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At least 1,189 records · Page 66

Additive Manufacturing: Disrupting Global Supply Chains and Enabling Sustainable Development

The integration of new materials and innovative manufacturing technologies into product supply chains are critically needed to address human and societal needs and to promote sustainable development and economic competitiveness. Recently, there has been tremendous growth in the additive manufacturing (AM) landscape with the introduction of high- end machines suitable for industrial applications. In addition, availability of desktop 3-D printers as well as open source printers and platforms have also facilitated the large scale growth of distributed manufacturing. The paradigm shift in thinking, where one can turn their design into product on demand, is leading to new business models and challenging traditional models of product development and distribution. In this presentation, an overview of different AM technologies will be provided along with technical challenges and opportunities. Various examples of materials (polymers, ceramics, metals, hybrids, and multi material systems) and structures achieved from utilizing a wide variety of additive manufacturing approaches will be provided. Technical challenges and opportunities for the utilization of additive manufacturing as a powerful enabler for sustainable development and disruptive technological threat to global supply chains for different materials and systems will be presented.

Additive Manufacturing↗

High-Order/Low-Dissipation Chain-Rule Flux Solution Reconstruction Schemes in FUN3D

In this paper, we report progress in the development of economically high-order flux-solution-reconstruction (FSR) schemes, which are second-order accurate on general unstructured grids but achieve high-order accuracy when a grid is regular, i.e., has the same stencil (with the same spacing) throughout the domain. Two variants of the FSR schemes are discussed: chain-rule-flux-solution reconstruction (CFSR) and quadratic-form-flux-solution reconstruction (QFSR), where the former is based on the chain rule and the latter on the flux reconstruction expressed as a function of solution variables. These schemes are tested for flows with shock waves with a limiter incorporated in the flux and solution reconstructions. Improved results, compared with second-order methods are demonstrated for inviscid and viscous flows with smooth grids.

high-order↗

Shape Estimation for Elongated Deformable Object using B-spline Chained Multiple Random Matrices Model

In this paper, a B-spline chained multiple random matrix models (RMMs) representation is proposed to model geometric characteristics of an elongated deformable object. The hyper degrees of freedom structure of the elongated deformable object make its shape estimation challenging. Based on the likelihood function of the proposed B-spline chained multiple RMMs, an expectation-maximization (EM) method is derived to estimate the shape of the elongated deformable object. A split and merge method based on the Euclidean minimum spanning tree (EMST) is proposed to provide initialization for the EM algorithm. The proposed algorithm is evaluated for the shape estimation of the elongated deformable objects in scenarios, such as the static rope with various configurations (including configurations with intersection), the continuous manipulation of a rope and a plastic tube, and the assembly of two plastic tubes. The execution time is computed and the accuracy of the shape estimation results is evaluated based on the comparisons between the estimated width values and its ground-truth, and the intersection over union (IoU) metric.

Gang Yao↗

The International Supply Chain and Manufacturing Costs for Photovoltaic Modules, and Project Economics of Systems Including Storage

This talk will highlight the most recent efforts from the National Renewable Energy Laboratory's (NREL) Strategic Energy Analysis Center (SEAC) to track solar PV manufacturing capacities and trade flows, bottom-up manufacturing costs, and project capital costs including storage. We will begin with an overview of the global PV supply chain and the 2019 benchmark input data used to inform NREL's crystalline silicon (c-Si) and thin film module manufacturing cost models. The accounting framework that we follow and that will be reviewed during this talk provides a methodology to prepare cost models in accordance with the Generally Accepted Accounting Principles (GAAP) and the International Financial Reporting Standards (IFRS). For the polysilicon, wafer, cell conversion, and module assembly steps of the c-Si supply chain, and for thin film modules, we will review input data and methods useful for calculating the costs of goods sold (COGS); research and development (R&D) expenses; and sales, general, and business administration (S, G, &A) expenses. This 2019 benchmark analysis is compiled for state-of-the-art polysilicon-to-module manufacturing across the globe. We will also provide an overview of opportunities for solar plus storage and show our 2018 benchmark bottom-up capital cost model results for utility-scale PV systems with battery storage.

