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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 937 records · Page 52

All sources NASA NTRS DOE OSTI

NASA NTRS2015

Status of NASA's Evolutionary Xenon Thruster (NEXT) Long-Duration Test as of 50,000 h and 900 kg Throughput

The NASA's Evolutionary Xenon Thruster (NEXT) project is developing the next-generation solar electric propulsion ion propulsion system with significant enhancements beyond the state-of-the-art NASA Solar Electric Propulsion Technology Application Readiness (NSTAR) ion propulsion system in order to provide future NASA science missions with enhanced propulsion capabilities. As part of a comprehensive thruster service life assessment, the NEXT Long-Duration Test (LDT) was initiated in June 2005 to demonstrate throughput capability and validate thruster service life modeling. The NEXT LDT exceeded its original qualification throughput requirement of 450 kg in December 2009. To date, the NEXT LDT has set records for electric propulsion lifetime and has demonstrated 50,170 h of operation, processed 902 kg of propellant, and delivered 34.9 MN-s of total impulse. The NEXT thruster design mitigated several life-limiting mechanisms encountered in the NSTAR design, dramatically increasing service life capability. Various component erosion rates compare favorably to the pretest predictions based upon semi-empirical ion thruster models. The NEXT LDT either met or exceeded all of its original goals regarding lifetime demonstration, performance and wear characterization, and modeling validation. In light of recent budget constraints and to focus on development of other components of the NEXT ion propulsion system, a voluntary termination procedure for the NEXT LDT began in April 2013. As part of this termination procedure, a comprehensive post-test performance characterization was conducted across all operating conditions of the NEXT throttle table. These measurements were found to be consistent with prior data that show minimal degradation of performance over the thruster's 50 kh lifetime. Repair of various diagnostics within the test facility is presently planned while keeping the thruster under high vacuum conditions. These diagnostics will provide additional critical information on the current state of the thruster, in regards to performance and wear, prior to destructive post-test analyses performed on the thruster under atmosphere conditions.

Ion thruster↗

DOE OSTI2021

EpitopeVec: linear epitope prediction using deep protein sequence embeddings

Motivation: B-cell epitopes (BCEs) play a pivotal role in the development of peptide vaccines, immuno-diagnostic reagents and antibody production, and thus in infectious disease prevention and diagnostics in general. Experimental methods used to determine BCEs are costly and time-consuming. Therefore, it is essential to develop computational methods for the rapid identification of BCEs. Although several computational methods have been developed for this task, generalizability is still a major concern, where cross-testing of the classifiers trained and tested on different datasets has revealed accuracies of 51–53%. Results: We describe a new method called EpitopeVec, which uses a combination of residue properties, modified antigenicity scales, and protein language model-based representations (protein vectors) as features of peptides for linear BCE predictions. Extensive benchmarking of EpitopeVec and other state-of-the-art methods for linear BCE prediction on several large and small datasets, as well as cross-testing, demonstrated an improvement in the performance of EpitopeVec over other methods in terms of accuracy and area under the curve. As the predictive performance depended on the species origin of the respective antigens (viral, bacterial and eukaryotic), we also trained our method on a large viral dataset to create a dedicated linear viral BCE predictor with improved cross-testing performance.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

MLOps for Beam Controls

Machine learning operations (MLOps) is the standardization and streamlining of the ML development lifecycle to address the challenges associated with large-scale machine learning applications. The full MLOps pipeline consists of open-source tools: DataHub, MinIO and MLflow. It is being used for dataset management and model development to handle changing data dependencies, varying business needs, reproducibility, and diverse teams working with differing tools and skills. To demonstrate the completion of an MLOps pipeline for particle accelerator operations, we are deploying a simple script that computes settings for the Booster’s gradient magnet power supply. Once the demonstration is complete, we will develop and deploy ML-based optimization algorithms to improve Booster’s overall efficiency. This MLOps pipeline opens the gate to systematically develop and deploy ML applications for accelerator controls and diagnostics.

