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At least 541 records · Page 30

Challenges in Markov Chain Monte Carlo for Bayesian Neural Networks

Markov chain Monte Carlo (MCMC) methods have not been broadly adopted in Bayesian neural networks (BNNs). This paper initially reviews the main challenges in sampling from the parameter posterior of a neural network via MCMC. Such challenges culminate to lack of convergence to the parameter posterior. Nevertheless, this paper shows that a nonconverged Markov chain, generated via MCMC sampling from the parameter space of a neural network, can yield via Bayesian marginalization a valuable posterior predictive distribution of the output of the neural network. Further, classification examples based on multilayer perceptrons showcase highly accurate posterior predictive distributions. The postulate of limited scope for MCMC developments in BNNs is partially valid; an asymptotically exact parameter posterior seems less plausible, yet an accurate posterior predictive distribution is a tenable research avenue.

97 MATHEMATICS AND COMPUTING↗

Technical Challenges and Gaps in Integration of Advanced Sensors, Instrumentation, and Communication Technologies with Digital Twins for Nuclear Application

This paper explores integrating advanced sensor, instrumentation, and communication technologies with digital twin technologies for nuclear energy application. Digital twins and digital-twin-enabling technologies are expected to integrate with future nuclear reactor designs and have the potential to impact currently operating nuclear power plants. Greater digital integration, advanced instrumentation and control systems, and advanced operations and maintenance practices are all associated with digital-twin-enabling technologies. Advanced sensors, instrumentation, and communication technology are expected to comprise important elements of the infrastructure required to develop and operate a nuclear digital twin system. This paper identifies and discusses challenges and gaps in developing and implementing advanced sensors and instrumentation and communication technology to be integrated with a digital twin in current and advanced reactor applications. It is important to address some challenge and gap to enable a successful near-term deploying advanced sensors, instrumentation, and communication technologies integrated with digital twins.

Yadav, Vaibhav↗

Challenges and needs for system-level electrochemical lithium-ion battery management and diagnostics

The desire for energy-dense and fast-charged battery technology in consumer-electronics, electric-vehicle, grid, and aviation applications is pushing the envelope on materials to cells and pack designs to use and operating conditions. However, some approaches could inherently decrease safety of the battery—thus requiring the development of advanced management and diagnostics. Safety of lithium-ion batteries (LiBs), particularly in multicell configurations, is highly variable and could evolve with use. Existing works primarily focus on cell life and safety diagnostics without considering module and pack-level uncertainties and sometimes imply that cell-level electrochemical diagnostics would work in modules or packs, a naive oversimplification. Using example case studies, we highlight the potential and challenges associated with extending single-cell diagnostics to multiple cells, note the challenge involved, and motivate the research and development and support communities to devote efforts to fill the gap by developing diagnostics at these levels for current- and future-generation LiBs.

25 ENERGY STORAGE↗

A prospective on machine learning challenges, progress, and potential in polymer science

Abstract Artificial intelligence and machine learning (ML) continue to see increasing interest in science and engineering every year. Polymer science is no different, though implementation of data-driven algorithms in this subfield has unique challenges barring widespread application of these techniques to the study of polymer systems. In this Prospective, we discuss several critical challenges to implementation of ML in polymer science, including polymer structure and representation, high-throughput techniques and limitations, and limited data availability. Promising studies targeting resolution of these issues are explored, and contemporary research demonstrating the potential of ML in polymer science despite existing obstacles are discussed. Finally, we present an outlook for ML in polymer science moving forward. Graphical Abstract

Struble, Daniel C. (ORCID:0009000093410612)↗

Analysis of the Challenges in Developing Sample-Based Multi-fidelity Estimators for Non-deterministic Models

Multifidelity (MF) uncertainty quantification (UQ) seeks to leverage and fuse information from a collection of models to achieve greater statistical accuracy with respect to a single-fidelity counterpart, while maintaining an efficient use of computational resources. Despite many recent advancements in MF UQ, several challenges remain and these often limit its practical impact in certain application areas. In this manuscript, we focus on the challenges introduced by nondeterministic models to sampling MF UQ estimators. Nondeterministic models produce different responses for the same inputs, which means their outputs are effectively noisy. MF UQ is complicated by this noise since many state-of-the-art approaches rely on statistics, e.g., the correlation among models, to optimally fuse information and allocate computational resources. Here, we demonstrate how the statistics of the quantities of interest, which impact the design, effectiveness, and use of existing MF UQ techniques, change as functions of the noise. With this in hand, we extend the unifying approximate control variate framework to account for nondeterminism, providing for the first time a rigorous means of comparing the effect of nondeterminism on different multifidelity estimators and analyzing their performance with respect to one another. Numerical examples are presented throughout the manuscript to illustrate and discuss the consequences of the presented theoretical results.