14 SOLAR ENERGY↗

Verifying status of a termination end cap of a microinverter chain

A photovoltaic (PV) system includes a system control module that determines the presence of a microinverter chain termination end cap. The end cap includes an embedded circuit. The embedded circuit includes components having resistive, reactive, or impedance values. A signal source provides a signal through the microinverter chain. A parameter for a sensed signal detected by the system control module is used to determine the presence of the end cap using a change between the parameter for the sense signal and a reference parameter.

14 SOLAR ENERGY↗

Approximate Inverse Chain Preconditioner: Iteration Count Case Study for Spectral Support Solvers

As the growing availability of computational power slows, there has been an increasing reliance on algorithmic advances. However, faster algorithms alone will not necessarily bridge the gap in allowing computational scientists to study problems at the edge of scientific discovery in the next several decades. Often, it is necessary to simplify or precondition solvers to accelerate the study of large systems of linear equations commonly seen in a number of scientific fields. Preconditioning a problem to increase efficiency is often seen as the best approach; yet, preconditioners which are fast, smart, and efficient do not always exist. Following the progress of [1], we present a new preconditioner for symmetric diagonally dominant (SDD) systems of linear equations. These systems are common in certain PDEs, network science, and supervised learning among others. Based on spectral support graph theory, this new preconditioner builds off of the work of [2], computing and applying a V-cycle chain of approximate inverse matrices. This preconditioner approach is both algebraic in nature as well as hierarchically-constrained depending on the condition number of the system to be solved. Due to its generation of an Approximate Inverse Chain of matrices, we refer to this as the AIC preconditioner. We further accelerate the AIC preconditioner by utilizing precomputations to simplify setup and multiplications in the con-text of an iterative Krylov-subspace solver. While these iterative solvers can greatly reduce solution time, the number of iterations can grow large quickly in the absence of good preconditioners. Initial results for the AIC preconditioner have shown a very large reduction in iteration counts for SDD systems as compared to standard preconditioners such as Incomplete Cholesky (ICC) and Multigrid (MG). We further show significant reduction in iteration counts against the more advanced Combinatorial Multigrid (CMG) preconditioner. We have further developed no-fill sparsification techniques to ensure that the computational cost of applying the AIC preconditioner does not grow prohibitively large as the depth of the V-cycle grows for systems with larger condition numbers. Our numerical results have shown that these sparsifiers maintain the sparsity structure of our system while also displaying significant reductions in iteration counts.1 2

97 MATHEMATICS AND COMPUTING↗

Approximate Inverse Chain Preconditioner: Iteration Count Case Study for Spectral Support Solvers

As the growing availability of computational power slows, there has been an increasing reliance on algorithmic advances. However, faster algorithms alone will not necessarily bridge the gap in allowing computational scientists to study problems at the edge of scientific discovery in the next several decades. Often, it is necessary to simplify or precondition solvers to accelerate the study of large systems of linear equations commonly seen in a number of scientific fields. Preconditioning a problem to increase efficiency is often seen as the best approach; yet, preconditioners which are fast, smart, and efficient do not always exist. Following the progress of [1], we present a new preconditioner for symmetric diagonally dominant (SDD) systems of linear equations. These systems are common in certain PDEs, network science, and supervised learning among others. Based on spectral support graph theory, this new preconditioner builds off of the work of [2], computing and applying a V-cycle chain of approximate inverse matrices. This preconditioner approach is both algebraic in nature as well as hierarchically-constrained depending on the condition number of the system to be solved. Due to its generation of an Approximate Inverse Chain of matrices, we refer to this as the AIC preconditioner. We further accelerate the AIC preconditioner by utilizing precomputations to simplify setup and multiplications in the con-text of an iterative Krylov-subspace solver. While these iterative solvers can greatly reduce solution time, the number of iterations can grow large quickly in the absence of good preconditioners. Initial results for the AIC preconditioner have shown a very large reduction in iteration counts for SDD systems as compared to standard preconditioners such as Incomplete Cholesky (ICC) and Multigrid (MG). We further show significant reduction in iteration counts against the more advanced Combinatorial Multigrid (CMG) preconditioner. We have further developed no-fill sparsification techniques to ensure that the computational cost of applying the AIC preconditioner does not grow prohibitively large as the depth of the V-cycle grows for systems with larger condition numbers. Our numerical results have shown that these sparsifiers maintain the sparsity structure of our system while also displaying significant reductions in iteration counts.1 2

