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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Energy resolution of the LZ detector for high-energy electronic recoils

The LUX-ZEPLIN (LZ) detector is a dual-phase liquid xenon time projection chamber (TPC) installed at the Sanford Underground Research Facility (Lead, South Dakota) at a depth of 1478 meters. Although the main objective of LZ is the direct detection of dark matter, its low background environment allows for the search of other rare processes, such as the neutrinoless double beta decay of xenon isotopes 134 Xe and 136 Xe with the respective Q-values of 826 keV and 2458 keV. The sensitivity of the detector to these decays is directly determined by the energy resolution, which, in turn, is degraded by non-uniformities in detector response. In this work, we present a novel method to correct, in the data, the non-uniformity of the light collected by an array of photosensors in a scintillation detector. This method is based on the knowledge of the light response functions of individual photosensors. With these techniques, we report, at a very early phase of the detector operations, a state-of-the-art energy resolution (σ/μ) of (0.67 ± 0.01)% at 2614 keV for the fiducial volume of 5.6 tonnes of liquid xenon.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Optimal Management of Grid-Interactive Efficient Buildings via Safe Reinforcement Learning

Reinforcement learning (RL)-based methods have achieved significant success in managing grid-interactive efficient buildings (GEBs). However, RL does not carry intrinsic guarantees of constraint satisfaction, which may lead to severe safety consequences. Besides, in GEB control applications, most existing safe RL approaches rely only on the regularisation parameters in neural networks or penalty of rewards, which often encounter challenges with parameter tuning and lead to catastrophic constraint violations. To provide enforced safety guarantees in controlling GEBs, this paper designs a physics-inspired safe RL method whose decision-making is enhanced through safe interaction with the environment. Different energy resources in GEBs are optimally managed to minimize energy costs and maximize customer comfort. The proposed approach can achieve strict constraint guarantees based on prior knowledge of a set of developed hard steady-state rules. Simulations on the optimal management of GEBs, including heating, ventilation, and air conditioning (HVAC), solar photovoltaics, and energy storage systems, demonstrate the effectiveness of the proposed approach.

Huo, Xiang↗
DOE OSTI2022

Progress toward a universal biomedical data translator

Clinical, biomedical, and translational science has reached an inflection point in the breadth and diversity of available data and the potential impact of such data to improve human health and well-being. However, the data are often siloed, disorganized, and not broadly accessible due to discipline-specific differences in terminology and representation. To address these challenges, the Biomedical Data Translator Consortium has developed and tested a pilot knowledge graph-based “Translator” system capable of integrating existing biomedical data sets and “translating” those data into insights intended to augment human reasoning and accelerate translational science. Having demonstrated feasibility of the Translator system, the Translator program has since moved into development, and the Translator Consortium has made significant progress in the research, design, and implementation of an operational system. Herein, we describe the current system’s architecture, performance, and quality of results. We apply Translator to several real-world use cases developed in collaboration with subject-matter experts. Finally, we discuss the scientific and technical features of Translator and compare those features to other state-of-the-art, biomedical graph-based question-answering systems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Lithium-Ion Battery Life Model with Electrode Cracking and Early-Life Break-in Processes

This paper develops a physically justified reduced-order capacity fade model from accelerated calendar- and cycle-aging data for 32 lithium-ion (Li-ion) graphite/nickel-manganese-cobalt (NMC) cells. The large data set reveals temperature-, charge C-rate-, depth-of-discharge-, and state of charge (SOC)-dependent degradation patterns that would be unobserved in a smaller test matrix. Model structure is informed by incremental capacity analysis that shows loss of lithium inventory and cathode-material loss as the dominant capacity fade mechanisms. The model includes terms attributable to solid-electrolyte interface (SEI) growth, electrode cracking, cycling-driven acceleration of SEI growth, and "break-in" mechanisms that slightly decrease or increase available Li inventory early in life. The study explores what mathematical couplings of these mechanisms best describe calendar aging, cycle aging, and mixed calendar/cycle aging. Various approaches are discussed for extracting relevant stress factors from complex cycling profiles to predict lifetime during real-world battery loads using models trained on constant-current laboratory test results. The complexity of the present human-driven model identification process motivates future work in machine learning to more widely search and statistically discern the optimal model that correctly extrapolates capacity fade based on physical knowledge.

