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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 1,225 records · Page 68
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

HUMAN-ARTIFICIAL INTELLIGENCE TEAMING FOR THE U.S. NAVY: DEVELOPING A HOLISTIC RESEARCH ROADMAP

With the ever-increasing deluge of data and demand for warfighters to make decisions upon its analysis, U.S. defense strategy has prioritized the development of artificial intelligence (AI)/machine learning (ML) systems that can analyze multi-source data streams and suggest courses of action. However, there is a history of systems that have failed to be adopted by the warfighter due not only to unsolved technical challenges, but also a lack of usability or contributions to mission effectiveness, perceived or otherwise. To avoid this, research into human-AI teaming shows promise for developing AI systems that work with frontline operators. A recent National Academies of Sciences, Engineering, and Medicine report (NASEM, 2022) presented 57 research objectives in this area; however, the U.S. Navy requires a more-focused set of priorities, as it is impossible to tackle every priority. A workshop involving 23 human factors scientists, computer scientists, and active-duty sailors was organized at the Naval Information Warfare Center Pacific, resulting in a set of five research priorities spanning near-, mid-, and far-term time frames. This panel will summarize the results of this workshop, with a focus on the big questions both going into this workshop and coming out of it. The panel participants come from government, academia, and industry, providing perspective from the different kinds of organizations required to accomplish these research goals.

Wong, Jason↗
DOE OSTI2022

Supplementary Data for "Evaluation of the Economic Implications of Varied Pressure Drawdown Strategies Generated Using a Real-time, Rapid Predictive, Multi-fidelity Model for Unconventional Oil and Gas Wells" by Bello, K., Vikara, D., Sheriff, A., Viswanathan, H., Carr, T., Sweeney, M., O'Malley, D., Marquis, M., Vactor, R.T., and Cunha, L.

The Bello et al. study evaluates the impact of contrasting pressure drawdown on gas productivity and the resulting economics of a well in the Marcellus Shale of the Appalachian Basin. This research applies a techno-economic analysis approach to help identify potential ways pressure management strategies can be used to improve cumulative recovery of hydraulically fractured horizontal wells while maintaining project profitability. Gas production forecast outlook scenarios of the Marcellus Shale Energy and Environment Laboratory Laboratory's MIP-3H well were generated under varying pressure drawdowns using two approaches: 1) a novel physics-informed machine learning (PIML) workflow and 2) via traditional reservoir simulation in Computer Modeling Group’s (CMG) GEM Compositional & Unconventional Simulator. Cash flow and other economic metrics of interest were compiled on the production outlook using the U.S. Department of Energy's (DOE) National Energy Technology Laboratory (NETL) Unconventional Shale Well Economic Model (UShWEM).The sheets within this Microsoft ExcelTM workbook provide the economic metric outputs for the baseline condition and the one-at-a-time (OAT) sensitivity analysis of UShWEM's input parameters for each of the production scenarios evaluated.

Fracture Network Model↗
DOE OSTI2020

Outcomes of the DOE Workshop on Atmospheric Challenges for the Wind Energy Industry

The U.S. Department of Energy-funded Mesoscale-to-Microscale Coupling (MMC) project team planned and conducted a virtual Workshop on Atmospheric Challenges for the Wind Energy Industry on October 19 and 20, 2020. The goal of the workshop was to forge a dialog with the community, including industry representatives, on how modeling tools are currently being used, the present active atmospheric modeling research in support of wind energy, and required advancements in capabilities and technology to continue to advance wind energy deployment. The workshop was planned in collaboration with an industry advisory panel that included representatives from wind power plant developers, turbine manufacturers, and companies that provide resource assessment and forecasting services. The format of the workshop included panels from government research sponsors, visionaries from industry, and mixed panels of researchers discussing research status and needs. A shared keynote presentation from the Technical University of Denmark experts anchored the second day of the workshop. An emphasis was placed on understanding the research needs in the offshore environment. In addition, breakout opportunities were provided each day. On the first day, the breakout discussions addressed predesigned questions configured to elicit participants’ thoughts on needed research directions. The second-day breakouts treated three important technical topics through a combination of presentations and group conversations. Each workshop participant chose their breakout preference from among downscaling details, modeling for turbines, and using artificial intelligence for atmospheric modeling. The discussions were robust and productive. The outcomes of the workshop include archiving a series of recommendations from industry and the research community on research directions required to further advance wind energy deployment. Discussions confirmed the need for high-fidelity modeling but that there are specific areas of applicability and other areas where the time and cost of computation is prohibitive. In those cases, the high-fidelity models can inform low-order models that are more practical for real-time or widely deployed applications. Industry must consider the financial cost of performing more expensive modeling approaches, but industry engineers and researchers are using these approaches where there appears to be a return on investment. An emerging type of low-order model is based on machine learning (ML). Participants confirmed that there are many atmospheric phenomena that need to be modeled better, including low-level jets, cold air outbreaks, land-sea induced circulations, diurnal variability, thin stable boundary layers, dynamic changes such as from frontal passage, interaction of wakes and blockage, and more. For the offshore environment, there is wide agreement that some level of ocean-wave-atmospheric coupling is necessary to capture variations in rotor-level winds needed to plan and operate offshore wind plants. Another recurring recommendation is that more observations are needed, particularly for the offshore environment. Those observations should consider the needs for model improvement, both for physically based models and for ML models. Observations must capture atmospheric profiles of variables that are important to understanding and modeling atmospheric and oceanic phenomena that impact boundary layer winds. Models must be validated with data and the uncertainty quantified, particularly those that are sensitive to initial and boundary conditions. Finally, a repeated request was to consider the holistic needs of hybrid plants of wind, solar, and storage resources because those types of plants are likely to be the wave of the future. In addition, industry wishes to understand impacts of the resource under a changing climate for long-term planning.

