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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Assessment of the Impact of the Single Stream Recycling on Paper Contamination in Recovery Facilities and Paper Mills

Contaminated recyclables pose one of the largest roadblocks to an effective implementation of a circular economy system. Current knowledge surrounding the effects of various recycling methods on contamination rates is limited, thus preventing effective quantification of contamination effects. This study aims to fill this knowledge gap by assessing the impact of single stream recycling on paper contamination in MRFs and their end-products based on different composition studies conducted by facilities nationwide. This project expects to provide the foundational knowledge that could catalyze significant cost savings and energy consumption at MRFs and paper mills by understanding the levels of inbound and outbound contamination rates in SSR systems. Statistical analysis conducted in this project utilizes the available nationwide inbound and outbound samples collected as part of material composition and audit studies to identify and measure the significance of various factors on the resultant contamination levels. The research results provide benefits to both the recycling industry and its customers by addressing the causes and impact of contamination in recycled products, particularly with the wide applicability of single stream recycling within the United States.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Materials Scorecards, Phase 2: Advanced Materials and Manufacturing Technology

This report presents updated material score cards with detailed justification of the proposed rating provided and quantifies the readiness of the advanced manufacturing of materials for nuclear applications. Based on an expanded literature review of available data, the scores in this report (Phase 2) have either been modified or remained constant as compared with Phase 1 scoring. The rating justification are provided for each scoring criteria whereas the maturity quantification of additively manufactured alloys and the knowledge gaps are discussed with reference traceability. This report focuses on the detailed justification and traceability for the adoption of additively manufactured 316SS, SS304, Alloy 800H, Graphite C/C, Alloy N, Silicon Carbide, HT9, Alloy 617, and Alloy 718. Significant variability in data and knowledge gaps exist among the identified materials. The scorecards are therefore based on published literature data, industry response to a survey, stakeholder input collected at workshops, and expert opinion. The interpretation and assessment were somewhat challenged as experimental process parameters and methodology varied and best judgment was exercised for score quantification. This assessment reflects the overall level of technical maturity for AM materials and their deployment in Gen-VI nuclear systems. These Phase 2 scorecards are likely to be revised based on additional input from stakeholders and as new research results become available.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Deriving Transmissibility Functions from Finite Elements for Specifications

This work explores deriving transmissibility functions for a missile from a measured location at the base of the fairing to a desired location within the payload. A pressure on the outside of the fairing and the rocket motor’s excitation creates an acceleration at a measured location and a desired location. Typically, the desired location is not measured. In fact, it is typical that the payload may change, but measured acceleration at the base of the fairing is generally similar to previous test flights. Given this knowledge, it is desired to use a finite-element model to create a transmissibility function which relates acceleration from the previous test flight’s measured location at the base of the fairing to acceleration at a location in the new payload. Four methods are explored for deriving this transmissibility, with the goal of finding an appropriate transmissibility when both the pressure and rocket motor excitation are equally present. In conclusion, these methods are assessed using transient results from a simple example problem, and it is found that one of the methods gives good agreement with the transient results for the full range of loads considered.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2024

Physically Motivated Deep Learning to Superresolve and Cross Calibrate Solar Magnetograms

Abstract Superresolution (SR) aims to increase the resolution of images by recovering detail. Compared to standard interpolation, deep learning-based approaches learn features and their relationships to leverage prior knowledge of what low-resolution patterns look like in higher resolution. Deep neural networks can also perform image cross-calibration by learning the systematic properties of the target images. While SR for natural images aims to create perceptually convincing results, SR of scientific data requires careful quantitative evaluation. In this work, we demonstrate that deep learning can increase the resolution and calibrate solar imagers belonging to different instrumental generations. We convert solar magnetic field images taken by the Michelson Doppler Imager (resolution ∼2″ pixel −1 ; space based) and the Global Oscillation Network Group (resolution ∼2.″5 pixel −1 ; ground based) to the characteristics of the Helioseismic and Magnetic Imager (resolution ∼0.″5 pixel −1 ; space based). We also establish a set of performance measurements to benchmark deep-learning-based SR and calibration for scientific applications.

