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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

The kinetic analog of the pressure–strain interaction

Energy transport in weakly collisional plasma systems is often studied with fluid models and diagnostics. However, the applicability of fluid models is limited when collisions are weak or absent, and using a fluid approach can obscure kinetic processes that provide key insights into the physics of energy transport. Kinetic diagnostics retain all of the information in 3D-3V phase space and thereby reach beyond the insights of fluid models to elucidate the mechanisms responsible for collisionless energy transport. In this work, we derive the Kinetic Pressure–Strain (KPS): a kinetic analog of the pressure–strain interaction, which is the channel between flow energy density and internal energy density in fluid models. Through two case studies of electron Landau damping, we demonstrate that the KPS diagnostic can elucidate kinetic mechanisms that are responsible for energy transport in this channel, just as the related field–particle correlation is known to identify kinetic mechanisms of transport between electromagnetic field energy density and kinetic energy density in particle flows. In addition, we show that resonant electrons play a major role in transferring energy between fluid flows and internal energy during the process of Landau damping.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2004

Age and Pathway Diagnostics for a Stratospheric General Circulation Model

Using a variety of age diagnostic experiments we examine the stratospheric age spectrum of the Goddard Finite Volume Generd Circulation Model. Pulse tracer release age-of-air computations are compared to forward and backward trajectory computations. These comparisons show good agreement, and the age-of-air also compares well with observed long lived tracers. Pathway diagnostics show how air arrives in the lowermost stratosphere and the age structure of that region. Using tracers with different lifetimes we can estimate the age spectrum - this technique should be useful in diagnosing transport from various trace gas observations.

Schoeberl, Mark R.↗
NASA NTRS1993

Fuels and Combustion Technology for Advanced Aircraft Engines [Les Propergols et les Systèmes de Combustion pour les Moteurs d'Aéronefs]

The Conference Proceedings contains the 38 papers presented at the Propulsion and Energetics Panel 81st Symposium on "Fuels and Combustion Technology for Advanced Aircraft Engines" which was held from 10th-14th May 1993, in Fiuggi, Italy. The Technical Evaluation Report and the Keynote Address are included at the beginning, and discussions follow most papers. The Symposium was arranged in the following Sessions: Technology Overview Papers (2); Modelling: Pollutant Formation (4); Modelling: Combustor Design (5); High Temperature Fuels and Fuel Systems (6); Combustion Research: Performance (6); Combustion Research: Emissions (5); Fuel Atomization: Diagnostics and Modelling (5); and Combustion Research: Flowfleld and Mixing (4). The last paper is a contribution from Russia not allocated to a session. New technologies for low NOx combustors and advanced high-pressure/high temperature cycle engines result in unique problems in design and performance. There have been significant advances in modelling and diagnostics to aid the development of these technologies. The purpose of the Symposium was to bring together experts from industry, research establishments and universities to discuss fundamental and applied research in these areas as relevant to the development of advanced gas turbine engines, to exchange practical experience and to discuss the state of the art.

Fuels for aircraft↗
DOE OSTI2021

Scalable Technologies Achieving Risk-Informed Condition-Based Predictive Maintenance Enhancing the Economic Performance of Operating Nuclear Power Plants

The primary objective of the research presented in this report is to develop scalable technologies that are deployable across plant assets and across the nuclear fleet to achieve risk-informed predictive maintenance (PdM) strategies at commercial nuclear power plants (NPPs). Over the years, the nuclear fleet has relied on labor-intensive and time-consuming preventive maintenance (PM) programs, driving up operation and maintenance (O&M) costs to achieve high capacity factors. A well-constructed risk-informed PdM approach for an identified plant asset has been developed in this research, taking advantage of advancements in data analytics, machine learning (ML), artificial intelligence (AI), physics-informed modeling, and visualization. These technologies would allow commercial NPPs to reliably transition from current labor-intensive PM programs to a technology driven PdM program, eliminating unnecessary O&M costs. The work presented in the report is being developed as part of a collaborative research effort between Idaho National Laboratory and Public Service Enterprise Group Nuclear, LLC. This report (1) reflects the results of work by LWRS Program researchers with PSEG, Nuclear LLC-owned Salem and Hope Creek Nuclear Power Plants; (2) presents utilization of circulating water system (CWS) heterogeneous data and fault modes from both the Salem and Hope Creek nuclear power plant sites to develop salient fault signatures associated with each fault mode; (3) describes the integration of component-level predictive models into a robust system-level model enabled by the federated-transfer learning; (4) describes the development of physics-informed model of circulating water pump and motor; (5) develops a scalable risk and economic model; and (6) outlines the development of a user-centric visualization application. The outcomes presented in this report lays the foundation and provides a much-needed technical basis to focus on explainability and trustworthiness of ML and AI-based technologies, as part of future research.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1996

JANNAF Combustion Subcommittee Meeting

This volume, the first of four volumes, is a collection of 33 unclassified/unlimited papers which were presented at the 33rd Joint Army-Navy-NASA-Air Force (JANNAF) Combustion Subcommittee Meeting in conjunction with the Propulsion Systems Hazards at the Naval Postgraduate School, Monterey, CA. The JANNAF papers contained in this volume review airbreathing combustion measurement, performance and control; electrothermal-chemical gun experiment and modeling; liquid propellant gun experiment and modeling; solid propellant gun interior ballistics; formulation modeling and diagnostics, ignition, and gun barrel wear modeling, blast and flash; and ram, fastcore and gas generator gun propulsion concepts.