97 MATHEMATICS AND COMPUTING↗

Induced Markov chain for wind farm generation forecasting

Systems and methods for forecasting power generation in a wind farm are disclosed. The systems and methods utilize an induced Markov chain model to generate a forecast of power generation of the wind farm. The forecast is at least one of a point forecast or a distributional forecast. Additionally, the systems and methods modify at least one of: (i) a generation of electricity at a power plant coupled to a common power grid as the wind farm; or (ii) a distribution of electricity in the common power grid based on the forecast of power generation of the wind farm. In an exemplary approach, utilizing the induced Markov chain model to generate the forecast may include determining a series of time adjacent power output measurements based on historical wind power measurements and calculating a time series of difference values based on the series of time adjacent power output measurements.

17 WIND ENERGY↗

Verifying status of a termination end cap of a microinverter chain

A photovoltaic (PV) system includes a system control module that determines the presence of a microinverter chain termination end cap. The end cap includes an embedded circuit. The embedded circuit includes components having resistive, reactive, or impedance values. A signal source provides a signal through the microinverter chain. A parameter for a sensed signal detected by the system control module is used to determine the presence of the end cap using a change between the parameter for the sense signal and a reference parameter.

Chapman, Patrick L.↗

Achieving American Leadership in the Grid Storage Supply Chain Factsheet

To meet growing demand for long duration energy storage, domestic manufacturing will have to increase significantly. The use of renewables is rapidly increasing, and the adaption of electric vehicles is on the rise, which will require the national grid to not only produce and deliver electricity, but also store it reliably and cost-effectively. The International Energy Agency (IEA) recently released a report showing that to reach a goal of net-zero emissions by 2050, grid storage will need to grow to almost 2,500 gigawatt hours (GWh) in less than a decade. Currently, across the globe, battery technologies provide over 30 GWh of grid storage(BloombergNEF, 2020) while pumped storage hydropower (PSH)provides 160 gigawatts (GW) of long-duration energy storage (LDES) (PSH) (U.S. Department of Energy, 2020). This fact sheet summarizes strategies to address key vulnerabilities in the grid storage supply chain, the United States. These strategies include: • Developing domestic, sustainable manufacturing and recycling capabilities along the energy storage supply chain. • Maximizing the use of domestic resources by focusing on second-life and recycling technologies. • Enabling the diversification and deployment of grid storage technologies through targeted research activities. Addressing these opportunities will have significant impacts with respect to increasing well-paying skilled domestic jobs, improving the gross domestic product (GDP), and ensuring minimal environmental and climate impacts.

Source record↗

Achieving American Leadership in the Hydropower Supply Chain

In 2020, hydropower accounted for 36.7% of U.S. renewable electricity generation and 7.3% of total electricity generation. While the existing U.S. supply chain effectively supports the nation’s hydropower fleet—the fourth-largest in the world, alongside the third-largest pumped storage hydropower (PSH) fleet—refurbishments, upgrades, and new development are required to modernize and expand the fleet and facilitate a clean, emissions-free U.S. electricity grid by 2035. To achieve these goals, researchers compiled a report examining the U.S. hydropower energy supply chain, including raw materials, processed materials, components, subcomponents, and recycling.

Source record↗

Achieving American Leadership in the Rare Earth Magnets Supply Chain Factsheet

Rare earth permanent magnets are a critical technology for the clean energy transition and electrified transport revolution. The global supply chain for rare earth metals and magnets is almost completely dominated by China, which controlled 58% of rare earth mining and 92% of magnet manufacturing in 2020. The United States produces only 15% of the global supply of raw materials for these magnets, and while the nation has several nascent downstream processing and manufacturing efforts, current domestic production is limited. Investments and incentives to boost the domestic supply chain, especially in the separation and refining phases, point the way to securing stable supplies of these critical clean energy components.

Source record↗

Engineered microbes for conversion of organic compounds to medium chain length alcohols and methods of use

This disclosure provides a genetically-modified bacterium from the genus Pseudomonas that comprises an exogenous nucleic acid encoding an enoyl-CoA reductase and an exogenous nucleic acid encoding an acyl-CoA reductase that produces medium chain length alcohols. The disclosure further provides methods for producing medium chain alcohols using such genetically-modified bacterium. This disclosure provides a renewable, bio-based production platform for valuable mcl-alcohols that have a wide range of industrial applications. Current production of mcl-alcohols typically occurs through the hydrogenation of plant oils and waxes. This process leads to issues of deforestation and is largely unsustainable. Utilizing waste lignin streams as the carbon source provides a more sustainable feedstock that can be generated from plant waste like corn stover. Along with this, the use of lignin avoids competition with food resources as traditional starch and sugar feedstocks.

