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FAD-Toolset (Floating Array Design Toolset) [SWR-26-056]

The Floating Array Design (FAD) Toolset is a collection of tools for modeling and designing arrays of floating offshore structures. It was originally designed for floating wind systems but has applicability for many offshore applications. A core part of the FAD Toolset is the floating array model, which serves as a high-level library for efficiently modeling a floating array, such as a floating wind array. It combines site condition information and a description of the floating array design, and contains functions for evaluating the array's behavior considering the site conditions. For example, it combines information about site soil conditions, mooring line loads, and an array's anchor characteristics to estimate the holding capacity of each anchor. The library works in conjunction with the tools RAFT, MoorPy, and FLORIS to model floating platforms, wind turbines, mooring systems, power cables, and array wakes respectively. Layered on top of the floating array model is a set of design tools that can be used for algorithmically adjusting or optimizing parts of the a floating array. Specific tools existing for mooring lines, shared mooring systems, dynamic power cables, static power cable routing, and overall array layout. These capabilities work with the design representation and evaluation functions in the floating array model, and they can be applied by users in various combinations to suit different purposes. In addition to standalone uses of the FAD Toolset, a coupling has been made with Ard, (https://github.com/NLRWindSystems/Ard) a sophisticated and flexible wind farm optimization tool. This coupling allows Ard to use certain mooring system capabilities from FAD to perform layout optimization of floating wind farms with Ard's more advanced layout optimization capabilities. The FAD Toolset works with the IEA Wind Task 49 Ontology (https://github.com/IEAWindTask49/Ontology), which provides a standardized format for describing floating wind farm sites and designs. See example use cases in our examples folder (https://github.com/NLRWindSystems/FAD-Toolset/blob/main/examples/README.md) For working with the library, it is important to understand the floating array model structure, which is described more here: https://github.com/NLRWindSystems/FAD-Toolset/blob/main/fad/README.md.

Sirkis, Leah [National Laboratory of the Rockies (

Floating Wind Array Ontology and Modeling Framework

While there are many tools for designing and modeling a single floating turbine, array level design and modeling has much more to consider. Designing floating wind arrays requires a coupled approach considering many variables, from bathymetry to installation and maintenance to failure and risk analysis. With all of these considerations, an array-level modeling tool is needed to quickly evaluate array designs. The Floating Array Model (FAModel) tool developed at the National Renewable Energy Laboratory was created to fill this gap in low-fidelity array modeling. FAModel is a python framework created to streamline holistic low-fidelity floating wind modeling for array-level analysis. FAModel integrates site data and models with a variety of open-source modeling tools developed by NREL, including FLORIS, RAFT, MoorPy, and anchor capacity models. The integration of these tools allows users to quickly and holistically design an array by considering forces, area analysis, visualization, annual energy production, failure modeling, and component costs.

17 WIND ENERGY

IEA Wind Task 49: Reference Site Conditions for Floating Wind Arrays

The commercial-scale deployment of floating offshore wind (FOW) projects is expected to take place in a diverse range of sites that may differ significantly from existing fixed-bottom projects. FOW farms are particularly sensitive to the water depth and the meteorological and oceanographic (metocean) and geotechnical conditions at the project site due to the wave-induced system motions and loads as well as the anchoring system constraints imposed by the seafloor conditions. Uncertainty around the site conditions will permeate through all aspects of project design, leading to suboptimal and overly conservative designs, increased costs, and adversely affected performance. As FOW expands into a global industry, metocean and geotechnical conditions will increasingly vary for projects located in different geographic regions or in far-from-shore, deep-water sites. This study represents the outputs of work package 1 of International Energy Agency Wind Task 49, which focuses on the integrated design of floating wind arrays. The primary goal of this study is to establish the type of parameters and constraints required to characterize FOW array reference sites; provide a realistic and publicly available set of reference site conditions to the FOW community as a baseline set of data for individual research projects; identify and categorize any critical gaps in the existing data or methodologies required to define reference site characteristics; and inform and support the design of reference FOW arrays. A building block concept was developed for synthesizing reference sites for the design of FOW arrays. The building blocks include three classes of site conditions focusing on the techno-economic design of FOW projects: metocean conditions, seabed conditions, and coastal infrastructure. All reference site data produced and collected in this study are publicly available.

