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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Diagnostics for multiple frequency heating and investigation of underlying processes

The development of new facilities routinely challenges ion source designers to build and operate sources that can achieve ever higher beam intensities and energies. As shown in this work, electron cyclotron resonance ion sources have proven to be extremely capable in meeting these challenges through the production of intense beams of medium and high-charge state ions. As performance boundaries are pushed, source stability becomes an issue as does the technology required to meet the challenge. Multiple frequency heating, the simultaneous use of two or more plasma heating frequencies, is a powerful tool in meeting the simultaneous need of intensity and stability. Relatively straightforward to utilize, the technique has been employed at numerous facilities to increase beam current and achievable charge state while also stabilizing the plasma. Its application has expanded the operational boundaries of existing and next generation sources, demonstrating that these devices have not yet achieved their full operational potential. To better understand the underlying physics, the diagnostics used to probe the source operational boundaries and the plasma properties have become increasingly sophisticated. In concert with detailed modeling, they are beginning to provide insight into the heating mechanism and, with that, the prospect of future advances.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Decision-tree structures utilizing a phase-transition material

The rich internal physics due to competing electronic phases present in phase-transition materials such as VO2 offer the potential for compact building block design for emerging non-von Neumann computing technologies. Here, based on the relaxation dynamics of an insulator-metal phase transition, we demonstrate experimentally a decision-tree classifier embedded within a single volatile resistive switching device. The tree is constructed by the combination of the voltage pulse and relaxation time and can adapt to different tasks. We use machine learning to analyze the relaxation process, enabling a predictive voltage-relaxation time phase diagram for the electrical resistance state. Classification of the etiology of the chronic cough is presented as a proof-of-principle use case. Further, our approach can be generalized to broader classes of solid-state and solid-liquid interfacial systems that demonstrate a variety of phase relaxations.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Generative deep-learning reveals collective variables of Fermionic systems

Complex processes of fermionic systems ranging from protein folding to nuclear fission often follow a low-dimensional reaction path parametrized in terms of a few collective variables. In nuclear theory, variables related to the shape of the nuclear density in a mean-field picture are key to describing the large amplitude collective motion of the neutrons and protons. Exploring the adiabatic energy landscape spanned by these degrees of freedom reveals the possible reaction channels while simulating the dynamics in this reduced space yields their respective probabilities. Unfortunately, this theoretical framework breaks down whenever the systems encounters a quantum phase transition with respect to the collective variables. Here, in this study, we introduce a novel generative deep-learning algorithm designed to build reaction paths that ensure that the many-fermion wave function stays differentiable with respect to the collective variables. This approach is applicable to any fermionic system described by a coherent state. We use the case of potential energy curves in the 16 O nucleus within the Hartree-Fock theory to illustrate its main features.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Leveraging Fine-Grained Occupancy Estimation Patterns for Effective HVAC Control

As occupancy sensing technologies become mature, various occupancy sensors are increasingly deployed in commercial buildings for pervasive occupancy monitoring. These sensors provide occupant-count data, which contains rich spatiotemporal information about occupancy patterns. With long-term occupant-count data collected from a commercial building, we design three different predictive models that capture the occupancy dynamics and examine how a model predictive control of the HVAC system benefits from actual occupancy count prediction. Our analysis reveals that mispredictions of occupancy states, especially false positives and false negatives, may introduce inefficient control that leads to energy waste or user discomfort. To address this issue, we take a step further to design an adaptive model predictive controller that minimizes inefficient control actions according to misprediction types and distributions. A comprehensive evaluation is performed in OpenBuild and EnergyPlus simulators to study the effectiveness of the proposed end-to-end control strategy. The evaluation shows that the proposed solution reduces energy consumption by 29.5% while improving the average weighted occupants comfort by 86.7% in Predicted Mean Vote (PMV) over the fixed schedule strategy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

The Application of Coordination to Magnetic Measurement Automation: An SSW System Example

Magnetic measurements are an integral part of the development cycle of accelerator magnets, a part which provides necessary feedback to drive design and manufacturing improvements and corrections. Automation of these measurements allows for reduction of human errors and reproducibility of measurements. Realization of automation via coordination that is separated at the architectural level from the functional code provides flexibility in defining measurement procedures and substantial extensibility. The described Single Stretched Wire (SSW) system herein is an implementation of such a solution with the use of a component-based framework designed to build a family of measurement systems.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Fast Neutron Irradiation of a Multichannel JFET-Based Optical Encoder

