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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Thermomechanical properties and microstructures of yttrium hydride

Yttrium hydride is an optimal choice for a high-temperature moderator material in advanced thermal neutron spectrum reactors that require small core volumes. However, a complete database of the thermomechanical properties of yttrium hydride is not available yet, although it is much needed to understand and predict the moderator performance during service in reactors. In this paper, we report the properties of unirradiated bulk yttrium hydride as a function of hydrogen concentration—including density, crystal structure, specific heat capacity, thermal diffusivity, thermal conductivity, hardness, elastic/shear moduli, Poisson’s ratio, fracture strength, microstructure, and thermal stability—providing a baseline measurement for the subsequent neutron irradiation response study of yttrium hydride. Furthermore, the recommended empirical treatment of the data is suggested. In addition, other properties (i.e., hydrogen retention, thermal hydrogen migration, and irradiation response) that needs to be investigated are discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Local atomic configurations in mechanically alloyed amorphous (FeCoNi) 70 Ti 10 B 20 powders

Here, the atomic structure of amorphous (FeCoNi) 70 Ti 10 B 20 alloy synthesized by mechanical alloying was investigated using high energy synchrotron X-ray diffraction and inverse Monte Carlo simulations of pair distribution functions. Empirical potential structure refinement indicates a chemical short-range order at the length scales of 2.1–2.5 Å via local atomic arrangements forming deformed bcc-like clusters. The structural model obtained was described by bond lengths, coordination numbers, and bond angle distribution functions determined for the first neighbor atoms by x-ray scattering supplemented with 3D Monte Carlo simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Crystallographic stability of the BCC phase during quenching of metastable beta titanium alloy Ti-5553 and comparison of structural criteria for the predictive capability of α” martensite

This work evaluates the compositional standard currently developed for Ti-5553 powder and explores the metastable β region for sensitivity to martensitic formation during rapid quenching from the melt. Ti-5553 is a beta-stabilized titanium alloy that is increasingly being used in additive manufacturing applications. A series of alloys within the Ti-5553 compositional space was processed using two-piston spat quenching to perform rapid solidification and quenching for each of these alloy compositions. Typical empirical models such as molybdenum equivalency are not able to fully separate the retained BCC and martensitic compositions. Further, the use of thermodynamic data to estimate the transformation energy needed to form martensite can differentiate the alloys and provide a metric to further develop compositional limits for metastable beta titanium alloy development.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Structural basis of DNA binding by the WhiB-like transcription factor WhiB3 in Mycobacterium tuberculosis

Mycobacterium tuberculosis (Mtb) WhiB3 is an iron–sulfur cluster-containing transcription factor belonging to a subclass of the WhiB-Like (Wbl) family that is widely distributed in the phylum Actinobacteria. WhiB3 plays a crucial role in the survival and pathogenesis of Mtb. It binds to the conserved region 4 of the principal sigma factor (σ A 4 ) in the RNA polymerase holoenzyme to regulate gene expression like other known Wbl proteins in Mtb. However, the structural basis of how WhiB3 coordinates with σ A 4 to bind DNA and regulate transcription is unclear. Here we determined crystal structures of the WhiB3:σ A 4 complex without and with DNA at 1.5 Å and 2.45 Å, respectively, to elucidate how WhiB3 interacts with DNA to regulate gene expression. These structures reveal that the WhiB3:σ A 4 complex shares a molecular interface similar to other structurally characterized Wbl proteins and also possesses a subclass-specific Arg-rich DNA-binding motif. We demonstrate that this newly defined Arg-rich motif is required for WhiB3 binding to DNA in vitro and transcriptional regulation in Mycobacterium smegmatis. Together, our study provides empirical evidence of how WhiB3 regulates gene expression in Mtb by partnering with σ A 4 and engaging with DNA via the subclass-specific structural motif, distinct from the modes of DNA interaction by WhiB1 and WhiB7.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Evaporation from thin porous Coatings: Pore size effects and predictive equation for homogeneous coatings