43 PARTICLE ACCELERATORS↗

NASA NTRS1983

An evaluation of the synoptic- and mesoscale predictability of the Mesoscale Atmospheric Simulation System (MASS 2.0) model

A report is presented regarding the synoptic- and mesoscale predictive capabilities of a regional-scale numerical weather prediction model known as the Mesoscale Atmospheric Simulation System (MASS, Version 2.0). The development of this model has been discussed by Kaplan et al. (1982). An evaluation of the performance of MASS 2.0 is based on the study of a sample of approximately thirty 12 h and 24 h forecasts of atmospheric flow patterns over the U.S. during spring and early summer of 1982. A description of model systems is provided, and synoptic-scale evaluation methods are considered along with aspects of mesoscale evaluation methodology, examples of coherent mesoscale information provided by MASS 2.0, the results of a diagnostic study of mesoscale convective systems (MCS), and the results of a limited real-time forecast experiment.

Koch, S. E.↗

NASA NTRS1988

Graph-based real-time fault diagnostics

A real-time fault detection and diagnosis capability is absolutely crucial in the design of large-scale space systems. Some of the existing AI-based fault diagnostic techniques like expert systems and qualitative modelling are frequently ill-suited for this purpose. Expert systems are often inadequately structured, difficult to validate and suffer from knowledge acquisition bottlenecks. Qualitative modelling techniques sometimes generate a large number of failure source alternatives, thus hampering speedy diagnosis. In this paper we present a graph-based technique which is well suited for real-time fault diagnosis, structured knowledge representation and acquisition and testing and validation. A Hierarchical Fault Model of the system to be diagnosed is developed. At each level of hierarchy, there exist fault propagation digraphs denoting causal relations between failure modes of subsystems. The edges of such a digraph are weighted with fault propagation time intervals. Efficient and restartable graph algorithms are used for on-line speedy identification of failure source components.

Padalkar, S.↗

NASA NTRS1997

Rocket Ejector Studies for Application to RBCC Engines: An Integrated Experimental/CFD Approach

Recent interest in low cost, reliable access to space has generated increased interest in advanced technology approaches to space transportation systems. A key to the success of such programs lies in the development of advanced propulsion systems capable of achieving the performance and operations goals required for the next generation of space vehicles. One extremely promising approach involves the combination of rocket and air- breathing engines into a rocket-based combined-cycle engine (RBCC). A key element of that engine is the rocket ejector which is utilized in the zero to Mach two operating regime. Studies of RBCC engine concepts are not new and studies dating back thirty years are well documented in the literature. However, studies focused on the rocket ejector mode of the RBCC cycle are lacking. The present investigation utilizes an integrated experimental and computation fluid dynamics (CFD) approach to examine critical rocket ejector performance issues. In particular, the development of a predictive methodology capable of performance prediction is a key objective in order to analyze thermal choking and its control, primary/secondary pressure matching considerations, and effects of nozzle expansion ratio. To achieve this objective, the present study emphasizes obtaining new data using advanced optical diagnostics such as Raman spectroscopy and CFD techniques to investigate mixing in the rocket ejector mode. A new research facility for the study of the rocket ejector mode is described along with the diagnostic approaches to be used. The CFD modeling approach is also described along with preliminary CFD predictions obtained to date.

Pal, S.↗

NASA NTRS1999

Regional Climate Simulation Experiments with a Variable Resolution Stretched Grid GCM

The variable resolution stretched grid (SG) version of the Goddard Earth Observing System (GEOS) GCM has been recently developed and tested in a regional climate simulation mode. The SG-approach is an alternative to the widely used nested grid approach introduced a decade ago as a pioneering step to regional climate modeling. The region of interest with a uniform about 60 km resolution used in experiments is a rectangle over the U.S. The results of one annual as well as two-month simulations for the anomalous climate event of the U.S. drought of 1988, are validated against data analysis fields and diagnostics. The efficient regional down-scaling as well as the positive impact of fine regional resolution, are obtained. The SG-concept appeared to be a promising candidate for regional and subregional climate studies and applications.

