of the United States. Tasks summarized in the report include Task 2.3 (including controller design for the turbine and the substructure and final design of the SpiderFLOAT), Task 2.4 (final design of the active can ballasting actuator), Task 2.5 (planning for a scale demonstrator), Task 3.1 (technology to market plan), and Task 3.3 (approval in principle). The report also summarizes project dissemination activities at a variety of venues including the lead author and/or presenter for each.

17 WIND ENERGY↗

Reference Site Conditions for Floating Wind Arrays in the United States

Floating offshore wind farm design is highly site-specific, requiring detailed information about the specific conditions of a project area for realistic design studies. Unfortunately, publicly available site condition data for potential floating offshore wind project sites in the United States is scarce. To support U.S. offshore wind research, we developed reference site condition datasets, including metocean and seabed information, for four potential floating wind project areas in the U.S.: Humboldt Bay, Morro Bay, the Gulf of Maine, and the Gulf of Mexico. These datasets were compiled using publicly available data. Our metocean analysis, covering wind, waves, and surface currents, utilized measurement data from 2000 to 2020. Sources included the National Renewable Energy Laboratory’s National Offshore Wind Dataset for wind data, National Data Buoy Center buoys for wave data, and the High Frequency Radar Network for surface currents. These data were integrated into hourly time series used to compute extreme return periods up to 500 years, monthly statistics, and joint probability clusters for fatigue analysis. Soil conditions were evaluated using the usSEABED database and bathymetry grids were interpolated from the NCEI Digital Elevation Model Global Mosaic. In addition to providing curated reference site condition datasets for four U.S. areas, our assessment highlights the need for more publicly available metocean and soil condition data.

17 WIND ENERGY↗

Innovative Deep-Water Mooring Systems for Floating Wind Farms (DeepFarm): Cooperative Research and Development (Final Report)

The DeepFarm project, led by Principle Power Inc (PPI), focused on design, modeling and development of innovative mooring and anchoring systems for semi-submersible floating wind turbine platforms in a deep water, commercial-size wind farm. It explored the possibility of sharing anchors amongst the floating platforms within the farm, and considers site conditions representative of the West Coast and Hawaii, with a variety of water depths, as case studies to ensure that the sensitivity to water depth and park size are well understood. NREL and PPI worked together to develop and apply NREL's computational tools for modeling floating wind farm dynamics and costs.

17 WIND ENERGY↗

Verification of New Floating Capabilities in FAST v8: Preprint

In the latest release of NREL's wind turbine aero-hydro-servo-elastic simulation software, FAST v8, several new capabilities and major changes were introduced. FAST has been significantly altered to improve the simulator's modularity and to include new functionalities in the form of modules in the FAST v8 framework. This paper is focused on the improvements made for the modeling of floating offshore wind systems. The most significant change was to the hydrodynamic load calculation algorithms, which are embedded in the HydroDyn module. HydroDyn is now capable of applying strip-theory (via an extension of Morison's equation) at the member level for user-defined geometries. Users may now use a strip-theory-only approach for applying the hydrodynamic loads, as well as the previous potential-flow (radiation/diffraction) approach and a hybrid combination of both methods (radiation/diffraction and the drag component of Morison's equation). Second-order hydrodynamic implementations in both the wave kinematics used by the strip-theory solution and the wave-excitation loads in the potential-flow solution were also added to HydroDyn. The new floating capabilities were verified through a direct code-to-code comparison. We conducted a series of simulations of the International Energy Agency Wind Task 30 Offshore Code Comparison Collaboration Continuation (OC4) floating semisubmersible model and compared the wind turbine response predicted by FAST v8, the corresponding FAST v7 results, and results from other participants in the OC4 project. We found good agreement between FAST v7 and FAST v8 when using the linear radiation/diffraction modeling approach. The strip-theory-based approach inherently differs from the radiation/diffraction approach used in FAST v7 and we identified and characterized the differences. Enabling the second-order effects significantly improved the agreement between FAST v8 and the other OC4 participants.

17 WIND ENERGY↗

Modeling the TetraSpar Floating Offshore Wind Turbine Foundation as a Flexible Structure in OrcaFlex and OpenFAST

Floating offshore wind turbine technology has seen an increasing and continuous development in recent years. When designing the floating platforms, both experimental and numerical tools are applied, with the latter often using time-domain solvers based on hydro-load estimation from a Morison approach or a boundary element method. Commercial software packages such as OrcaFlex, or open-source software such as OpenFAST, are often used where the floater is modeled as a rigid six degree-of-freedom body with loads applied at the center of gravity. However, for final structural design, it is necessary to have information on the distribution of loads over the entire body and to know local internal loads in each component. This paper uses the TetraSpar floating offshore wind turbine design as a case study to examine new modeling approaches in OrcaFlex and OpenFAST that provide this information. The study proves the possibility of applying the approach and the extraction of internal loads, while also presenting an initial code-to-code verification between OrcaFlex and OpenFAST. As can be expected, comparing the flexible model to a rigid-body model proves how motion and loads are affected by the flexibility of the structure. OrcaFlex and OpenFAST generally agree, but there are some differences in results due to different modeling approaches. Since no experimental data are available in the study, this paper only forms a baseline for future studies but still proves and describes the possibilities of the approach and codes.

17 WIND ENERGY↗