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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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High-Lift Common Research Model Aeroacoustic Test
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X-57 “Maxwell” High-Lift Propeller Testing and Model Development
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On Characterization of Flow Disturbances in Arc-Jet Testing
This paper reports computational simulations and analysis of flow characterization tests in high enthalpy arc-jet facilities at NASA Ames Research Center. Flow disturbances and their characterization are presented through case studies, and their implications for testing are discussed. The case studies include free-jet test configurations from three different arc heaters and nozzles: the 60-MW IHF 30-inch conical nozzle, the 10-MW TP3 15-inch conical nozzle, and the 20-MW AHF 12-inch conical nozzle. For all cases, test articles are placed in the jet exiting the conical nozzle, and the existence of a flow disturbance is confirmed through flow survey data using pitot pressure and heat flux probes and accompanying analysis. The paper focuses on cases where the effects of the disturbances on test article surface quantities are important. The present analysis comprises computational fluid dynamics simulations of the nonequilibrium flowfield in the facility nozzles and test box, including the models tested. Comparisons of computations with the experimental measurements are presented. The computations that reproduce the probe sweep data approximately are essential to interpret the arc-jet test data accurately, while providing insights into several observed calorimeter anomalies caused by flow disturbances.
On Characterization of Flow Disturbances in Arc-Jet Testing
This paper reports computational simulations and analysis of flow characterization tests in high enthalpy arc-jet facilities at NASA Ames Research Center. Flow disturbances and their characterization are presented through case studies, and their implications for testing are discussed. The case studies include free-jet test configurations from three different arc heaters and nozzles: the 60-MW IHF 30-inch conical nozzle, the 10-MW TP3 15-inch conical nozzle, and the 20-MW AHF 12-inch conical nozzle. For all cases, test articles are placed in the jet exiting the conical nozzle, and the existence of a flow disturbance is confirmed through flow survey data using pitot pressure and heat flux probes and accompanying analysis. The paper focuses on cases where the effects of the disturbances on test article surface quantities are important. The present analysis comprises computational fluid dynamics simulations of the nonequilibrium flowfield in the facility nozzles and test box, including the models tested. Comparisons of computations with the experimental measurements are presented. The computations that reproduce the probe sweep data approximately are essential to interpret the arc-jet test data accurately, while providing insights into several observed calorimeter anomalies caused by flow disturbances.
Fundamental Experimental Tests and Modeling of LOX/CH4 Engines at High Pressures
In recent years, the space travel industry has grown exponentially, resulting in the need for a low-cost, efficient rocket engine fuel. As such, there has been renewed interest in utilizing liquid natural gas (LNG) as it is less likely to soot than kerosene-based fuels and has widespread availability and low cost of use as compared to the traditional RP-X and even liquid methane (LCH4) fuels due to the reduced need for refinement. However, current literature does not fully cover varying blends of natural gas and the effects that impurities have on natural gas/methane fuels, with most studies being confined to low-pressure applications. Therefore, new experimental ignition delay time measurements at rocket engine relevant pressures are essential to ensure current chemical kinetic models capture the ignition behavior at these elevated conditions and to determine the optimal purity levels for a reliable, cost-efficient, aerospace-grade LCH4/LNG fuel. The current work explores various blends of natural gas/methane fuel with various impurities, including higher hydrocarbons as well as nitrogen-carriers. A shock tube study was carried out to study the ignition delay times and carbon monoxide time histories of these natural gas blends utilizing chemiluminescence and laser absorption spectroscopy at 20, 50 and 100 atm over temperatures ranging from 1400-1700K and at an equivalence ratio of 1. The experimental data was then compared to two different chemical kinetic mechanisms: the industry-standard GRI 3.0 and the in-house UCF 2022. The data is used to improve chemical kinetic mechanisms and modeling of LNG fuels for rocket engine applications.
Test Particle Model Predictions of SEP Electron Transport and Precipitation at Mars
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Testing and Model Validation of a Prototype Moving Packed-Bed Particle-to-sCO2 Heat Exchanger.
Abstract not provided.
U.S. Wind to Hydrogen Modeling, Analysis, Testing, and Collaboration
Presentation for U.S. Department of Energy Hydrogen Program's Annual Merit Review and Peer Evaluation Meeting for TA060, NREL activities for offshore wind-hydrogen.
Modeling and Testing of Wireless Diamond Radiation Detector Prototype for Dry Spent Fuel Canister Monitoring.
