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DOE OSTI2024

Composition Quantification of SiGeSn Alloys Through Time-of-Flight Secondary Ion Mass Spectrometry: Calibration Methodologies and Validation With Atom Probe Tomography

Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) is a powerful technique for elemental compositional analysis and depth profiling of materials. However, it encounters the problem of matrix effects that hinder its application. In this work, we introduce a pioneering ToF-SIMS calibration method tailored for SixGeySnz ternary alloys. SixGe1-x and Ge1-zSnz binary alloys with known compositions are used as calibration reference samples. Through a systematic SIMS quantification study of SiGe and GeSn binary alloys, we unveil a linear correlation between secondary ion intensity ratio and composition ratio for both SiGe and GeSn binary alloys, effectively mitigating the matrix effects. Extracted relative sensitivity factor (RSF) value from SixGe1-x (0.07 < x < 0.83) and Ge1-zSnz (0.066 < z < 0.183) binary alloys are subsequently applied to those of SixGeySnz (0.011 < x < 0.113, 0.863 < y < 0.935 and 0.023 < z < 0.103) ternary alloys for elemental compositions quantification. These values are cross-checked by Atom Probe Tomography (APT) analysis, an indication of the great accuracy and reliability of as-developed ToF-SIMS calibration process. Furthermore, the proposed method and its reference sample selection strategy in this work provide a low-cost as well as simple-to-follow calibration route for SiGeSn composition analysis, thus driving the development of next-generation multifunctional SiGeSn-related semiconductor devices.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Achieving extraordinary thermal stability of salt hydrate eutectic composites by amending crystallization behaviour with thickener

Commercial thermal energy storage (TES) systems necessitate reliable thermal performance throughout their operational lifetime. Repeated volume changes of the phase change materials (PCMs) during thermal cycling disengages thermal contacts between the conductive fillers like expanded graphite (EG) in TES composites, causing unstable thermal conductivity (k) that fades with cycling. Our in-operando crystallization studies on eutectic PCM made of zinc nitrate hexahydrate (ZNH) and KNO 3 revealed that the thermal stability of PCM composites can be considerably degraded by the formation of large and sharp-cornered PCM crystals during the freezing cycle. While the crystals can push EG particles, disengaging thermal contacts between them, we have newly discovered that carboxymethyl cellulose (CMC) can be used to retain the thermal contacts by forming networks of smaller PCM crystals. Furthermore, the scalable synthesis methodology of EG/ZNH eutectic composites was introduced. Here, in this study, EG particles were strongly connected into a matrix via a stable, corrosion resistant polydimethylsiloxane (PDMS) binder, stabilizing thermal networks and thereby maintaining k up to 1000 melt/freeze cycles. The thermal conductivity of our 25 vol% EG sample (13.3 W m -1 K -1 ) is more than 48% higher than other salt hydrate eutectic composites reported in the literature. We expect this study to provide insights on cooperative interaction between different components of TES systems (PCM, filler, thickener, binder) for exceptionally robust thermal properties.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Control System Upgrade for Battery State-of-Charge Indications

The Advanced Test Reactor (ATR) Complex at Idaho National Laboratory (INL) relies on Battery Backed Power (BBP) systems and Uninterruptible Power Supplies (UPS) to ensure continuous power supply to critical components. This project aims to enhance the reliability and functionality of the battery monitoring and control systems by updating the State-of-Charge (SOC) system, Programmable Logic Controller (PLC), and Human-Machine Interface (HMI) for the nuclear safety-related battery banks. The current system, while functional, has areas for improvement, particularly in recharging calculations and alarm functions. The project objectives include developing flow charts, programming the new PLC and HMI, conducting bench tests, and updating design documentation. Additionally, the project ensures compliance with safety standards, develops training materials, creates comprehensive documentation, and integrates seamlessly with existing ATR infrastructure. The new SOC system is designed to be scalable for future upgrades, improve efficiency, enhance data accuracy, implement redundancy features, and achieve project goals within budget constraints while considering environmental impact. The methodology involved familiarizing with BBP and UPS systems, collecting current readings, rescaling signals, learning ladder logic, and updating the HMI. The transition from SLC 5/03 PLC using RS Logix 500 to CompactLogix 5380 using Studio 5000 was a key step. Despite challenges in transferring outdated PLC ladder logic and HMI code, starting from scratch led to a more accurate and efficient monitoring system, contributing to improved safety and operational efficiency. The project is currently awaiting approval of the Engineering Calculation and Analysis Report (ECAR) before implementation.

