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Results for “phase identification task”

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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Phase identification using co‐association matrix ensemble clustering

Calibrating distribution system models to aid in the accuracy of simulations such as hosting capacity analysis is increasingly important in the pursuit of the goal of integrating more distributed energy resources. The recent availability of smart meter data is enabling the use of machine learning tools to automatically achieve model calibration tasks. This research focuses on applying machine learning to the phase identification task, using a co‐association matrix‐based, ensemble spectral clustering approach. The proposed method leverages voltage time series from smart meters and does not require existing or accurate phase labels. This work demonstrates the success of the proposed method on both synthetic and real data, surpassing the accuracy of other phase identification research.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1971

IMBLMS phase B4, additional tasks 5.0. Microbial identification system

A laboratory study was undertaken to provide simplified procedures leading to the presumptive identification (I/D) of defined microorganisms on-board an orbiting spacecraft. Identifications were to be initiated by nonprofessional bacteriologists, (crew members) on a contingency basis only. Key objectives/constraints for this investigation were as follows:(1) I/D procedures based on limited, defined diagnostic tests, (2) testing oriented about ten selected microorganisms, (3) provide for definitive I/D key and procedures per selected organism, (4) define possible occurrences of false positives for the resulting I/D key by search of the appropriate literature, and (5) evaluation of the I/D key and procedure through a limited field trial on randomly selected subjects using the I/D key.

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DOE OSTI2026

Advanced Semi-Supervised Learning with Uncertainty Estimation for Phase Identification in Distribution Systems

The integration of advanced metering infrastructure (AMI) into power distribution networks generates valuable data for tasks such as phase identification; however, the limited and unreliable availability of labeled data in the form of customer phase connectivity presents challenges. To address this issue, we propose a semi-supervised learning (SSL) framework that effectively leverages labeled and unlabeled data. Our approach incorporates self-training, label spreading, and Bayesian neural networks (BNNs) to enhance phase identification with AMI data. Our method uses an ensemble of multilayer perceptron classifiers in a self-training setup, iteratively adding high-confidence pseudo-labels to improve robustness. We also apply label spread to propagate labels based on data similarity, which enhances generalization across diverse distributions. In addition, we employ a BNNs with uncertainty estimation, boosting confidence in predictions and reducing phase identification errors. In our case study, we achieved approximately 98% +/- 0.08 accuracy with uncertainty using minimal and unreliable labeled data from a real U.S. utility, Duquesne Light Company. Our SSL approach, combined with uncertainty estimation, provides an efficient solution for phase identification in AMI data, ultimately improving the reliability of smart grid applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Advanced Semi-Supervised Learning With Uncertainty Estimation for Phase Identification in Distribution Systems

The integration of advanced metering infrastructure (AMI) into power distribution networks generates valuable data for tasks such as phase identification; however, the limited and unreliable availability of labeled data in the form of customer phase connectivity presents challenges. To address this issue, we propose a semi-supervised learning (SSL) framework that effectively leverages labeled and unlabeled data. Our approach incorporates self-training, label spreading, and Bayesian neural networks (BNNs) to enhance phase identification with AMI data. Our method uses an ensemble of multilayer perceptron classifiers in a self-training setup, iteratively adding high-confidence pseudo-labels to improve robustness. We also apply label spread to propagate labels based on data similarity, which enhances generalization across diverse distributions. In addition, we employ a BNNs with uncertainty estimation, boosting confidence in predictions and reducing phase identification errors. In our case study, we achieved approximately 98% +/- 0.08 accuracy with uncertainty using minimal and unreliable labeled data from a real U.S. utility, Duquesne Light Company. Our SSL approach, combined with uncertainty estimation, provides an efficient solution for phase identification in AMI data, ultimately improving the reliability of smart grid applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Toward autonomous design and synthesis of novel inorganic materials

Autonomous experimentation driven by artificial intelligence (AI) provides an exciting opportunity to revolutionize inorganic materials discovery and development. Herein, we review recent progress in the design of self-driving laboratories, including robotics to automate materials synthesis and characterization, in conjunction with AI to interpret experimental outcomes and propose new experimental procedures. We focus on efforts to automate inorganic synthesis through solution-based routes, solid-state reactions, and thin film deposition. In each case, connections are made to relevant work in organic chemistry, where automation is more common. Characterization techniques are primarily discussed in the context of phase identification, as this task is critical to understand what products have formed during synthesis. The application of deep learning to analyze multivariate characterization data and perform phase identification is examined. To achieve “closed-loop” materials synthesis and design, we further provide a detailed overview of optimization algorithms that use active learning to rationally guide experimental iterations. Lastly, we highlight several key opportunities and challenges for the future development of self-driving inorganic materials synthesis platforms.

