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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Membrane Carbonation for 100% Efficient Delivery of Industrial CO 2 Gases

Industrial processes generate about one quarter of all greenhouse gases emissions in the US, about 80% of which is carbon dioxide (CO 2 ). Biological processes are one of the largest natural sinks for atmospheric CO 2 , but the rate of capture is limited by the low concentration in air (~0.04%). Industrial emissions have significantly higher CO 2 concentrations, ranging from 5–80% CO 2 , which can significantly increase the rate of biological CO 2 capture, including many-fold improvements for cultivating microalgae to produce food, fertilizer, and renewable fuel. However, traditional methods for delivering CO 2 to microalgae using bubbling is < 40% efficient, leading to significant residual CO 2 emissions and increased cost. This project developed the Membrane Carbonation (MC) technology to significantly improve the CO 2 delivery efficiency to microalgae from power plant flue gas, wastewater treatment plant anaerobic digesters biogas (Figure 1), and other CO 2 -containing industrial sources.

09 BIOMASS FUELS↗
DOE OSTI2024

Q4Q: Quantum Computation for Quantum Prediction of Materials and Molecular Properties

We aim at exploring the potential of quantum computation to solve practical problems that are currently targeted by “traditional” high performance computing (HPC). Along the way, we will develop theoretical frameworks and algorithmic strategies. We propose computational research activities that will advance the application of quantum computers to selected materials/molecular properties: (1) Band structure of solids; (2) Molecular electronic/vibronic properties; (3) Free energies and phase transitions.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Integrating HPC, AI, and Workflows for Scientific Data Analysis: Report from Dagstuhl Seminar 23352

The Dagstuhl Seminar 23352, titled “Integrating HPC, AI, and Workflows for Scientific Data Analysis,” held from August 27 to September 1, 2023, was a significant event focusing on the synergy between High-Performance Computing (HPC), Artificial Intelligence (AI), and scientific workflow technologies. The seminar recognized that modern Big Data analysis in science rests on three pillars: workflow technologies for reproducibility and steering, AI and Machine Learning (ML) for versatile analysis, and HPC for handling large data sets. These elements, while crucial, have traditionally been researched separately, leading to gaps in their integration. The seminar aimed to bridge these gaps, acknowledging the challenges and opportunities at the intersection of these technologies. The event highlighted the complex interplay between HPC, workflows, and ML, noting how ML has increasingly been integrated into scientific workflows, thereby enhancing resource demands and bringing new requirements to HPC architectures, like support for GPUs and iterative computations. The seminar also addressed the challenges in adapting HPC for large-scale ML tasks, including in areas like deep learning, and the need for workflow systems to evolve to leverage ML in data analysis fully. Moreover, the seminar explored how ML could optimize scientific workflow systems and HPC operations, such as through improved scheduling and fault tolerance. A key focus was on identifying prestigious use cases of ML in HPC and understanding their unique, unmet requirements. The stochastic nature of ML and its impact on the reproducibility of data analysis on HPC systems was also a topic of discussion.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Feature Extraction: Improving Remote Sensor Classification of Non-Proliferation

This research focuses on developing algorithms for nuclear non-proliferation detection using remote sensor modeling. To improve the performance of classification models, we implemented a data pipeline with feature extraction. This pipeline takes raw data and transforms it into smaller data points called features that still describe the model. Improving this classification works towards the departments of energy’s missions of ensuring American’s security and prosperity by creating technology that addresses nuclear challenges. To conduct this analysis, we used the Python programming language and some key packages, including tsfresh and TSFEL. Originally tsfresh was selected because it has the most statistical features out of all the packages. Later TSFEL was incorporated due to the additional features it can extract from data, such as temporal and spectral. However, feature extraction becomes challenging in the presence of missing values. In this case, two additional Python packages were added to our workflow, NumPy and pandas, allowing for the feature extraction process to handle unknown values. Our data pipeline was tested on data collected from a simulation that describes the process state of a physical example. The results show the pipeline’s capability to consume and extract a total 17 features from tabular data. Future work includes producing classifications using decision tree-based models such as XGBoost and improving data collection by analyzing feature importance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

The reverse quantum limit and its implications for unconventional quantum oscillations in YbB 12

The quantum limit in a Fermi liquid, realized when a single Landau level is occupied in strong magnetic fields, gives rise to unconventional states, including the fractional quantum Hall effect and excitonic insulators. Stronger interactions in metals with nearly localized $f$-electron degrees of freedom increase the likelihood of these unconventional states. However, access to the quantum limit is typically impeded by the tendency of $f$-electrons to polarize in a strong magnetic field, consequently weakening the interactions. In this study, we propose that the quantum limit in such systems must be approached in reverse, starting from an insulating state at zero magnetic field. In this scenario, Landau levels fill in the reverse order compared to regular metals and are closely linked to a field-induced insulator-to-metal transition. We identify YbB 12 as a prime candidate for observing this effect and propose the presence of an excitonic insulator state near this transition.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Reliability of Materials and Components for Solid Oxide Fuel Cells

