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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Toward the implementation of self-powered, wireless, real-time reactor power sensing

The successful development of real-time, self-powered, wireless reactor power sensing technology would enable key operating parameters of every fuel assembly in a commercial reactor core to be continuously monitored, and potentially be utilized for applications such as closed-loop reactor control. This paper discusses the expansion of successful work on the application of Self Powered Neutron Detectors (SPND) for reactor power monitoring and the wireless transmission of these detector signals using advanced vacuum micro-electronic (VME) transmitter technology developed by Westinghouse in collaboration with the Penn State University Radiation Science and Engineering Center. Results demonstrate that this technology may be successfully applied via long-term testing in the Penn State TRIGA reactor. SPND are inherently slower than, for example, ion chambers due to their dependence on radioactive decay to generate a signal. Additional research developed delay compensation algorithms to accelerate the response of these detectors for use in near-real-time closed-loop reactor control. Initial results indicate that practical signal acceleration can be obtained digitally or with an equivalent analog circuit. Future research will apply an inverse model of the sensor’s balance differential equations to more-effectively accelerate the SPND signal. Use of inverse models will also prove to be adaptive in the sense that time-dependent burn-up of these detectors, due to neutron activation of the detector material, can be modeled and applied in the acceleration algorithm.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Line identification of boron and nitrogen emissions in EUV and VUV wavelength ranges in the impurity powder dropping experiments of LHD and its application to spectroscopic diagnostics

An impurity powder dropper was installed in the 21st campaign of the Large Helical Device experiment (Oct. 2019–Feb. 2020) under a collaboration between the National Institute for Fusion Science and the Princeton Plasma Physics Laboratory for the purposes of real-time wall conditioning and edge plasma control. In order to assess the effective injection of the impurity powders, spectroscopic diagnostics were applied to observe line emission from the injected impurity. Therefore, extreme-ultraviolet (EUV) and vacuum-ultraviolet (VUV) emission spectra were analyzed to summarize observable impurity lines with B and BN powder injection. Emission lines released from B and N ions were identified in the EUV wavelength range of 5–300 Å measured using two grazing incidence flat-field EUV spectrometers and in the VUV wavelength range of 300–2400 Å measured using three normal incidence 20 cm VUV spectrometers. BI–BV and NIII–NVII emission lines were identified in the discharges with the B and BN powder injection, respectively. Useful B and N emission lines which have large intensities and are isolated from other lines were successfully identified as follows: BI (1825.89, 1826.40) Å (blended), BII 1362.46 Å, BIII (677.00, 677.14, 677.16) Å (blended), BIV 60.31 Å, BV 48.59 Å, NIII (989.79, 991.51, 991.58) Å (blended), NIV 765.15 Å, NV (209.27, 209.31) Å (blended), NVI 1896.80 Å, and NVII 24.78 Å. Applications of the line identifications to the advanced spectroscopic diagnostics were demonstrated, such as the vertical profile measurements for the BV and NVII lines using a space-resolved EUV spectrometer and the ion temperature measurement for the BII line using a normal incidence 3 m VUV spectrometer.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Rethinking U.S. Biosecurity Strategy for the Decade Ahead (Workshop Summary)

The Center for Global Security Research (CGSR) at Lawrence Livermore National Laboratory (LLNL) hosted a workshop titled, “Rethinking U.S. Biosecurity Strategy for the Decade Ahead” on October 27-29, 2020. The workshop included participants from the policy, military, and technical communities in the United States as well as from U.S. allies. The workshop, convened virtually due to the ongoing global COVID-19 pandemic, was a close collaboration between CGSR and the Physical Life Sciences (PLS) Directorate at LLNL. While the scope of conference extended beyond current circumstances to include precedents and predictions for both public health and bioweapons, contemporary events weighed heavily on the discussion participants.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

