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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

High- χ diblock copolymers containing poly(vinylpyridine- N -oxide) segments

Block copolymers exhibiting an enhanced segregation strength due to an underlying high Flory–Huggins interaction parameter (high χ) have attracted considerable attention because of their potential of forming microphase-separated domains with very small feature sizes (<10 nm) useful for next-generation lithography. Here, we report the synthesis, characterization, and self-assembly of poly(styrene)- block -poly(2-vinylpyridine N -oxide) (PS- b -P2VPNO) and poly(styrene)- block -poly(4-vinylpyridine N -oxide) (PS- b -P4VPNO) block copolymers. These PS- b -PVPNOs were obtained from the oxidation of their precursors, poly(styrene)- block -poly(2-vinylpyridine) (PS- b -P2VP) and poly(styrene)- block -poly(4-vinylpyridine) (PS- b -P4VP), respectively. The PS- b -PVPNOs exhibit an enhanced segregation as revealed by ordered cylindrical and lamellar structures in the sub-10 nm scale from copolymers with relatively low molecular weight. The morphologies and periodicities of the ordered structures were determined by small-angle X-ray scattering, while atomic-force microscopy was used to image the self-assembly in thin films. Estimates of the changes in disorder–order transition temperature and domain spacing are derived from a theory based on the effects of dipolar interactions. All the experimentally observed morphological changes resulting from the oxidation of P2VP and P4VP precursors can be qualitatively explained in terms of an increased dipole moment of vinylpyridine N -oxide segments. Therefore, our results demonstrate that PVPNO based block copolymers are versatile candidates toward nanopatterned structures with small feature sizes critical for the future microelectronics industry and beyond.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Insights into water extraction and aggregation mechanisms of malonamide-alkane mixtures

Structure at the nanoscale in the organic phase of liquid–liquid extraction systems is often tied to separation performance. However, the weak interactions that drive extractant assembly lead to poorly defined structures that are challenging to identify. Here, in this work, we investigate the mechanism of water extraction for a malonamide extractant commonly applied to f-element separations. We measure extractant concentration fluctuations in the organic phase with small angle X-ray scattering (SAXS) before and after contact with water at fine increments of extractant concentration, finding no qualitative changes upon water uptake that might suggest significant nanoscopic reorganization of the solution. The critical composition for maximum fluctuation intensity is consistent with small water–extractant adducts. The extractant concentration dependence of water extraction is consistent with a power law close to unity in the low concentration regime, suggesting the formation of 1 : 1 water–extractant adducts as the primary extraction mechanism at low concentration. At higher extractant concentrations, the power law slope increases slightly, which we find is consistent with activity effects modeled using Flory–Huggins theory without introduction of additional extractant–water species. Molecular dynamics simulations are consistent with these findings. The decrease in interfacial tension with increasing extractant concentration shows a narrow plateau region, but it is not correlated with any change in fluctuation or water extraction trends, further suggesting no supramolecular organization such as reverse micellization. This study suggests that water extraction in this system is particularly simple: it relies on a single mechanism at all extractant concentrations, and only slightly enhances the concentration fluctuations characteristic of the dry binary extractant/diluent mixture.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Proof-of-concept of a reinforcement learning framework for wind farm energy capture maximization in time-varying wind

Here, we present a proof-of-concept distributed reinforcement learning framework for wind farm energy capture maximization. The algorithm we propose uses Q-Learning in a wake-delayed wind farm environment and considers time-varying, though not yet fully turbulent, wind inflow conditions. These algorithm modifications are used to create the Gradient Approximation with Reinforcement Learning and Incremental Comparison (GARLIC) framework for optimizing wind farm energy capture in time-varying conditions, which is then compared to the FLOw Redirection and Induction in Steady State (FLORIS) static lookup table wind farm controller baseline.