17 WIND ENERGY

Frequency-domain modeling of floating wind arrays with shared mooring lines

A frequency-domain model for floating wind turbine dynamics has been extended to model floating wind farms with couplings from shared mooring systems. The model, called RAFT, could previously calculate the mean offsets and wave-induced response spectra for single floating wind turbines. Now, the model supports multiple floating wind turbines, each with their own properties and responses, along with mooring lines that run directly between floating wind turbines in the array, meaning that shared mooring lines or fully suspended dynamic power cables can be included. This capability is achieved by setting up an array-level solution of the system mean offsets and assembling the full system matrices for solving the dynamic response. The quasi-static mooring model MoorPy is used to linearize the mooring system properties. To compute the floating wind turbine relative motions, phase offsets are applied to each turbine's response as a function of wave frequency based on the wavelength and relative positions in the array. These differential motions are then applied to mooring system tension Jacobians to compute the tension loads in the shared mooring lines. Overall, the capability provides a frequency-domain analog to the modeling capabilities of the floating support structure in FAST.Farm. Mean offsets and power spectral density plots of responses are compared between RAFT and FAST.Farm to verify the implementation. The results indicate good agreement within the expectations of a frequency-domain modeling approach and suggest correct implementation of the shared mooring aspects. Additionally, a unique comb-like frequency response in the shared mooring line tensions has been observed. This phenomenon has a clear physical basis and may be an important design consideration for future shared mooring systems.

17 WIND ENERGY

Reference floating wind array designs for three representative regions

This work presents the systematic development of three open-source reference floating wind array designs. The designs are tailored to representative site conditions for three regions of the United States: Humboldt Bay off the coast of California, the Gulf of Maine, and the Gulf of America. We adopted existing reference designs for the individual 15 MW turbines, semisubmersible floating platforms, substations, mooring systems, and power cables – integrating and adapting them as needed for each location. We adapted existing dynamic cable designs to use larger conductor sizes to meet the arrays' power transmission requirements, and we set up redundant mooring systems for each substation. The layout of each array is a uniform-grid design optimized to approximately minimize the levelized cost of energy (LCOE) within a square lease area while satisfying spatial constraints. These constraints ensure adequate clearances between adjacent turbines and between underwater components during the layout optimization to prevent clashing and ensure that all components reside within the lease boundaries. Substations are included to allow accounting for intra-array cable costs. They are placed within the uniform grid to maintain the navigability of the arrays. For each feasible layout considered, annual energy production and cable routing costs are calculated and updated in the LCOE objective function. After the optimization, we refined the cable routing with a mix of algorithmic and manual methods to ensure that the cables avoid mooring system components and approach the substation with adequate clearances. We confirmed the suitability of each reference array's layout by comparing the wake losses at each wind heading angle to the wind rose, observing that the optimized layouts largely avoid wake losses in the predominant wind directions. These reference arrays provide open-source baseline designs to enable future research and innovation of floating wind technology at the array scale.

17 WIND ENERGY

Dynamic Wind Loading on Compliant, Floating PV Arrays: Cooperative Research and Development (Final Report)

PV panels are subjected to wind loads during normal outdoor operation, where th wind speed, wind direction, panel angle, and array layout play a large role in the overall loading magnitude. For floating PV systems, where panels are installed on floating rafts, these forces can lead to a dynamic displacement of the raft and mounted PV hardware. The Contractor and Participant will perform fluid-structure interaction simulations of this phenomenon for a variety of wind speeds, directions, and panel angles to characterize these forces and help design mooring/tethering lines to resist and anchor raft movement.