AI@DOE Interim Executive Report

Interim executive report for DOE’s Office of Science, NNSA, and Applied Energy Offices, in collaboration with the Artificial Intelligence and Technology Office, DOE AI roundtable workshops (December 2021 through February 2022) (“AI@DOE”). These workshops were used to help identify AI research requirements and priorities for the next decade for Department of Energy in developing machine-learning-based prediction and decision-support capabilities that address and anticipate DOE mission challenges. These missions’ challenges for AI include: AI to advance scientific discovery and technological innovation AI to support high consequence decisions while managing risk AI to improve DOE’s responsiveness to national & global issues AI to assist stewardship of the environment and national critical infrastructure These will be addressed in large part through the development and application of new and powerful artificial intelligence and/or machine learning algorithms and strategies. https://web.cvent.com/event/fc3922f8-fc75-4041-a317-f13a1da44f7c/summary?locale=en-US&i=2Wx2tjbStkydRFq_N2whPw

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Advanced Thin Film Core Technology: CIGS Final Technical Report (FTR)

Cu(In,Ga)Se 2 (CIGS) thin-film photovoltaics are a high-efficiency and reliable technology. This project completed research in two important areas and was designed to work collaboratively with industrial partners. Task 1: Alkali Science focused on alkali post-deposition treatments (PDT). PDTs have been instrumental in the dramatic voltage improvements that have moved CIGS device efficiencies from 20% to 23.35% [1]. Based on a survey of CIGS companies at the beginning of the project, the single biggest breakthrough for the CIGS community would be a mechanistic understanding of the role of alkalis in the material system. Significant accomplishments of Task 1: Alkali Science: 1) KF post-deposition treatments were shown to improve lifetime, open-circuit voltage (VOC), and efficiency of industrial partner samples, even when done as a later step, separate from the original CIGS deposition. 2) KF boosted efficiency when incorporated at the end of the third stage of NREL CIGS growth. 3) XPS characterization of CIGS surfaces with and without PDTs led to a proposed mechanism whereby K drives structural transformation at 350 degrees C that is locked in at room temperature even after K is rinsed away. 4) Published recipes for KF and RbF PDTs. Literature to date did not provide enough detail to quickly reproduce experimental results. 5) Identified most important parameters (RbF cell temperature and lamp setpoint temperature) and set boundaries for successful RbF PDTs. The purpose of Task 2: Cell-level Reliability was to overcome the largest challenges to investor confidence and long product lifetime in CIGS-based photovoltaic products: metastability, shading-induced hot spots, and potential-induced degradation (PID). Key findings were made in each of these areas by studying CIGS reliability at the cell level: 1) Published NREL's cell-level reliability testing procedures along with challenges that were encountered while developing them. These were also distributed to the community through an MRS conference presentation. 2) Decreased metastability by adding a CdS hole-injection layer between the CIGS and Zn(O,S) in the device stack. It also improved device performance. Materials other than CdS can be used for the same purpose. 3) Reduced front-glass PID by replacing soda-lime glass with low-Na borosilicate glass. 4) Found that PID depends on leakage current and light/electrical bias. This will help labs avoid test-specific degradation. 5) Discovered that CIGS can suffer from two different types of PID with different mechanisms. Front is slower and leads to shunting ZnO. Back is faster and degrades the p-n junction. 6) Holding cells at open circuit slows PID compared to short circuit. This affects testing protocols for glass/glass modules.

14 SOLAR ENERGY↗
DOE OSTI2024

CEERP SM3 Research, Monitoring, and Evaluation Workshop: Summary Report

The Columbia Estuary Ecosystem Restoration Program (CEERP) workshop, “CEERP Synthesis Memorandum (SM3): Research, Monitoring, and Evaluation,” was held in Portland, Oregon on June 24–25 2024. A diverse group of 26 domain experts with demonstrated knowledge and experience working in the Lower Columbia River Estuary (LCRE) participated. The workshop supported development of the forthcoming third Synthesis Memorandum for CEERP. The workshop furthered the collaborative understanding of the state of the science regarding the LCRE, helped to identify remaining knowledge gaps and uncertainties, and assisted in the prioritization of future restoration research and monitoring.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Data-Enabled Fusion Technology (Final Scientific/Technical Report)