Evaporation of small water droplets on solids is hindered because surface tension pulls the droplet into a spherical cap that has a small perimeter. Our solution is to coat a solid with a very thin, porous layer into which the droplet flows to create a large-area disk with concomitant high rate of evaporation. We investigate evaporation by varying factors that have not been previously considered: pore size and distribution, contact angle, temperature, and relative humidity (RH). A larger pore size resulted in faster evaporation, which we explain through faster transport within the coating. Even faster evaporation occurred for a bilayer structure with small particles on the air side and larger particles on the solid side. Further, the water advancing contact angle had an insignificant effect in the range from < 10° through to 60°. Our results for different pore sizes, temperature, humidity, and contact angle all collapse onto a single curve when appropriately normalized. This validates an equation that can be used for the evaporation from a homogeneous coating that depends only one empirical factor and the droplet volume. Since the volume is often user-controlled, we envisage that this equation can be used to predict evaporation and guide design of fast-drying coatings.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Choices in land representation materially affect modeled biofuel carbon intensity estimates

Estimates of biofuel carbon intensity are uncertain and depend on modeled land use change (LUC) emissions. While analysts have focused on economic and agronomic assumptions affecting the quantity of land converted, researchers have paid less attention to how models classify land into broad categories and designate some categories as ineligible for LUC. To explore the effect of these land representation attributes, we use three versions of a global human and Earth systems model, GCAM, and compute the “carbon intensity of land-use change” (CI-LUC) from increased U.S. corn ethanol production. We consider uncertainty in model parameters along with the choice of land representation and find the latter is one of the most influential parameters on estimated CI-LUC. A version of the model that protects 90% of non-commercial land reduced estimated CI-LUC by an average of 32% across Monte Carlo trials compared to our baseline model. Another version that mimics the GTAP-BIO-ADV land representation, which protects all non-commercial land, reduced CI-LUC by an average of 19%. The results of this experiment suggest that analyses of LUC that exclude non-commercial land categories or assume more land is protected than empirically justified underestimate biofuel induced LUC GHG emissions.

09 BIOMASS FUELS↗
DOE OSTI2020

NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations

In the last 50 years there has been a tremendous progress in solving numerically the Navier-Stokes equations using finite differences, finite elements, spectral, and even meshless methods. Yet, in many real cases, we still cannot incorporate seamlessly (multi-fidelity) data into existing algorithms, and for industrial-complexity applications the mesh generation is time consuming and still an art. Moreover, solving ill-posed problems (e.g., lacking boundary conditions) or inverse problems is often prohibitively expensive and requires different formulations and new computer codes. Here, we employ physics-informed neural networks (PINNs), encoding the governing equations directly into the deep neural network via automatic differentiation, to overcome some of the aforementioned limitations for simulating incompressible laminar and turbulent flows. We develop the Navier-Stokes flow nets (NSFnets) by considering two different mathematical formulations of the Navier-Stokes equations: the velocity-pressure (VP) formulation and the vorticity-velocity (VV) formulation. Since this is a new approach, we first select some standard benchmark problems to assess the accuracy, convergence rate, computational cost and flexibility of NSFnets; analytical solutions and direct numerical simulation (DNS) databases provide proper initial and boundary conditions for the NSFnet simulations. The spatial and temporal coordinates are the inputs of the NSFnets, while the instantaneous velocity and pressure fields are the outputs for the VP-NSFnet, and the instantaneous velocity and vorticity fields are the outputs for the VV-NSFnet. This is unsupervised learning and, hence, no labeled data are required beyond boundary and initial conditions and the fluid properties. The residuals of the VP or VV governing equations, together with the initial and boundary conditions, are embedded into the loss function of the NSFnets. No data is provided for the pressure to the VP-NSFnet, which is a hidden state and is obtained via the incompressibility constraint without extra computational cost. Unlike the traditional numerical methods, NSFnets inherit the properties of neural networks (NNs), hence the total error is composed of the approximation, the optimization, and the generalization errors. Here, we empirically attempt to quantify these errors by varying the sampling (“residual”) points, the iterative solvers, and the size of the NN architecture. For the laminar flow solutions, we show that both the VP and the VV formulations are comparable in accuracy but their best performance corresponds to different NN architectures. The initial convergence rate is fast but the error eventually saturates to a plateau due to the dominance of the optimization error. For the turbulent channel flow, we show that NSFnets can sustain turbulence at , but due to expensive training we only consider part of the channel domain and enforce velocity boundary conditions on the subdomain boundaries provided by the DNS data base. We also perform a systematic study on the weights used in the loss function for balancing the data and physics components, and investigate a new way of computing the weights dynamically to accelerate training and enhance accuracy. In the last part, we demonstrate how NSFnets should be used in practice, namely for ill-posed problems with incomplete or noisy boundary conditions as well as for inverse problems. We obtain reasonably accurate solutions for such cases as well without the need to change the NSFnets and at the same computational cost as in the forward well-posed problems. As a result, we also present a simple example of transfer learning that will aid in accelerating the training of NSFnets for different parameter settings.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Bayesian sparse learning with preconditioned stochastic gradient MCMC and its applications