36 MATERIALS SCIENCE↗
NASA NTRS1976

STS payloads mission control study continuation phase A-1. Volume 2-C, task 3: Identification of joint activities and estimation of resources in preparation for joint flight operations

Payload mission control concepts are developed for real time flight operations of STS. Flight planning, training, simulations, and other flight preparations are included. Payload activities for the preflight phase, activity sequences and organizational allocations, and traffic and experience factors to establish composite man-loading for joint STS payload activities are identified for flight operations from 1980 to 1985.

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NASA NTRS1973

A system for analysis and classification of voice communications

A method for analysis and classification of verbal communications typically associated with manned space missions or simulations was developed. The study was carried out in two phases. Phase 1 was devoted to identification of crew tasks and activities which require voice communication for accomplishment or reporting. Phase 2 entailed development of a message classification system and a preliminary test of its feasibility. The classification system permits voice communications to be analyzed to three progressively more specific levels of detail and to be described in terms of message content, purpose, and the participants in the information exchange. A coding technique was devised to allow messages to be recorded by an eight-digit number.

Older, H. J.↗
NASA NTRS1991

Expert system verification and validation study. Phase 2: Requirements Identification. Delivery 2: Current requirements applicability

The second phase of a task is described which has the ultimate purpose of ensuring that adequate Expert Systems (ESs) Verification and Validation (V and V) tools and techniques are available for Space Station Freedom Program Knowledge Based Systems development. The purpose of this phase is to recommend modifications to current software V and V requirements which will extend the applicability of the requirements to NASA ESs.

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NASA NTRS1983

Research on the use of space resources

The second year of a multiyear research program on the processing and use of extraterrestrial resources is covered. The research tasks included: (1) silicate processing, (2) magma electrolysis, (3) vapor phase reduction, and (4) metals separation. Concomitant studies included: (1) energy systems, (2) transportation systems, (3) utilization analysis, and (4) resource exploration missions. Emphasis in fiscal year 1982 was placed on the magma electrolysis and vapor phase reduction processes (both analytical and experimental) for separation of oxygen and metals from lunar regolith. The early experimental work on magma electrolysis resulted in gram quantities of iron (mixed metals) and the identification of significant anode, cathode, and container problems. In the vapor phase reduction tasks a detailed analysis of various process concepts led to the selection of two specific processes designated as ""Vapor Separation'' and ""Selective Ionization.'' Experimental work was deferred to fiscal year 1983. In the Silicate Processing task a thermophysical model of the casting process was developed and used to study the effect of variations in material properties on the cooling behavior of lunar basalt.

Carroll, W. F.↗
DOE OSTI2026

Dara: Automated Multiple-Hypothesis Phase Identification and Refinement from Powder X-ray Diffraction

Powder X-ray diffraction (XRD) is a foundational technique for characterizing crystalline materials. However, the reliable interpretation of XRD patterns, particularly in multiphase systems, remains a manual and expertise-demanding task. As a characterization method that only provides structural information, multiple reference phases can often be fit to a single pattern, leading to potential misinterpretation when alternative solutions are overlooked. To ease humans’ efforts and address the challenge, we introduce Dara (data-driven automated Rietveld analysis), a framework designed to automate the robust identification and refinement of multiple phases from powder XRD data. Dara performs an exhaustive tree search over all plausible phase combinations within a given chemical space and validates each hypothesis using the BGMN Rietveld refinement routine. Key features include structural database filtering, automatic clustering of isostructural phases during tree expansion, and peak-matching-based scoring to identify promising phases for refinement. When ambiguity exists, Dara generates multiple hypothesis which can then be decided between by human experts or with further characterization tools. By enhancing the reliability and accuracy of phase identification, Dara enables scalable analysis of realistic complex XRD patterns and provides a foundation for integration into multimodal characterization workflows, moving toward fully self-driving materials discovery.