Fry, Ronald S.↗
NASA NTRS2001

Plasma CVD of Carbon Nanotubes

Carbon nanotubes(CNT) exhibit remarkable mechanical and unique electronic properties and thus have created excitement in the research community about their potential in electronics, computing, sensor and structural applications. Realization of these applications critically depends on the ability to control the properties(such as diameter, chirality) as well purity. We have investigated CNT growth using an inductively coupled plasma(ICP) process using hydrocarbon feedstock. The catalyst required for nanotube growth consists of thin sputtered layers of aluminum and iron(10 nm each) and aligned carbon nanotubes have been obtained. Optical emission diagnostics as well as a plasma modeling effort have been undertaken to understand growth mechanisms. This presentation will discuss growth characteristics under various pressure, power and feedgas compositions and our understanding from modeling and diagnostics.

Delzeit, Lance↗
DOE OSTI2024

eagles-project/asediag

Aerosol process analysis in model-native Spectral Element (SE) grid. Aerosol Diagnostics on Model Native Grid is a Python-based tool designed for diagnosing aerosol processes in E3SM. It is particularly designed to analyze simulation data on the model-native spectral element (SE) grid, soit is also known as Aerosol SE Diagnostics or “asediag”

Hassan, Taufiq↗
NASA NTRS2001

Mixing in the Extratropical Stratosphere: Model-measurements Comparisons using MLM Diagnostics

We evaluate transport processes in the extratropical lower stratosphere for both models and measurements with the help of equivalent length diagnostic from the modified Lagrangian-mean (MLM) analysis. This diagnostic is used to compare measurements of long-lived tracers made by the Cryogenic Limb Array Etalon Spectrometer (CLAES) on the Upper Atmosphere Research Satellite (UARS) with simulated tracers. Simulations are produced in Chemical and Transport Models (CTMs), in which meteorological fields are taken from the Goddard Earth Observing System Data Assimilation System (GEOS DAS), the Middle Atmosphere Community Climate Model (MACCM2), and the Geophysical Fluid Dynamics Laboratory (GFDL) "SKYHI" model, respectively. Time series of isentropic equivalent length show that these models are able to capture major mixing and transport properties observed by CLAES, such as the formation and destruction of polar barriers, the presence of surf zones in both hemispheres. Differences between each model simulation and the observation are examined in light of model performance. Among these differences, only the simulation driven by GEOS DAS shows one case of the "top-down" destruction of the Antarctic polar vortex, as observed in the CLAES data. Additional experiments of isentropic advection of artificial tracer by GEOS DAS winds suggest that diabatic movement might have considerable contribution to the equivalent length field in the 3D CTM diagnostics.

Ma, Jun↗
DOE OSTI2024

Results from a synthetic model of the ITER XRCS-Core diagnostic based on high-fidelity x-ray ray tracing

A high-fidelity synthetic diagnostic has been developed for the ITER core x-ray crystal spectrometer diagnostic based on x-ray ray tracing. This synthetic diagnostic has been used to model expected performance of the diagnostic, to aid in diagnostic design, and to develop engineering tolerances. The synthetic model is based on x-ray ray tracing using the recently developed xicsrt ray tracing code and includes a fully three-dimensional representation of the diagnostic based on the computer aided design. The modeled components are: plasma geometry and emission profiles, highly oriented pyrolytic graphite pre-reflectors, spherically bent crystals, and pixelated x-ray detectors. Plasma emission profiles have been calculated for Xe 44+ , Xe 47+ , and Xe 51+ , based on an ITER operational scenario available through the Integrated Modelling & Analysis Suite database, and modeled within the ray tracing code as a volumetric x-ray source; the shape of the plasma source is determined by equilibrium geometry and an appropriate wavelength distribution to match the expected ion temperature profile. All individual components of the x-ray optical system have been modeled with high-fidelity producing a synthetic detector image that is expected to closely match what will be seen in the final as-built system. Particular care is taken to maintain preservation of photon statistics throughout the ray tracing allowing for quantitative estimates of diagnostic performance.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Systematic benchmarking demonstrates large language models have not reached the diagnostic accuracy of traditional rare-disease decision support tools

Large language models (LLMs) show promise in supporting differential diagnosis, but their performance is challenging to evaluate due to the unstructured nature of their responses, and their accuracy compared to existing diagnostic tools is not well characterized. To assess the current capabilities of LLMs to diagnose genetic diseases, we benchmarked these models on 5213 previously published case reports using the Phenopacket Schema, the Human Phenotype Ontology and Mondo disease ontology. Prompts generated from each phenopacket were sent to seven LLMs, including four generalist models and three LLMs specialized for medical applications. The same phenopackets were used as input to a widely used diagnostic tool, Exomiser, in phenotype-only mode. The best LLM ranked the correct diagnosis first in 23.6% of cases, whereas Exomiser did so in 35.5% of cases. While the performance of LLMs for supporting differential diagnosis has been improving, it has not reached the level of commonly used traditional bioinformatics tools. Future research is needed to determine the best approach to incorporate LLMs into diagnostic pipelines.