25 ENERGY STORAGE↗
DOE OSTI2024

A 15-Year Retrospective on Immersive Visualization Lessons from the Applied Visualization Laboratory

This article shares the experiences of operating an immersive visualization laboratory over a 15-year period. The paper discusses valuable insights into the lessons learned from various projects, including the challenges they faced, such as technical difficulties, user adoption, and opportunities, and practical solutions for overcoming them. One crucial element for successful immersive visualization projects is interdisciplinary collaboration. The paper presents the advantages of immersive visualization technology, such as improved data comprehension, enhanced communication, and increased engagement in complex scenarios. We present the dynamic realm of virtual and augmented truth (VR/AR) technology in the context of a immersive laboratory system. We spotlight rising trends inside the integration of VR/AR tools for visualization. We also present our experience in dealing with the hardware and software additives in an immersive VR/AR labs, dropping light on the demanding situations and successes encountered throughout everyday operations. Furthermore, it offers practical knowledge and guidance based on years of experience in the field, which can help you overcome challenges and achieve success in your project.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Significantly enhanced near-field coupling via tip engineering

The ability to significantly enhance near-field coupling between light and matter at the nanoscale is crucial for advancing the fields of nanophotonics and nanopolariotonics. However, conventional probes face challenges in achieving optimal light–matter interaction. In this study, we propose a novel, to the best of our knowledge, simulation-based strategy that leverages tip engineering to dramatically amplify the scattering field through tailored double-layer geometries. By employing a core-shell structure with a thin shell layer optimized for specific dielectric permittivity and effective polarizability, we demonstrate a near-field enhancement of up to 10 times compared to conventional probes. Our findings highlight exciting new possibilities for optimizing near-field interactions through probe designs with customized resonances, paving the way for substantially improved nano-optical sensing, imaging, and detection.

Shiravi, H.↗
DOE OSTI2020

Influence of Nanoconfinement and Elevated Temperatures on Geocolloid-Facilitated Transport of Energy related Contaminants

Fossil fuels obtained using hydraulic fracturing and nuclear energy are deemed crucial for meeting the current and future energy demands of the United States. The utilization of these resources, especially fossil fuels from hydraulic fracturing, has increased rapidly within the last decade. However, with the increasing levels of hydraulic fracturing activities, the occurrence and fate of chemicals used in these processes in the geosystem, as well as the potential consequences of their interactions with environment and geosystems (e.g. mineral-based geocolloids and geosurfaces), have been increasingly recognized as issues warranting consideration owing to the toxic nature of these chemicals. Scientific results originated from this project has brought better understandings on how such contaminants can preferentially or adversely interact with geosystems and how geocolloids can facilitate their transport by realistically mimicking geological conditions and environments in the laboratory level for the first time. Based on such knowledge, it may eventually lead to the development of mitigation strategies for reducing and minimizing the transport and wide distribution of contaminants and pollutants. This aspect is important for the society to ensure that energy-related contaminants do not find their way to waterbodies that are used as drinking water or irrigation. The project is also anticipated to increase public awareness about potential adverse effects of energy systems on the geosphere and environment and may motivate society to consider better mitigation strategies to prevent such occurrences.

(Nano)Confinement↗
DOE OSTI2021

Development of a Framework for Data Integration, Assimilation, and Learning for Geological Carbon Sequestration (DIAL-GCS) (Final Report)

This project aimed to develop and demonstrate a Data Integration, Assimilation, and Learning framework for geologic carbon sequestration projects (DIAL-GCS). DIAL-GCS is an intelligence monitoring system (IMS) for automating GCS closed-loop management by leveraging recent developments in machine learning technologies, complex event processing (CEP), and reduced-order modeling. The safe and efficient operation of GCS repositories requires integrated monitoring to track the injected CO¬2 as it moves within a storage reservoir. GCS projects are data intensive, as a result of proliferation of digital instrumentation and smart-sensing technologies. GCS projects are also resource intensive, often requiring multidisciplinary teams performing different monitoring, verification, accounting (MVA) tasks throughout the lifecycle of a project to ensure secure containment of injected CO2. The success of GCS thus depends in a large part on our ability to access, assimilate, and analyze heterogeneous data and information sources in a timely manner. This project included a number of meaningful and necessary tasks to transform the human domain knowledge into machine-interpretable rules for automating knowledge extraction and discovery in GCS. The specific technical objectives of the proposed DIAL-GCS project were to develop an ontology-driven GCS data management module for storing, querying, and exchanging GCS data (both historic and live sensor data) from multiple sources and in heterogeneous formats. Incorporate a CEP engine for detecting abnormal situations by seamlessly combining expert knowledge, rule-based reasoning, and machine learning. Enable uncertainty quantification and predictive analytics using a combination of coupled-process modeling, AI/ML methods, and reduced-order modeling, and integrate and demonstrate the system’s capabilities with both real and simulated data. As far as we know, this is one of the first projects aimed to develop intelligent monitoring systems (IMS) targeting the GCS. Under this project, the team had developed a large number of web applications and scientific algorithms that contribute the main theme of intelligent monitoring. The team has published more than a dozen peer reviewed papers and disseminated the research results at multiple technical meetings.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Evaluations of the Fates of Alkali Metals, Actinides, Mercury, and Iodine During DWPF Recycle Diversion