Guss, Adam M.↗

An example of neutronic penalizations in reactivity transient analysis using 3D coupled chain HEMERA

HEMERA (Highly Evolutionary Methods for Extensive Reactor Analyses), is a fully coupled 3D computational chain developed jointly by IRSN and CEA. It is composed of CRONOS2 (core neutronics, cross sections library from APOLLO2), FLICA4 (core thermal-hydraulics) and the system code CATHARE. Multi-level and multi-dimensional models are developed to account for neutronics, core thermal-hydraulics, fuel thermal analysis and system thermal-hydraulics, dedicated to best-estimate, conservative simulations and sensitivity analysis. In IRSN, the HEMERA chain is widely used to study several types of reactivity accidents and for sensitivity studies. Just as an example of the HEMERA possibilities, we present here two types of neutronic penalizations and their impact on a power transient due to a REA (Rod Ejection Accident): in the first one, we studied a bum-up distribution modification and in the second one, a delayed-neutron fraction modification. Both modifications are applied to the whole core or localized in a few assemblies. Results show that it is possible to use global or local changes but 1) in case of bum-up modification, the total core power can increase when assembly peak power decrease so, care has to be taken if the goal is to maximize a local power peak and 2) for delayed-neutron fraction, a local modification can have the same effect as the one on the whole core, provided that it is large enough. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Panel Session 68: Key EFCOG Actions: Risk Communication, Supply Chain Management, Human Capital and More

Overview on the Energy Facility Contractor Group's (EFCOG) most recent work in partnership with DOE to improve performance and EFCOG's mission is to ensure safety, security, and quality across the US DOE complex. Best practices were highlighted in discussions with US DOE/ NNSA leaders and key project executives. EFCOG's key priorities for 2020 were featured in the panel discussions to include: addressing human capital issues to ensure future mission needs can be met; addressing supply chain issues for nuclear projects; improving the data quality and performance assurance; and improving risk communication and stakeholder relations. EFCOG accomplishes its mission through working groups that provide forums to address common challenges and exchange proven techniques and other management and technical information among member contractors. Panelists with presentations: WM EFCOG Panel Slides (Morgan Smith); EFCOG DOE Supply Chain Initiative (SCI) - Enabling The DOE Mission - (Darrell Graddy); Data Quality and Performance Assurance (JD Dowell); Risk Communication - Different Events - Very Different Responses from the Community (Rick McLeod); Current State of Cybersecurity (Liz Porter)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

An Approach for Assessing Consequences of Potential Supply Chain and Insider Contributed Cyber Attacks on Nuclear Power Plants

The Stuxnet attack at the Natanz facility is an example of a targeted and successful cyber attack on a nuclear facility. Snowden's release of National Security Agency documents demonstrated the consequences of the insider threat. More recently, the United States tried to attack North Korea but failed, South Korea was attempting to attack North Korea, and both applied Stuxnet-like approaches. These sophisticated targeted attacks differ from web-site hacking events that are reported almost daily in the news mainly because targeted attacks require detailed design and operation information of the systems attacked and/or are often carried out by insiders. For instance, in order to minimize disruption of facilities around the world, Stuxnet remained idle until it recognized the specific configuration of the Natanz facility, demonstrating that the attackers possessed extremely detailed information about the facility. Such targeted cyber attacks could become a national-level military weapon and be used in coercion of hostile countries. While U.S. nuclear power plants (NPPs) are well designed and protected and not easily attacked through internet hacking, there have been a few digital-system-related incidents. For example, in 2003, the Slammer worm breached the private network of the Davis Besse plant and disabled a safety monitoring system for almost five hours. The breach did not pose a safety hazard because the plant was offline, but it demonstrates the vulnerability of NPP systems. The worm did not enter the plant systems directly; it began by entering the systems of a Davis Besse contractor, and entered through the T1 line bridging the contractor's computer and Davis Besse's corporate networks. The T1 line was not protected by the plant's firewall. The success of the Stuxnet-Natanz case demonstrates that supply-chain level attack (i.e., physically tampering with digital systems to install undetectable malware for the purpose of bringing harm to a player further down the supply chain network) plays a crucial role in a successful attack. Although attacking an NPP naturally requires more complex strategies and tactics than internet hacking, NPP's have a variety of sophisticated and possibly sensitive systems that may be attacked. Previous studies, for example, Baylon, have identified and confirmed that such attack strategies and tactics can be designed and could produce severe damage to an NPP. Current NPPs were designed according to design basis accidents that predate use of the internet by the general public and did not take into consideration potential cyber attacks that could be carried out by state-sponsored attackers and malicious insiders. To date, no detailed engineering analysis has been performed to realistically examine the consequences these attacks may have on the plants. In this paper, we discuss an approach for assessing the potential consequences of Stuxnet type of attacks. We will examine the engineering criteria used in designing the plants, develop possible attack scenarios in terms of the structures, components, and systems (SSCs) being controlled by digital systems at the NPPs, perform engineering analysis to determine if the attacks would impose conditions that are beyond design bases, and categorize the consequences of the potential accidents. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Efficient Subset Simulation using Hamiltonian Neural Network enhanced Markov Chain Monte Carlo Methods