Deep neural networks have been successfully employed in an extensive variety of research areas, including solving partial differential equations. Despite its significant success, there are some challenges in effectively training DNN, such as avoiding overfitting in over-parameterized DNNs and accelerating the optimization in DNNs with pathological curvature. Here, we propose a Bayesian type sparse deep learning algorithm. The algorithm utilizes a set of spike-and-slab priors for the parameters in the deep neural network. The hierarchical Bayesian mixture will be trained using an adaptive empirical method. That is, one will alternatively sample from the posterior using preconditioned stochastic gradient Langevin Dynamics (PSGLD), and optimize the latent variables via stochastic approximation. The sparsity of the network is achieved while optimizing the hyperparameters with adaptive searching and penalizing. A popular SG-MCMC approach is Stochastic gradient Langevin dynamics (SGLD). However, considering the complex geometry in the model parameter space in nonconvex learning, updating parameters using a universal step size in each component as in SGLD may cause slow mixing. To address this issue, we apply a computationally manageable preconditioner in the updating rule, which provides a step-size parameter to adapt to local geometric properties. Moreover, by smoothly optimizing the hyperparameter in the preconditioning matrix, our proposed algorithm ensures a decreasing bias, which is introduced by ignoring the correction term in the preconditioned SGLD. According to the existing theoretical framework, we show that the proposed algorithm can asymptotically converge to the correct distribution with a controllable bias under mild conditions. Numerical tests are performed on both synthetic regression problems and learning solutions of elliptic PDE, which demonstrate the accuracy and efficiency of the present work.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

When and why PINNs fail to train: A neural tangent kernel perspective

Physics-informed neural networks (PINNs) have lately received great attention thanks to their flexibility in tackling a wide range of forward and inverse problems involving partial differential equations. However, despite their noticeable empirical success, little is known about how such constrained neural networks behave during their training via gradient descent. More importantly, even less is known about why such models sometimes fail to train at all. Here in this work, we aim to investigate these questions through the lens of the Neural Tangent Kernel (NTK); a kernel that captures the behavior of fully-connected neural networks in the infinite width limit during training via gradient descent. Specifically, we derive the NTK of PINNs and prove that, under appropriate conditions, it converges to a deterministic kernel that stays constant during training in the infinite-width limit. This allows us to analyze the training dynamics of PINNs through the lens of their limiting NTK and find a remarkable discrepancy in the convergence rate of the different loss components contributing to the total training error. To address this fundamental pathology, we propose a novel gradient descent algorithm that utilizes the eigenvalues of the NTK to adaptively calibrate the convergence rate of the total training error. Finally, we perform a series of numerical experiments to verify the correctness of our theory and the practical effectiveness of the proposed algorithms.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

A conservative, interface-resolved, compressible framework for the modeling and simulation of liquid/gas phase change