Muñoz-Jaramillo, Andrés (ORCID:0000000247160840)↗
DOE OSTI2023

Integrative visualization of the molecular structure of a cellular microdomain

Abstract An integrative approach to visualization is used to create a visual snapshot of the structural biology of the polar microdomain of Caulobacter crescentus . The visualization is based on the current state of molecular and cellular knowledge of the microdomain and its cellular context. The collaborative process of researching and executing the visualization has identified aspects that are well determined and areas that require further study. The visualization is useful for dissemination, education, and outreach, and the study lays the groundwork for future 3D modeling and simulation of this well‐studied example of a cellular condensate.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Insight into the nanostructure of “water in salt” solutions: A SAXS/WAXS study on imide-based lithium salts aqueous solutions

"Water-in-salt " electrolyte (WISE) series have been studied for lithium-ion batteries and supercapacitor applications, but so far, most of the focus has been on the LiTFSI salt-based systems. Herein, we used small-angle X-ray scattering/wide-angle X-ray scattering (SAXS/WAXS) and molecular dynamics (MD) simulation to investigate the solvation structure of a series of lithium salts with four different symmetric anions: (bis(fluoro sulfonyl)imide (FSI), bis(trifluoromethane sulfonyl)imide (TFSI), bis(pentafluoroethane sulfonyl) imide (BETI) and bis(nonafluorobutane sulfonyl)imide (BNTI)), which have similar parent structures but different lengths of the fluorocarbon chains. Two competing structures were found in the four lithium salts aqueous solutions: anion solvated structure and anion network. The anion network plays a crucial role in obtaining a stable and enlarged electrochemical window. The d spacing of the anion solvated structure follows a linear correlation with the number of carbons in the fluorocarbon chains and an exponential correlation with the concentrations. Further, we also observed that the transition from one structure to another is predominantly controlled by the salt volume fraction for all imide-based aqueous solutions. This work provides a systematic study of the atomistic scale structures of a series of imide-based lithium salt aqueous solutions that could extend the knowledge of WIS electrolytes.

25 ENERGY STORAGE↗
DOE OSTI2023

Cohort-based learning for microbiome research community standards

Microbiome data standards are key to enabling data reuse, yet awareness and community adoption continue to be significant barriers to their broad implementation. The National Microbiome Data Collaborative launched an Ambassador Program based around a community learning model to broaden foundational knowledge and technical skills regarding microbiome metadata standards and best practices in data stewardship.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Suppressed phase segregation for triple-junction perovskite solar cells

The tunable band gaps and facile fabrication of perovskites make them attractive for multi-junction photovoltaics. However, light-induced phase segregation limits their efficiency and stability: this occurs in wide band gap (> 1.65 eV) I/Br mixed perovskite absorbers, and becomes even more acute in the top cells of triple-junction solar photovoltaics that requires a fully 2.0 eV band gap absorber. We report herein that lattice distortion in I/Br mixed perovskites is correlated with the suppression of phase segregation, generating an increased ion migration energy barrier arising from the decreased average interatomic distance between A-site cation and iodide. Using a ~2.0 eV Rb/Cs mixed-cation inorganic perovskite with large lattice distortion in the top subcell, we fabricated all-perovskite triple-junction solar cells and achieved an efficiency of 24.3% (23.3% certified quasi-steady-state efficiency) with an open-circuit voltage of 3.21 V. This is, to our knowledge, the first reported certified efficiency for perovskite-based triple-junction solar cells. The triple-junction devices retain 80% of their initial efficiency following 420 hours of operation at the maximum power point.