Takacs, Lawrence L.↗

DOE OSTI2024

MLOps for Beam Controls

43 PARTICLE ACCELERATORS↗

DOE OSTI2024

MLOps for Beam Controls

43 PARTICLE ACCELERATORS↗

DOE OSTI2021

National Virtual Biotechnology Laboratory: Report on Rapid R&D Solutions to the COVID-19 Crisis

With funding from the CARES Act, the U.S Department of Energy (DOE) established the National Virtual Biotechnology Laboratory (NVBL) in March 2020 to address key challenges associated with the COVID-19 crisis. NVBL brought together the broad scientific and technical expertise and resources of DOE’s 17 national laboratories to help tackle medical supply short ages, discover potential drugs to fight the virus, develop and validate COVID-19 testing methods, model disease spread and impact across the nation, and understand virus transport in buildings and the environment. National laboratory resources leveraged for this effort include a suite of world-leading user facilities broadly available to the research community, such as light and neutron sources, nanoscale science research centers, sequencing and biocharacterization facilities, and high-performance computing facilities. Within months, NVBL teams produced innovations in materials and advanced manufacturing that mitigated shortages in test kits and personal protective equipment (PPE), creating nearly 1,000 new jobs. They used DOE’s high-performance computers and light and neutron sources to identify promising candidates for antibodies and antivirals that universities and drug companies are now evaluating. NVBL researchers also developed new diagnostic targets and sample collection approaches, and supported U.S. Food and Drug Administration (FDA), Centers for Disease Control and Prevention (CDC), and U.S. Department of Defense (DoD) efforts to establish national guidelines used in administering millions of tests. Researchers used artificial intelligence and high-performance computing to produce near-real-time data analysis to forecast disease transmission, stress on public health infrastructure, and economic impact, which supported decision-makers at the local, state, and national levels. NVBL teams also studied how to control indoor virus movement to minimize uptake and protect human health. NVBL’s accomplishments demonstrate not only the powerful resource represented by DOE’s national laboratories working together to meet national needs, but also the effectiveness of the integrated NVBL framework for rapidly responding to emergencies with research and development (R&D) solutions. As the fight against COVID continues, sustained efforts are needed to confront this pandemic as well as future threats. Examples include: 1) Establishing “supply chains on demand” to meet emergency production needs by leveraging the materials and manufacturing expertise of DOE national laboratories and developing advances in electronics, sensing, robotics, and automation capabilities; 2) Improving the speed and robustness of drug discovery by integrating experimental platforms with DOE’s computational and experimental user facilities, which provide unique resources to support the discovery of high-potential therapeutic agents; 3) Protecting public, environmental, and animal health by developing new testing protocols and instrumentation adaptable to diverse sample types (both physiological and environmental) to quickly detect a wide range of pathogens and monitor other biorisks; 4) Supporting near-real-time data needs of decision-makers at the local, regional, state, and national levels by advancing data curation, analysis, and modeling using artificial intelligence and new data science tools for managing and evaluating large diverse datasets; 5) Harnessing DOE’s expertise in environmental modeling to design rooms and air handling for offices, classrooms, restaurants, and other structures to minimize biorisk transmissions. Going forward, NVBL is poised to apply the unique capabilities and expertise of the national laboratory complex to future national and international emergencies, both natural and engineered. Through this framework, the Office of Science will continue to be an integral component of agency wide efforts to prepare for and respond to biorisks and other crises.