Advancing Scientific Understanding in Fusion Energy and Machine Learning This research represented a significant step forward in machine learning (ML) applications for fusion energy experiments. The project integrated advanced data-driven modeling, optimization techniques, and artificial intelligence to enhance the predictive capabilities and operational efficiency of plasma-based fusion systems. Specifically, tasks focused on ML-enhanced diagnostics, operator guidance tools, and predictive modeling helped improve the ability to interpret complex fusion experiments. Key areas of advancement included: 1) data-driven plasma control, i.e., using ML algorithms to optimize experimental conditions and classify plasma behaviors based on historical data; 2) spectroscopy and diagnostics, i.e., applying AI models to extract previously inaccessible insights from experimental spectroscopy data; and 3) configuration mapping and operator guidance, i.e., developing a predictive framework to assist scientists in identifying the most effective experimental parameters, reducing reliance on manual adjustments. By refining these ML-driven techniques, the project contributed to the broader scientific community’s understanding of plasma dynamics and fusion energy viability. Technical Effectiveness and Economic Feasibility The methods investigated demonstrated high technical effectiveness, as reflected in milestones assessing the predictive accuracy, performance, and optimization of fusion configurations. The development of an Operator Guidance Tool (OGT), for example, led to more precise control of plasma conditions by learning from experimental data and offering real-time adjustments. From an economic standpoint, DeFT provided: 1) the ability to reduce trial-and-error experimentation, which lowered operational costs; 2) improved data interpretation methods, which enabled more efficient resource allocation in large-scale fusion research projects; and 3) the automation of key diagnostic tasks, which reduced manual labor and human error, increasing overall efficiency. 13 The final assessments of predictive models and optimization strategies demonstrated that these approaches were scalable and could be implemented across multiple fusion energy research programs. Public Benefit and Societal Impact This project contributed directly to the broader goal of achieving sustainable and commercially viable fusion energy, which had profound implications for clean energy production and climate change mitigation. The integration of AI-driven solutions into fusion research: 1) sped up scientific discovery, accelerating progress towards achieving energy breakthroughs; 2) reduced the cost of experimentation, making fusion research more accessible; and 3) provided a framework for future AI applications in high-energy physics, benefiting adjacent fields like space exploration, material science, and renewable energy. Additionally, by fostering collaborations between AI researchers and plasma physicists, this project promoted interdisciplinary innovation that could lead to broader applications beyond fusion research.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Science of the Van Allen Probes Science Operations Centers

The Van Allen Probes mission operations materialized through a distributed model in which operational responsibility was divided between the Mission Operations Center (MOC) and separate instrument specific SOCs. The sole MOC handled all aspects of telemetering and receiving tasks as well as certain scientifically relevant ancillary tasks. Each instrument science team developed individual instrument specific SOCs proficient in unique capabilities in support of science data acquisition, data processing, instrument performance, and tools for the instrument team scientists. In parallel activities, project scientists took on the task of providing a significant modeling tool base usable by the instrument science teams and the larger scientific community. With a mission as complex as Van Allen Probes, scientific inquiry occurred due to constant and significant collaboration between the SOCs and in concert with the project science team. Planned cross-instrument coordinated observations resulted in critical discoveries during the seven-year mission. Instrument cross-calibration activities elucidated a more seamless set of data products. Specific topics include post-launch changes and enhancements to the SOCs, discussion of coordination activities between the SOCs, SOC specific analysis software, modeling software provided by the Van Allen Probes project, and a section on lessons learned. One of the most significant lessons learned was the importance of the original decision to implement individual team SOCs providing timely and well-documented instrument data for the NASA Van Allen Probes Mission scientists and the larger magnetospheric and radiation belt scientific community.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Application of Quantum Machine Learning to High Energy Physics Analysis at LHC Using Quantum Computer Simulators and Quantum Computer Hardware