14 SOLAR ENERGY↗
DOE OSTI2020

High-resolution neutron time-of-flight measurements for light water at the Spallation Neutron Source (SNS), Oak Ridge National Laboratory

Series of light water inelastic neutron scattering experiments have been made at the Oak Ridge National Laboratory (ORNL), Spallation Neutron Source (SNS) covering temperatures ranging from 295 K to 600 K and pressures of 1 bar and 150 bar. The temperatures and pressures ranges correspond to that of pressurized light water reactors. The inelastic scattering measurements will help the development of light water thermal scattering kernels, also known as S (α,β) thermal scattering law (TSL), in a consistent fashion given the amount and the quality of the measured data. Light water thermal scattering evaluations available in existing nuclear data libraries have certain limitations and pitfalls. This paper introduces the state of the art of the light water thermal scattering cross-section data not only for room temperature but as well as for reactor operating temperatures, i.e. 550 - 600 K. During the past few years there has been a renewed interest in re-investigating the existing TSL models and utilize the recent experimental data or perform molecular dynamics simulations. It should be pointed out that no single TSL evaluation is based entirely on experimental data and one has to rely on TSL models or a combination of both. New TOF measurement of light water at the SNS, with a detailed description of the experimental setup, measurement conditions, and the associated foreseen results is presented in this paper. The analysis of the experimental data would help in validating the existing approach based on old experimental data or based on molecular dynamic simulations using classical water models, knowledge of which is very important to generate TSL libraries at reactor operating conditions.

Leal, Luiz C.↗
DOE OSTI2024

Thinking Bayesian for plasma physicists

Bayesian statistics offers a powerful technique for plasma physicists to infer knowledge from the heterogeneous data types encountered. To explain this power, a simple example, Gaussian Process Regression, and the application of Bayesian statistics to inverse problems are explained. The likelihood is the key distribution because it contains the data model, or theoretic predictions, of the desired quantities. By using prior knowledge, the distribution of the inferred quantities of interest based on the data given can be inferred. Because it is a distribution of inferred quantities given the data and not a single prediction, uncertainty quantification is a natural consequence of Bayesian statistics. The benefits of machine learning in developing surrogate models for solving inverse problems are discussed, as well as progress in quantitatively understanding the errors that such a model introduces.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

High-dimensional maximum-entropy phase space tomography using normalizing flows

Particle accelerators generate charged-particle beams with tailored distributions in six-dimensional position-momentum space (phase space). Knowledge of the phase space distribution enables model-based beam optimization and control. In the absence of direct measurements, the distribution must be tomographically reconstructed from its projections. In this paper, we highlight that such problems can be severely underdetermined and that entropy maximization is the most conservative solution strategy. We leverage —invertible generative models—to extend maximum-entropy tomography to six-dimensional phase space and perform numerical experiments to validate the model's performance. Our numerical experiments demonstrate consistency with exact two-dimensional maximum-entropy solutions and the ability to fit complicated six-dimensional distributions to large measurement sets in reasonable time. Published by the American Physical Society 2024

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

cuZ-Checker: A GPU-Based Ultra-Fast Assessment System for Lossy Compressions

Lossy compression is becoming an indispensable technique for the success of today's extreme-scale high-performance computing projects that produce vast volumes of data during scientific simulations or instrument data acquisitions. Comprehensively understanding the compression quality and performance of different lossy compressors is critical to selecting the best-fit compressors and using them properly and efficiently in practice. A few lossy compression assessment tools (e.g., Z-checker) have been developed, but none of them support the execution in a GPU environment. This is a significant gap because many recent extreme-scale applications and lossy compressors (e.g., cuSZ) can run entirely within GPUs. In this work, we develop an efficient lossy compression measuring system (called cuZ-Checker) on the GPU platform, which aims to perform the lossy compression quality and performance assessment completely within the GPU environment. Our contribution is threefold. (1) We develop a novel GPU-based lossy compression measuring framework using a computation pattern-based design approach. This approach classifies the computing-intensive metrics into three categories based on their patterns which creates large opportunities for kernel fusion and data reuse. (2) For each pattern in cuZ-Checker, we develop a CUDA kernel and provide fine-grained optimizations to boost its performance. (3) We thoroughly evaluate our cuZ-checker on a V100 GPU using four real-world scientific application datasets. Experiments show that cuZ-Checker can significantly accelerate the overall lossy compression assessment performance by 23X similar to 31X compared with the OpenMP-based multithreading CPU performance. To the best of our knowledge, this is the first lossy compression measuring system designed for GPU devices.

