Results for “CHAINS” — Page 11

DOE OSTI2018

A neoclassical drift-magnetohydrodynamical fluid model of the interaction of a magnetic island chain with a resonant error-field in a high temperature tokamak plasma

A two-fluid, neoclassical theory of the interaction of a single magnetic island chain with a resonant error-field in a quasi-cylindrical, low-β, tokamak plasma is presented. The plasmas typically found in large hot tokamaks lie in the so-called weak neoclassical flow-damping regime in which the neoclassical ion stress tensor is not the dominant term in the ion parallel equation of motion. Nevertheless, flow-damping in such plasmas dominates ion perpendicular viscosity, and is largely responsible for determining the phase velocity of a freely rotating island chain (which is in the ion diamagnetic direction relative to the local E × B frame at the rational surface). The critical vacuum island width required to lock the island chain is mostly determined by the ion neoclassical poloidal flow damping rate at the rational surface. The stabilizing effect of the average field-line curvature, as well as the destabilizing effect of the perturbed bootstrap current, is the same for a freely rotating, a non-uniformly rotating, and a locked island chain. The destabilizing effect of the error-field averages to zero when the chain is rotating and only manifests itself when the chain locks. The perturbed ion polarization current has a small destabilizing effect on a freely rotating island chain, but a large destabilizing effect on both a non-uniformly rotating and a locked island chain. Finally, this behavior may account for the experimentally observed fact that locked island chains are much more unstable than corresponding freely rotating chains.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Gravitational wave spectrum of chain inflation

Chain inflation is an alternative to slow-roll inflation in which the inflaton tunnels along a large number of consecutive minima in its potential. In this work we perform the first comprehensive calculation of the gravitational wave (GW) spectrum of chain inflation. In contrast to slow-roll inflation the latter does not stem from quantum fluctuations of the gravitational field during inflation, but rather from the bubble collisions during the first-order phase transitions associated with vacuum tunneling. Our calculation is performed within an effective theory of chain inflation which builds on an expansion of the tunneling rate capturing most of the available model space. The effective theory can be seen as chain inflation’s analog of the slow-roll expansion in rolling models of inflation. The near scale-invariance of the scalar power spectrum translates to a quasiperiodic shape of the inflaton potential in chain inflation, with the tunneling rate changing very slowly during the e-folds leading to cosmic microwave background observables. We show that chain inflation produces a very characteristic double-peak GW spectrum: a faint high-frequency peak associated with the gravitational radiation emitted during inflation, and a strong low-frequency peak associated with the graceful exit from chain inflation (marking the transition to the radiation-dominated epoch). There exist very exciting prospects to test the gravitational wave signal from chain inflation at the aLIGO-aVIRGO-KAGRA network, at LISA and /or at pulsar timing array experiments. A particularly intriguing possibility we point out is that chain inflation could be the source of the stochastic gravitational wave background recently detected by NANOGrav, PPTA, EPTA, and CPTA. We also show that the gravitational wave signal of chain inflation is often accompanied by running/ higher running of the scalar spectral index to be tested at future cosmic microwave background experiments. Published by the American Physical Society 2024

Freese, Katherine↗

DOE OSTI2022

Competitiveness and Commercialization of Energy Technologies: Supply Chain Deep Dive Assessment