97 MATHEMATICS AND COMPUTING↗

BEAM DYNAMICS CHALLENGES IN THE DESIGN OF THE ELECTRON-ION COLLIDER

The Electron-Ion Collider (EIC), presently under construction at Brookhaven National Laboratory, will collide polarized high-energy electron beams with hadron beams, achieving luminosities up to 1 × 1034 cm¿2 s¿1 in the center-of-mass energy range of 20-140 GeV. To achieve such high luminosity, we adopt high bunch intensities for both beams, small and flat transverse beam sizes at the interaction point (IP), a large crossing angle of 25 mrad, and a novel strong hadron cooling in the Hadron Storage Ring (HSR) to counteract intra-beam scattering (IBS) at the collision energy. In this talk, we will review the beam dynamics challenges in the design of the EIC, particularly the single-particle dynamic aperture, polarization maintenance, beam-beam interaction, impedance budget and instabilities. We will also briefly mention some technical challenges associated with beam dynamics, such as strong hadron cooling, multipoles and noises of crab cavities, power supply current ripples, and the vacuum upgrade to existing beam pipes of the Hadron Storage Ring of the EIC.

Luo, Y.↗

Beam Instrumentation Challenges for the Fermilab PIP-II Accelerator

Fermilab is undertaking the development of a new 800 MeV superconducting RF linac to replace it’s present normal conducting 400 MeV linac. The PIP-II linac warm front-end consists of an ion source, LEBT, RFQ and MEBT which includes an arbitrary pattern bunch chopper, to generate a 2.1 MeV, 2mA H⁻ beam. This is followed immediately by a series of superconducting RF cryomodules to produce a 800 MeV beam. Commissioning, operate and safety present challenges to the beam instrumentation. This paper describes these beam instrumentation challenges and the choices made for PIP-II.

43 PARTICLE ACCELERATORS↗

The National Virtual Biotechnology Laboratory Symposium: Harnessing the unique strengths of DOE to tackle the biotechnology challenges of COVID-19, October 28, 2020

This virtual symposium was held to highlight the impact the U.S. Department of Energy’s (DOE) National Virtual Biotechnology Laboratory (NVBL) has had utilizing the unique capabilities of the DOE to tackle the science and technology challenges associated with COVID-19, and to discuss areas in which the NVBL can have impact in the future. With perspectives from Chris Fall, Director, Office of Science, DOE, and William A. Bookless, Principal Deputy Administrator of the National Nuclear Security Administration, the event featured Presentations from lead investigators reporting progress in: Epidemiological modeling, Therapeutics, Testing, Understanding transport of the virus, and Solving issues around supply chain challenges. In addition, keynote speakers from outside the NVBL discussed the upcoming science and technology needs in computing, testing and surveillance, and vaccines. The event was aimed at the S&T community, media, and general public.

59 BASIC BIOLOGICAL SCIENCES↗

Opportunities and Challenges from Artificial Intelligence and Machine Learning for the Advancement of Science, Technology, and the Office of Science Missions

In February 2019, the President signed Executive Order 13859, Maintaining American Leadership in Artificial Intelligence. This order launched the American Artificial Intelligence Initiative, a concerted effort to promote and protect AI technology and innovation in the United States. The Initiative implements a government-wide strategy in collaboration and engagement with the private sector, academia, the public, and like-minded international partners. Among other actions, key directives in the Initiative called for Federal agencies to: Prioritize AI research and development investments, Enhance access to high-quality cyberinfrastructure and data, Ensure that the US maintains an international leadership role in the development of technical standards for AI, and Provide education and training opportunities to prepare the American workforce for the new era of AI. The mission of the Department of Energy (DOE) is to ensure America’s security and prosperity by addressing its energy, environmental, and nuclear challenges through transformative science and technology solutions. In terms of Science and Innovation, the DOE’s mission is to maintain a vibrant US effort in science and engineering as a cornerstone of our economic prosperity with clear leadership in strategic areas. From July to October in 2019, the Argonne, Oak Ridge, and Berkeley National Laboratories hosted a series of four AI for Science Town Hall meetings in Chicago, Oak Ridge, Berkeley, and Washington DC. The four meetings were attended by over 1300 scientists from the 17 DOE Labs, 39 companies, and over 90 universities. The goal of the Town Hall series was ‘to examine scientific opportunities in the areas of artificial intelligence, Big Data, and high-performance computing (HPC) in the next decade, and to capture the big ideas, grand challenges, and next steps to realizing these.’ The discussions at the meetings were captured in the final report of the AI for Science Town Hall meetings.