Reese, Justin T. [Lawrence Berkeley National Labor↗
DOE OSTI2024

Evaluation of Deep Learning Model Architectures for Point-of-Care Ultrasound Diagnostics

Point-of-care ultrasound imaging is a critical tool for patient triage during trauma for diagnosing injuries and prioritizing limited medical evacuation resources. Specifically, an eFAST exam evaluates if there are free fluids in the chest or abdomen but this is only possible if ultrasound scans can be accurately interpreted, a challenge in the pre-hospital setting. In this effort, we evaluated the use of artificial intelligent eFAST image interpretation models. Widely used deep learning model architectures were evaluated as well as Bayesian models optimized for six different diagnostic models: pneumothorax (i) B- or (ii) M-mode, hemothorax (iii) B- or (iv) M-mode, (v) pelvic or bladder abdominal hemorrhage and (vi) right upper quadrant abdominal hemorrhage. Models were trained using images captured in 27 swine. Using a leave-one-subject-out training approach, the MobileNetV2 and DarkNet53 models surpassed 85% accuracy for each M-mode scan site. The different B-mode models performed worse with accuracies between 68% and 74% except for the pelvic hemorrhage model, which only reached 62% accuracy for all model architectures. These results highlight which eFAST scan sites can be easily automated with image interpretation models, while other scan sites, such as the bladder hemorrhage model, will require more robust model development or data augmentation to improve performance. With these additional improvements, the skill threshold for ultrasound-based triage can be reduced, thus expanding its utility in the pre-hospital setting.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Recent progress in atomic-scale controlled plasma processing

Atomic-scale control in plasma processing is becoming increasingly critical for fabricating of advanced semiconductor devices, particularly as the industry shifts toward three-dimensional (3D) architectures and high-aspect-ratio (HAR) structures. This review presents a comprehensive overview of recent developments in atomic-scale controlled plasma processes, organized along two key directions: the hierarchical structure of plasma–surface interactions and the generational evolution of atomic layer processing (ALP) technologies. We examined the gas phase, where molecular design enables selective generation of ions and radicals; the boundary layer, where transport phenomena govern species delivery into nanoscale features, and the surface, where temperature-dependent reactions and cyclic processing determine etching selectivity and precision. Building on this foundation, we outline five generations of ALP—from thermal atomic layer deposition to transport-aware, temporally and structurally decoupled processes—highlighting the increasing sophistication of process control. The review further explores the transition from empirical recipe development to science-based, data-driven methodologies. By integrating quantum-chemical modeling, advanced diagnostics, and machine learning, we demonstrated how predictive models can link plasma species composition to process outcomes, enabling autonomous and adaptive control strategies. Finally, this review discusses the broader societal implications of plasma process innovation through the E4 quartet: energy and resource efficiency, environmental sustainability, evolutionary advancement, and educational promotion. These principles guide the development of sustainable and intelligent atomic-scale manufacturing technologies that are not only technically advanced but also socially responsible.

Ishikawa, Kenji [Nagoya Univ. (Japan)] (ORCID:0000↗
NASA NTRS1994

Qualitative model-based diagnosis using possibility theory

The potential for the use of possibility in the qualitative model-based diagnosis of spacecraft systems is described. The first sections of the paper briefly introduce the Model-Based Diagnostic (MBD) approach to spacecraft fault diagnosis; Qualitative Modeling (QM) methodologies; and the concepts of possibilistic modeling in the context of Generalized Information Theory (GIT). Then the necessary conditions for the applicability of possibilistic methods to qualitative MBD, and a number of potential directions for such an application, are described.

Joslyn, Cliff↗
NASA NTRS2002

Hybrid Neural-Network: Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics Developed and Demonstrated

As part of the NASA Aviation Safety Program, a unique model-based diagnostics method that employs neural networks and genetic algorithms for aircraft engine performance diagnostics has been developed and demonstrated at the NASA Glenn Research Center against a nonlinear gas turbine engine model. Neural networks are applied to estimate the internal health condition of the engine, and genetic algorithms are used for sensor fault detection, isolation, and quantification. This hybrid architecture combines the excellent nonlinear estimation capabilities of neural networks with the capability to rank the likelihood of various faults given a specific sensor suite signature. The method requires a significantly smaller data training set than a neural network approach alone does, and it performs the combined engine health monitoring objectives of performance diagnostics and sensor fault detection and isolation in the presence of nominal and degraded engine health conditions.

Kobayashi, Takahisa↗