17 WIND ENERGY↗
DOE OSTI2023

Machine Learning for Geothermal Resource Exploration in the Tularosa Basin, New Mexico

Geothermal energy is considered an essential renewable resource to generate flexible electricity. Geothermal resource assessments conducted by the U.S. Geological Survey showed that the southwestern basins in the U.S. have a significant geothermal potential for meeting domestic electricity demand. Within these southwestern basins, play fairway analysis (PFA), funded by the U.S. Department of Energy’s (DOE) Geothermal Technologies Office, identified that the Tularosa Basin in New Mexico has significant geothermal potential. This short communication paper presents a machine learning (ML) methodology for curating and analyzing the PFA data from the DOE’s geothermal data repository. The proposed approach to identify potential geothermal sites in the Tularosa Basin is based on an unsupervised ML method called non-negative matrix factorization with custom k-means clustering. This methodology is available in our open-source ML framework, GeoThermalCloud (GTC). Using this GTC framework, we discover prospective geothermal locations and find key parameters defining these prospects. Our ML analysis found that these prospects are consistent with the existing Tularosa Basin’s PFA studies. This instills confidence in our GTC framework to accelerate geothermal exploration and resource development, which is generally time-consuming.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Digital Tools for the Preventive Conservation of Built Heritage: The Church of Santa Ana in Seville

Historic Building Information Modelling (HBIM) plays a pivotal role in heritage conservation endeavours, offering a robust framework for digitally documenting existing structures and supporting conservation practices. However, HBIM’s efficacy hinges upon the implementation of case-specific approaches to address the requirements and resources of each individual asset and context. This paper defines a flexible and generalisable workflow that encompasses various aspects (i.e., documentation, surveying, vulnerability assessment) to support risk-informed decision making in heritage management tailored to the peculiar conservation needs of the structure. This methodology includes an initial investigation covering historical data collection, metric and condition surveys and non-destructive testing. The second stage includes Finite Element Method (FEM) modelling and structural analysis. All data generated and processed are managed in a multi-purpose HBIM model. The methodology is tested on a relevant case study, namely, the church of Santa Ana in Seville, chosen for its historical significance, intricacy and susceptibility to seismic action. The defined level of detail of the HBIM model is sufficient to inform the structural analysis, being balanced by a more accurate representation of the alterations, through linked orthophotos and a comprehensive list of alphanumerical parameters. This ensures an adequate level of information, optimising the trade-off between model complexity, investigation time requirements, computational burden and reliability in the decision-making process. Field testing and FEM analysis provide valuable insight into the main sources of vulnerability in the building, including the connection between the tower and nave and the slenderness of the columns.