The report “America’s Strategy to Secure the Supply Chain for a Robust Clean Energy Transition” lays out the challenges and opportunities faced by the United States in the energy supply chain as well as the federal government plans to address these challenges and opportunities. It is accompanied by several issue-specific deep dive assessments, including this one, in response to Executive Order 14017 “America’s Supply Chains,” which directs the Secretary of Energy to submit a report on supply chains for the energy sector industrial base. The Executive Order is helping the federal government to build more secure and diverse U.S. supply chains, including energy supply chains. Competitive U.S.-based clean energy manufacturers and rapid commercialization of U.S.-developed technologies are critical to secure energy supply chains, generate high quality jobs, and meet the United States’ national security, energy and climate objectives. The February 2021 “Executive Order on America’s Supply Chains” (E.O. 14017) directs the U.S. Department of Energy (DOE) to evaluate supply chains that encompass the energy industrial base, focusing on technologies that are critical to meet U.S. decarbonization goals by 2050. Understanding and analyzing the end-to-end supply chain through economic analysis is crucial to mitigating risks and identifying opportunities to enhance U.S. competitiveness in the clean energy industry. This insight will allow the Department of Energy (DOE) to leverage its research, development, demonstration and deployment (RDD&D) capabilities to most fully realize the objectives of E.O. 14017.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRS1997

Doping of Semiconducting Atomic Chains

Due to the rapid progress in atom manipulation technology, atomic chain electronics would not be a dream, where foreign atoms are placed on a substrate to form a chain, and its electronic properties are designed by controlling the lattice constant d. It has been shown theoretically that a Si atomic chain is metallic regardless of d and that a Mg atomic chain is semiconducting or insulating with a band gap modified with d. For electronic applications, it is essential to establish a method to dope a semiconducting chain, which is to control the Fermi energy position without altering the original band structure. If we replace some of the chain atoms with dopant atoms randomly, the electrons will see random potential along the chain and will be localized strongly in space (Anderson localization). However, if we replace periodically, although the electrons can spread over the chain, there will generally appear new bands and band gaps reflecting the new periodicity of dopant atoms. This will change the original band structure significantly. In order to overcome this dilemma, we may place a dopant atom beside the chain at every N lattice periods (N > 1). Because of the periodic arrangement of dopant atoms, we can avoid the unwanted Anderson localization. Moreover, since the dopant atoms do not constitute the chain, the overlap interaction between them is minimized, and the band structure modification can be made smallest. Some tight-binding results will be discussed to demonstrate the present idea.

Toshishige, Yamada↗

NASA NTRS1998

Substrate Effects on Electronic Properties of Atomic Chains

Atomic chains, precise structures of adatoms created on an atomically regulated surface, are candidates for constituent elements in future electronics. It was predicted that Si chains were metallic and Mg chains were semiconducting, and a doping method was also discussed. The substrate was assumed to work as a non-interacting template holding the adatoms. However, this scheme requires a low-temperature environment so that the adatoms will not displace from their ideal positions due to unwanted thermal agitation. For better structural stability, we may seek a scheme to allow the adatoms to form chemical bonding with the substrate atoms and secure their positions. The chemical bonding has two major effects on the chain electronic properties. First, only the remaining s and p orbitals in an adatom not used for the chemical bonding can decide chain band structures, rather than the full set of orbitals previously assumed. Second, because of a possible HOMO energy difference between adatom and substrate atom, semiconducting chains are unintentionally doped. These effects are studied with a self-consistent tight-binding method with universal parameters. With one adatom per unit cell, adatom chains are semiconducting: (1) if adatoms are of group III and form one chemical bond per adatom, or (2) if adatoms are of group IV and form two chemical bonds. 'Me previous result [I I of realizing semiconducting chains by group II adatoms without chemical bonds is consistent with this picture. When the HOMO energy in an adatom is shallower than that in a substrate atom, the entire chain is positively polarized, unintentionally achieving p-type doping. When deeper, the chain is negatively charged, achieving n-type doping.