42 - ENGINEERING↗

DOE OSTI2025

Innovative Method for Reliable Measurement of PEM Water Electrolyzer Component Resistances

Understanding the sheet resistance of porous electrodes is essential for improving the performance of polymer electrolyte membrane (PEM) water electrolyzers and related technologies. Despite its importance, existing methods often fail to provide reliable and comprehensive data, especially for porous materials with complex morphologies and non‐uniform thicknesses. This study introduces a robust and straightforward method for determining the sheet resistance of porous electrodes using a novel probe concept based on industrial printed circuit board (PCB) technology. This probe measures resistance across ten distances, ranging from 250 µm to 2500 µm, enabling local mapping of resistance. The study focuses on the sheet resistance of key components in PEM water electrolyzers, including the gas diffusion layer (GDL), porous transport layer (PTL), and catalyst layers deposited on a membrane. Additionally, an image‐processing‐based method is presented to obtain the thickness distribution of the studied catalyst layers, facilitating a detailed analysis of the electrical in‐plane resistivity with thickness variations. Overall, this methodology has the potential to expedite material integration and bridge the gap between electrode engineering and single‐cell testing, thereby advancing the development of PEM water electrolyzers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Comparison of Conventional and Constrained Variational Methods for Computing Large-Scale Budgets and Forcing Fields

Analyses of atmospheric heat and moisture budgets serve as an effective tool to study convective characteristics over a region and to provide large-scale forcing fields for various modeling applications. This paper examines two popular methods for computing large-scale atmospheric budgets: the conventional budget method (CBM) using objectively gridded analyses based primarily on radiosonde data and the constrained variational analysis (CVA) approach which supplements vertical profiles of atmospheric fields with measurements at the top of the atmosphere and at the surface to conserve mass, water, energy, and momentum. Successful budget computations are dependent on accurate sampling and analyses of the thermodynamic state of the atmosphere and the divergence field associated with convection and the large-scale circulation that influences it. Utilizing analyses generated from data taken during Dynamics of the Madden-Julian Oscillation (DYNAMO) field campaign conducted over the central Indian Ocean from October to December 2011, we evaluate the merits of these budget approaches and examine their limitations. While many of the shortcomings of the CBM, in particular effects of sampling errors in sounding data, are effectively minimized with CVA, accurate large-scale diagnostics in CVA are dependent on reliable background fields and rainfall constraints. Finally, for the DYNAMO analyses examined, the operational model fields used as the CVA background state provided wind fields that accurately resolved the vertical structure of convection in the vicinity of Gan Island. However, biases in the model thermodynamic fields were somewhat amplified in CVA resulting in a convective environment much weaker than observed.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

A Compact 50kW High Power Density, Hybrid 3-Level Paralleled T-type Inverter for More Electric Aircraft Applications

The demand for high performing, lightweight, reliable inverters, increased the scope of wide bandgap and high-switching frequency based solutions. To achieve such high efficiency inverters, it is vital to focus on the system level design considerations to maximize the benefits of these advanced technologies. This paper presents an improved design based on considerations to further reap the benefits of choosing the right inverter topology; increased capabilities through paralleling devices, with reduced total number of switches; and designing a planarized inverter with PCB based busbar. Appropriate thermal analysis and heatsink design has aided in increased system power density along with the overall efficiency. Demonstration of a 50kW 3-phase 3-level paralleled T-type SiC inverter operating at 40kHz switching frequency for aircraft applications is shown to evaluate the benefits of proposed design methodology. Here, the prototype achieves a high power density of 11kW/L.

