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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

Integrating Oil Debris and Vibration Measurements for Intelligent Machine Health Monitoring

A diagnostic tool for detecting damage to gears was developed. Two different measurement technologies, oil debris analysis and vibration were integrated into a health monitoring system for detecting surface fatigue pitting damage on gears. This integrated system showed improved detection and decision-making capabilities as compared to using individual measurement technologies. This diagnostic tool was developed and evaluated experimentally by collecting vibration and oil debris data from fatigue tests performed in the NASA Glenn Spur Gear Fatigue Rig. An oil debris sensor and the two vibration algorithms were adapted as the diagnostic tools. An inductance type oil debris sensor was selected for the oil analysis measurement technology. Gear damage data for this type of sensor was limited to data collected in the NASA Glenn test rigs. For this reason, this analysis included development of a parameter for detecting gear pitting damage using this type of sensor. The vibration data was used to calculate two previously available gear vibration diagnostic algorithms. The two vibration algorithms were selected based on their maturity and published success in detecting damage to gears. Oil debris and vibration features were then developed using fuzzy logic analysis techniques, then input into a multi sensor data fusion process. Results show combining the vibration and oil debris measurement technologies improves the detection of pitting damage on spur gears. As a result of this research, this new diagnostic tool has significantly improved detection of gear damage in the NASA Glenn Spur Gear Fatigue Rigs. This research also resulted in several other findings that will improve the development of future health monitoring systems. Oil debris analysis was found to be more reliable than vibration analysis for detecting pitting fatigue failure of gears and is capable of indicating damage progression. Also, some vibration algorithms are as sensitive to operational effects as they are to damage. Another finding was that clear threshold limits must be established for diagnostic tools. Based on additional experimental data obtained from the NASA Glenn Spiral Bevel Gear Fatigue Rig, the methodology developed in this study can be successfully implemented on other geared systems.

Dempsey, Paula J.↗
NASA NTRS2005

A Method to Analyze Tail Buffet Loads of Aircraft

Aircraft designers commit significant resources to the design of aircraft in meeting performance goals. Despite fulfilling traditional design requirements, many fighter aircraft have encountered buffet loads when demonstrating their high angle-of-attack maneuver capabilities. As a result, during test or initial production phases of fighter development programs, many new designs are impacted, usually in a detrimental way, by resulting in reassessing designs or limiting full mission capability. These troublesome experiences usually stem from overlooking or completely ignoring the effects of buffet during the design phase of aircraft. Perhaps additional requirements are necessary that addresses effects of buffet in achieving best aircraft performance in fulfilling mission goals. This paper describes a reliable, fairly simple, but quite general buffet loads analysis method to use in the initial design phases of fighter-aircraft development. The method is very similar to the random gust load analysis that is now commonly available in a commercial code, which this analysis capability is based, with some key modifications. The paper describes the theory and the implementation of the methodology. The method is demonstrated on a JSF prototype example problem. The demonstration also serves as a validation of the method, since, in the paper, the analysis is shown to nearly match the flight data. In addition, the paper demonstrates how the analysis method can be used to assess candidate design concepts in determining a satisfactory final aircraft configuration.

Pototzky, Anthony S.↗
NASA NTRS2005

A Tail Buffet Loads Prediction Method for Aircraft at High Angles of Attack

Aircraft designers commit significant resources to the design of aircraft in meeting performance goals. Despite fulfilling traditional design requirements, many fighter aircraft have encountered buffet loads when demonstrating their high angle-of-attack maneuver capabilities. As a result, during test or initial production phases of fighter development programs, many new designs are impacted, usually in a detrimental way, by resulting in reassessing designs or limiting full mission capability. These troublesome experiences usually stem from overlooking or completely ignoring the effects of buffet during the design phase of aircraft. Perhaps additional requirements are necessary that addresses effects of buffet in achieving best aircraft performance in fulfilling mission goals. This paper describes a reliable, fairly simple, but quite general buffet loads analysis method to use in the initial design phases of fighter-aircraft development. The method is very similar to the random gust load analysis that is now commonly available in a commercial code, which this analysis capability is based, with some key modifications. The paper describes the theory and the implementation of the methodology. The method is demonstrated on a JSF prototype example problem. The demonstration also serves as a validation of the method, since, in the paper, the analysis is shown to nearly match the flight data. In addition, the paper demonstrates how the analysis method can be used to assess candidate design concepts in determining a satisfactory final aircraft configuration.