Biological databases↗
NASA NTRS1986

Sliding Seal Materials for Adiabatic Engines, Phase 2

An essential task in the development of the heavy-duty adiabatic diesel engine is identification and improvements of reliable, low-friction piston seal materials. In the present study, the sliding friction coefficients and wear rates of promising carbide, oxide, and nitride materials were measured under temperature, environmental, velocity, and loading conditions that are representative of the adiabatic engine environment. In addition, silicon nitride and partially stabilized zirconia disks were ion implanted with TiNi, Ni, Co, and Cr, and subsequently run against carbide pins, with the objective of producing reduced friction via solid lubrication at elevated temperature. In order to provide guidance needed to improve materials for this application, the program stressed fundamental understanding of the mechanisms involved in friction and wear. Electron microscopy was used to elucidate the micromechanisms of wear following wear testing, and Auger electron spectroscopy was used to evaluate interface/environment interactions which seemed to be important in the friction and wear process. Unmodified ceramic sliding couples were characterized at all temperatures by friction coefficients of 0.24 and above. The coefficient at 800 C in an oxidizing environment was reduced to below 0.1, for certain material combinations, by the ion implanation of TiNi or Co. This beneficial effect was found to derive from lubricious Ti, Ni, and Co oxides.

Lankford, J.↗
DOE OSTI2025

What Is the Limit of Quantification for the Minor Phase in Time-of-Flight Neutron Diffraction? A Case Study on Fe and Ni Powder Mixtures at VULCAN

A phase present in small quantities within materials may not simply serve as a secondary component; it can play a crucial role in determining the integrity, properties, and performance of the material. These minor but important phases usually draw attention in material design and processing for fundamental understanding as well as material quality control. Accurately quantifying a minor phase amid a majority phase, especially at extremely low fractions, remains a challenging task. Time-of-flight neutron diffraction, coupled with advanced pattern analysis techniques like Rietveld refinement, is a powerful tool for crystal structure identification and phase quantification. The deep penetrating capability of neutrons enables the detection and quantification of trace phases within materials. In this study, the quantification limits of time-of-flight neutron diffraction were explored using the VULCAN diffractometer at the Spallation Neutron Source, using Fe–Ni powder mixtures as a sample system. By comparing the refinement results to the known weighed values, it was determined that the reliable quantification of a minor Ni phase is achievable down to about 0.1 wt% while a Ni fraction as low as 0.02 wt% is difficult to trace. Effective control of the refinement parameters, especially the profile function parameters, are found to significantly influence the convergence of fittings and the accuracy of phase quantification.

Rietveld refinement↗
DOE OSTI2021

Risk Assessment and Management Workflow—An Example of the Southwest Regional Partnership

This paper summarizes the risk assessment and management workflow developed and applied to the Southwest Regional Partnership on Carbon Sequestration (SWP) Phase III Demonstration Project. The risk assessment and management workflow consists of six primary tasks, including management planning, identification, qualitative analysis, quantitative analysis, response planning, and monitoring. Within the workflow, the SWP assembled and iteratively updated a risk registry that identifies risks for all major activities of the project. Risk elements were ranked with respect to the potential impact to the project and the likelihood of occurrence. Both qualitative and quantitative risk analyses were performed. To graphically depict the interactions among risk elements and help building risk scenarios, process influence diagrams were used to represent the interactions. The SWP employed quantitative methods of risk analysis including Response Surface Method (RSM), Polynomial Chaos Expansion (PCE), and the National Risk Assessment Partnership (NRAP) toolset. The SWP also developed risk response planning and performed risk control and monitoring to prevent the risks from affecting the project and ensure the effectiveness of risk management. As part of risk control and monitoring, existing and new risks have been tracked and the response plan was subsequently evaluated. Findings and lessons learned from the SWP’s risk assessment and management efforts will provide valuable information for other commercial geological CO2 storage projects.