Machine learning enjoys widespread success in High Energy Physics (HEP) analyses at LHC. However the ambitious HL-LHC program will require much more computing resources in the next two decades. Quantum computing may offer speed-up for HEP physics analyses at HL-LHC, and can be a new computational paradigm for big data analyses in High Energy Physics.We have successfully employed three methods (1) Variational Quantum Classifier (VQC) method, (2) Quantum Support Vector Machine Kernel (QSVM-kernel) method and (3) Quantum Neural Network (QNN) method for two LHC flagship analyses: ttH (Higgs production in association with two top quarks) and H->mumu (Higgs decay to two muons, the second generation fermions). We shall address the progressive improvements in performance from method (1) to method (3).We will present our experiences and results of a study on LHC High Energy Physics data analyses with IBM Quantum Simulator and Quantum Hardware (using IBM Qiskit framework), Google Quantum Simulator (using Google Cirq framework), and Amazon Quantum Simulator (using Amazon Braket cloud service). The work is in the context of a Qubit platform (a gate-model quantum computer). Taking into account the present limitation of hardware access, different quantum machine learning methods are studied on simulators and the results are compared with classical machine learning methods (BDT, classical Support Vector Machine and classical Neural Network). Furthermore, we do apply quantum machine learning on IBM quantum hardware to compare performance between quantum simulator and quantum hardware. The work is performed by an international and interdisciplinary collaboration with the Department of Physics and Department of Computer Sciences of University of Wisconsin, CERN Quantum Technology Initiative, IBM Research Zurich, IBM T.J. Watson Research Center, Fermilab Quantum Institute, BNL Computational Science Initiative, State University of New York at Stony Brook, and Quantum Computing and AI Research of Amazon Web Services. This work pioneers a close collaboration of academic institutions with industrial corporations in the High Energy Physics analyses effort. Though the size of event samples in future HL-LHC physics and the limited number of qubits pose some challenges to the Quantum Machine learning studies for High Energy Physics, more advanced quantum computers with larger number of qubits, reduced noise and improved running time (as envisioned by IBM and Google) may outperform classical machine learning in both classification power and in speed.Although the era of efficient quantum computing may still be years away, we have made promising progress and obtained preliminary results in applying quantum machine learning to High Energy Physics. A PROOF OF PRINCIPLE.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Crowdsourcing Global Perspectives in Ecology Using Social Media

Transparent, open, and reproducible research is still far from routine, and the full potential of open science has not yet been realized. Crowdsourcing–defined as the usage of a flexible open call to a heterogeneous group of individuals to recruit volunteers for a task –is an emerging scientific model that encourages larger and more outwardly transparent collaborations. While crowdsourcing, particularly through citizen- or community-based science, has been increasing over the last decade in ecological research, it remains infrequently used as a means of generating scientific knowledge in comparison to more traditional approaches. We explored a new implementation of crowdsourcing by using an open call on social media to assess its utility to address fundamental ecological questions. We specifically focused on pervasive challenges in predicting, mitigating, and understanding the consequences of disturbances. In this paper, we briefly review open science concepts and their benefits, and then focus on the new methods we used to generate a scientific publication. We share our approach, lessons learned, and potential pathways forward for expanding open science. Our model is based on the beliefs that social media can be a powerful tool for idea generation and that open collaborative writing processes can enhance scientific outcomes. We structured the project in five phases: (1) draft idea generation, (2) leadership team recruitment and project development, (3) open collaborator recruitment via social media, (4) iterative paper development, and (5) final editing, authorship assignment, and submission by the leadership team. We observed benefits including: facilitating connections between unusual networks of scientists, providing opportunities for early career and underrepresented groups of scientists, and rapid knowledge exchange that generated multidisciplinary ideas. We also identified areas for improvement, highlighting biases in the individuals that self-selected participation and acknowledging remaining barriers to contributing new or incompletely formed ideas into a public document. While shifting scientific paradigms to completely open science is a long-term process, our hope in publishing this work is to encourage others to build upon and improve our efforts in new and creative ways.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