Pototzky, Anthony S.↗
DOE OSTI2021

Development of Integrated Safety and Security Models for Comprehensive Reliability and Resiliency Evaluation

The security of the electric grid and supporting energy systems is crucial to national security. One of the complexities in analyzing the security of energy systems is the safety consequences that may result from accidents. For energy systems, the goal is to ensure that they operate as intended and that any consequences are mitigated or prevented. The integration of safety and security is paramount to protecting these systems from attacks and ensuring that large consequences are prevented. This report describes an integrated safety and security methodology to evaluate cybersecurity events that can lead to large consequences. This novel approach first describes how Systems-Theoretic Process Analysis (STPA) provides a digital causal analysis for Bayesian Networks (BNs). The use of STPA causal analysis provides a systematic approach to constructing BNs that adequately model cyber scenarios that result in consequences. When combined with the technical principles described in Risk-Informed Management of Enterprise Systems (RIMES), a comprehensive risk-informed cybersecurity analysis results that allows decision-makers to prioritize systems that most impact risk.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Probabilistic Zonal Reserve Requirements for Improved Energy Deliverability with Wind Power

In power systems with high penetration of renewable energy resources, uncertainty and variability of these stochastic resources introduce additional challenges for the operation of power systems. To improve the power system's reliability in the face of uncertainty, reserves are required as additional generation capacity to rebalance the power system following random disturbances. However, reserve deliverability is not guaranteed, because it may encounter potential transmission line congestion. Zonal reserve requirements can address this issue, but operators lack efficient ways to allocate reserves to zones while accounting for wind power forecast uncertainty. Herein we propose a methodology for probabilistic zonal reserve requirements to address wind power forecast uncertainties. This method estimates the probability distribution of line flows based on the system generation margin and injection shift factor. This estimate is then used to construct pre-defined and post-zonal reserve requirements. Case studies demonstrate that the proposed method efficiently schedules energy and reserves to balance energy and manage deliverability with wind power forecast uncertainty.We also discuss operational implications of the proposed method.

17 WIND ENERGY↗
NASA NTRS1984

Coating Life Prediction

Hot-section gas-turbine components typically require some form of coating for oxidation and corrosion protection. Efficient use of coatings requires reliable and accurate predictions of the protective life of the coating. Currently engine inspections and component replacements are often made on a conservative basis. As a result, there is a constant need to improve and develop the life-prediction capability of metallic coatings for use in various service environments. The purpose of this present work is aimed at developing of an improved methodology for predicting metallic coating lives in an oxidizing environment and in a corrosive environment.