42 ENGINEERING↗

DOE OSTI2025

Soot formation and precursor chemistry in Counterflow flames of aviation fuel surrogates

To meet market demands, the aviation sector is interested in utilizing drop-in Synthetic Aviation Turbine Fuels (SATF), either as neat fuels or in blends with conventional Jet A. SATF currently approved in standard specifications may have lower aromatic content with significant fractions of normal, branched, and cyclo-alkanes. Fundamental studies on soot formation from aviation fuels (Jet A, SATF) and their surrogate components are essential to understand how fuel composition influences soot and soot precursor formation. Here, this study reports new measurements of polycyclic aromatic hydrocarbons (PAH) and soot in counterflow diffusion flames (CDFs) of aviation fuel surrogates. Both intrusive and non-intrusive diagnostics are employed to determine the profiles of temperature, gas phase species, PAHs (up to C16), and soot volume fraction (SVF) in CDFs of iso-octane and surrogate mixtures. These measurements shed light on the transition of soot precursors to primary soot particles. In addition to serving as a common surrogate component in Jet A surrogate mixtures, iso-octane is a template species for larger, less volatile branched alkanes found in SATF mixtures. The newly developed Lawrence Livermore National Laboratory (LLNL) PAH and soot model successfully captures temperature, precursor species, and SVF profiles for the mixtures and conditions discussed in this work. Finally, a high-fidelity surrogate for Jet A is proposed that matches targeted physical and chemical properties well, while leveraging the wide range of candidate fuel molecules available in the LLNL detailed chemical model. The proposed surrogate formulation is validated against newly acquired measurements of the surrogate and literature measurements of Jet A. These new experiments and simulations provide critical insights into the PAH and soot formation from aviation fuels. Reaction pathways which require further investigation are highlighted, such that future work may bridge the remaining quantitative gaps in predicting soot formation from aviation fuel surrogates and surrogate components.

Aviation fuels↗

DOE OSTI2020

The Importance of Convective Stage on Out-of-Cloud Convectively Induced Turbulence from High-Resolution Simulations

Turbulence (clear-air, mountain wave, convectively induced) is an aviation hazard that is a challenge to forecast due to the coarse resolution ultilized in operational weather models. Turbulence indices are commonly used to aid pilots in avoiding turbulence, but these indices have been designed and calibrated for midlatitude clear-air turbulence prediction (e.g., the Ellrod index). A significant limitation with current convectively induced turbulence (CIT) prediction is the lack of storm stage dependency. In this study, six high-resolution simulations of tropical oceanic and midlatitude continental convection are performed to characterize the turbulent environment near various convective types during the developing and mature stages. Second-order structure functions, a diagnostic commonly used to identify turbulence in turbulence prediction systems, are used to characterize the probability of turbulence for various convective types. Turbulence likelihood was found to be independent of region (i.e., tropical vs midlatitude) but dependent on convective stage. The probability of turbulence increased near developing convection for the majority of cases. Additional analysis of static stability and vertical wind shear, indicators of turbulence potential, showed that the convective environment near developing convection was more favorable for turbulence production than mature convection. Near developing convection, static stability decreased and vertical wind shear increased. Vertical wind shear near mature and developing convection was found to be weakly correlated to turbulence intensity in both the tropics and the midlatitudes. Finally, this study emphasizes the need for turbulence avoidance guidelines for the aviation community that are dependent on convective stage.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2004

Development of In-Situ Erosion Measurement Techniques for Application to Real-Time Determination of Plasma Thruster Component Lifetimes

This research has resulted in advancing the laser-based diagnostic capability and the ion optics development in the ion propulsion program at NASA GRC. Laser-based plasma diagnostics have been demonstrated in tabletop experiments and, in the case of LDI, on laboratory hollow cathodes. Assessment by GRC of its resources and priorities two years into the grant lead to a refocusing of the research effort away from the development of a real-time erosion rate measurement technique. The extension of the diagnostic techniques to diagnostic tools has been transferred to graduate students under the technical direction of the PI. These diagnostics may facilitate the development of ion thruster with significantly improved throughput capability for lower-power (10 kW) missions High-Isp, Long-lived ion optics development has proceeded from simple extensions of state-of-the-art geometries to radically different geometries and materials. Full-scale testing of these ion optics has demonstrated a significant advance in the throughput capability of ion thrusters enabling significantly more demanding missions. The capability to predict the throughput was developed and will continue to be upgraded. The performance models have been validated via full-scale testing. Partial validation of the throughput prediction will be completed via an upcoming wear test of the ion optics.