Abstract not provided.
Instrumenting Bifacial Fields for Capacity Testing and Modeling
This presentation discusses why we measure irradiance, what makes bifacial irradiance challenging, recommendations for monitoring rear POA irradiance, and a field comparison study.
A Co-Simulation Platform for Modeling and Testing Dynamic Boundary Fractal Microgrids
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Testing- and Model- Based Optimization of Coal-fired Primary Heater Design for Indirect Supercritical CO2 Power Cycles
Project overview
Automation of Vulnerability and Patch Management: Information Extraction, Association, and Optimization
Vulnerability and patch management is an integral part of a robust cybersecurity program, yet it grows increasingly complex due to the sheer amount of data that must be analyzed. Particularly in Operational Technology (OT) environments, analysis must be done manually because of the lack of automated solutions. Additionally, there are many steps in this process, from the initial discovery of the vulnerability to the implementation of its remediation, and each step in the process requires different data in order to be performed effectively. In this work, we provide approaches and strategies to assist operators in industrial or OT environments throughout the vulnerability management cycle. Security advisories provide key information about mitigation strategies, or actions that can be taken when a patch is unavailable or cannot be installed. Details of these strategies are not shared in public vulnerability databases and must be found manually. We approach this problem by designing a solution to automatically identify that information within vendor security advisories and retrieve it for operator use. We start with an approach that requires domain-specific knowledge of certain frequently-seen reference websites. Next, an approach that can work on an arbitrary website but relies on certain keywords. Finally, an approach that uses Natural Language Processing (NLP) methods and does not require specific knowledge or keywords. Each of these approaches is more general than its predecessor; we demonstrate high accuracy for all approaches Advisories also often contain details of affected products in non-standard or natural language formats. While this information can be easily understood when read by an operator, the non-standard format acts as a barrier to effective automation. We provide an approach for the first step in this process: identifying vendors in security advisories and mapping them to a standard framework for representing digital assets and software products. We evaluate five established string similarity algorithms, plus one of our own design that combines string similarity and information theory, on the task of mapping vendors to their corresponding entries in the Common Platform Enumeration (CPE) repository. Our results show that our proposed metric outperforms all others. Due to the constraints on time, finances, and personnel for organizations, Large Language Models (LLMs) may seem like attractive opportunities for security operators to speed up information gathering; however, it is still not clear whether LLMs can handle vulnerability management tasks well. To answer this question, we perform an empirical study of LLMs’ ability to provide consistent, accurate information about vulnerabilities in order to guide organizations in their adoption of LLMs. We observe poor performance for all models tested, suggesting that these models are not well-suited to the consistent retrieval of accurate vulnerability information. Finally, once vulnerabilities have been identified and any additional information has been obtained, operators must decide which remediation actions to implement based on their available resources. This already-complex problem becomes even more so when we consider that a vulnerability may have multiple avenues for remediation. We formulate this scenario as two knapsack problems and provide solutions, which we then compare against several existing strategies for vulnerability prioritization seen in real operational environments.
Testing and Modeling Thermophotovoltaic Devices
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Python codes for the paper - "Trade-offs in the latent representation of microstructure evolution"
SAND2024-00946O Python codes used in "Trade-offs in the latent representation of microstructure evolution," a manuscript accepted for publication in Acta Materialia, are part of a repository. The code was developed to perform analysis of microstructure evolution. The repository consists of two main directories: models, which train and test models such as autoencoders and diffusion maps, and analysis, which analyzes microstructures. Source code is used to perform dimensionality reduction of microstructure data for analysis of its evolution in time. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
Shape change of Galileo probe models in free-flight tests
Scale models of the Galileo Probe made of polycarbonate, AXF5Q graphite, carbon-carbon composite, and carbon-phenolic were flown in a free flight range in an ambient gas of air, krypton, or xenon. Mach numbers varied between 14 and 24, Reynolds numbers between 300,000 and 1,000,000, stagnation pressures between 31 and 200 atm, and stagnation point heat transfer rates between 10 and 1,000 kW/sq cm. Shadowgraphs indicate gouging ablation of the aft portion of the frustum; the gouging was moderate in air and severe in the noble gases. The graphite models break in the same region. An explanation of the phenomena is offered in terms of the strong compression and shear caused by the reattachment of a turbulent separated flow. Conditions are calculated for similar tests appropriate for Von Karman Facility of the Arnold Engineering Development Center in which a larger model can be flown in argon.