Nesbitt, J. A.↗
NASA NTRS1988

AGT (Advanced Gas Turbine) technology project

An overall summary documentation is provided for the Advanced Gas Turbine Technology Project conducted by the Allison Gas Turbine Division of General Motors. This advanced, high risk work was initiated in October 1979 under charter from the U.S. Congress to promote an engine for transportation that would provide an alternate to reciprocating spark ignition (SI) engines for the U.S. automotive industry and simultaneously establish the feasibility of advanced ceramic materials for hot section components to be used in an automotive gas turbine. As this program evolved, dictates of available funding, Government charter, and technical developments caused program emphases to focus on the development and demonstration of the ceramic turbine hot section and away from the development of engine and powertrain technologies and subsequent vehicular demonstrations. Program technical performance concluded in June 1987. The AGT 100 program successfully achieved project objectives with significant technology advances. Specific AGT 100 program achievements are: (1) Ceramic component feasibility for use in gas turbine engines has been demonstrated; (2) A new, 100 hp engine was designed, fabricated, and tested for 572 hour at operating temperatures to 2200 F, uncooled; (3) Statistical design methodology has been applied and correlated to experimental data acquired from over 5500 hour of rig and engine testing; (4) Ceramic component processing capability has progressed from a rudimentary level able to fabricate simple parts to a sophisticated level able to provide complex geometries such as rotors and scrolls; (5) Required improvements for monolithic and composite ceramic gas turbine components to meet automotive reliability, performance, and cost goals have been identified; (6) The combustor design demonstrated lower emissions than 1986 Federal Standards on methanol, JP-5, and diesel fuel. Thus, the potential for meeting emission standards and multifuel capability has been initiated; (7) Small turbine engine aerodynamic and mechanical design capability has been initiated; and (8) An infrastructure of manpower, facilities, materials, and fabrication capabilities has been established which is available for continued development of ceramic component technology in gas turbine and other heat engines.

Source record↗
NASA NTRS1993

Lunar lander and return propulsion system trade study

This trade study was initiated at NASA/JSC in May 1992 to develop and evaluate main propulsion system alternatives to the reference First Lunar Outpost (FLO) lander and return-stage transportation system concept. Thirteen alternative configurations were developed to explore the impacts of various combinations of return stage propellants, using either pressure or pump-fed propulsion systems and various staging options. Besides two-stage vehicle concepts, the merits of single-stage and stage-and-a-half options were also assessed in combination with high-performance liquid oxygen and liquid hydrogen propellants. Configurations using an integrated modular cryogenic engine were developed to assess potential improvements in packaging efficiency, mass performance, and system reliability compared to non-modular cryogenic designs. The selection process to evaluate the various designs was the analytic hierarchy process. The trade study showed that a pressure-fed MMH/N2O4 return stage and RL10-based lander stage is the best option for a 1999 launch. While results of this study are tailored to FLO needs, the design date, criteria, and selection methodology are applicable to the design of other crewed lunar landing and return vehicles.

Hurlbert, Eric A.↗
DOE OSTI2026

AI-Based Analytics and Energy Modeling Framework for Characterizing Urban Energy Systems

Developing location-specific district energy models is essential for understanding energy patterns and supporting efficient management and planning decisions. However, accurately characterizing these models remains challenging due to gaps in building characteristics and labor-intensive traditional modeling workflows. To address these challenges, we develop an AI-based framework that integrates top-down and bottom-up building energy data to automate urban energy model characterization. The framework trains multimodal deep learning models using heterogeneous ResStockTM datasets to infer missing building characteristics from varying levels of known information and generate simulation-ready inputs for district-scale energy modeling. It also employs a conditioning-based injection approach to generate ”what-if” scenarios, enabling users to explore retrofit, efficiency, and technology-upgrade pathways. Integrated within URBANoptTM, a bottom-up district energy modeling platform for simulating co-located buildings, the framework infers detailed building-level inputs required for bottom-up simulations. Both localized and generalized AI models are developed to learn relationships across categorical, numerical, and time-series data, enabling reconstruction of missing attributes and generation of targeted upgrade scenarios. We demonstrate this methodology on a residential neighborhood in Baltimore, MD, assessing internal consistency against ResStock reference data and URBANopt simulation, and comparing selected attributes against real-world building characteristics. Results show strong overall predictive accuracy in data completion and scenario generation, with localized and generalized models offering complementary trade-offs between precision and scalability. Overall, our automated framework streamlines energy modeling and provides a reliable framework for urban building energy characterization.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Edge AI-Enhanced Traffic Monitoring and Anomaly Detection Using Multimodal Large Language Models