GPU↗
DOE OSTI2026

Quantum entanglement distribution coexisting with high-rate, broadband classical optical communications over a real-world fiber connecting remote, synchronized nodes

Compatibility with existing classical network infrastructure offers a scalable path towards deploying large-scale quantum networks. Here, we demonstrate O-band polarization-encoded quantum entanglement distribution over an installed 24.4-km fiber while coexisting with a state-of-the-art fully loaded C-band classical communications line system and a picosecond-level precision L-band synchronization signal. The classical system carries two 800-Gbps channels while the remainder of the C-band is filled with amplified spontaneous emission, as is standard for such state-of-the-art communications systems. We examine the spontaneous Raman scattering spectrum generated from this broadband C-band light and offer insights into wavelength allocation for O-band quantum channels. Optimal wavelength selection and narrow filtering enable well-preserved Bell state fidelity when coexisting with 21.4-dBm aggregate launch power across the C-band suitable for 36-Tbps transmission. To the best of our knowledge, this is the first implementation of entanglement-based quantum communications between two remote nodes coexisting with independent classical communications traffic. We demonstrate coexistence of quantum entanglement with ultra-high power levels and record classical bandwidth, offering promise for real-world entanglement-based networking integrated within high-capacity communications infrastructure.

Talcott, Gina M. [Northwestern U.] (ORCID:00000002↗
DOE OSTI2020

Extending the range of constant strain rate nanoindentation testing

Constant strain rate nanoindentation hardness measurements at high sustained strain rates cannot be made in conventional nanoindentation testing systems using the commonly employed continuous stiffness measurement technique (CSM) because of the “plasticity error” recently reported by Merle et al. [Acta Mater.134, 167 (2017)]. To circumvent this problem, here we explore an alternative testing and analysis procedure based on quasi-static loading and an independent knowledge of the Young's modulus, which is easily obtained by standard nanoindentation testing. In theory, the method applies to any indentation strain rate, but in practice, an upper limit on the rate arises from hardware limitations in the testing system. The new methodology is developed and applied to measurements made with an iMicro nanoindenter (KLA, Inc.), in which strain rates up to 100 s -1 were successfully achieved. The origins of the hardware limitations are documented and discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Final Technical Report: Center for Mechanistic Control of Unconventional Formations

The overarching mission of the Center for Mechanistic Control of Unconventional Formations (CMC-UF) was to garner cross-cutting, fundamental, geoscience knowledge to achieve mechanistic control over the strongly coupled nonequilibrium physical and geochemical processes in extreme geological environments including shale, mudstone, marls, and other tight rocks with nanoscale pores. Collectively, these are referred to as unconventional formations and they often play the role of seals for other subsurface storage formations. The fundamental knowledge garnered by CMC-UF enabled science-based management of US unconventional formations for subsurface storage of carbon dioxide and TWh quantities of renewable energy as hydrogen and/or compressed air over longer timeframes as well as for natural gas production, with reduced environmental impacts, in the short term.