Yamada, Toshishige↗

NASA NTRS2010

Using SCOR as a Supply Chain Management Framework for Government Agency Contract Requirements

This paper will present a model that uses the Supply-Chain Operations Reference (SCOR) model as a foundation for a framework to illustrate the information needed throughout a product lifecycle to support a healthy supply chain management function and the subsequent contract requirements to enable it. It will also show where in the supply chain the information must be extracted. The ongoing case study used to exemplify the model is NASA's (National Aeronautics and Space Administration) Ares I program for human spaceflight. Effective supply chain management and contract requirements are ongoing opportunities for continuous improvement within government agencies, specifically development of systems for human spaceflight operations. Multiple reports from the Government Accountability Office (GAO) reinforce this importance. The SCOR model is a framework for describing a supply chain with process building blocks and business activities. It provides a set of metrics for measuring supply chain performance and best practices for continuously improving. This paper expands the application of the SCOR to also provide the framework for defining information needed from different levels of the supply chain and at different phases of the lifecycle. These needs can be incorporated into contracts to enable more effective supply chain management. Depending on the phase of the lifecycle, effective supply chain management will require involvement from different levels of the organization and different levels of the supply chain.

Paxton, Joseph↗

DOE OSTI2026

SENTRA: A Modular Computational Graph Framework for Critical Mineral and Materials Supply Chains: Part I: Network Construction Latent-Quantity Estimation, and Temporal Graph Forecasting

Global supply chains for critical minerals and materials are complex, evolving networks of countries, products, production stages, and trade relationships. Existing analytical approaches are limited by fragmented data and static network representations that do not capture the dynamic production dependencies linking raw materials, intermediate products, and final goods across multiple countries. Trade and production statistics provide only a partial view of domestic production, inventories, and material flows, making it difficult to identify indirect sourcing pathways, hidden dependencies, and embedded foreign exposures. This paper introduces the Supply Chain Exposure Network Tracking and Risk Assessment (SENTRA) framework, a modular graph-based computational framework for constructing, analyzing, and forecasting dynamic supply chain networks. As the first paper in a three-part methodological series, it establishes the computational foundation of SENTRA by constructing a temporal attributed multi-relational graph whose nodes represent product–country pairs and whose edges encode observed trade and within-country value-chain relationships. Statistical estimation and constrained optimization recover latent production, final demand, and product input dependency coefficients while enforcing economic accounting constraints. Graph-derived exposure measures quantify direct, transshipment, value-chain, and multi-hop supply chain dependencies independently of the forecasting model. A temporal graph forecasting architecture based on a relational graph neural network then forecasts the evolution of the graph under mass-balance constraints with distribution-free conformal uncertainty quantification. Validation on the global aluminum supply chain shows that the learned graph representations recover economically meaningful supply chain structure, accurately forecast out-of-sample trade relationships, and produce well-calibrated prediction intervals. Subsequent papers apply this computational foundation to exposure assessment, disruption analysis, and scenario-based policy analysis, and extend the framework to multimaterial supply chain modeling and decision support.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Deconstructing the Nuclear Supply Chain Cyber-Attack Surface

The nuclear supply chain cyber-attack surface is a large, complex network of interconnected stakeholders and activities. The global economy has widened and deepened the supply chain resulting in larger numbers of geographically dispersed locations and increased difficulty ensuring the authenticity and security of digital assets. Although the nuclear industry has made significant strides in securing facilities from cyber-attacks, the supply chain remains vulnerable. This paper provides further details on each of the elements in the Digital I&C System Supply Chain Cyber-Attack Surface, including supply chain lifecycle activities, key stakeholders, touchpoints, and attack types. Deconstructing this attack surface provides insights into supply chain threats, vulnerabilities, and consequences. These insights will lead to improvements in cybersecurity supply chain risk analysis, development of new cybersecurity supply chain processes and tools, and enhancement of overall supply chain resilience.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

Maximized Hole Trapping in a Polystyrene Transistor Dielectric from a Highly Branched Iminobis(aminoarene) Side Chain