Modern electrical components are susceptible to damage from high levels of radiation and extreme temperatures found near reactors in terrestrial nuclear power plants and in aerospace applications. Radiation-hardened electronics are being developed, largely for the aerospace industry, but they sometimes rely on application-specific, small-batch semiconductor fabrication processes. These processes tend to be prohibitively expensive to develop and maintain outside major industrial facilities or governmental agencies. Recently, commercially available, nonradiation-rated junction-gate field-effect transistors (JFETs) were shown to maintain their functionality at gamma doses exceeding 1 MGy, suggesting that nonrated, commercially available electrical components could be used to develop systems that are tolerant to ionizing radiation. However, gamma ray survival is not indicative of neutron dose survival, and few studies characterize JFETs under neutron irradiation. To address this knowledge gap, a JFET-based analog multiplexer and optical pulsewidth modulation (PWM) encoder was developed and irradiated using a 252 Cf source to 1.6×10 13 n/cm 2 . The multiplexed optical encoder (MOE) system maintained functionality throughout testing and showed little evidence of radiation effects. These results indicate that circuitry tolerant to fast neutron damage can be developed using low-cost, nonradiation-rated, commercially available JFETs, which could provide a lower production cost alternative to specialized semiconductor processes when designing and building electronics better able to survive neutron irradiation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Impacts of the Additive Manufacturing Process on the Roughness of Engine Scale Vanes and Cooling Channels

Abstract By leveraging the additive manufacturing (AM) platform, development time and costs for turbine component testing can be reduced relative to traditional investment casting. Surface roughness is a key characteristic of the additive manufacturing process that can impact flow, heat transfer, and mechanical integrity of printed components. There are multiple design and build considerations that result in variability in surface roughness, especially when additively fabricating complicated three-dimensional vanes and internal cooling passages. This study characterizes the surface roughness of internal cooling passages, vanes, and flat external surface samples made using additive manufacturing, specifically the direct metal laser sintering process. The samples were manufactured with various wall thicknesses, layer thicknesses, build locations, build directions, and on different AM machines. A combination of computed tomography scanning and optical profilometry was used to evaluate surface roughness levels. The data indicate that the dominant factors in roughness for a given layer thickness are a function of wall thickness, build location, and build direction.

Engineering↗
DOE OSTI2022

Synthetic genetic circuits as a means of reprogramming plant roots

We report the shape of a plant’s root system influences its ability to reach essential nutrients in the soil and to acquire water during drought. Progress in engineering plant roots to optimize water and nutrient acquisition has been limited by our capacity to design and build genetic programs that alter root growth in a predictable manner. We developed a collection of synthetic transcriptional regulators for plants that can be compiled to create genetic circuits. These circuits control gene expression by performing Boolean logic operations and can be used to predictably alter root structure. This work demonstrates the potential of synthetic genetic circuits to control gene expression across tissues and reprogram plant growth.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

AviSense: A Real-time System for Detection, Classification, and Analysis of Aviation Signals

Wireless systems are an integral part of aviation. Apart from their apparent use in air-to-ground communication, wireless systems play a crucial role in avionic functions including navigation and landing. An interference-free wireless environment is therefore critical for the uninterrupted operation and safety of an aircraft. Hence, there is an urgency for airport facilities to acquire the capability to continuously monitor aviation frequency bands for real-time detection of interference and anomalies. To meet this critical need, we design and build AviSense, an SDR-based real-time, versatile system for monitoring aviation bands. AviSense detects and characterizes signal activities to enable practical and effective anomaly detection. We identify and tackle the challenges posed by a diverse set of critical aviation bands and technologies. We evaluate our methodology with real-world aviation signal measurements and two custom datasets of anomalous signals. We find that our signal classification capability achieves a true positive rate of ~99%, with few exceptions, and a false positive rate of less than 4%. We also demonstrate that AviSense can effectively distinguish between different types of anomalies. We build and evaluate a prototype implementation of AviSense that supports distributed monitoring.

42 ENGINEERING↗
DOE OSTI2023

ctrl-flow v0.0.1

ctrl-flow is a software that assists users in selecting a building control sequence from a library of control sequences that are implemented in the Modelica Buildings Library. The software allows selecting, configuring and editing the control sequences. It also allows exporting the sequence for use in building project design documents in Modelica-based simulations. The export will be in a json intermediate format, in the Modelica Language, in the Control Description Language and in English language format, such as in a Word document. The software functions as a web based application that includes a graphical user interface.