14 SOLAR ENERGY

PV Modules Temperature Variation and Patterns in Medium and Utility-Scale Floating PV Systems

This paper presents the preliminary results and findings of the four operational Floating PV systems across the USA. At each site, temperature of five PV modules located at North-West, North-East, Middle, South-West, and, South-East have been monitored through the Resistant Temperature Detector (RTD) sensors. Three RTDs were attached to each PV module on the rear-side along the diagonal at top, middle and bottom cells. The preliminary results reveal wide temperature differences among the inter and intra PV modules. Besides this, wave pattern temperatures were observed in a few PV modules. The final results, findings, and, factors responsible will be investigated during the next few months.

array

Optimal load attachment of a deeply embedded ring anchor in clay

A Deeply Embedded Ring Anchor (DERA) system has been developed as a cost-effective solution for mooring arrays of floating offshore wind turbines (FOWTs) to the seabed. The DERA boasts several key features, including its versatility in various soil types, compact size, compatibility with diverse mooring systems, multi-line potential, and robust performance even under unintentional loading conditions. While prior preliminary studies have provided valuable insights into how the DERA can enhance cost-effectiveness by offering a high load capacity, these studies have predominantly focused on optimizing anchor performance under translational horizontal and vertical loading. However, to design the DERA optimally, we must also consider its ability to handle inclined loading conditions in addition to lateral and axial loadings. Due to its shorter length compared to a conventional caisson, the DERA has less resistance to moments, making it more sensitive to horizontal load capacity and the optimal load attachment depth concerning load angle. For this reason, our study introduces an analytical approach to evaluate the effects of inclined loading on anchor performance, utilizing the previously validated upper bound plastic limit analysis (PLA) method. In investigating the optimal load attachment of the DERA, this paper conducts a parametric study to analyze how factors such as load attachment depth, anchor aspect ratio, and load inclination affect the DERA’s load capacity. Our findings indicate that PLA can serve as a valuable analytical tool for assessing the ultimate load capacity of the DERA, particularly under inclined loading conditions.

42 ENGINEERING

Focal Fisheries Requirements for Floating Offshore Wind Co-Design in the Gulf of Maine and the Mid-Atlantic Bight

Floating offshore wind is an emerging renewable energy technology in U.S. waters and globally. Floating offshore wind technology is more complex than fixed-bottom installations, requiring platforms, moorings, and power cables that take up a larger footprint, both in the water column and along the seafloor. This complexity affects opportunities for compatibility between floating offshore wind and other ocean users, particularly fishermen, whose fishing gear also takes up space in the water column and along the seafloor. This report describes the results of semi-structured interviews with key fishing informants from two focal fisheries: (1) Gulf of Maine lobster (fixed gear) and (2) Mid-Atlantic Bight pelagic longline and recreational tuna (mobile gear). A total of six fishermen were interviewed: four from the Gulf of Maine and two from the Mid-Atlantic Bight. The interviews were designed to elicit information and opinions on the co-design of marine space for particular fishing gear and floating offshore wind infrastructure. Here, we provide an overview of participant fishing experience, the spatial requirements of specific gear types, self-reported comfort levels in different fishing scenarios, and concerns about accessing and operating within floating offshore wind arrays. This summary of fishermen s concerns around floating offshore wind infrastructure can guide future co-existence conversations.