42 ENGINEERING↗

DOE OSTI2021

Comparison of methodologies used to determine aromatic lignin unit ratios in lignocellulosic biomass

Abstract Background Multiple analytical methods have been developed to determine the ratios of aromatic lignin units, particularly the syringyl/guaiacyl (S/G) ratio, of lignin biopolymers in plant cell walls. Chemical degradation methods such as thioacidolysis produce aromatic lignin units that are released from certain linkages and may induce chemical changes rendering it difficult to distinguish and determine the source of specific aromatic lignin units released, as is the case with nitrobenzene oxidation methodology. NMR methods provide powerful tools used to analyze cell walls for lignin composition and linkage information. Pyrolysis-mass spectrometry methods are also widely used, particularly as high-throughput methodologies. However, the different techniques used to analyze aromatic lignin unit ratios frequently yield different results within and across particular studies, making it difficult to interpret and compare results. This also makes it difficult to obtain meaningful insights relating these measurements to other characteristics of plant cell walls that may impact biomass sustainability and conversion metrics for the production of bio-derived fuels and chemicals. Results The authors compared the S/G lignin unit ratios obtained from thioacidolysis, pyrolysis-molecular beam mass spectrometry (py-MBMS), HSQC liquid-state NMR and solid-state (ss) NMR methodologies of pine, several genotypes of poplar, and corn stover biomass. An underutilized approach to deconvolute ssNMR spectra was implemented to derive S/G ratios. The S/G ratios obtained for the samples did not agree across the different methods, but trends were similar with the most agreement among the py-MBMS, HSQC NMR and deconvoluted ssNMR methods. The relationship between S/G, thioacidolysis yields, and linkage analysis determined by HSQC is also addressed. Conclusions This work demonstrates that different methods using chemical, thermal, and non-destructive NMR techniques to determine native lignin S/G ratios in plant cell walls may yield different results depending on species and linkage abundances. Spectral deconvolution can be applied to many hardwoods with lignin dominated by S and G units, but the results may not be reliable for some woody and grassy species of more diverse lignin composition. HSQC may be a better method for analyzing lignin in those species given the wealth of information provided on additional aromatic moieties and bond linkages. Additionally, trends or correlations in lignin characteristics such as S/G ratios and lignin linkages within the same species such as poplar may not necessarily exhibit the same trends or correlations made across different biomass types. Careful consideration is required when choosing a method to measure S/G ratios and the benefits and shortcomings of each method discussed here are summarized.

09 BIOMASS FUELS↗

DOE OSTI2025

Machine Learning-Based Process Control for Injection Molding of Recycled Polypropylene

The increased interest in artificial intelligence in manufacturing has driven the adoption of machine learning to optimize processes and improve efficiency. A key challenge in injection molding is the variability of recycled materials, which affects part quality and processing stability. This study presents a novel closed-loop process control approach for injection molding, leveraging machine learning to adaptively predict processing inputs and quality outcomes. The methodology was tested on five blends of recycled polypropylene (rPP), using artificial neural networks (ANNs), linear regression, and polynomial regression to model the relationships between material properties and process parameters. The dataset was split 80/20 into training and testing sets. The ANN model was implemented using TensorFlow and Keras, with six hidden layers of 32 neurons per layer, ReLU activation, and an Adam optimizer. Empirical tuning and early stopping were used to optimize performance and prevent overfitting. Predictions were evaluated based on mean absolute error (MAE), mean squared error (MSE), and percentage error. The results showed that yield stress, ultimate elongation, and part weight were accurately predicted within a 5% error for linear and polynomial regression models and within a 10% error for the ANN. However, modulus predictions were less reliable, with errors of ~11% for ANN and linear regression and ~40% for polynomial regression, reflecting the inherent variability of this property in rPP blends. Predictions of processing inputs had errors ranging from 3% to 25%, depending on the model and response variable. No single modeling approach was consistently superior across all responses, highlighting the complexity of the relationship between material properties, process parameters, and quality metrics. Overall, the work demonstrates that closed-loop process control, powered by machine learning, can effectively predict key quality parameters in injection molding of recycled materials. The proposed approach can improve process stability and material utilization, facilitating increased adoption of sustainable materials.

Krantz, Joshua↗

DOE OSTI2023

AI-Driven Smart Community Control for Accelerating PV Adoption and Enhancing Grid Resilience

The U.S. Department of Energy (DOE) has launched a Connected Communities program that supports projects that expand DOE's network of grid-interactive, efficient building communities nationwide. As an early pilot of Connected Communities, the National Renewable Energy Laboratory (NREL) and its partners have developed and demonstrated a community-scale, hierarchical control solution to address the potential grid issues arising from high penetration of solar photovoltaics (PV) as well as to improve grid reliability and resilience in this residential community. A field pilot study has been performed at an affordable housing development called the Basalt Vista Community, which was built for school teachers and other professionals in the local workforce and represents an autonomous energy grid with all-electric, energy-efficient homes. With no natural gas line in the community, this is the first all-electric net-zero community in rural Colorado. In the field pilot study, NREL has validated the performance of the hierarchical control solution, which consists of NREL's foresee home energy management systems (HEMS) and community aggregators, in increasing demand flexibility and self-consuming PV, reducing potential over-voltages, and supporting critical loads during emergency events. In this presentation, we will present the methodology, simulation and field pilot results, and lessons learned from the project.