42 ENGINEERING↗

Benefit-Cost Analysis for Utility-Facing Grid Modernization Investments: Trends, Challenges, and Considerations

In recent years, many electric utilities have submitted grid modernization plans for review by state public utility commissions. A central objective of these plans is to demonstrate that grid modernization investments will provide net benefits to customers, be reasonable and be in the public interest. The plans typically include some form of benefit-cost analysis, but the assumptions, methodologies and frameworks vary considerably between utilities. This report: -Describes utility-facing technologies and projects for modernizing distribution systems, including interdependencies of grid components -Highlights benefit-cost considerations related to grid modernization -Summarizes recent trends in benefit-cost analysis for grid modernization based on a review of 21 plans by U.S. electric utilities -Discusses challenges state public utility commissions face in reviewing utility plans and provides options for addressing challenges

24 POWER TRANSMISSION AND DISTRIBUTION↗

Solar Photovoltaic (PV) Manufacturing Expansions in the United States, 2017-2019: Motives, Challenges, Opportunities, and Policy Context

This report analyzes U.S. PV manufacturing announcements from 2017 through July-2019 and explores the potential impact of tariffs on the competitiveness of U.S. PV module manufacturing. First, the fraction of announcements that resulted in established facilities in the time frame specified is analyzed, as well as firm nationality and the existence of any other U.S. facilities for the firm. The prevalence of different PV product types among the announcements was also analyzed, as well as the type of facilities selected for development (greenfield, existing PV factories, or repurposed buildings). The cost to build these facilities was also analyzed in the context of their capacity, the value of incentives received by state and local governments, and the number of jobs created. Case studies of Auxin Solar, First Solar, Hanwha Q Cells, JinkoSolar, LG, Silfab, Solaria, and SunPower were conducted and highlight five factors influencing firms’ decisions to expand their U.S. PV production capacity. These included: 1) proximity to demand, including supply contracts and demand for domestic content 2) tariffs and other incentives. The Section 201 tariffs on PV module imports were often described helpful but tariffs on cell imports (Section 201) or materials (Section 301, Section 232) were often described as counterproductive since U.S. supply chains do not exist for many necessary goods. The corporate tax decrease and local incentives were often mentioned as helpful. 3) the ability to move quickly in order to maximize the benefit of the Section 201 tariffs. This affected decisions to select localities with faster permitting or moving into existing buildings. 4) competition with scale, where competing with Asian PV imports or avoiding imports of material from China was challenging due to the larger scale of Asian manufacturers. Additionally, smaller firms expressed concerns that larger firms may not need their U.S. facilities to profitable. 5) access to capital, which was a particular challenge for smaller firms. Several U.S. tariffs affect the economics of PV cells, modules, and balance-of-module (BOM) materials, some of which are applicable to a long list of countries and others applicable only to China or Taiwan. We analyze how the tariffs interact to affect the prices of c-Si modules sourced under various assumptions about domestic and imported components and assembly. We estimate module prices under a range of baseline scenarios featuring U.S.-assembled modules with Southeast Asian cells (not from China or Taiwan). When all BOM components are sourced from the United States, baseline module prices are $0.37/W (below the 2.5-GW Section 201 cell cap) to $0.42/W (above the 2.5-GW cell cap). If all BOM components are sourced from China, the module price in this baseline U.S.-assembly scenario can reach up to $0.60/W. Imported module prices are lower than or within the range of the U.S.-assembled baseline prices under some conditions. Importing bifacial modules from Southeast Asia is the lowest-priced alternative, at $0.35/W, because bifacial modules are excluded from the Section 201 tariffs. Importing Southeast Asia modules that contain U.S. cells results in a price of $0.38/W, also owing to a Section 201 exclusion. Importing Southeast Asian modules that have Southeast Asian cells yields a price of $0.44/W, and importing Chinese modules that have U.S. cells yields a price of $0.58/W. These results suggest that U.S. tariff policy in 2019 may encourage U.S. assembly of mono-facial c-Si modules if the 2.5-GW cell import cap is not reached and Chinese BOM imports are negligible. However, some imported module options may offer the lowest prices if the 2.5-GW cell import cap is reached and/or U.S. module manufacturers must rely on significant Chinese BOM content. The analysis shows the 2.5-GW cell cap may be exceeded in 2019 and following years. It is also likely that U.S. module manufacturers will need to use at least some Chinese BOM content, since U.S. module manufacturers have stated that U.S. supply chains do not exist (or do not exist at a sufficient scale) for most BOM components, which makes them at least partially reliant on Chinese BOM and thus subject to Section 301 tariffs.