Chaves, Estefanía↗
DOE OSTI2020

The CYBER security – Competency Health and Maturity Progression (CYBER-CHAMP) model: Extending the National Initiative for Cybersecurity Education (NICE) Framework Across Organizational Security

Problem Statement: There is a pervasive talent deficit in the cybersecurity industry that prevents employers from being able to fill their open positions efficiently. A holistic approach to security is required to ensure organizations have adequate prevention and response capabilities in case of a cyberattack. Specifically, industrial control systems (ICS’s) and their operational technology (OT) components have become a constant target for cyberattacks. Research Questions: It is proposed that the NICE Framework should be extended in the following areas: 1) Include guidance regarding the job roles and competencies for both IT and OT professionals. 2) Offer step-by-step solutions, based on the work role mappings from the NICE Framework, to increase cybersecurity through employee training and education. 3) Provide a streamlined, lifecycle approach to building a cybersecurity program. Contribution: The CYBER security – Competency Health and Maturity Progression (CYBER-CHAMP©) model provides a customized solution for businesses to understand their education gaps in organizational security and target areas for improvement. Rationale: The Framework for Improving Critical Infrastructure Cybersecurity v1.1 addresses ICS but does not offer a measurement of cybersecurity maturity or clear methods to ascertain an organization’s current risk profile. In Phases 1 and 5 of the model, measurements are provided to help an organization build their current and target risk profiles. The NICE framework provides a structure for planning an IT cybersecurity workforce, but the OT aspects of cybersecurity are only briefly discussed. The model uses Phases 2-3 to examine the competencies of an organization’s workforce, which includes both IT and OT roles. Current frameworks do not offer next steps to increase an organization’s cybersecurity. During Phase 4, employees’ roles are mapped to training, education, and/or certifications from common vendors. Investigative Approach: The model provides measurements and metrics for both an organization’s status and continual improvement. This improvement methodology includes guidance for creating an overall strategic plan for security improvement via products designed to increase an organization’s operational readiness through workforce competency health. Lessons Learned: Depending on who was participating, there were contradicting answers given in Phase 1 due to different security cultures in the organization. This revelation has influenced the steps listed in the User’s Guide, where Phase 1’s first recommended step is to assemble a team that champions the facilitation and implementation of the model in the organization. During Phase 2, the discovery was made that organizations may be missing roles that are necessary to perform critical cybersecurity functions. By understanding the functional roles and competencies needed, they can contract or hire cybersecurity help to fill these gaps. Implications: Using the model, organizations can discuss quantitative measures for improvement as a business case for advancing their security program. Future research can validate and extend the present theory and model to a variety of environments. It is of interest to investigate additional security roles and knowledge domains that are used to build standardized cybersecurity curriculum.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Iterative Workflow for Quantification and Minimization of Reduced Chemistry-Induced Uncertainties in Reacting Flow Simulation

Reduced chemistry models mitigate computational cost but introduce two sources of uncertainties in reacting flow simulation, including chemical information loss due to model reduction, and approximation errors due to non-optimal projection. We present an iterative workflow for quantification and minimization of reduced chemistry-induced uncertainties in reacting flow simulations.

chemistry↗
DOE OSTI2022

Differential holography

Differential Holography technology measures the amplitude and/or phase of, e.g., an incident linearly polarized spatially coherent quasi-monochromatic optical field by optically computing the first derivative of the field and linearly mapping it to an irradiance signal detectable by an image sensor. This information recorded on the image sensor is then recovered by a simple algorithm. In some embodiments, an input field is split into two or more beams to independently compute the horizontal and vertical derivatives (using amplitude gradient filters in orthogonal orientations) for detection on one image sensor in separate regions of interest (ROIs) or on multiple image sensors. A third unfiltered beam recorded in a third ROI directly measures amplitude variations in the input field to numerically remove its contribution as noise before recovering the original wavefront using a numerical in algorithm. When combined, the measured amplitude and phase constitute a holographic recording of the incident optical field.

Ravizza, Frank↗
DOE OSTI2023

Molten Salt Tritium Transport Experiment: A Versatile Fluoride Salt Loop for Validation of Tritium Transport Phenomena

Tritium is generated in Molten Salt Reactors (MSRs) from neutron capture by lithium and other constituents of the molten salt FLiBe. Tritium is a unique radionuclide as it readily permeates through hot structural materials. Thus, any material in contact with tritium laden molten salt is a potential pathway for release. Understanding tritium transport and devising adequate control strategies is necessary for the safe operation of MSRs. The Molten Salt Tritium Transport Experiment (MSTTE) is a forced-convection fluoride salt loop with the capability to inject hydrogen isotopes into flowing molten salt and to measure transport phenomena such as permeation through metals and evolution from free-surfaces. MSTTE is designed to be versatile to test potential control technology in future campaigns. This report focuses on the current design and analysis of MSTTE. Custom designed and fabricated experiment components such as the hydrogen injection system, permeation test section, diagnostics, and gas distribution system are discussed in detail. System-level tritium transport modeling using the System Analysis Module investigates experiment parameters such as hydrogen source terms, salt flow rate, and salt temperature. Computational fluid dynamics of the salt flow in the permeation test section informs design choices to establish fully developed flow in the measured permeation zone.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