17 WIND ENERGY

Shifting Between Compute and Memory Bounds: A Compression-Enabled Roofline Model

In the evolving landscape of high-performance computing, especially to fight the end of Moore’s Law and Dennard’s Scaling, the ability to shift between compute-bound and memory-bound states is critical for enhancing adaptability and flexibility to diverse system and domain-specific architectures. Such capability is vital for optimizing performance across distinguished hardware configurations, such as accelerators, memory hierarchies, and cache systems. Despite that ad hoc optimization techniques, such as compressed/approximate computation, have been enabled for compute-/data-intensive computing for improved performance in distinct hardware settings, there lacks an understanding of 1) the rational behind performance improvement; 2) capability of different optimizations; 3) what optimization to respond to specific computational and memory demands. This work proposes a compression-enabled roofline model to facilitate this adaptability with data compression techniques to balance and transform between computational and memory demands. This model enables applications to adjust in response to the specific strengths and limitations of the underlying hardware and system to optimize resource utilization. The effectiveness of this approach is demonstrated with matrix multiplication kernels on different input sizes, with turning on/off various compression techniques, including 1) low-precision floating point; 2) sparse matrix formulation; and 3) compressed arrays with ZFP. By reducing memory transfer volumes and cache misses and increasing data locality and computational intensity through compression, the specific roofline model can transform between compute and memory bounds to align more efficiently with system capabilities. This advancement not only improves overall performance but also maximizes adaptability in diverse computing environments.

Naraparaju, Ramasoumya [University of Washington]

AquaPV: Foundational Analysis and Industry Guidance on Floating PV Results

This dataset contains results of the technical potential analysis of floating photovoltaics (FPV) on federally owned or permitted reservoirs in the United States. Estimates of the area of reservoirs that is technically feasible for FPV development are provided for reservoirs that are owned by the US Army Corps of Engineers or the US Bureau of Reclamation, or are associated with hydropower dams licensed by the Federal Energy Regulatory Commission. Other associated data are included, such as estimates of the potential evaporative losses of FPV development, and whether the waterbody is associated with an energy community or disadvantaged community that could qualify it for investment or production tax credits. Data is provided both as a spreadsheet and in a geospatial format including the waterbody geometries. Please read the included readme for more detailed field definitions.

area

Deploying and Tracking Software with NCCS Software Provisioning

The National Center for Computational Sciences (NCCS) at Oak Ridge National Laboratory has a long history of deploying ground-breaking leadership-class supercomputers for the U.S. Department of Energy. The latest in this line of supercomputers is Frontier, the first supercomputer to break the exascale barrier (1018 floating-point operations per second) on the TOP500 list. Frontier serves a wide array of scientific domains, from traditional simulation-based workloads to newer AI and Machine Learning workloads. To best serve the NCCS user community, NCCS uses Spack to deploy a comprehensive software stack of scientific software packages, providing straightforward access to these packages through Lmod Environment Modules. Maintaining a large software stack while also including multiple new compiler releases each year is a very time-consuming task. Additionally, it is not straightforward to provide a software stack alongside existing vendor-provided software such as the HPE/Cray Programming Environment (CPE), and existing CPE, Spack, and Lmod integration does not allow for multiple versions of GPU libraries such as AMD’s ROCm to be used. To address these challenges and shortcomings, NCCS has developed the NCCS Software Provisioning tool (NSP)1, a tool for deploying and monitoring software stacks on HPC systems. NSP allows NCCS to quickly and effectively provision software stacks from the ground up using template-driven recipes and configuration files. NSP is successfully deployed on Frontier and several other NCCS clusters, enabling the NCCS software team to quickly deploy software stacks for newly-released compilers, expand current software offerings, better support GPU-based software, and monitor Lmod module usage to identify unused software packages that can be removed from the software stack. In this work, we discuss the shortcomings of the previous CPE, Spack, and Lmod usage at NCCS, provide further details on the implementation and structure of NSP, then discuss the benefits that NSP provides.

Rentschler, Asa [ORNL] (ORCID:0009000597694743)

Distributed Acoustic Sensing for Whale Vocalization Monitoring: A Vertical Deployment Field Test