Wetter, Michael↗
DOE OSTI2024

AEGIS (Air Emissions Grouped by Industrial Sectors) [SWR-24-49]

AEGIS is a robust framework designed to build and compile emissions inventories for various industrial sectors. It integrates multiple emissions databases provided by the USEPA – including GHGRP, NEI, and TRI – and leverages the STEWI and STEWICOMBO tools to retrieve, merge, and process data. The framework produces facility- and process-level inventories, identifies discrepancies (e.g., NAICS or FRS mismatches), and performs exploratory analysis including emission concentration calculations and visualization.

Atnoorkar, Swaroop↗
DOE OSTI2023

Generalized Aerosol/Chemistry Interface (GIANT): A Community Effort to Advance Collaborative Science across Weather and Climate Models

Atmospheric aerosol and chemistry modules are key elements in Earth system models (ESMs), as they predict air pollutant concentrations and properties that can impact human health, weather, and climate. The current uncertainty in climate projections is partly due to the inaccurate representation of aerosol direct and indirect forcing. Aerosol/chemistry parameterizations used within ESMs and other atmospheric models span large structural and parameter uncertainties that are difficult to assess independently of their host models. Moreover, there is a strong need for a standardized interface between aerosol/chemistry modules and the host model to facilitate portability of aerosol/chemistry parameterizations from one model to another, allowing not only a comparison between different parameterizations within the same modeling framework, but also quantifying the impact of different model frameworks on aerosol/chemistry predictions. To address this need, we have initiated a new community effort to coordinate the construction of a Generalized Aerosol/Chemistry Interface (GIANT) for use across weather and climate models. We aim to organize a series of community workshops and hackathons to design and build GIANT, which will serve as the interface between a range of aerosol/chemistry modules and the physics and dynamics components of atmospheric host models. GIANT will leverage ongoing efforts at the U.S. modeling centers focused on building next-generation ESMs and the international AeroCom initiative to implement this common aerosol/chemistry interface. GIANT will create transformative opportunities for scientists and students to conduct innovative research to better characterize structural and parametric uncertainties in aerosol/chemistry modules, and to develop a common set of aerosol/chemistry parameterizations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Trust in Artificial Intelligence: Meta-Analytic Findings

Objective The present meta-analysis sought to determine significant factors that predict trust in artificial intelligence (AI). Such factors were divided into those relating to (a) the human trustor, (b) the AI trustee, and (c) the shared context of their interaction. Background There are many factors influencing trust in robots, automation, and technology in general, and there have been several meta-analytic attempts to understand the antecedents of trust in these areas. However, no targeted meta-analysis has been performed examining the antecedents of trust in AI. Method Data from 65 articles examined the three predicted categories, as well as the subcategories of human characteristics and abilities, AI performance and attributes, and contextual tasking. Lastly, four common uses for AI (i.e., chatbots, robots, automated vehicles, and nonembodied, plain algorithms) were examined as further potential moderating factors. Results Results showed that all of the examined categories were significant predictors of trust in AI as well as many individual antecedents such as AI reliability and anthropomorphism, among many others. Conclusion Overall, the results of this meta-analysis determined several factors that influence trust, including some that have no bearing on AI performance. Additionally, we highlight the areas where there is currently no empirical research. Application Findings from this analysis will allow designers to build systems that elicit higher or lower levels of trust, as they require.

Behavioral Sciences↗
DOE OSTI2020

Real-Time Acoustic Analysis of Batteries

The project explored development and commercialization of an acoustical approach to battery diagnostics. The technology has significant potential impacts for battery manufacturing and quality assurance, battery management during operation, and could be an enabler for the second-life battery market. Research was focused on inspecting and analyzing the physical condition of batteries using ultrasound. This consisted of designing and building hardware for gathering high-quality acoustic data, as well as using battery charge-discharge cyclers and temperature chambers to operate and age the batteries. Over the course of the 2-year program, we were able to build our first prototypes that are robust enough to be used by industry partners to evaluate our technology. Using the same prototypes, we also showed preliminary evidence that our acoustic inspection method may have distinct advantages over standard electrical methods in terms of detecting differences in the physical quality of battery cells. The project also expanded the participant’s core capabilities beyond time-of-flight acoustic measurements.