This paper addresses the challenge of traffic monitoring and incident detection in remote areas, utilizing multimodal large language models (LLMs) deployed on edge AI devices. The key novelty of the LLM is to convert real-time video streams into descriptive texts, enabling low-bandwidth transmissions and reliable detection of anomalies and incidents in environments of intermittent connectivity. The model is developed based on fine-tuning open-source LLMs and extending it with multi-modal capabilities to analyze video frames. Our work also involves deploying this model on edge devices such as Nvidia IGX Orin and is planned to be tested in realistic environments in future work. The methodology includes data set curation, iterative model fine-tuning and compression, and hardware-based optimization. This approach aims to enhance traffic safety and response speed in remote areas, marking a significant advancement in the application of AI for traffic monitoring and safety management.

Peruski, Ryan [University of Tennessee, Knoxville ↗
DOE OSTI2026

A large interlaboratory electron diffraction study of monolayer graphene

Standardisation of data collection and analysis is essential to enable commercialisation of 2D materials in a wide range of technologies. Selected area electron diffraction (SAED) in the transmission electron microscope (TEM) is one of the key methods for distinguishing monolayer from bilayer and few-layer graphene by comparing the 1st and 2nd order diffraction spot intensities. Yet there are many factors that can affect the reliability of data collection and interpretation, causing the measurement of monolayer samples to deviate from the literature boundary condition of $I_{\{\bar{2}110\}}$$/$$I_{\{1\bar{1}00\}}$ < 1 for monolayer graphene (1LG). Here we present the results of a large interlaboratory SAED comparison study, where 15 international laboratories measured and analysed nominally identical samples of chemical vapour deposited graphene. Large variations were observed in the measured ratios of diffraction spot intensities, with the largest variance associated with poor quality SAED data resulting from inadequate specimen handling and storage. To inform the reliable determination of monolayer thickness from SAED patterns we provide a description of best practice for specimen handling, TEM operation, data collection and analysis. This work was undertaken within VAMAS Technical Working Area 41: Graphene and related 2D materials—Project 9, the results of which have been directly incorporated into ISO/TS 21356–2 for the characterisation of graphene sheets. We find that when this methodology is followed, 1LG can be distinguished from bilayer or thicker material with high confidence where analysis of a single SAED pattern gives $I_{\{\bar{2}110\}}$$/$$I_{\{1\bar{1}00\}}$ < 1.2, even in the absence of precise specimen tilting.

2D materials↗
DOE OSTI2024

Coupling of the Electricity and Transportation Sectors - Part II: Risk Assessment

This comprehensive report explores the intricate interplay through “sector coupling” between the Transportation Sector, Energy Sector, and the critical infrastructure challenges faced by the United States. Aligned with the ambitious federal goal of reducing greenhouse gas emissions, the report analyzes the present vulnerabilities in the fossil fuel supply chain, highlighting instances where the dependence on this industry has been susceptible to local and global disruptions. With a focus on risk assessment, this report establishes a framework to compare the fossil fuel-based transportation sector with the burgeoning electrified sector, considering the evolving technologies in electric vehicles (EVs) and the nascent charging infrastructure. This study delves into the historical reliability of fossil fuel-based transportation and identifies opportunities for strengthening sector coupling between transportation and the electric sector. Assessments of the gasoline-diesel supply chain provide insights into both opportunities and threats, offering a roadmap for creating a more reliable and resilient electrified transportation system. We modeled sectoral coupling scenarios for the current internal combustion engine (ICE)-based and future EV-dominant landscapes, leveraging qualitative sector attributes to identify relative strengths and weaknesses in Part 1 of the report. In this report (Part 2) we continue to understand the role of the electricity sector on transportation and develop a semi-quantitative methodology is employed to evaluate and compare the risks associated with the fossil fueled and electric transportation sector during emergency scenarios, drawing from real-world events like hurricanes and geopolitical disruptions. Different operational scenarios for the fossil fuel and electrified sectors have been developed to understand the risks associated with them. This report concludes with recommendations for the architectural designs of electric charging infrastructure, underlining the need for informed decisions in the transition towards a sustainable and resilient transportation future.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2017