14 SOLAR ENERGY↗

Embedded Sensor Development in Metallic Structures for the Transformational Challenge Reactor

The ability to make minimally intrusive measurements of process conditions has tremendous potential for improving system performance and control in applications such as nuclear power generation. Towards this end, emerging and evolving advanced manufacturing methods, such as additive manufacturing, present new opportunities for more seamlessly integrating sensing with structure. This technical report summarizes the progress of work performed to assess the feasibility of embedding commercial off-the-shelf (COTS) sensors in a metallic reactor core using advanced manufacturing methods. The progress of four research and development tasks are documented: 1) A survey of COTS sensors for nuclear applications that are candidates for embedded fabrication, 2) Development of a conceptual framework for investigating the feasibility of sensor embedment, 3) Definition of structural geometries and other characteristics that will represent compelling proof-of-concept, and 4) Preliminary prototyping of embedded sensors. For the purposes of this initial study, nuclear grade K-type thermocouples were selected for investigation of embedment methodologies. This sensor was selected as an initial candidate because it represents one of the simplest sensor configurations that can provide useful process information. A selective laser melting (SLM) additive manufacturing process was used to fabricate structures with complexity representative of the core design of the transformational challenge reactor (TCR) and methods were developed to embed a thermocouple with a complex two-dimensional routing path. There are many challenges associated with embedding sensors during the additive manufacturing process including securing the sensor in the structure, relocating the part in the additive machine if using a “pick and place” sensor placement method, mitigating the potential for powder contamination, optimizing energy deposition parameters to build as closely as possible around the sensor path and improve structural integrity of the part, and tuning energy deposition to mitigate thermally induced warpage of the sensor and part as well as potential for damaging the sensing device. Preliminary results indicate that it is feasible to integrate the sensor while minimally compromising the structural integrity of the part and preserving sensor performance. This first phase of this work focused on developing a workflow for embedding sensors and controlling energy deposition during the additive manufacturing process to better understand the build characteristics around the sensor. Future work will perform more in-depth characterization of prototype parts using X-ray computed tomography and scanning electron microscopy to better understand fabricated structures, performance testing of embedded sensors in high temperature environments, adding new embedded measurement modalities, embedding devices with dissimilar materials in contact with additive powders, and the development of methods for embedding sensors with three-dimensional routing paths.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

2021 GeoAI Workshop Report: The Trillion Pixel Challenge

The convergence of geospatial big data with advancements from artificial intelligence, cloud infrastructure, and high-performance computing continues to revolutionize mapping and analysis of Earth's surface in unprecedented detail. Rapid innovations in sensing technologies will soon collect geospatial data in even higher resolution and throughput. These developments offer the potential for breakthroughs in science, policy, and national security via end-to-end GeoAI systems that can provide fresh insights into how humans occupy and alter their environment over time. At the 2021 GeoAI Trillion Pixel workshop, international subject matter experts from government, academia, industry, and nonprofit organizations gathered virtually to discuss the Trillion Pixel GeoAI Challenge. The event focused on six major themes currently influencing scientific innovation and breakthroughs. Particular focus was paid to societal impacts. As an additional takeaway message, the gathering identified remaining application gaps and challenges that are in need of stronger community partnerships and collaborations.