This paper presents a method for simulating evaporation in a compressible, interface-resolved framework appropriate for modeling problems of engineering interest. In order to achieve robustness and broad applicability, the method has been designed to discretely enforce consistent mass and thermal energy transport at the phase interface, to globally conserve mass, momentum, and energy, and to be capable of modeling compressible and incompressible systems. Verification is performed via the Sod-shock test, one-dimensional heat conduction, evaporation from a planar interface, and evaporation of three-dimensional droplets. Convergence with increasing mesh resolution is demonstrated in all tested configurations, and conservation is maintained near machine precision for a translating droplet. Conservation and accurate phase change rates are preserved at the low numerical resolutions commonly encountered in engineering calculations. Following verification, the method is validated by comparison to an empirical correlation for evaporating droplets in high temperature crossflow, and the presentation concludes with the simulation of an iso-octane spray at conditions representative of gasoline direct injection. In conclusion, successful verification, validation, and demonstrated practical utility suggest the method to be an accurate, efficient, and robust approach for the study of phase change in engineering systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Convergence of variational Monte Carlo simulation and scale-invariant pre-training

We provide theoretical convergence bounds for the variational Monte Carlo (VMC) method as applied to optimize neural network wave functions for the electronic structure problem. Here, we study both the energy minimization phase and the supervised pre-training phase that is commonly used prior to energy minimization. For the energy minimization phase, the standard algorithm is scale-invariant by design, and we provide a proof of convergence for this algorithm without modifications. The pre-training stage typically does not feature such scale-invariance. We propose using a scale-invariant loss for the pretraining phase and demonstrate empirically that it leads to faster pre-training.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Multi-head physics-informed neural networks for learning functional priors and uncertainty quantification

In numerous applications, the integration of prior knowledge and historical information is essential, particularly for tasks requiring the solution of ordinary or partial differential equations (ODEs/PDEs) in data-sparse or noisy environments. For instance, achieving accurate solutions to time-dependent PDEs with limited initial condition measurements necessitates an effective strategy for embedding prior knowledge. Hard-parameter sharing architectures in neural networks (NNs) have demonstrated success in both traditional and scientific machine learning domains, facilitating the learning of informative representations. Here, in this study, we introduce a novel, yet efficient, method to enhance physics-informed neural networks (PINNs) by incorporating a multi-head structure that enables the learning of functional priors from both empirical data and governing physical laws. This prior information can then be used to address data sparsity and high-level noise in solving ODE/PDE problems with uncertainty quantification (UQ). The approach, termed Multi-Head PINN (MH-PINN), consists of a shared body NN and multiple head NNs, each corresponding to an individual PINN instance. Our framework for functional prior learning is carried out in two stages: (1) training the MH-PINNs to develop a shared body NN alongside multiple head NNs, and (2) employing these trained head NNs to estimate a prior distribution through a normalizing flow-based density estimator. The learned functional prior can then be applied as a regularization mechanism in deterministic contexts or as an informative prior within a Bayesian inference framework, aiding in the resolution of subsequent ODE/PDE tasks. We evaluate the efficacy of MH-PINNs across five benchmark problems, including a high-dimensional parametric PDE, all characterized by data sparsity or substantial noise levels. Our findings reveal that MH-PINNs deliver accurate solutions and robust UQ, demonstrating adaptability across a range of complex and challenging scenarios.

Bayesian inference↗
DOE OSTI2022

The lay of the land: What we know about non-operating agricultural and absentee forest landowners in the U.S. and Europe

While non-operating agricultural and absentee forest landowners across the U.S. and Europe are an important group of landowners, our understanding of them remains relatively limited. In this paper, we conduct a systematic literature review on these landowners to encapsulate a current lay of the land in terms of what we know about these landowners and move the dialogue on this topic forward. Eighty-one articles are identified in our search of empirical literature. For each of the landowner types, we discuss their demographics and the three primary themes that emerged related to land management: participation in land management decisions, attitudes regarding land use and ownership, and resource needs in working with these landowners. For agricultural non-operating landowners, we find limited participation in land management decisions, particularly among women, a variety of individual and social factors play a role in involvement, and while they have pro-conservation attitudes, implementation of conservation practices is more limited. Absentee forest landowners we find are more willing to use management plans, yet less willing to engage in active management and risk reduction. These landowners have a range of attitudes regarding land use, with studies highlighting recreation, conservation, and profit motivations. In conclusion, our review concludes with identifying specific needs for more research and outreach on these landowners.