Selection of High Temperature Organic Materials for Future Stirling Convertors

In the future higher temperature Stirling convertors for improved efficiency and performance, various high temperature organic materials have been demanded as essential components for their unique properties and functions such as bonding, potting, sealing, thread locking, insulation, and lubrication. The higher temperature capabilities would also allow current state-of-the-art (SOA) convertors to be used in additional missions, particularly those that require a Venus flyby for a gravity assist. Stirling convertor radioisotope generators have been developed for potential future space applications including Lunar/Mars surface power or a variety of spacecraft and vehicles, especially with a long mission cycle, sometimes up to 17 years, such as deep space exploration. Thus, performance, durability, and reliability of the organics should be critically evaluated in terms of comprehensive structure-process-service environment relations based on the potential mission specifications. The initial efforts in screening the high temperature candidates focused on the most susceptible organics, such as adhesive, potting compound, o-ring, shrink tubing, and thread locker materials in conjunction with commercially available materials. More systematic and practical test methodologies that were developed and optimized based on the extensive organic evaluations and validations performed for various Stirling convertor types were employed to determine thermal stability, outgassing, and material compatibility of the selected organic candidates against their functional requirements. Processing and fabrication conditions and procedures were also optimized. This paper presents results of the three-step candidate evaluation processes, their application limitations, and the final selection recommendations.

Organics↗
NASA NTRS2018

Risk Management at NASA and Its Applicability to the Oil and Gas Industry

NASA has a world-class capability for quantitatively assessing the risk of highly-complex, isolated engineering structures operated in extremely hostile environments. In particular, the International Space Station (ISS) represents a reasonable risk analog for High Pressure, High Temperature drilling and production operations on deepwater rigs. Through a long-term U.S. Government Interagency Agreement, BSEE has partnered with NASA to modify NASA's Probabilistic Risk Assessment (PRA) capabilities for application to deepwater drilling and production operations. The immediate focus of the activity will be to modify NASA PRA Procedure Guides and Methodology Documents to make them applicable to the Oil &Gas Industry. The next step will be for NASA to produce a PRA for a critical drilling system component, such as a Blowout Preventer (BOP). Subsequent activities will be for NASA and industry partners to jointly develop increasingly complex PRA's that analyze other critical drilling and production system components, including both hardware and human reliability. In the presentation, NASA will provide the objectives, schedule, and current status of its PRA activities for BSEE. Additionally, NASA has a Space Act Agreement with Anadarko Petroleum Corporation to develop a PRA for a generic 20K BOP. NASA will summarize some of the preliminary insights gained to date from that 20K BOP PRA as an example of the distinction between quantitative versus qualitative risk assessment.

PRA↗
DOE OSTI2023

Grid Enhancing Technologies Modeling Methodology: Telos Approach

Grid Enhancing Technologies Modeling Methodology Introduction -Grid Enhancing Technology -PLEXOS and TARA Modeling Methodology Overview -PLEXOS/TARA agreement -Flowgate identification -DLR and PFC Location Identification Key Lessons Learned and Possible Improvements

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Uncertainty-based weighted least squares density integration for background-oriented schlieren

We propose an improved density integration methodology for Background-Oriented Schlieren (BOS) measurements that overcomes the noise sensitivity of the commonly used Poisson solver. Here, the method employs a weighted least-squares (WLS) optimization of the 2D integration of the density gradient field by solving an over-determined system of equations. Weights are assigned to the grid points based on density gradient uncertainties to ensure that a less reliable measurement point has less effect on the integration procedure. Synthetic image analysis with a Gaussian density field shows that WLS constrains the propagation of random error and reduces it by 80% in comparison to Poisson for the highest noise level. Using WLS with experimental BOS measurements of flow induced by a spark plasma discharge shows a 30% reduction in density uncertainty in comparison to Poisson, thereby increasing the overall precision of the BOS density measurements.