The fates of alkali metals, actinides, mercury, and iodine in the Defense Waste Processing Facility Recycle Diversion process (as currently conceptualized) have been evaluated through paper studies based on available knowledge of the chemistry, physical properties, solubility, and volatility of the various species involved. The effect of pH in the range from 9 to 13 has been discussed. Recommendations for additional studies to close technology gaps have been provided, many of which are contingent upon the results of pending testing and sample characterization efforts. There is uncertainty in the amounts of soluble actinides passing through the process filter, though the bulk of the actinides should be captured on the filter with the Recycle Collection Tank solids and the total amounts of actinides should be relatively low. The Recycle Collection Tank pH could impact the fraction of actinides reaching the evaporator, but the primary factors determining the actinide fate are expected to be the amount of CO 2 sorption from air sparging or, for certain actinides (such as plutonium), oxidation and/or sorption to MnO 2 solids from permanganate additions to destroy the glycolate anion. Process optimization could minimize the amounts of actinides passing the filter. Depending upon the levels of mercury observed in recycle stream samples and because of the volatility of mercury, the evaporator should be designed with the capability to remove dense mercury phases from the condensate to avoid exceeding ETP WAC limits. The facility design must be adequate to transfer dense mercury phases and testing to confirm mercury transfer is needed. Simulant containing mercury is recommended for both filtration and evaporation testing. OLI Modeling of the various recycle streams is recommended to provide insight on the fate of iodine. Iodine-spiked simulants are recommended for upcoming evaporation tests. The pro) ect should consider the likelihood and impact of NAS scale formation in the evaporators. Process optimization may be needed to minimize the accumulation of NAS scale and possibly the sorption of actinides in the evaporator. Actual waste testing of the Recycle Diversion filtration and evaporation should include the analysis of actinides, mercury, and iodine to determine their partitioning.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Unifying Combinatorial and Graphical Methods in Artificial Intelligence

Recently, a new graph Laplacian, called the inner product Laplacian, was introduced which generalizes many existing Laplacians, including the normalized and combinatorial Laplacian and their weighted variants. The key observation behind the inner product Laplacian is that by defining appropriate inner product spaces on the vertices and edges, the standard Laplacians can be recovered as Hodge Laplacians over the simplicial complex formed by the edges and vertices. These inner product spaces form a natural way to incorporate non-combinatorial information into the definition of a domain-specific Laplacian. In particular, in contrast to current domain-specific weighting schemes which rely solely on edge weights, information regarding the similarity of non-adjacent vertices and arbitrary pairs of edges can be effectively incorporated into the Laplacian. In order to illustrate this approach we consider the problem of calculating the potential energy of an atomistic configuration using Graph Neural Networks. In comparison with start-of-the-art approaches, such as SchNet, our approach replaces a learned (via auto-encoder) representation of the atom types with an inner product space on atoms based on scientific knowledge (e.g., electronegativity). We will illustrate how this approach captures key chemical properties of the molecules and compare the energy calculations with state-of-the-art neural network approaches. However, to compute the resulting Laplacian involves a mixture of sparse and dense matrix computation and yields a dense matrix as the basis for the graph convolution. This dense convolutional kernel necessitates moving away from the standard message passing framework for graph neural networks and increases the computational cost of applying the kernel. In order to mitigate these costs we investigate means of leveraging the mixed sparse and dense computations to reduce the overall computational cost and how these approaches can be automatically transferred to energy efficient hardware (e.g., field programmable gate arrays (FPGAs)).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

CO2 Streams Captured From Industrial Processes and Corrosion of Steel Pipelines

Based on the knowledge accumulated in the last decade related to Carbon Capture and Storage (CCS) processes and the identified gaps in knowledge, our study identifies and quantifies the key issues that impact the corrosion of materials, specifically relating to the integrity of structures for the CO2 transport pipelines and provides a better understanding of the materials’ compatibility with CO2 steams with impurities. This will help to understand the corrosion risks associated with CO2 transport and injection and leverage existing infrastructure or rebuild new pipeline networks to transport CO2 with impurities for EOR or geological storage.