all-electric community↗

DOE OSTI2023

Tensor train continuous time solver for quantum impurity models

The simulation of strongly correlated quantum impurity models is a significant challenge in modern condensed matter physics that has multiple important applications. Thus far, the most successful methods for approaching this challenge involve Monte Carlo techniques that accurately and reliably sample perturbative expansions to any order. However, the cost of obtaining high precision through these methods is high. Recently, tensor train decomposition techniques have been developed as an alternative to Monte Carlo integration. In this study, we apply these techniques to the single-impurity Anderson model at equilibrium by calculating the systematic expansion in power of the hybridization of the impurity with the bath. Furthermore, we demonstrate the performance of the method in a paradigmatic application, examining the first-order phase transition on the infinite-dimensional Bethe lattice, which can be mapped to an impurity model through dynamical mean field theory. Our results indicate that using tensor train decomposition schemes allows the calculation of finite-temperature Green's functions and thermodynamic observables with unprecedented accuracy. The methodology holds promise for future applications to frustrated multiorbital systems, using a combination of partially summed series with other techniques pioneered in diagrammatic and continuous time quantum Monte Carlo.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2022

Scalable deep learning for watershed model calibration

Watershed models such as the Soil and Water Assessment Tool (SWAT) consist of high-dimensional physical and empirical parameters. These parameters often need to be estimated/calibrated through inverse modeling to produce reliable predictions on hydrological fluxes and states. Existing parameter estimation methods can be time consuming, inefficient, and computationally expensive for high-dimensional problems. In this paper, we present an accurate and robust method to calibrate the SWAT model (i.e., 20 parameters) using scalable deep learning (DL). We developed inverse models based on convolutional neural networks (CNN) to assimilate observed streamflow data and estimate the SWAT model parameters. Scalable hyperparameter tuning is performed using high-performance computing resources to identify the top 50 optimal neural network architectures. We used ensemble SWAT simulations to train, validate, and test the CNN models. We estimated the parameters of the SWAT model using observed streamflow data and assessed the impact of measurement errors on SWAT model calibration. We tested and validated the proposed scalable DL methodology on the American River Watershed, located in the Pacific Northwest-based Yakima River basin. Our results show that the CNN-based calibration is better than two popular parameter estimation methods (i.e., the generalized likelihood uncertainty estimation [GLUE] and the dynamically dimensioned search [DDS], which is a global optimization algorithm). For the set of parameters that are sensitive to the observations, our proposed method yields narrower ranges than the GLUE method but broader ranges than values produced using the DDS method within the sampling range even under high relative observational errors. The SWAT model calibration performance using the CNNs, GLUE, and DDS methods are compared using R 2 and a set of efficiency metrics, including Nash-Sutcliffe, logarithmic Nash-Sutcliffe, Kling-Gupta, modified Kling-Gupta, and non-parametric Kling-Gupta scores, computed on the observed and simulated watershed responses. The best CNN-based calibrated set has scores of 0.71, 0.75, 0.85, 0.85, 0.86, and 0.91. The best DDS-based calibrated set has scores of 0.62, 0.69, 0.8, 0.77, 0.79, and 0.82. The best GLUE-based calibrated set has scores of 0.56, 0.58, 0.71, 0.7, 0.71, and 0.8. The scores above show that the CNN-based calibration leads to more accurate low and high streamflow predictions than the GLUE and DDS sets. Our research demonstrates that the proposed method has high potential to improve our current practice in calibrating large-scale integrated hydrologic models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Network Security Challenges and Countermeasures for Software-Defined Smart Grids: A Survey