Source record↗

NASA NTRS2011

Land Surface Verification Toolkit (LVT) - A Generalized Framework for Land Surface Model Evaluation

Model evaluation and verification are key in improving the usage and applicability of simulation models for real-world applications. In this article, the development and capabilities of a formal system for land surface model evaluation called the Land surface Verification Toolkit (LVT) is described. LVT is designed to provide an integrated environment for systematic land model evaluation and facilitates a range of verification approaches and analysis capabilities. LVT operates across multiple temporal and spatial scales and employs a large suite of in-situ, remotely sensed and other model and reanalysis datasets in their native formats. In addition to the traditional accuracy-based measures, LVT also includes uncertainty and ensemble diagnostics, information theory measures, spatial similarity metrics and scale decomposition techniques that provide novel ways for performing diagnostic model evaluations. Though LVT was originally designed to support the land surface modeling and data assimilation framework known as the Land Information System (LIS), it also supports hydrological data products from other, non-LIS environments. In addition, the analysis of diagnostics from various computational subsystems of LIS including data assimilation, optimization and uncertainty estimation are supported within LVT. Together, LIS and LVT provide a robust end-to-end environment for enabling the concepts of model data fusion for hydrological applications. The evolving capabilities of LVT framework are expected to facilitate rapid model evaluation efforts and aid the definition and refinement of formal evaluation procedures for the land surface modeling community.

Kumar, Sujay V.↗

NASA NTRS2010

Monitoring System for Storm Readiness and Recovery of Test Facilities: Integrated System Health Management (ISHM) Approach

Severe weather events are likely occurrences on the Mississippi Gulf Coast. It is important to rapidly diagnose and mitigate the effects of storms on Stennis Space Center's rocket engine test complex to avoid delays to critical test article programs, reduce costs, and maintain safety. An Integrated Systems Health Management (ISHM) approach and technologies are employed to integrate environmental (weather) monitoring, structural modeling, and the suite of available facility instrumentation to provide information for readiness before storms, rapid initial damage assessment to guide mitigation planning, and then support on-going assurance as repairs are effected and finally support recertification. The system is denominated Katrina Storm Monitoring System (KStorMS). Integrated Systems Health Management (ISHM) describes a comprehensive set of capabilities that provide insight into the behavior the health of a system. Knowing the status of a system allows decision makers to effectively plan and execute their mission. For example, early insight into component degradation and impending failures provides more time to develop work around strategies and more effectively plan for maintenance. Failures of system elements generally occur over time. Information extracted from sensor data, combined with system-wide knowledge bases and methods for information extraction and fusion, inference, and decision making, can be used to detect incipient failures. If failures do occur, it is critical to detect and isolate them, and suggest an appropriate course of action. ISHM enables determining the condition (health) of every element in a complex system-of-systems or SoS (detect anomalies, diagnose causes, predict future anomalies), and provide data, information, and knowledge (DIaK) to control systems for safe and effective operation. ISHM capability is achieved by using a wide range of technologies that enable anomaly detection, diagnostics, prognostics, and advise for control: (1) anomaly detection algorithms and strategies, (2) fusion of DIaK for anomaly detection (model-based, numerical, statistical, empirical, expert-based, qualitative, etc.), (3) diagnostics/prognostics strategies and methods, (4) user interface, (5) advanced control strategies, (6) integration architectures/frameworks, (7) embedding of intelligence. Many of these technologies are mature, and they are being used in the KStorMS. The paper will describe the design, implementation, and operation of the KStorMS; and discuss further evolution to support other needs such as condition-based maintenance (CBM).