17 WIND ENERGY↗
DOE OSTI2024

Wind plant wake losses: Disconnect between turbine actuation and control of plant wakes with engineering wake models

Wake losses from neighboring plants may become a major factor in wind plant design and control as additional plants are constructed in areas with high wind resource availability. Because plant wakes span a large range of physical scales, from turbine rotor diameter to tens of kilometers, it is unclear whether conventional wake models or turbine control strategies are effective at the plant scale. Wake steering and axial induction control are evaluated in the current work as means of reducing the impact of neighboring wind plants on power and levelized cost of electricity. FLOw Redirection and Induction in Steady State (FLORIS) simulations were performed with the Gauss–Curl Hybrid and TurbOPark wake models as well as two operation and maintenance models to investigate control setpoint sensitivity to wake representation and economic factors. Both wake models estimate losses across a range of atmospheric conditions, although the wake loss magnitude is dependent on the wake model. Annual energy production and levelized cost of electricity are driven by wind direction frequency, with frequently aligned plants experiencing the greatest losses. However, both wake steering and axial induction are unable to mitigate the impact of upstream plants. Wake steering is constrained by plant geometry, since wake displacement is much less than the plant wake width, while axial induction requires curtailing the majority of turbines in upstream plants. Individual turbine strategies are limited by their effective scale and model representation. New wake models that include plant-scale physics are needed to facilitate the design of effective plant wake control strategies.

Scott, Ryan (ORCID:0000000328107574)↗
DOE OSTI2024

Coupled modeling of wake steering and platform offsets for floating wind arrays

Wake effects are a key challenge in the design and analysis of wind farms. For floating wind farms, the platforms offset under the aerodynamic loading of the turbine and are constrained by mooring systems that can vary significantly in allowable offsets. When considering wake steering, the crosswind offset of the turbine can counteract the lateral deflection of the wake. This work presents a tool to efficiently model the coupled impacts of wake steering and platform offsets for floating wind farms. The tool relies on the frequency-domain wind farm model RAFT and the steady-state wake model FLORIS. A verification with FAST.Farm is presented, then the tool is applied to a simple two-turbine case study. A range of mooring systems with increasing platform offsets and varied yaw misalignment angles are considered while comparing the impact on turbine power. Additional sensitivities to turbine spacing and mooring system orientation are explored. The results show that there is a least-optimal watch circle width for downwind turbine power production that varies with yaw misalignment angle and turbine spacing. Additionally, the turbine offsets under yaw-misaligned conditions vary significantly depending on mooring system orientation relative to the rotor plane, which in turn impacts the optimal misalignment angle. These results highlight the importance of including floating platform offsets and mooring systems in the evaluation of wake steering strategies for floating wind arrays.

17 WIND ENERGY↗
DOE OSTI2020

Absolute intensity calibration for carbon-edge soft X-ray scattering

Resonant soft X-ray scattering (RSOXS) has become a premier probe to study complex three-dimensional nanostructures in soft matter through combining the robust structural characterization of small-angle scattering with the chemical sensitivity of spectroscopy. This technique borrows many of its analysis methods from alternative small-angle scattering measurements that utilize contrast variation, but thus far RSOXS has been unable to reliably achieve an absolute scattering intensity required for quantitative analysis of domain compositions, volume fraction, or interfacial structure. Here, a novel technique to calibrate RSOXS to an absolute intensity at the carbon absorption edge is introduced. It is shown that the X-ray fluorescence from a thin polymer film can be utilized as an angle-independent scattering standard. Verification of absolute intensity is then accomplished through measuring the Flory–Huggins interaction parameter in a phase-mixed polymer melt. As a result, the necessary steps for users to reproduce this intensity calibration in their own experiments to improve the scientific output from RSOXS measurements are discussed.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

The physical basis for solvent flow in organic solvent nanofiltration

Organic solvent nanofiltration (OSN) is an emerging membrane technology that could revolutionize chemical separations in numerous vital industries. Despite its significance, there remains a lack of fundamental understanding of solvent transport mechanisms in OSN membranes. Here, we use an extended Flory-Rehner theory, nonequilibrium molecular dynamic simulations, and organic solvent transport experiments to demonstrate that solvent flow in OSN membranes is driven by a pressure gradient. We show that solvent molecules migrate as clusters through interconnected pathways within the membrane pore structure, challenging the widely accepted diffusion-based view of solvent transport in OSN. We further reveal that solvent permeance is dependent on solvent affinity to the OSN membrane, which, in turn, controls the membrane pore structure. Our fundamental insights lay the scientific groundwork for the development of next-generation OSN membranes.