R&D Towards CUPID, a Tonne-Scale Bolometric 0νββ Experiment (Collaborative Proposal)

As endorsed by the 2015 Long Range Plan for Nuclear Science, development of the ton-scale 0νββ experiment with the sensitivity to the effective neutrino mass at the level of 15 meV is one of the highest priorities for the nuclear physics. Bolometric detector based on CUORE design is very well matched to this goal. We have assembled a Collaboration to develop CUPID, a next-generation bolometric 0νββ detector based on CUORE experience and design, but improving upon CUORE sensitivity by more than an order of magnitude.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Advanced Research Directions on AI for Science, Energy, and Security: Report on Summer 2022 Workshops

Over the past decade, fundamental changes in artificial intelligence (AI)—from foundational to applied—have delivered dramatic insights across a wide breadth of U.S. Department of Energy (DOE) mission space. AI is helping to augment and improve scientific and engineering workflows (e.g., for control, design, and dramatic performance gains through surrogate models) in national security, the Office of Science, and DOE’s applied energy programs. The progress and potential for AI in DOE science was captured in the 2020 “AI for Science” report from the DOE laboratory community in collaboration with academia and industry. Specific scientific areas ready to further leverage the power of AI ranged from the scale and performance of computational models to data analysis to creating new classes of observations using computer vision. Since that report, the scale and scope of scientific AI have accelerated, revealing new, emergent properties that yield insights that go beyond enabling opportunities to being potentially transformative in the way that scientific problems are posed and solved. Thus, under the guidance of both the Office of Science (SC) and the National Nuclear Security Administration (NNSA), the DOE national laboratories organized a series of workshops in 2022 to gather input on new and rapidly emerging opportunities and challenges of scientific AI. This 2023 report is a synthesis of those workshops. The scientific community believes AI can have a foundational impact on a broad range of DOE missions, including science, energy, and national security. Further, DOE has unique capabilities that enable the community to drive progress in scientific use of AI, building on long-standing DOE strengths and investments in computation, data, and communications infrastructure, spanning the Energy Sciences Network (ESnet), the Exascale Computing Project (ECP), and integrative programs such as the NNSA Office of Defense Programs Advanced Simulation and Computing (ASC) and the SC Scientific Discovery through Advanced Computing (SciDAC) programs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Deuterium Concentration Effects on Cell Cycle Progression

Deuterium (D), which is found in natural water at ~ 150 ppm, seems to play an important role in biology. For example, D concentrations above 150 ppm are known to produce toxic effects in many organisms. There is also evidence to suggest D levels significantly less than 150 ppm can cause delays in progression through the normal mitotic cell cycle. Some have even theorized that the D:H ratio in cells may impact an organism’s radiation resistance. Therefore, evaluating the role of D and the D:H ratio in eukaryotic and prokaryotic cells should lead to a better understanding of cell cycle progression and radiation resistance in these organisms. Research in this field has likely been stalled by the limited availability of D 2 O with varying D concentrations needed to accurately study the deuterium effects. However, SRNL can currently manufacture D 2 O in varying concentrations, and we have assembled a unique team of radiation biologists, microbiologists, radiochemists, and health physics to form an interdisciplinary research group to study the cell cycle as a function of D concentration in order to address several fundamental science questions. Proposed work in FY20 was a collaborative effort with Augusta University to utilized BSL-2 mammalian cell lines. Lab work was halted due to the COVID-19 pandemic. An intensive literature review was performed and identified pertinent knowledge gaps that could be filled in future research efforts.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2024

Discovery Science and Inertial Fusion Energy Research at the Jupiter Laser Facility (Research Performance Progress Report)