Figueroa, Fernando↗

DOE OSTI2026

Flame Chemistry Workshop: a perspective on challenges and strategic actions in combustion experiments and chemical kinetics modeling

Continued progress in the development of predictive models for combustion chemistry—including ignition and flame behavior, species evolution, and combustion system performance—relies on overcoming enduring and emerging challenges in experimental measurements, theoretical formulations, and chemical kinetics mechanism construction. As combustion science continues to coincide with advances in sustainable fuels development, plasma technologies, and automated modeling capabilities, the need for coordinated, community-driven strategies is essential. The Flame Chemistry Workshop (FCWS), held biennially before the International Symposium on Combustion, serves as a dedicated platform to identify, consolidate, and address these challenges in a structured and collaborative manner. This perspective arises from discussions at the 7th FCWS in Milan, Italy (2024), and presents a collective view of the critical barriers currently limiting progress. Across the five technical domains discussed during the 7th FCWS – sustainable fuels combustion, advanced diagnostics for combustion measurements, experiments and modeling in plasma combustion, artificial intelligence and automated methods for theory and mechanisms generation, and chemical kinetic models—a series of persistent and emerging scientific challenges were identified, highlighting the need for deeper integration between three areas: theory, experiments, and modeling. In conclusion, the present article concisely describes present challenges that were identified in each of the technical domains in an effort to streamline and coordinate solutions to accelerate progress in combustion science.

Chemical kinetics↗

NASA NTRS1986

A simple diagnostic tool for the investigation of persistent phenomena with application to the summer 1980 U.S. heat wave

A simple and readily-computed index for diagnostic and numerical weather prediction studies of heat waves and other persistent anomalies has been developed. The index takes into account the two important factors of intensity and persistence and it is highly correlated to the surface air temperature on a variety of time-scales. The utilization of this index for investigating the severe, summer 1980 U.S. heat wave and drought results in an instructive description of the spatial and temporal development of the phenomenon. An assessment of the skill of the Goddard Laboratory for Atmospheres (GLA) model in forecasting the initiation and breakdown of the heat wave in terms of the index is also illustrated.

Wolfson, Noah↗

NASA NTRS2000

Ion Thruster Used to Propel the Deep Space 1 Spacecraft to Comet Encounters

The NASA Solar Electric Propulsion Technology Applications Readiness (NSTAR) Project provided a xenon ion propulsion system to the Deep Space 1 (DS1) spacecraft to validate the propulsion system as well as perform primary propulsion for asteroid and comet encounters. The On-Board Propulsion Branch of the NASA Glenn Research Center at Lewis Field developed engineering model versions of the 30-cm-diameter ion thruster and the 2.5-kW power processor unit (PPU). Glenn then transferred the thruster and PPU technologies to Hughes Electron Dynamics and managed the contract, which supplied two flight sets of thrusters and PPU s to the Deep Space 1 spacecraft and to a ground-based life verification test at the Jet Propulsion Laboratory (JPL). In addition to managing the DS1 spacecraft development, JPL was responsible for the NSTAR Project management, thruster life tests, the feed system, diagnostics, and propulsion subsystem integration. The ion propulsion development team included NASA Glenn, JPL, Hughes Electronics, Moog Inc., and Spectrum Astro Inc. The overall NSTAR subsystem dry mass, including thruster, PPU, controller, cables, and the xenon storage and feed system, is 48 kg. The mass of the xenon stored onboard DS1 was about 81 kg, and the spacecraft wet mass was approximately 500 kg.The DS1 spacecraft was launched on October 24, 1998, and on July 29, 1999, it flew within 16 miles of the small asteroid Braille (formerly 1992KD) at a relative speed of 35,000 mph. As of November 1999, the ion propulsion system had performed flawlessly for nearly 149 days of thrusting. NASA has approved an extension to the mission, which will allow DS1 to continue thrusting to encounters with two comets in 2001. The DS1 optical and plasma diagnostic instruments will be used to investigate the comet and space environments. The spacecraft is scheduled to fly past the dormant comet Wilson- Harrington in January 2001 and the very active comet Borrelly in September 2001, at which time approximately 500 days of ion engine thrusting will have been completed.

Sovey, James S.↗