42 ENGINEERING↗
DOE OSTI2026

AnnualReportingWind [SWR-26-026]

AnnualReportingWind is a version-controlled repository used to generate the cost and performance results published in major wind energy reports such as the Cost of Wind Energy Review (COWER) and the Annual Technology Baseline (ATB). The repository consolidates the model configurations, assumptions, and scripts used in each reporting year so that published results can be transparently reproduced and traced back to the exact analytical setup used in the analysis. The workflows rely on several techno-economic modeling tools developed at the National Laboratory of the Rockies (NLR), including WAVES, ORBIT, LandBOSSE, WOMBAT, and FLORIS.

Mulas Hernando, Daniel [National Laboratory of the↗
DOE OSTI2026

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 (↗
DOE OSTI2023

Direct conversion of syngas to lower olefins precursors of jet fuel and chemicals

This study investigates the direct conversion of syngas to olefins as a pathway to produce sustainable aviation fuels. This approach combines methanol/ dimethyl ether synthesis with methanol-to-olefins into one unit operation to avoid the equilibrium limitation of methanol synthesis and the Anderson–Schulz–Flory distribution limitations from the Fischer Tropsch process. The ability to combine reactor functionalities into a single step are also projected to significantly lower capital costs over current synthesis approaches for the syngas to olefins process.

10 SYNTHETIC FUELS↗
DOE OSTI2023

AWAKEN Wind Plant Simulation Comparison

A series of numerical simulations of wind farms, using different model fidelities and for different atmospheric stability conditions, were performed as a part of the American WAKE ExperimeNt. The simulations included using FLORIS wake models, a number of microscale AMR-Wind and Nalu-Wind runs, as well as idealized and complex terrain WRF runs. The largest computations used the AMR-Wind LES solver to simulate a 100 km x 100 km domain containing 541 turbines under unstable atmospheric conditions matching previous measurements, while other LES computations focused on sections of the King Plains wind farm. Results of this qualitative comparison illustrate the interactions with wind farms with large-scale ABL structures in the flow, as well as the extent of downstream wake penetration in the flow and blockage effects around wind farms.

17 WIND ENERGY↗
DOE OSTI2025

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↗
DOE OSTI2021

Network Based Estimation of Wind Farm Power and Velocity Data Under Changing Wind Direction

This paper describes an estimation algorithm for velocity and power output signals in a wind farm under changing wind direction. A graph-theoretic definition describes the wind farm as a collection of nodes (turbines) and time-varying weighted edges (inter-turbine wake propagation) that change as a function of incoming wind direction. The velocity at each turbine is determined through a discrete input-output model. Changes in wind direction serve as the input and the output is defined in terms of a time-varying weighted adjacency matrix that depends on the time-delay of information propagation between turbines. These delays, which are defined in terms of the advection speed of the wind and the distance between the turbines, capture the delayed effect of wind direction changes on the inter-connectivity of the graph as the wind conditions at the farm inlet propagate through the turbine array. An event-based update framework is employed to capture time-dependent topology changes due to shifts in wind direction. Simulation results for dynamically changing wind inlet directions to a circular wind farm are compared to predictions from both the static and dynamic versions of the FLOw Redirection and Induction in Steady State (FLORIS) model. The approach is shown to enable real-time tracking of dynamic changes to wind farm power output within a framework that can be easily integrated into real-time, horizon-based, control strategies that typically do not account for wind direction changes.