absentee↗
DOE OSTI2022

Floating solar panels on reservoirs impact phytoplankton populations: A modelling experiment

Floating solar photovoltaic (FPV) deployments are increasing globally as the switch to renewable energy intensifies, representing a considerable water surface transformation. FPV installations can potentially impact aquatic ecosystem function, either positively or negatively. However, these impacts are poorly resolved given the challenges of collecting empirical data for field or modelling experiments. In particular, there is limited evidence on the response of phytoplankton to changes in water body thermal dynamics and light climate with FPV. Given the importance of understanding phytoplankton biomass and species composition for managing ecosystem services, we use an uncertainty estimation approach to simulate the effect of FPV coverage and array siting location on a UK reservoir. FPV coverage was modified in 10% increments from a baseline with 0% coverage to 100% coverage for three different FPV array siting locations based on reservoir circulation patterns. Results showed that FPV coverage significantly impacted thermal properties, resulting in highly variable impacts on phytoplankton biomass and species composition. The impacts on phytoplankton were often dependent on array siting location as well as surface coverage. Changes to phytoplankton species composition were offset by the decrease in phytoplankton biomass associated with increasing FPV coverage. We identified that similar phytoplankton biomass reductions could be achieved with less FPV coverage by deploying the FPV array on the water body's faster-flowing area than the central or slower flowing areas. The difference in response dependent on siting location could be used to tailor phytoplankton management in water bodies. Simulation of water body-FPV interactions efficiently using an uncertainty approach is an essential tool to rapidly develop understanding and ultimately inform FPV developers and water body managers looking to minimise negative impacts and maximise co-benefits.

14 SOLAR ENERGY↗
DOE OSTI2024

Developing expert scientific consensus on the environmental and societal effects of marine artificial structures prior to decommissioning

Thousands of artificial (‘human-made’) structures are present in the marine environment, many at or approaching end-of-life and requiring urgent decisions regarding their decommissioning. No consensus has been reached on which decommissioning option(s) result in optimal environmental and societal outcomes, in part, owing to a paucity of evidence from real-world decommissioning case studies. To address this significant challenge, we asked a worldwide panel of scientists to provide their expert opinion. They were asked to identify and characterise the ecosystem effects of artificial structures in the sea, their causes and consequences, and to identify which, if any, should be retained following decommissioning. Experts considered that most of the pressures driving ecological and societal effects from marine artificial structures (MAS) were of medium severity, occur frequently, and are dependent on spatial scale with local-scale effects of greater magnitude than regional effects. The duration of many effects following decommissioning were considered to be relatively short, in the order of days. Overall, environmental effects of structures were considered marginally undesirable, while societal effects marginally desirable. Experts therefore indicated that any decision to leave MAS in place at end-of-life to be more beneficial to society than the natural environment. However, some individual environmental effects were considered desirable and worthy of retention, especially in certain geographic locations, where structures can support improved trophic linkages, increases in tourism, habitat provision, and population size, and provide stability in population dynamics. The expert analysis consensus that the effects of MAS are both negative and positive for the environment and society, gives no strong support for policy change whether removal or retention is favoured until further empirical evidence is available to justify change to the status quo. The combination of desirable and undesirable effects associated with MAS present a significant challenge for policy- and decision-makers in their justification to implement decommissioning options. Decisions may need to be decided on a case-by-case basis accounting for the trade-off in costs and benefits at a local level.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Climate-extreme modeling framework for sustainable flood management in the Arabian Peninsula