Abstract There is growing interest in floating offshore wind turbine (FOWT) technology, where turbines are installed on floating structures anchored to the seabed, allowing wind energy development in areas unsuitable for traditional fixed-platform turbines. Responsible development requires monitoring the impact of FOWTs on marine wildlife, such as whales, throughout the operational lifecycle of the turbines. Distributed acoustic sensing (DAS)—a technology that transforms fiber-optic cables into vibration sensor arrays—has been demonstrated for acoustic monitoring of whales using seafloor telecommunications cables. However, no studies have yet evaluated DAS performance in dynamic, engineered environments, such as floating platforms or moving vessels with complex, dynamic strain loads, despite their relevance to FOWT settings. This study addresses that gap by deploying DAS aboard a boat in Monterey Bay, California, where a fiber-optic cable was lowered using a weighted and suspended mooring line, enabling vertical deployment. Humpback whale vocalizations were captured and identified in the DAS data, noise sources were identified, and DAS data were compared to audio captured by a standalone hydrophone attached to the mooring line and a nearby hydrophone on a cabled observatory. This study is unique in: (1) deploying DAS in a vertical deployment mode, where noise from turbulence, cable vibrations, and other sources posed additional challenges compared to seafloor DAS applications; (2) demonstrating DAS in a dynamic, nonstationary setup, which is uncommon for DAS interrogators typically used in more stable environments; and (3) leveraging looped sections of the cable to reduce the noise floor and mitigate the effects of excessive cable vibrations and strain. This research demonstrates DAS’s ability to capture whale vocalizations in challenging environments, highlighting its potential to enhance underwater acoustic monitoring, particularly in the context of renewable energy development in offshore environments.

Saw, Jaewon

Floating Solar at Lima's Twin Lakes Reservoir

The City of Lima, Ohio is developing a floating photovoltaic (FPV) facility on the surface of Twin Lake Reservoir, adjacent to the City’s water treatment plant. The FPV array is designed with a capacity of 2 megawatts (MW) and will consist of approximately 3,500 solar panels spanning more than four acres of water surface. Based on regional solar conditions, the system will generate 2,000 megawatt-hours (MWh) of electricity annually. While the FPV system will interconnect to the electrical infrastructure serving the water treatment plant, power produced by the system will be net-metered through the utility grid, with energy credits applied to offset the plant’s overall electrical demand rather than directly supplying all plant loads in real time.

14 SOLAR ENERGY

Bridging the Gap Between LLMs and LNS with Dynamic Data Format and Architecture Codesign

Deep neural networks (DNNs) have achieved tremendous success in the past few years. However, their training and inference demand exceptional computational and memory resources. Quantization has been shown as an effective approach to mitigate the cost, with the mainstream data types reduced from FP32 to FP16/BF16 and recently FP8 in the latest NVIDIA H100 GPUs. With increasingly aggressive quantization, however, the conventional floating-point formats suffer from limited precision in representing numbers around zero. Recently, NVIDIA demonstrated the potential of using a Logarithmic Number System (LNS) for the next generation of tensor cores. While LNS mitigates the hurdles in representing small numbers, in this work we observed a mismatch between LNS and the emerging Large Language Models (LLM), where LLM exhibits significant outliers when directly adopting the LNS format. In this paper, we present a data-format/architecture codesign to bright this gap. On the format side, we propose a dynamic LNS format to flexibly represent outliers at a higher precision, by exploiting asymmetry in the LNS representation and identifying outliers through a per-vector basis. On the architecture side, for demonstration, we realize the dynamic LNS format in a systolic array, which can handle the irregularity of the outliers at runtime. We implement our approach on an Alveo U280 FPGA as a prototype. Experimental results show that our design can effectively handle the outliers and resolve the mismatch between LNS and LLM, contributing to an accuracy improvement of 15.4% and 16% over the floating-point and the original LNS baselines, using four state-of-the-art LLM models. Our observation and design lay a solid foundation for the large-scale adoption of the LNS format in the next-generation deep learning hardware.