Lee, Si-Yong↗
DOE OSTI2024

Bulk high-temperature superconductivity in pressurized tetragonal La 2 PrNi 2 O 7

The Ruddlesden–Popper (R–P) bilayer nickelate, La 3 Ni 2 O 7 , was recently found to show signatures of high-temperature superconductivity (HTSC) at pressures above 14 GPa . Subsequent investigations achieved zero resistance in single-crystalline and polycrystalline samples under hydrostatic pressure conditions. Yet, obviousdiamagnetic signals, the other hallmark of superconductors, are still lacking owing to the flamentary nature with low superconducting volume fraction. The presence of a new 1313 polymorph and competing R–P phases obscured proper identification of the phase for HTSC. Thus, achieving bulk HTSC and identifying the phase at play are the most prominent tasks. Here we address these issues in the praseodymium (Pr)-doped La 2 PrNi 2 O 7 polycrystalline samples. We find that substitutions of Pr for La efectively inhibit the intergrowth of diferent R–P phases, resulting in a nearly pure bilayer structure. For La 2 PrNi 2 O 7 , pressure-induced orthorhombic to tetragonalstructural transition takes place at P c ≈ 11 GPa, above which HTSC emerges gradually on further compression. The superconducting transition temperatures at 18–20 GPa reach $T$ $^{onset}_{c}$ = $82.5$ $K$ and $T$ $^{zero}_{c}$ = $60$ $K$, which are the highest values, to our knowledge, among known nickelate superconductors. Importantly, bulk HTSC was testified by detecting clear diamagnetic signals below about 75 K with appreciable superconducting shielding volume fractions at a pressure of above 15 GPa. Further, our results not only resolve the existing controversies but also provide directions for exploring bulk HTSC in the bilayer nickelates.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1980

Atmospheric lidar multi-user instrument system definition study

A spaceborne lidar system for atmospheric studies was defined. The primary input was the Science Objectives Experiment Description and Evolutionary Flow Document. The first task of the study was to perform an experiment evolutionary analysis of the SEED. The second task was the system definition effort of the instrument system. The third task was the generation of a program plan for the hardware phase. The fourth task was the supporting studies which included a Shuttle deficiency analysis, a preliminary safety hazard analysis, the identification of long lead items, and development studies required. As a result of the study an evolutionary Lidar Multi-User Instrument System (MUIS) was defined. The MUIS occupies a full Spacelab pallet and has a weight of 1300 kg. The Lidar MUIS laser provides a 2 joule frequency doubled Nd:YAG laser that can also pump a tuneable dye laser wide frequency range and bandwidth. The MUIS includes a 1.25 meter diameter aperture Cassegrain receiver, with a moveable secondary mirror to provide precise alignment with the laser. The receiver can transmit the return signal to three single and multiple photomultiple tube detectors by use of a rotating fold mirror. It is concluded that the Lidar MUIS proceed to program implementation.

Greco, R. V.↗
NASA NTRS1993

Nonlinear stability and control study of highly maneuverable high performance aircraft

This project is intended to research and develop new nonlinear methodologies for the control and stability analysis of high-performance, high angle-of-attack aircraft such as HARV (F18). Past research (reported in our Phase 1, 2, and 3 progress reports) is summarized and more details of final Phase 3 research is provided. While research emphasis is on nonlinear control, other tasks such as associated model development, system identification, stability analysis, and simulation are performed in some detail as well. An overview of various models that were investigated for different purposes such as an approximate model reference for control adaptation, as well as another model for accurate rigid-body longitudinal motion is provided. Only a very cursory analysis was made relative to type 8 (flexible body dynamics). Standard nonlinear longitudinal airframe dynamics (type 7) with the available modified F18 stability derivatives, thrust vectoring, actuator dynamics, and control constraints are utilized for simulated flight evaluation of derived controller performance in all cases studied.

Mohler, R. R.↗
NASA NTRS1991

Beyond the Baseline 1991: Proceedings of the Space Station Evolution Symposium. Volume 1: Space Station Freedom, part 1

Personnel responsible for Advanced Systems Studies and Advanced Development within the Space Station Freedom Program reported on the results of their work to date. The results of SSF Advanced Studies provide a road map for the evolution of Freedom in terms of user requirements, utilization and operations concepts, and growth options for distributed systems. Regarding these specific systems, special attention is given to: highlighting changes made during restructuring; description of growth paths thru the follow-on and evolution phases; identification of minimum impact provisions to allow flexibility in the baseline; and identification of enhancing and enabling technologies. Products of these tasks include: engineering fidelity demonstrations and evaluations of advanced technology; detailed requirements, performance specifications, and design accommodations for insertion of advanced technology.

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