Evaluating extreme precipitation events (EPEs) is essential for building climate-resilient water management strategies, but it remains a major challenge in ungauged basins. Using the 26,070 km 2 Wadi al-Rummah basin in central Saudi Arabia as a case study, we developed an alternative, reliable, cost-effective satellite-based framework that combines empirically derived EPE thresholds, imagery-calibrated 2D hydrodynamic modeling, GRACE water-storage diagnostics, and bias-corrected CMIP6 projections to assess flood hazards and recharge potential under current and future climate scenarios in ungauged basins. The integrated approach and the resulting findings followed four key steps: (1) Identified a 22.5 mm EPE threshold, the 80th percentile of 3-day GPM/IMERG rainfall (2000–2024), aligned with flood-triggering events (Nov 2018: 23–28 mm; Apr, 2023: 42 mm); (2) Developed and calibrated a RiverFlow2D model using Sentinel-2 and PlanetScope imagery for the November 2018 flood, accurately reproducing flood depth and extent (RMSE ≤0.31 m; fuzzy-Dice ≥0.91), and estimating runoff (41 %), infiltration (25 %), and evaporation (34 %); (3) Independently validated the model with the April 2023 event (RMSE ≤0.35 m; fuzzy-Dice ≥0.86); (4) Conducted climate projections (2025–2100) from five bias-corrected NEX-GDDP CMIP6 models that revealed a 34 % increase in EPE intensity under SSP2-4.5 and 48 % under SSP5-8.5 scenarios, relative to 20th-century baselines. Our findings indicate that while intensifying extremes in the 21st century increase flood risk, the results highlight the potential for episodic recharge if effective retention strategies are employed, and offer a transferable model for climate-informed planning in data-scarce arid regions.

CMIP6↗
DOE OSTI2025

Quantifying fission gas adsorption onto natural clinoptilolite in the presence of environmental air and water

Adsorption of noble gas fission products onto naturally occurring minerals is of interest for its potential to retain or retard emissions from nuclear fuel reprocessing operations or underground nuclear explosions. However, experimental studies of trace noble gas adsorption in the presence of air and water have largely focused on synthetic materials, such as activated carbon or metal-organic frameworks. Here, in this study, adsorption of Kr and Xe onto the naturally occurring zeolitic mineral clinoptilolite is studied in the presence of nitrogen and water. By varying the composition of the gas phase and monitoring the change in the combined adsorbate mass, the adsorbed concentration of noble gas is calculated gravimetrically. For dry clinoptilolite, the concentration of adsorbed Kr and Xe is linearly correlated with noble gas pressure and Henry's Law appears satisfactory, despite the presence of nitrogen at atmospheric pressures. However, the presence of water significantly reduces the adsorbed concentration of both Kr and Xe, which is typical in nanoporous sorbents. Here, an empirical bivariate model is presented, combining the Henry's Law adsorption model for a dry adsorbent with the exponential reduction in the presence of water, as reported by Lungu and Underhill in 1999. This model provides a means to estimate the adsorbate concentration at the trace partial pressures and higher water contents relevant to field-scale modeling of fission gas transport through the vadose zone.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Time-Domain Vortex Induced Vibration Modeling of Reference Dynamic Power Cable for the Gulf of Maine

Vortex-induced vibration (VIV) is a phenomenon known to decrease the fatigue life of dynamic power cables used in floating offshore wind systems through increased bending loading cycles. A variety of modeling solutions have been proposed to study this, but none enable fully-coupled simulations leveraging open-source tools. To address this, a time-domain VIV model has been successfully implemented into the open-source mooring dynamics model MoorDyn. The semi-empirical VIV model describes a lift force acting on segments of a flexible cylinder, making it well suited for implementation into MoorDyn. The new capability successfully predicted the frequencies and magnitudes of strain in both steady and oscillating flows when compared to the original validation results and matched peak spectral response when compared to experimental results, verifying successful implementation. MoorDyn with VIV was then leveraged to simulate a 15 MW floating turbine with a dynamic power cable in the Gulf of Maine using OpenFAST for six return periods of combined wind, wave, and current conditions. Increased curvature and tension fluctuations in higher flow speeds were observed when simulating VIV. Maximum tensions and curvatures also increased, with the 500 year conditions violating the curvature factor of safety of 2.0. These results highlight the importance of considering VIV when designing dynamic power cables for the Gulf of Maine, and indicate that cost effective mitigation strategies should be explored for the area. They also demonstrate the utility of this new modeling capability, which provides the first open-source tool for fully coupled time-domain floating offshore wind simulations with dynamic power cable VIV.

17 WIND ENERGY↗