CO2 corrosion↗
DOE OSTI2023

Compaction of crushed salt for safe containment – a summary of the KOMPASS projects

Abstract. For the underground disposal of high-level nuclear waste in rock salt formations, the safety concept includes the backfilling of open cavities with crushed salt. For the prognosis of the sealing function of the backfill for the safe containment of the radioactive waste, it is crucial to have a comprehensive process understanding of the crushed-salt compaction behavior. The crushed-salt compaction process is influenced by internal properties (e.g., grain size, mineralogy, and moisture content) and boundary conditions (e.g., temperature, stress state, and compaction rate) and, therefore, involves several coupled thermal–hydro–mechanical (THM) processes (Hansen et al., 2014; Kröhn et al., 2017). With the paradigm shift from the limited release of radionuclides to safe containment due to the German Repository Site Selection Act passed in 2017, the importance of crushed salt as geotechnical barrier has increased, with a focus on the evolution of its hydraulic properties. Based on the knowledge gaps in the current process understanding, the “Compaction of crushed salt for safe containment” (KOMPASS) projects were initiated to improve the scientific basis behind using crushed salt for the long-term isolation of high-level nuclear waste within rock salt repositories. The efforts to improve the prediction of crushed-salt compaction begun during the first phase of the KOMPASS projects (Czaikowski et al., 2020) and were followed up in a second phase ending in June 2023. The primary achievements of the projects are as follows (Czaikowski et al., 2020; Friedenberg et al., 2022): specification of the KOMPASS reference material, an easily available and reproducible synthetic crushed-salt material, for generic investigations; development of pre-compaction methods and successful production of samples in the short term and under in situ loading conditions; formulation of an extended laboratory program addressing the isolated investigation of known relevant factors influencing the compaction behavior of crushed salt (Düsterloh et al., 2022); execution of long-term compaction tests addressing isotropic and deviatoric load changes, temperature, and compaction state; construction of a backfill body using the KOMPASS reference material in the Sondershausen mine through collaboration with the SAVER (Entwicklung eines salzgrusbasierten Versatzkonzepts unter der Option Rückholbarkeit) project (Schaarschmidt and Friedenberg, 2022); advancement of the tools for microstructure investigation methods (Svensson and Laurich, 2022); generation (first stages) of a microphysical process list combining literature research with our own findings; benchmarking of long-term compaction test for model development and optimization of various existing models as well as the development of new models; application of a virtual demonstrator (2D model representing a backfilled drift in rock salt) for the visualization of developments and the quantification of the models (Rabbel, 2022). In summary, the KOMPASS projects contributed to the reduction of uncertainties and the strengthening of the safety case for using crushed salt within rock salt repositories.

Friedenberg, Larissa↗
DOE OSTI2020

Polycrystal thermo-elasticity revisited: theory and applications

The self-consistent (SC) theory is the most commonly used mean-field homogenization method to estimate the mechanical response behavior of polycrystals based on the knowledge of the properties and orientation distribution of constituent single-crystal grains. The original elastic SC method can be extended to thermo-elasticity by adding a stress-free strain to an elastic constitutive relation that expresses stress as a linear function of strain. With the addition of this independent term, the problem remains linear. Although the thermo-elastic self-consistent (TESC) model has important theoretical implications for the development of self-consistent homogenization of non-linear polycrystals, in this paper, we focus on TESC applications to actual thermo-elastic problems involving non-cubic (i.e. thermally anisotropic) materials. To achieve this aim, here we provide a thorough description of the TESC theory, which is followed by illustrative examples involving cooling of polycrystalline non-cubic metals. The TESC model allows studying the effect of crystallographic texture and single-crystal elastic and thermal anisotropy on the effective thermo-elastic response of the aggregate and on the internal stresses that develop at the local level.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Uncertainty-refined image segmentation under domain shift

Digital image segmentation is provided. The method comprises training a neural network for image segmentation with a labeled training dataset from a first domain, wherein a subset of nodes in the neural net are dropped out during training. The neural network receives image data from a second, different domain. A vector of N values that sum to 1 is calculated for each image element, wherein each value represents an image segmentation class. A label is assigned to each image element according to the class with the highest value in the vector. Multiple inferences are performed with active dropout layers for each image element, and an uncertainty value is generated for each image element. Uncertainty is resolved according to expected characteristics. The label of any image element with an uncertainty above a threshold is replaced with a new label corresponding to a segmentation class based on domain knowledge.