The goal of this project is to provide operational support to run the Jupiter Laser Facility within the LaserNetUS network to achieve the following four objectives: • Develop new science, techniques, and platforms for discovery science and IFE research, in partnership with academia, other LaserNetUS nodes, and the greater community. • Advance the development of secondary sources of photons and particles for applications relevant to discovery science and IFE; • Serve as a testbed for new laser, optical, target, and diagnostic capabilities that will advance IFE; • Attract, train, and retain talent in high-energy-density and laser science that will be essential in furthering development of IFE, and help build new collaborations between national laboratory and academic researchers. We have accomplished our four objectives by: • Providing access to the three JLF platforms (Titan, Janus, and COMET) to LaserNetUS users after their proposals have been independently reviewed and ranked by the LaserNetUS PRP; • Providing expertise and technical capabilities to support focused science research thrusts at the facility; • Promoting and maintaining technical relationships and collaborations with other members of the LaserNetUS community to nurture and grow the discovery science and IFE workforce— especially students and early career scientists—on LaserNetUS facilities. • Augmenting JLF with specific improvements while coordinating efforts with LLNL organizations and collaborators to provide users with optimal laser, target, and diagnostic resources to help them maximize technical impact.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Modeling Water Quality in Watersheds: From Here to the Next Generation

In this synthesis, we assess present research and anticipate future development needs in modeling water quality in watersheds. We first discuss areas of potential improvement in the representation of freshwater systems pertaining to water quality, including representation of environmental interfaces, in-stream water quality and process interactions, soil health and land management, and (peri-)urban areas. In addition, we provide insights into the contemporary challenges in the practices of watershed water quality modeling, including quality control of monitoring data, model parameterization and calibration, uncertainty management, scale mismatches and provisioning of modeling tools. Finally, we make three recommendations to provide a path forward for improving watershed water quality modeling science, infrastructure and practices. These include building stronger collaborations between experimentalists and modelers, bridging gaps between modelers and stakeholders, and cultivating and applying procedural knowledge to better govern and support water quality modeling processes within organizations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Isotope Production Education and Research via a Systematic Study of Photo-nuclear Reaction Yields and Excitation Functions

A new collaboration between the New Mexico Institute of Mining and Technology, Idaho State University, and Idaho National Laboratory builds the domestic workforce in isotope production and related science and technologies by conducting research and education programs related to photo-nuclear reactions. Workforce development was identified by the Department of Energy’s Office of Science in the Reaching a New Energy Sciences Workforce (RENEW) program that seeks to engage underrepresented university students in isotope production activities. This collaboration includes coursework development, student training, and isotope production research utilizing photo-nuclear reactions at electron linear accelerators. Bremsstrahlung-weighted excitation functions and cross-sections will be measured and the use of nanomaterials to exploit kinematic recoil reactions will be investigated. Students will be hosted at the Idaho National Laboratory where mentors will provide opportunities to explore careers in nuclear science and technology. This presentation regards the ongoing progress of the research and education objectives of the collaboration that was performed under the Office of Science Isotopes Program under award number D000-22-2765.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2024

The contributions of microclimatic information in advancing ecosystem science

Drawing upon over 100 years of scholarly work on microclimate, we first present an overview of the history, key references, and critical issues surrounding the collection and utilization of microclimate records in ecosystem studies. We place particular emphasis on addressing specific and pressing issues related to the applications of microclimate at the community-ecosystem-landscape level, excluding those of controlled experiment such as growth chambers and greenhouses. Specifically, we: (1) highlight some key issues concerning the collection, quality assurance/quality control (QA/QC), and utilization of microclimatic data in ecosystem studies; (2) revisit microclimatic responses to the structural changes of ecosystems and landscapes; and (3) emphasize the significance of microclimate in understanding major ecosystem/landscape processes and functions. Vapor pressure deficit (VPD) is particularly emphasized for its calculation and use because of its burgeoning applications in the literature. Case studies for each of the three thematic topics are provided with selected references to demonstrate challenges and solutions. As the scientific community gears up to enhance microclimatic stations, we envision significant increases in the use of smart sensors, wireless access, networking, open databases, and computational capabilities. Understanding and addressing some of the issues raised in this synthesis paper may help advance microclimate research and foster collaboration with other relevant disciplines, such as ecosystem science.

54 ENVIRONMENTAL SCIENCES↗