58 GEOSCIENCES↗
DOE OSTI2021

Induction of Protection in Mice against a Chlamydia muridarum Respiratory Challenge by a Vaccine Formulated with the Major Outer Membrane Protein in Nanolipoprotein Particles

Chlamydia trachomatis is a sexually transmitted bacterium that infects over 130 million individuals worldwide annually. To implement a vaccine, we developed a cell-free co-translational system to express the Chlamydia muridarum major outer membrane protein (MOMP). This approach uses a nanolipoprotein particles (tNLP) made from ApoA1 protein, amphiphilic telodendrimer and lipids that self-assemble to form 10–25 nm discs. These tNLP provide a protein-encapsulated lipid support to solubilize and fold membrane proteins. The cell-free system co-translated MOMP and ApoA1 in the presence of telodendrimer mixed with lipids. The MOMP-tNLP complex was amenable to CpG and FSL-1 adjuvant addition. To investigate the ability of MOMP-tNLP+CpG+FSL-1 to induce protection against an intranasal (i.n.) C. muridarum challenge, female mice were vaccinated intramuscularly (i.m.) or i.n. and i.m. simultaneously 4 weeks apart. Following vaccination with MOMP-tNLP+CpG+FSL-1, mice mounted significant humoral and cell-mediated immune responses. Following the i.n. challenge, mice vaccinated with MOMP-tNLP+CpG+FSL-1 i.n. + i.m. group were protected as determined by the percentage change in body weight and by the number of C. muridarum inclusion forming units (IFU) recovered from the lungs. To our knowledge, this is the first time a MOMP-based vaccine formulated in tNLP has been shown to protect against C. muridarum.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Optimizing Ventilation Using Low-Cost Sensors to Improve Health, Safety, and Energy Efficiency

Air is the primary carrier of hazards within a space, whether it be hazardous byproducts of laboratory research activities or airborne pathogens. As a result, building ventilation is a primary defense against unseen airborne hazards. Critical laboratory facilities require effective mitigation of exposure to research-related, airborne hazards, providing a proving ground for effective ventilation strategies that optimize safety of occupants and reduce energy use. The heart of smart laboratory building operation is dynamic, analytics-based ventilation, which requires an in-depth intimate knowledge of building environmental conditions achieved through contaminant-detection systems. Unfortunately, currently many contaminant-detection solutions are expensive, elaborate systems that raise barriers for building managers. Through the successful deployment of a novel low-cost, modular sensor technology, we have developed a demand-control ventilation protocol effective in improving safety and reducing energy in critical laboratory environments. In this article, we will highlight best practices and lessons learned through this deployment that can be applied beyond laboratories. This article describes a low-cost sensor to support providing a safe, healthy building environment and reduce energy use through effective and efficient ventilation.

dynamic management of indoor air quality↗
DOE OSTI2026

Learning to Trigger: Reinforcement Learning at the Large Hadron Collider

High-throughput scientific facilities such as the Large Hadron Collider depend on real-time event filtering (\textit{triggering}) under tight constraints on bandwidth, latency, and storage. In practice, trigger menus are largely static and hand-tuned and can become suboptimal as detector conditions, pileup, and background composition drift over time. We cast online threshold tuning as a sequential decision-making problem: a reinforcement learning agent ingests streaming summaries of recent rates and signal-sensitive features and updates trigger thresholds to maximize signal efficiency while tracking a target background rate within a tolerance band. We adapt Group-Filtered Policy Optimization (GFPO) to streaming control and introduce two variants (GFPO-F, GFPO-FR) that enforce background rate feasibility during training. On a benchmark that emulates realistic collider operation, we study two representative triggers: a total transverse energy ($H_{T}$) trigger sensitive to pileup variation, and an anomaly-detection (AD) trigger based on reconstruction loss for rare or non-standard signatures. On Monte Carlo streams, our agent increases the fraction of in-tolerance time intervals by 48% ($H_T$) and 28% (AD), with a cumulative gain of up to 2% in signal efficiency on those in-tolerance intervals. Transferring from simulation to \emph{real} collision data (CMS Run 283408), the same agent, without fine-tuning, achieves a 56% ($H_T$) and 28% (AD) in-tolerance improvement over baselines, with further signal-efficiency gain on both triggers. To our knowledge, this is the \emph{first} demonstration of RL-based trigger control on real Large Hadron Collider collision data. Code is available at https://github.com/Zixind/GFPO_LHC (see repo for details).

Ding, Zixin [Chicago U.]↗