58 GEOSCIENCES↗

Workshop on Manufacturing and Integration Challenges for Analog and Neuromorphic Computing

The U.S. Department of Energy’s (DOE’s) Advanced Manufacturing Office (AMO) held its third virtual workshop on Semiconductor R&D for Energy Efficiency on August 11-13, 2021. This public workshop – titled “Manufacturing and Integration Challenges for Analog and Neuromorphic Computing” – brought together more than 180 leading scientific and technical experts to identify challenges and opportunities to improve manufacturing capabilities for analog and neuromorphic computing architectures to significantly increase energy efficiency of microelectronic devices. The workshop featured perspectives of researchers from national laboratories, universities, government agencies, and industry with expertise in novel and emerging devices, processes, and metrology and characterization. This workshop is intended to inform an AMO research, development, demonstration, and deployment (RDD&D) plan to significantly reduce semiconductor energy use by 2030. It was co-sponsored by the Semiconductor Research Corporation (SRC) and the DOE Office of Science’s Advanced Scientific Computing Research (ASCR).

97 MATHEMATICS AND COMPUTING↗

Nuclear Security Mission Growth: A regional strategy for meeting operational challenges [Slides]

The nation's growing nuclear security requirements have yielded three major conclusions: (1) LANL possesses an exceptionally diversified workforce, multidimensional infrastructure and mission-focused strategy to fulfill the responsibilities entrusted to us; (2) However, amidst significant opportunity and growth, regional considerations have exposed several operational challenges; and (3) Given these challenges and the Lab's many strengths, assistance is needed to successfully execute our mission.

99 GENERAL AND MISCELLANEOUS↗

Addressing Regulatory Challenges to Tribal Solar Deployment: Key Findings

Tribal land in the United States represents approximately 2% of the country's total landmass and holds more than 5% of solar photovoltaic potential (Doris, Lopez, and Beckley 2013). This resource is largely untapped. Many Tribes note that regulatory challenges often are roadblocks to taking advantage of solar potential. This report serves as a summary of a larger project, Addressing Regulatory Challenges to Tribal Solar Deployment. The project seeks to unlock some of this potential by bringing Tribal, regulatory, utility, and other stakeholders together to articulate key barriers to Tribal solar adoption and develop replicable solutions. By increasing institutional capacity and developing frameworks, trainings, and a technical document repository for regulatory bodies, utilities, and Tribes, this project can help expand an emerging market.

14 SOLAR ENERGY↗

Beam Dynamics Challenges of a Far-Future ERL-Based Collider - The Ghost Collider

Beam Dynamics Challenges of a Far-Future ERL-Based Collider - The Ghost Collider In a recent paper, Valery Telnov proposed a linear collider based on twin axis cavities [1]. In a subsequent presentation, Erk Jensen proposed a modification with intra-bucket energy recovery [2], which eliminates higher order mode excitation. Interestingly, this means that there is no need for large aperture SRF cavities and high-power HOM couplers. The Ghost Collider adopts these ideas, and adds the concept of four-beam collisions (initially proposed by Joel LeDuff [3]) to remove beam-beam interactions and disruption. This concept brings up a series of new beam dynamics problems which make optimization of the parameters difficult. The presentation will describe the concept, which has a series of beam-dynamics challenges to be solved before the concept can advance. [1] V.I. Telnov, JINST 16 (2021) no.12, P12025 [2] E. Jensen https://indico.cern.ch/event/1040671/?view=nicecompact [3] Status Report on D. C. I, The Orsay Storage Ring Group, IEEE Transactions on Nuclear Science, Vol. NS-26, No.3, June 1979

Hutton, Andrew↗

The case for an EIC Theory Alliance: Theoretical Challenges of the EIC

We outline the physics opportunities provided by the Electron Ion Collider (EIC). These include the study of the parton structure of the nucleon and nuclei, the onset of gluon saturation, the production of jets and heavy flavor, hadron spectroscopy and tests of fundamental symmetries. We review the present status and future challenges in EIC theory that have to be addressed in order to realize this ambitious and impactful physics program, including how to engage a diverse and inclusive workforce. In order to address these many-fold challenges, we propose a coordinated effort involving theory groups with differing expertise is needed. We discuss the scientific goals and scope of such an EIC Theory Alliance.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