Martinez, Carianne↗
DOE OSTI2024

Qualitative and Quantitative Evaluation for Representative Human Reliability Analysis Methods

The Korea Institute of Nuclear Safety (KINS) is the regulatory expert organization established by the Korean government to strengthen the nation’s technical capabilities relating to nuclear safety regulation. KINS oversees the technical aspects of nuclear safety regulation, including safety reviews, inspections, education, and safety research—all conducted based on technical knowledge and accumulated regulatory experience. In 2023, KINS requested that Idaho National Laboratory (INL) validates representative human reliability analysis (HRA) methods used throughout the world, thus affording KINS with a basis for determining an HRA method adequate for its domestic regulatory purposes. The present paper mainly examines INL’s efforts in this regard. The resulting INL study covered four representative HRA methods widely used by nuclear utilities and regulatory institutes. These methods were qualitatively evaluated by applying specific evaluation criteria and determining how well each method reflected critical HRA issues. For this assessment, INL benchmarked the Halden International HRA Empirical Study. Using the Halden empirical data, along with information on human failure events (HFEs), the present study employed the selected HRA methods to estimate human error probabilities (HEPs) for the HFEs. It also performed statistical analyses to compare the HEPs predicted via the HRA methods against those from the Halden empirical data.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Teachers’ Beliefs and Their Influence on Math Instructions for Gifted English Learners

The dynamic interplay between teachers’ beliefs and practices significantly impact the quality of instruction and the trajectory of talent development in young students. This case study explores the beliefs and practices of two elementary teachers instructing gifted ELs in mathematics. The constant comparison method was used to analyze data collected from classroom observations, semi-structured interviews, and field notes. Three factors were found to affect the (in)consistency between teachers’ expressed beliefs and observed practices: compatibility among core and peripheral beliefs, knowledge about evidence-based practices, and classroom management skills. Students exhibit higher levels of participation, communication, and engagement in critical thinking skills when their teacher embraces constructive perspectives in teaching mathematics, demonstrates pedagogical expertise, and employs a proactive classroom management approach. Conversely, students encounter restricted opportunities to independently construct their own understanding of mathematics when their teacher holds maladaptive beliefs about teaching mathematics, has limited knowledge of evidence-based practices, and has an authoritarian classroom management style. These findings underscore the need for a new approach to professional development (PD) that encourages teachers to critically examine the connection between their beliefs and instructional practices and their impact on the student’s mathematical talent development.

Yang, Jenny (ORCID:0000000296905466)↗
DOE OSTI2023

Addressing Regulatory Burdens to Accessing Solar Among Municipal, Commercial and Institutional Customers Through Collaborative Partnerships (Final Technical Report)

The project focused on providing education to large energy customers, with a focus on local governments, to help them better understand and overcome barriers to solar energy adoption that currently exist (or could exist) in utility planning processes and/or within wholesale energy markets. The project team created accessible educational materials — ranging from reports to case studies to webinars — that could connect with stakeholders who rarely engage in these venues, have limited bandwidth to do so, and possess little previous knowledge of these issues. The project team focused on sharing lessons learned and best practices based on the experiences of other local governments and large energy users that have engaged on these issues, including the PJM Cities and Communities Coalition (PJMCCC). The project sought to better connect these customers and other stakeholders to deepen and expand opportunities for them to engage together on these issues, as well as build peer networks to facilitate the exchange of information. The project team held convenings of key partners — such as local governments, developers, regulators, utilities and large solar buyers — to discuss best practices, develop research products to broaden stakeholders’ knowledge base, and to foster these peer networks.

14 SOLAR ENERGY↗
DOE OSTI2020

Delivering Contextual Knowledge and Critical Skills of Disruptive Technologies through Problem-Based Learning in Research Experiences for Undergraduates Setting

The recent development in transportation, such as energy-efficient and autonomous vehicles, defines a condition for the students in transportation engineering. Students in the field of transportation engineering should be ready upon their graduation with new knowledge and skills that are compatible with the need of the industry and sustainable engineering practices.During summers of 2018 and 2019, we developed and implemented an eight-week program to increase the knowledge and skills of students coming from multidisciplinary fields related to autonomous vehicles. Problem of “How much will platooning reduce fuel consumption and emissions per vehicle mile traveled?” was instrumentalized in subsequent activities to introduce the comprehensive knowledge structure of autonomous vehicles.The engineering concept of reducing the cost and sustainability was embedded in the leading research question that helped us to develop and implement activities on an overall knowledge structure in autonomous vehicles. The goal of using problem-based learning activities was not to encourage the students to focus on reaching the solution merely. We aimed to introduce the multidisciplinary knowledge and critical skills aspects of learning about disruptive technologies.In this paper, we will discuss how a multidisciplinary research approach was incorporated into a problem-based learning activity. The students were introduced the subjects related to math, physics, computer science, and biology as the integration of the knowledge structure of autonomous vehicles. We will also present the results on students’ use of critical skills such as machine learning and computer programming.

Comert, Gurcan↗