distributed↗
DOE OSTI2021

Deep Reinforcement Learning for Automatic Generation Control of Wind Farms

This paper provides a model-free framework for real-time control of wind farms to accurately track a power reference signal. This problem requires tractable dynamical models for capturing the aerodynamic interaction between wind turbines and controllers that can make decisions in realtime given varying atmospheric conditions. In this paper, we propose a deep reinforcement learning framework to provide real-time yaw control of a wind farm. Modifications have been made to FLOw Redirection and Induction in Steady State (FLORIS), a modeling tool that incorporates transient wake behavior. The control problem is formulated to track a synthetic power reference signal based on historical atmospheric (wind speed and direction) information, price signals, and regulation deployment data from U.S. regional transmission operators. Results indicate that a wind farm, with this control paradigm, can achieve good tracking performance when tested with real atmospheric data.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
DOE OSTI2020

Continued results from a field campaign of wake steering applied at a commercial wind farm – Part 2

This paper presents the results of a field campaign investigating the performance of wake steering applied at a section of a commercial wind farm. It is the second phase of the study for which the first phase was reported in Fleming et al. (2019). The authors implemented wake steering on two turbine pairs, and compared results with the latest FLORIS (FLOw Redirection and Induction in Steady State) model of wake steering, showing good agreement in overall energy increase. Further, although not the original intention of the study, we also used the results to detect the secondary steering phenomenon. Results show an overall reduction in wake losses of approximately 6.6 % for the regions of operation, which corresponds to achieving roughly half of the static optimal result.

17 WIND ENERGY↗
DOE OSTI2021

Experimental results of wake steering using fixed angles

In this article, the authors present a test of wake steering at a commercial wind farm. A single fixed yaw offset, rather than an optimized offset schedule, is alternately applied to an upstream wind turbine, and the effect on downstream turbines is analyzed. This experimental design allows for comparison with engineering wake models independent of the controller's ability to track a varying offset and correctly measure wind direction. Additionally, by applying the same offset in beneficial and detrimental conditions, we are able to collect important data for assessing second-order wake model predictions. Results of the article from collected data show good agreement with the FLOw Redirection and Induction in Steady State (FLORIS) engineering model and offer support for the asymmetry of wake steering predicted by newer models, such as the Gauss–curl hybrid model.

17 WIND ENERGY↗
DOE OSTI2023

Evolution of eddy viscosity in the wake of a wind turbine

The eddy viscosity hypothesis is a popular method in wind turbine wake modeling for estimating turbulent Reynolds stresses. We document the downstream evolution of eddy viscosity in the wake of a wind turbine from experimental and large-eddy-simulation data. Wake eddy viscosity is isolated from its surroundings by subtracting the inflow profile, and the driving forces are identified in each wake region. Eddy viscosity varies in response to changes in turbine geometry and nacelle misalignment with larger turbines generating stronger velocity gradients and shear stresses. We propose a model for eddy viscosity based on a Rayleigh distribution. Model parameters are obtained from scaling the eddy viscosity hypothesis and demonstrate satisfactory agreement with the reference data. The model is implemented in the curled wake formulation in the FLOw Redirection and Induction in Steady State (FLORIS) framework and assessed through comparisons with the previous formulation. Our approach produced more accurate flow field estimates with lower total error for the majority of cases.

17 WIND ENERGY↗
DOE OSTI2023

WAVES (Wind Asset Value Estimation System) [SWR-23-81]

The Wind Asset Value Estimation System (WAVES) model is a coupling framework for core NREL techno economic analysis software models to estimate capital expenditures (ORBIT), operational expenditures (WOMBAT), and energy production (FLORIS) for offshore wind power plants. Existing workflows to couple the three models for lifecycle performance and cost estimation require a large amount of manual and error-prone setup to combine both shared inputs and dependent outputs, as such WAVES's primary functionality is to wrap the core logic for running standard modeling workflows to ensure shared settings and entangled results are correctly and efficiently combined every time. SEE ALSO: https://pypi.org/project/WAVES/

Hammond, Robert↗