The rise of grid modernization has been prompted by the escalating demand for power, the deteriorating state of infrastructure, and the growing concern regarding the reliability of electric utilities. The smart grid encompasses recent advancements in electronics, technology, telecommunications, and computer capabilities. Smart grid telecommunication frameworks provide bidirectional communication to facilitate grid operations. Software-defined networking (SDN) is a proposed approach for monitoring and regulating telecommunication networks, which allows for enhanced visibility, control, and security in smart grid systems. Nevertheless, the integration of telecommunications infrastructure exposes smart grid networks to potential cyberattacks. Unauthorized individuals may exploit unauthorized access to intercept communications, introduce fabricated data into system measurements, overwhelm communication channels with false data packets, or attack centralized controllers to disable network control. An ongoing, thorough examination of cyber attacks and protection strategies for smart grid networks is essential due to the ever-changing nature of these threats. Previous surveys on smart grid security lack modern methodologies and, to the best of our knowledge, most, if not all, focus on only one sort of attack or protection. This survey examines the most recent security techniques, simultaneous multi-pronged cyber attacks, and defense utilities in order to address the challenges of future SDN smart grid research. The objective is to identify future research requirements, describe the existing security challenges, and highlight emerging threats and their potential impact on the deployment of software-defined smart grid (SD-SG).

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Cybersecurity Value-at-Risk Framework

As more variable renewable energy sources are added to the grid, the role of hydropower as a reliable baseline and firming resource is growing more critical. However, the U.S hydropower fleet is not fully prepared to face modern issues such as cybersecurity threats. Hydropower accounts for 37% of U.S. utility-scale renewable electricity but is challenged by diverse infrastructure and legacy devices that predate modern security practices. While new cybersecurity solutions cannot simply be added to current hydropower generation and operation technologies, custom cybersecurity assessments can reveal system-specific threats and risk probabilities and identify mitigating enhancements.

cybersecurity valuation methodology↗

DOE OSTI2026

A Robust Methodology to Elucidate Kinetics of Room Temperature Electrochemical Propane Adsorption on Platinum

Electrocatalytic activation of alkanes can further decarbonize chemical manufacturing by leveraging affordable renewable electricity and readily available shale gas reserves in the United States. Earlier works have identified the unique role of Pt in adsorbing and activating alkanes, like propane, at room temperature in acidic, aqueous electrolytes, revealing spontaneous formation of deeply dehydrogenated propane-derived surface species with an intact C 3 - backbone. Although an adsorption mechanism was hypothesized, it has not been explicitly investigated to date, preventing the quantification of kinetic rate parameters. A robust methodology to investigate and benchmark propane adsorption kinetics on Pt is critical for the rational design of electrocatalysts that exhibit higher selectivity toward desired partially oxidized products. Herein, we analyze an oxidative current transience that appears during the adsorption of propane on Pt in aqueous electrochemical conditions and develop a methodology that elucidates the adsorption mechanism and enables quantification of rate parameters such as order dependences and apparent activation barriers. This method yields an expected first-order dependence with respect to propane concentration at low coverage and reveals a second-order dependence with respect to the concentration of surface active sites. Additionally, the apparent activation barrier for propane adsorption was calculated using an Arrhenius analysis of the current transience under temperature control. The experimentally measured activation barrier of 35 kJ mol –1 is in excellent agreement with the theoretical barrier calculated by density functional theory (DFT). The kinetic analysis was extended, via the use of transition state theory, to extract entropy and enthalpy of activation, yielding consistent results with the proposed two-step adsorption mechanism and DFT calculations. These results demonstrate reliable quantification of kinetic parameters for electrocatalytic activation of C–H bonds in alkanes that can be employed for rational catalyst development for a versatile range of electrocatalytic conditions.

alkane activation↗

DOE OSTI2023

Influence of fatigue precracking and specimen size on Master Curve fracture toughness measurements of EUROFER97 and F82H steels