We synthesized highly branched and electron donating side chain subunits and attached them to polystyrene (PS) used as a dielectric layer in a pentacene field effect transistor. The influence of these groups on dielectric function, charge retention and threshold voltage shifts (ΔV th ) depending on their positions in dielectric multilayers was determined. We compared the observations made on an N-perphenylated iminobisaniline side chain with those from the same side chains modified with ZnO nanoparticles and with an adduct formed from tetracyanoethylene (TCNE). We also synthesized an analogue in which six methoxy groups are present instead of two amine nitrogens. At 6 mol % side chain, hopping transport was sufficient to cause shorting of the gate, while at 2 mol %, charge trapping was observable as transistor threshold voltage shifts (ΔV th ). Here, we created three types of devices: with the substituted PS layer as single layer dielectric, on top of a crosslinked PS layer but in contact with the pentacene (bilayers), and sandwiched between two PS layers in trilayers. Particularly large bias stress effects and ΔV th , larger than the hexamethoxy and previously studied dimethoxy analogs, were observed in the second case and the effects increased with the increasing electron donating properties of the modified side chains. The highest ΔV th was consistent with a majority of the side chains stabilizing trapped charge. Trilayer devices showed decreased charge storage capability compared to previous work in which we used less donating side chains but in higher concentrations. The ZnO and TCNE modifications resulted in slightly more and less negative ΔV th , respectively, when the side chain polystyrene was not in contact with the pentacene and isolated from the gate electrode. The results indicate a likely maximum combination of molecular charge stabilizing activity and side chain concentration that still allows gate dielectric function.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Structure and Interdigitation of Chain-Asymmetric Phosphatidylcholines and Milk Sphingomyelin in the Fluid Phase

We addressed the frequent occurrence of mixed-chain lipids in biological membranes and their impact on membrane structure by studying several chain-asymmetric phosphatidylcholines and the highly asymmetric milk sphingomyelin. Specifically, we report trans-membrane structures of the corresponding fluid lamellar phases using small-angle X-ray and neutron scattering, which were jointly analyzed in terms of a membrane composition-specific model, including a headgroup hydration shell. Focusing on terminal methyl groups at the bilayer center, we found a linear relation between hydrocarbon chain length mismatch and the methyl-overlap for phosphatidylcholines, and a non-negligible impact of the glycerol backbone-tilting, letting the sn1-chain penetrate deeper into the opposing leaflet by half a CH 2 group. That is, penetration-depth differences due to the ester-linked hydrocarbons at the glycerol backbone, previously reported for gel phase structures, also extend to the more relevant physiological fluid phase, but are significantly reduced. Moreover, milk sphingomyelin was found to follow the same linear relationship suggesting a similar tilt of the sphingosine backbone. Complementarily performed molecular dynamics simulations revealed that there is always a part of the lipid tails bending back, even if there is a high interdigitation with the opposing chains. The extent of this back-bending was similar to that in chain symmetric bilayers. For both cases of adaptation to chain length mismatch, chain-asymmetry has a large impact on hydrocarbon chain ordering, inducing disorder in the longer of the two hydrocarbons.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2013

Production of branched-chain alcohols by recombinant Ralstonia eutropha in fed-batch cultivation

Branched-chain alcohols are considered promising green energy sources due to their compatibility with existing infrastructure and their high energy density. We utilized a strain of Ralstonia eutropha capable of producing branched-chain alcohols and examined its production in flask cultures. In order to increase isobutanol and 3-methyl-1-butanol (isoamyl alcohol) productivity in the engineered strain, batch, fed-batch, and two-stage fed-batch cultures were carried out in this work. The effects of nitrogen source concentration on branched-chain alcohol production were investigated under four different initial concentrations in fermenters. A maximum 380 g m(-3) of branched-chain alcohol production was observed with 2 kg m(-3) initial NH4Cl concentration in batch cultures. A pH-stat control strategy was utilized to investigate the optimum carbon source amount fed during fed-batch cultures for higher cell density. In cultures of R. eutropha strains that did not produce polyhydroxyalkanoate or branched-chain alcohols, a maximum cell dry weight of 36 kg m(-3) was observed using a fed-batch strategy, when 10 kg m(-3) carbon source was fed into culture medium. Finally, a total branched-chain alcohol titer of 790 g m(-3), the highest branched-chain alcohol yield of 0.03 g g(-1), and the maximum branched-chain alcohol productivity of 8.23 g m(-3) h(-1) were obtained from the engineered strain Re2410/pJL26 in a two-stage fed-batch culture system with pH-stat control. Isobutanol made up over 95% (mass fraction) of the total branched-chain alcohols titer produced in this study. (C) 2013 Published by Elsevier Ltd.