Planar stack solid-oxide fuel cells (SOFCs) require seals that must operate reliably under demanding conditions for lifetimes of 40000 hours. This includes temperature fluctuations between 800°C and RT during on and off cycles, thermal stresses, oxidizing environments and chemical degradation to name a few. This comprehensive report provides results from long term testing of two commercially available multicomponent barium alkali silicate glasses: SCN and G6, chosen as sealing candidates. In this scope, the glass seals were deposited on YSZ and Al 2 O 3 substrates simulating electrolytes (Zrbased) and coatings (both zirconia and Al 2 O 3 ). The seal-substrate couples were subjected to 800°C under air and steam+H 2 +N 2 environments up to 40000 hours to test their integrity under real operating conditions. Extensive studies on the effects of exposure have been conducted over the span of testing at various time intervals. Within the context of characterization, mechanical properties such as density, roughness, thermal expansion and glass transition, viscosity and wettability behavior; and microstructural properties such as glass chemistries, defect formation (cracks and pores), phase transformations (devitrification) and glass-interface reactions are investigated. Results and discussions are provided with a focus on the degradation of the properties over long term interrupted testing.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2024

Workflows Community Summit 2024: Future Trends and Challenges in Scientific Workflows

The 2024 Workflows Community Summit report presents the outcomes of a three-day international gathering that brought together 109 experts from 18 countries to discuss future trends and challenges in scientific workflows. The summit focused on six key areas: time-sensitive workflows, convergence of AI and HPC workflows, multi-facility workflows, heterogeneous HPC environments, user experience and interfaces, and FAIR computational workflows. Discussions highlighted emerging challenges such as integrating AI with traditional HPC, managing workflows across diverse facilities, addressing heterogeneity in computing environments, and ensuring workflows are findable, accessible, interoperable, and reusable (FAIR). The report outlines recent advances, ongoing challenges, and provides recommendations for each topic area, emphasizing the need for standardization, improved interoperability, and the development of more sophisticated tools and frameworks to support the evolving landscape of scientific workflows in the era of exascale computing and AI integration.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Microgrid Handbook for Army Resilience: A Technical Review

The Army recognizes the need for its installations to operate as independently and efficiently as possible with secure energy and water resource that cannot be limited or shut off by external forces or events, whether natural or man-made . In an effort to make this possible, the Army has identified microgrids as an effective way to achieve the goal of secure power for operations, both for domestic and international installations. This document is designed to educate Army Installation leaders about what microgrids are, options for their components, financing, and operations, as well as other regulatory and technological considerations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Evaluating the Accuracy of Machine Learning Forecasts

To improve the accuracy of forecasting in machine learning, we must investigate multiple machine learning models and see how accurately they can predict values after training. We used seven machine learning models to try and get more accurate predictions. The models that were used were ARIMA, SES, MLP, CART, LightGBM, and XGBoost. We used a processed dataset from a Terminal at LAX that had the number of people traveling through terminal X every hour in March from 2015-2019. We trained our models with the dates March 6 - March 19 to predict the value for March 20th and the hours 6:00 am to 6:00 pm since those are the most popular traveling hours. By using the different models, we had varying results of accuracy when estimating the amount of people traveling through terminal X on March 20th. We know that machine learning models are helpful for forecasting and by seeing how accurately these models can predict, we can see how forecasting can be helpful for other issues. Using these methods, airports can use forecasting to predict the amount of people coming in and out and can use these predictions to prepare their resource management, operational efficiency, and overall passenger experience.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Coordinated Natural Gas and Electric Planning: Case Studies of Current Approaches and Practices

This paper examines how natural gas and electric utilities across eight U.S. states and two Canadian provinces are beginning to coordinate historically separate planning processes in response to growing system interdependencies, policy mandates, aging infrastructure, and changing customer energy choices. Electricity planning has long relied on robust integrated resource planning frameworks that weigh numerous objectives, risks, and costs. Natural gas planning, on the other hand, is typically less transparent and more narrowly focused on safety and system integrity. As economic, reliability, and policy drivers place new and shared demands on both systems, jurisdictions and utilities are experimenting with approaches such as coordinated forecasting, non pipeline alternatives, and combined planning pilots. A few have issued regulatory or statutory directives for greater data sharing and methodological alignment. Case studies from British Columbia, California, Colorado, Illinois, Massachusetts, Minnesota, New York, Québec, Rhode Island, and Washington illustrate a wide range of approaches to rethinking siloed planning. The paper identifies several common themes across the jurisdictions examined. It provides observations on the methods, processes, and organizational steps that may be needed in the future to address the challenges being faced by states and utilities. Lastly, it identifies some initial steps that states and utilities can consider if they would like to pursue more integrated, cost-effective, policy-aligned energy system planning.

03 NATURAL GAS↗