Haghi, Pouya

Public Reference Data for Megawatt-Scale Hydrogen Electrolysis - Simulated Marine Hydrokinetic Tidal Turbine

The U.S. Department of Energy and National Laboratory of the Rockies (NLR) demonstrate hydrogen electrolysis, hydrogen compression and storage, and variable hydrogen fuel cell power production using megawatt-scale equipment at NLR’s Flatirons Campus as part of the Advanced Research on Integrated Energy Systems (ARIES) initiative. This dataset is part of that effort and is intended for academic, national laboratory, industrial, and other stakeholders to plan, design, and validate models of megawatt-scale hydrogen technologies and diverse energy infrastructure. These data provide a baseline for how existing hydrogen electrolysis technologies perform when coupled with other energy technologies. This dataset contains inputs and outputs from simulations of a floating marine hydrokinetic turbine over approximately half a tidal cycle (~6.6 hours). Inflow conditions were derived from field measurements in Alaska’s Cook Inlet and represent a tidal environment in which the current speed ramps from near 0 m/s to a peak of 3 m/s and back. The original acoustic doppler current profiler dataset is publicly available on the Marine and Hydrokinetic Data Repository. In a full tidal cycle, the flow reverses and the rotor would reorient; this reversal was not modeled. In the Cook Inlet campaign , turbulence intensity was similar in both directions. Two inflow cases are included. In the first case, labeled “raw” in the files, the measured current time series was used directly in the InflowWind module of OpenFAST. Speed and direction were applied as a function of time and elevation, uniformly in the horizontal direction. With full spatial coherence, this approach captures high turbulent variability and results in pronounced power fluctuations, so it is considered a conservative, near-worst-case representation of loading. In the second case, labeled “average” in the files, a 30-minute moving average was applied to extract the slowly varying mean speed. The residual fluctuations about this mean were used to generate spatially varying, full-field turbulence inputs with TurbSim, giving a more physically realistic representation of the inflow across the rotor disk. Two random realizations were used to produce distinct inflow conditions for two OpenFAST simulations representing a two-turbine array. The same turbulence intensity is applied across the full time series, producing larger fluctuations at the start and end, where the mean speed is low. The second case is the more appropriate framework for performance and power assessment but overpredicts turbulence at lower flow speeds and underpredicts it at higher speeds. As the floating platform moves and the rotor changes its x-position, Taylor’s frozen turbulence hypothesis used by InflowWind assumes a constant rather than a time-varying mean velocity, introducing some inaccuracy in the velocity plane sampling. The turbine modeled is the 500-kW Reference Model 1, a horizontal-axis two-bladed hydrokinetic turbine on a four-column floating semisubmersible substructure . Simulations were performed using OpenFAST v4.1 with the Reference Open Source Controller (ROSCO) v2.10. All input files required to reproduce the simulations are included. The electrolyzer is a 1.25-MW proton exchange membrane type MC250 system manufactured by Nel . This unit supports up to 2.5 MW, but NLR has only a single 1.25-MW stack. The datasets report hydrogen balance-of-plant and system data, all captured at 1 Hz, including hydrogen mass production measured with an Emerson Coriolis flow meter. The system controls hydrogen production by varying direct current applied to the stack, from a maximum of 3,000 A to a minimum safe operating current of 300 A, or 10%. Because the current–voltage characteristic changes as the stack ages and efficiency degrades, the actual minimum safe operating power changes over time. The simulated tidal turbine time series data was translated from power (kilowatts) to current (amperes) using a curve fit with calibration data and sent to the electrolyzer power supply at 1-Hz. Each zip file represents a single tidal electrolysis experiment and is named: {technology}_{inflow method}_{number of 500 kW tidal turbines connected} For instance, “tidal-500kW-RM1_average_2.zip” is a 6-hour experiment using the 500-kW tidal reference model, scaled by 2x (1-MW) to better match the electrolyzer maximum of 1.25MW, fed with the 30-minute moving average current case. Each zip folder contains the following files: A .csv file of raw data. An .xlsx file explaining all the fields in the raw data. A .png plot showing the time series of hydrogen production in kilograms per hour, electrolysis power consumption, and input wave power. A .csv file combines all tidal profiles as "combined_tidal_experiments.csv." A separate experiment, “characterization_200.zip,” shows the MC250 electrolyzer steady-state response with 30-minute load steps over 5 hours and is accessible with this entry.

08 HYDROGEN