Fei, Q↗

DOE OSTI2014

Rouse mode analysis of chain relaxation in homopolymer melts

We use molecular dynamics simulations of the Kremer–Grest (KG) bead–spring model of polymer chains of length between 10 and 500, and a closely related analogue that allows for chain crossing, to clearly delineate the effects of entanglements on the length-scale-dependent chain relaxation in polymer melts. We analyze the resulting trajectories using the Rouse modes of the chains and find that entanglements strongly affect these modes. The relaxation rates of the chains show two limiting effective monomeric frictions, with the local modes experiencing much lower effective friction than the longer modes. The monomeric relaxation rates of longer modes vary approximately inversely with chain length due to kinetic confinement effects. The time-dependent relaxation of Rouse modes has a stretched exponential character with a minimum of stretching exponent in the vicinity of the entanglement chain length. None of these trends are found in models that allow for chain crossing. As a result, these facts, in combination, argue for the confined motion of chains for time scales between the entanglement time and their ultimate free diffusion.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Conformational explosion: Understanding the complexity of short chain para -dialkylbenzene potential energy surfaces

The single-conformation ultraviolet and infrared spectroscopy of three short-chain para-dialkylbenzenes (para-diethylbenzene, para-dipropylbenzene, and para-dibutylbenzene) is reported for the jet-cooled, isolated molecules. The present study builds off previous work on single-chain n-alkylbenzenes, where an anharmonic local mode Hamiltonian method was developed to account for stretch-bend Fermi resonance in the alkyl CH stretch region [D. P. Tabor et al., J. Chem. Phys. 144, 224310 (2016)]. The jet-cooled molecules are interrogated using laser-induced fluorescence (LIF) excitation, fluorescence dip infrared spectroscopy, and dispersed fluorescence. The LIF spectra in the S1 ← S0 origin region show a dramatic increase in the number of resolved transitions with increasing length of the alkyl chains, reflecting an explosion in the number of unique low-energy conformations formed when two independent alkyl chains are present. Since the barriers to isomerization of the alkyl chain are similar in size, this results in an “egg carton” shaped potential energy surface. A combination of electronic frequency shift and alkyl CH stretch infrared spectra is used to generate a consistent set of conformational assignments. Using these experimental techniques in conjunction with computational methods, subsets of origin transitions in the LIF excitation spectrum can be classified into different conformational families. Two conformations are resolved in para-diethylbenzene, seven in para-dipropylbenzene, and about nineteen in para-dibutylbenzene. These chains are largely independent of each other as there are no new single-chain conformations induced by the presence of a second chain. A cursory LIF excitation scan of para-dioctylbenzene shows a broad congested spectrum at frequencies consistent with interactions of alkyl chains with the phenyl π cloud

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Fault Diagnosis of Double Pitch Time-Sharing Meshing Toothed Conveyor Chain Transmission System Based on Neural Network

With the rapid development of industry, the production and demand of automobiles have increased significantly. The generation of cars often requires tens of thousands of processes, consisting of hundreds of assembly lines to complete. Chain conveyors are widely used in the transportation of automobile assembly lines. The existing conveyor chains are roller chains. With the improvement of the requirements for conveyor reliability, synchronization, and environmental friendliness, the characteristics of roller conveyor chains restrict the further development of chain conveyors. It is urgent to study a new conveyor chain system to improve the synchronization, reliability, and environmental friendliness of chain conveyors on hundreds of assembly lines. In this paper, the mathematical modeling, meshing analysis, reliability, and environmental friendliness of the parameters of the components of the double pitch time-sharing meshing toothed conveyor chain system are studied. In addition, a novel fault diagnosis method of double pitch time-sharing meshing toothed conveyor chain transmission system based on neural network model is proposed in this paper.