25 ENERGY STORAGE↗
DOE OSTI2020

FIU Projects 4 & 5: DOE-FIU Science and Technology Workforce Development Program

The DOE-FIU Science and Technology Workforce Development Program has been designed to build upon the existing DOE/FIU relationship by creating a “pipeline” of minority engineers specifically trained and mentored to enter the Department of Energy workforce in technical areas of need. The main objective of the program is to provide interested students with a unique opportunity to integrate course work, DOE field work, and research work at FIU into a well-structured academic program that leads to entry into DOE EM’s Pathways Program. Students selected as DOE EM Fellows perform research at FIU and at DOE sites, national laboratories, and DOE contractors. Graduation and completion of this fellowship leads to employment opportunities with DOE EM, DOE contractors, DOE national laboratories, other federal agencies, and private industry as well as the pursuit of post-master or post-doctoral positions at DOE national labs.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Dissolution of Additively Manufactured Mo Disks and Structures for Accelerator Based Production of Mo-99

The press-and-sinter approach for production of Mo targets has been successfully tested and demonstrated. The main advantage of this process is that it uses readily available technology and tools that have been tuned for production of various metal and metal oxide targets. However, certain target design specifications, such as dimensional accuracy of the diameter and thickness of the disks, are required for proper fit into a target holder and therefore place strict tolerances on sintered Mo disks intended for accelerator production. Furthermore, having a target composed of multiple disks poses certain challenges for post-irradiation processing, where there is a potential for spillage of individual disks during the manipulation for dissolution inside a processing hot cell. Additive manufacturing (AM), also referred to as 3D printing, provides capabilities to design and build a single-piece Mo target that provides the opportunity for better geometry for optimized production of Mo-99, improved cooling capability, and safer post-irradiation handling. A drawback of AM Mo structures is roughness of finished surfaces, which could reduce the effectiveness of gas cooling systems. Process development for AM Mo structures using selective laser melting is described in work by Gibson and Lowden. For post-irradiation processing, it is important to look at the dissolution behavior of AM Mo structures in comparison to readily dissolved press-and-sinter Mo targets. The dissolution behavior of press-and-sinter targets has been well documented in our previous reports. In this report, we discuss results of dissolution experiments conducted with AM Mo disks and structures (provided by Oak Ridge National Laboratory [ORNL]) in 50% hydrogen peroxide (H 2 O 2 ).

36 MATERIALS SCIENCE↗
DOE OSTI2021

COVID-19, An Exercise in Data Governance at Sandia National Laboratories

In April of 2020, Sandia National Laboratories had an urgent need to identify and manage the data that could be used to create mobile applications, models, reports, and visualizations to assist management in safely bringing the workforce onsite during the COVID-19 pandemic. Multiple divisions volunteered to design and build software solutions; meanwhile, requests for new data sources, including duplicate requests, were inundating Information Technology (IT) and data owners. The Enterprise Data Governance Team was assigned to resolve obtaining and accessing new sources of data in an accelerated timeframe. Through successful collaboration with multiple stakeholders and domain owners across Sandia, the Enterprise Data Governance Team rapidly developed a centralized data strategy and solution for use in safeguarding the Sandia workforce during the COVID-19 pandemic. This foundation enabled teams to successfully develop solutions, including reports for executives and management as well as the data for modeling and scientific analysis.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2021

Hydrogen Compatible Materials Workshop

This report serves as the proceedings of the Hydrogen Compatible Materials Workshop held virtually by Sandia National Laboratories on December 2-3, 2020. The purpose of the workshop was to assemble subject matter experts at Sandia and its national laboratory partners within the U.S. Department of Energy's (DOE) Hydrogen Materials Compatibility (H-Mat) Consortium with public and private stakeholders in the research, development and deployment of hydrogen technologies to discuss the topic of hydrogen compatible materials. This workshop was designed to build on past events and current research and development (R&D) efforts to develop a forward-looking vision that identifies gaps and challenges for the next decade. In particular, the workshop organizers sought to expand their understanding of hydrogen compatible materials needs for power, manufacturing and other industrial uses to enable deeper impact and widespread use of hydrogen while continuing to address open questions in hydrogen-powered transportation of concern to Original Equipment Manufacturers, hydrogen producers, materials & component suppliers and other private entities. The workshop was primarily organized as a series of panel-led discussions on the topics of hydrogen-enabled transportation, heating and power, and industrial uses. Each panel consisted of 2-3 subject matter experts who relayed their perspectives on a set of framing questions developed to facilitate discussion by the broader group of workshop participants. By the workshop's conclusion, the participants identified and prioritized a list of technical challenges for each panel topic where further R&D is warranted.

08 HYDROGEN↗