EUROFER97 and F82H steels are two leading reduced-activation ferritic-martensitic (RAFM) steels for fusion first wall and blanket applications. Exposure to the harsh environment of fusion reactors can result in severe degradation of fracture toughness. Thus, the post-irradiation evaluation of fracture toughness is critical for understanding the material behavior. Due to the space constraints of irradiation facilities and challenges in controlling a uniform irradiation condition for large size specimens, the development of small specimen test techniques (SSTT) is indispensable to evaluate the performance of irradiated materials. In this study, we evaluated specimen size effects on the Master Curve fracture toughness of EUROFER97 and F82H steels. A wide variety of specimens, including 0.5 T compact tension (C(T)) specimens, 0.16 T mini-compact tension (miniC(T)) specimens, and 1.65 mm miniature bend bar specimens, were tested. The testing methodology was based on the Master Curve method in the ASTM E1921 standard. No specimen size effect was observed in 0.5 T C(T) and 0.16 T miniC(T) specimens on the Master Curve reference temperature T0, while 1.65 mm miniature bend bar specimens yielded a higher T 0Q . A strong effect of fatigue precracking on T 0 for 0.5 T C(T) and 0.16 T miniC(T) specimens was observed, such that testing on specimens with skewed fatigue precrack fronts resulted in lower T 0 than for specimens with ASTM standard qualified straight fatigue precrack fronts. The results highlight the importance of experimental quality control in developing SSTT for Master Curve fracture toughness testing. Lastly, we also evaluated and provided recommendations on the minimum number of specimens needed for each specimen type for yielding reliable T 0Q values.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Synthetic Pathways to gamma-Graphyne and Related Allotropes of Carbon

Graphynes, two-dimensional carbon lattices combining sp 1 and sp 2 hybridized atoms, were predicted theoretically more than three decades ago, but few structures have been realized to date. These carbons are believed to possess remarkable mechanical and electronic properties, including high charge carrier mobilities comparable to those in graphene (10 4 to 10 5 cm 2 V -1 s -1 ). Unlike graphene, certain graphynes are predicted to be intrinsic semiconductors. Among these intriguing structures, γ-graphyne stands out as the structurally simplest and most symmetric sp 1 /sp 2 lattice. γ-Graphyne was first theorized in 1987. In contrast with graphene, γ-graphyne will be a semiconductor with a small band gap suitable for fabrication of electronic devices. This solves one of the fundamental problems of carbon-based electronics, the necessity for inducing a band gap in graphene. γ-Graphyne has the potential to form the basis for the next generation of carbon-based electronics operating at speeds unattainable by traditional silicon chips. Unlike silicon, γ-graphyne is a direct band gap semiconductor, and it will feature exceptional strength comparable to that of other 2D carbon allotropes. Such combination of properties may enable a new generation of highly efficient, ultra-light and flexible solar cells. Despite being a potentially “magical” material, γ-graphyne remained synthetically elusive for over three decades. The primary goals of this project were: (1) Synthesis of bulk γ-graphyne phases through solution-phase 2D polymerizations; (2) Experimental exploration of the physical and chemical properties of γ-graphyne; and (3) Mechanistic and theoretical studies of the novel chemical transformations developed in Goal 1. Common pyrolytic and vapor-deposition methodologies used for the synthesis of graphitic allotropes are unsuitable for graphyne and other sp 1 -contaning structures, as acetylenes readily convert to graphene and amorphous carbon at high temperatures. In contrast, this proposal is based on solution-based 2D polymerization. The major advantages of this approach over the traditional high temperature techniques are the potential to adjust the structure of the material with atomic precision, and the possibility of using structurally complex and relatively fragile repeat units. The outcomes of this research can revolutionize carbon nanotechnology, expanding the field’s structural toolbox beyond primarily graphitic and benzenoid structures. Understanding the chemistry of sp 1 carbon allotropes can lead to entirely new classes of structures with unique properties, including graphyne ribbons, nanotubes, quantum dots, and heterostructures with other 2D materials. Furthermore, the development of reliable and robust synthetic pathways towards periodic covalent molecular sheets with atomically precise structures shall have a profound impact on chemistry and materials science.

2D polymerization↗

DOE OSTI2023

The DG03: An Outline of Suggested Changes

The Design Guideline 03 (DG03) published by the National Renewable Energy Laboratory in 2009 is widely used in the wind industry. It allows engineers to get acquainted with design aspects and methodologies to determine pitch and yaw bearing static capacity and fatigue life. The combination of the detailed theoretical descriptions and practical examples makes it a highly application-oriented document. Since its publication, the knowledge about oscillating bearings, slewing bearings, and wind turbines has grown significantly. Some aspects of the DG03 need updates to reflect the current state of the art. This work covers proposed changes for an upcoming revision of DG03.

design guideline↗

DOE OSTI2023

The Wind Turbine Design Guideline DG03: Yaw and Pitch Rolling Bearing Life Revisited - An Outline of Suggested Changes: Preprint

design guideline↗