Ding, Song↗

DOE OSTI2026

Cyote-attack Chain Estimator

Attack Chain Estimator (ACE) Application Overview The Attack Chain Estimator (ACE) Application is a sophisticated tool designed for the ingestion, classification, sequencing, and enrichment of cybersecurity threat reports. This application leverages advanced machine learning models and extensive historical data to provide comprehensive insights into cyber threats, specifically targeting Industrial Control Systems (ICS). Purpose The primary functions of the ACE Application include: Ingestion of Cybersecurity Threat Reporting: Capable of ingesting text-based threat reports in markdown or text file format. Supports ingestion of structured data from other sources in STIX/JSON format. Classification of Report’s Text-Based Events: Utilizes a DeBERTa classifier, specifically trained on cybersecurity data, to map the events to MITRE ATT&CK for ICS Tactics and Techniques. Classification is performed using multiple Jupyter notebooks and machine learning workflows hosted as FastAPI microservices: regex_data deberta_base_35_train_hft_classifier_mlflow.ipynb hft_regex_classifier_mlflow.ipynb param_train_hft_classifier_mlflow.ipynb regex_tactic_tech.ipynb Ordering of Tactics, Techniques, and Observable Events: Sequences the identified tactics, techniques, and events to form a coherent attack chain. Enrichment with Historical Attack Chain Details: Enhances the attack chain with details from historical attacks using a Markov model developed from CyOTE Precursor Analysis Report data. The Markov model is available as a FastAPI endpoint for seamless integration. Enrichment with Adversary Emulation Capabilities Data: Integrates adversary emulation capabilities data using MITRE Caldera for OT adversary abilities UUIDs. Export of Output Files: Provides options to export the enriched attack chain in JSON or CSV formats. Routing of Output to Other Applications: Facilitates routing of output to various platforms and applications, including: Threat Intelligence Platforms COREII Scout for Threat Intelligence Analysis COREII Modeling and Simulation for Adversary Emulation Technical Description The ACE Application is an advanced cybersecurity tool designed to provide detailed threat analysis and sequence generation. It is built on a robust architecture that integrates natural language processing, machine learning, and historical data modeling. Key Components: Data Ingestion Module: Handles the input of threat reports and data from various formats, ensuring flexibility in data sources. Classification Engine: Employs DeBERTa-based classifiers hosted as FastAPI microservices to analyze and classify threat report events in accordance with the MITRE ATT&CK framework for ICS. Sequence Generator: Orders the classified events into a logical attack chain, providing clear insight into the sequence of tactics and techniques used in the threat. Enrichment Engine: Integrates historical data and adversary emulation capabilities to enhance the attack chain with valuable context and additional details. The historical data enrichment is powered by a Markov model, which is available as a FastAPI endpoint. Export and Routing Module: Facilitates the export of the enriched attack chain in multiple formats and routes the output to designated applications for further analysis or emulation.

Paul, Tony [Idaho National Laboratory (INL), Idaho↗

DOE OSTI2022

Carbon Capture, Transport, & Storage: Supply Chain Deep Dive Assessment (Final Report)