The Monte Carlo method delivers an unbiased estimate of the probability of failure. However, the variance of the estimate depends on the number of evaluated samples. This number must be very large for estimations of a low probability of failure. If the evaluation of each sample is computationally expensive, the crude Monte Carlo simulation strategy is impracticable. Therefore, subset simulations are used to reduce the required number of evaluations. Subset simulations require a Markov Chain Monte Carlo sampler, such as the random walk Metropolis-Hastings algorithm. The algorithm, however, struggles with sampling in low-probability regions, especially if they are narrow. As a consequence, advanced Markov Chain Monte Carlo simulations have been developed. In particular, the Hamiltonian Monte Carlo method explores the target distribution rapidly. Driven by the idea of Hamiltonian dynamics, this sampler provides a non-random walk through the target distribution. The incorporation of subset simulation and Hamiltonian Monte Carlo methods has shown promising results for reliability analysis. One downside of the Hamiltonian Monte Carlo method is that gradient evaluations are computationally expensive, especially when dealing with high-dimensional problems and evaluating long trajectories. We show that integrating Hamiltonian neural networks in Hamiltonian Monte Carlo simulations significantly speeds up the sampling task. Furthermore, the enhancement of adaptive trajectory length within the Hamiltonian Monte Carlo results in the efficient proposal of the following states. Based on this recent enhancement, we provide a fast sampling strategy for subset simulations using Hamiltonian neural networks to replace the evaluation of the gradient and significantly speed up the Hamiltonian Monte Carlo simulation.

97 MATHEMATICS AND COMPUTING↗

The 2022 Russian Invasion of Ukraine: Nuclear Supply Chains vs. Sanctions

The United States and European countries are unlikely to collectively approve of sanctions against Russian exports of nuclear fuel assemblies and uranium in the near term despite the 2022 invasion of Ukraine. Rather than bearing the costs that cutting off Russian imports would have on domestic nuclear energy production, the United States and European countries are more likely to prefer developing alternative supply chains of nuclear fuel and enriched uranium to reduce dependence on Russian resources. This outlook is primarily based on two areas of evaluation: (1) comparison of the Russian-made reactor fuel supply chain within the European Union (EU) in 2014 following the annexation of Crimea and in 2022 following the invasion of Ukraine, and (2) the dependence of France and the United States on Russia’s exports of enriched uranium. Additionally, the potential impacts to Kazakh uranium exports if trade routes for nuclear resources through Russia became unavailable were considered as an additional factor in the push for disentanglement from Russian nuclear resources. This work utilized BACI, a database of harmonized international trade data at the product level drawn from the United Nations Comtrade Database and created by the French international economics research institute CEPII.1 Comparisons of imports and exports were based on the trade value in U.S. dollars as reported in BACI instead of the quantity of the traded product. This evaluation only considers import and export data up until 2021, since the trade data for 2022 is incomplete within the Comtrade Database for several of the key countries presented in this work, including Australia, Canada, Namibia, Niger, and Russia.

Political science↗