42 ENGINEERING↗
DOE OSTI2022

Gamma-ray bursts, supernovae Ia, and baryon acoustic oscillations: A binned cosmological analysis

Abstract Cosmological probes at any redshift are necessary to reconstruct consistently the cosmic history. Studying properly the tension on the Hubble constant, H0, obtained by supernovae type Ia (SNe Ia) and the Planck measurements of the cosmic microwave background radiation would require complete samples of distance indicators at any epoch. Gamma-ray bursts (GRBs) are necessary for the aforementioned task because of their huge luminosity that allows us to extend the cosmic ladder to very high redshifts. However, using GRBs alone as standard candles is challenging, because their luminosity varies widely. To this end, we choose a reliable correlation for GRBs with a very small intrinsic scatter: the so-called fundamental plane correlation for GRB afterglows corrected for selection biases and redshift evolution. We choose a well defined sample: the platinum sample, composed of 50 long GRBs. To further constrain the cosmological parameters, we use baryon acoustic oscillations (BAOs) given their reliability as standard rulers. Thus, we have applied GRBs, SNe Ia, and BAOs in a binned analysis in redshifts so that the GRB contribution is fully included in the last redshift bin, which reaches z = 5. We use the fundamental plane correlation (also known as the 3D Dainotti relation), together with SNe Ia and BAOs, to constrain H0 and the density matter today, ΩM. This methodology allows us to assess the role of GRBs combined with SNe Ia and BAOs. We have obtained results for H0 and ΩM using GRBs+SNe Ia+BAOs with better precision than SNe Ia alone for every bin, thus confirming the beneficial role of BAOs and GRBs added together. In addition, consistent results between GRBs+SNe Ia+BAOs are obtained when compared with SNe Ia+BAOs, showing the importance of GRBs since the distance ladder is extended up to z = 5 with a similar precision obtained with other probes without including GRBs.

Dainotti, Maria Giovanna↗
DOE OSTI2023

Empirical radius formulas for canonical neutron stars from bidirectionally selecting features of equations of state in extended Bayesian analyses of observational data

Significant advancement in Bayesian inference of nuclear equation of state (EOS) from gravitational wave and x-ray observations of neutron stars (NSs) has been made by the nuclear astrophysics community especially since GW170817. By extending the traditional Bayesian analysis which normally ends at presenting the marginalized posterior probability distribution functions (PDFs) of individual EOS parameters and their correlations (or sometimes only the Pearson correlation coefficients which are only reliably useful when the variables are linearly correlated while they are actually often not), we search for a data-driven and robust empirical formula for the radius 𝑅 1.4 of canonical NSs in terms of the characteristic EOS parameters (features). We also identify the single most important but currently poorly known EOS parameter for determining the 𝑅 1.4 . Using three regression-model-building methodologies: bidirectional stepwise feature selection, least absolute shrinkage selection operator (LASSO) regression, and neural network regression on a large set of posterior EOSs and the corresponding 𝑅 1.4 values inferred from earlier comprehensive Bayesian analyses of NS observational data, we systematically and rigorously develop the most probable 𝑅 1.4 formulas with varying statistical accuracy and technical complexity. Here, the most important EOS parameters for determining 𝑅 1.4 are found consistently in each of the feature selection processes to be (in order of decreasing importance): curvature 𝐾 sym , slope 𝐿, skewness 𝐽 sym of nuclear symmetry energy, skewness 𝐽 0 , incompressibility 𝐾 0 of symmetric nuclear matter, and the magnitude 𝐸 sym ⁡(𝜌 0 ) of symmetry energy at the saturation density 𝜌 0 of nuclear matter.

Bayesian methods↗