The report “America’s Strategy to Secure the Supply Chain for a Robust Clean Energy Transition” lays out the challenges and opportunities faced by the United States in the energy supply chain as well as the federal government plans to address these challenges and opportunities. It is accompanied by several issue-specific deep dive assessments, including this one, in response to Executive Order 14017 “America’s Supply Chains,” which directs the Secretary of Energy to submit a report on supply chains for the energy sector industrial base. The Executive Order is helping the federal government to build more secure and diverse U.S. supply chains, including energy supply chains. This report was completed by the Department of Energy (DOE) to examine carbon dioxide (CO 2 ) capture, transport, and storage technologies and associated supply chains that will be required to support the United States (U.S.) decarbonization goals by 2050. Specifically, the analysis sought to understand supply chain bottlenecks to achievingan upper-bound 2.0 gigatonnes (Gt) of CO 2 capture and storage (CCS) per year in the United States. A literature review shows that in aggressive infrastructure deployment scenarios, the United States’ likely upper bound of CCS capacity is 1.7 Gigatons per annum (Gtpa) by 2050. This suggests the study design of 2.0 Gtpa capacity by 2050 is more aggressive yet and represents a conservative upper bound for supply chain analyses.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Grid Energy Storage: Supply Chain Deep Dive Assessment

The report “America’s Strategy to Secure the Supply Chain for a Robust Clean Energy Transition” lays out the challenges and opportunities faced by the United States in the energy supply chain as well as the Federal Government plans to address these challenges and opportunities. It is accompanied by several issue-specific deep dive assessments, including this one, in response to Executive Order 14017 “America’s Supply Chains,” which directs the Secretary of Energy to submit a report on supply chains for the energy sector industrial base. The Executive Order is helping the Federal Government to build more secure and diverse U.S. supply chains, including energy supply chains. To combat the climate crisis and avoid the most severe impacts of climate change, the U.S. is committed to achieving a 50 to 52 percent reduction from 2005 levels in economy-wide net greenhouse gas pollution by 2030, creating a carbon pollution-free power sector by 2035, and achieving net zero emissions economy-wide by no later than 2050. The U.S. Department of Energy (DOE) recognizes that a secure, resilient supply chain will be critical in harnessing emissions outcomes and capturing the economic opportunity inherent in the energy sector transition. Potential vulnerabilities and risks to the energy sector industrial base must be addressed throughout every stage of this transition. The DOE energy supply chain strategy report summarizes the key elements of the energy supply chain as well as the strategies the U.S. Government is starting to employ to address them. Additionally, it describes recommendations for Congressional action. DOE has identified technologies and crosscutting topics for analysis in the one-year time frame set by the Executive Order. Along with the capstone policy report, DOE is releasing 11 deep dive assessment documents, including this one, covering the following technology sectors: carbon capture materials; electric grid including transformers and high voltage direct current (HVDC); energy storage; fuel cells and electrolyzers; hydropower including pumped storage hydropower (PSH); neodymium magnets; nuclear energy; platinum group metals and other catalysts; semiconductors; solar photovoltaics (PV); and wind. DOE is also releasing two deep dive assessments on the following crosscutting topics: Commercialization and competitiveness; and cybersecurity and digital components. More information can be found at www.energy.gov/policy/supplychains.

25 ENERGY STORAGE↗

DOE OSTI2022

Wind Energy: Supply Chain Deep Dive Assessment

The report “America’s Strategy to Secure the Supply Chain for a Robust Clean Energy Transition” lays out the challenges and opportunities faced by the United States in the energy supply chain as well as the federal government plans to address these challenges and opportunities. It is accompanied by several issue-specific deep dive assessments, including this one, in response to Executive Order 14017 “America’s Supply Chains,” which directs the Secretary of Energy to submit a report on supply chains for the energy sector industrial base. The Executive Order is helping the federal government to build more secure and diverse U.S. supply chains, including energy supply chains. To inform the DOE team’s supply chain review, researchers at the National Renewable Energy Laboratory (NREL) conducted research and analyses that characterize supply chain strengths, weaknesses, opportunities, and threats within the wind industry, including both land-based and offshore wind. The team also conducted interviews with industry stakeholders and subject matter experts. This report documents these findings and provides a foundation for addressing the observed vulnerabilities and enhancing U.S. wind supply chain competitiveness.

17 WIND ENERGY↗