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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 865 records · Page 48

Data base architecture for instrument characteristics critical to spacecraft conceptual design

Spacecraft designs are driven by the payloads and mission requirements that they support. Many of the payload characteristics, such as mass, power requirements, communication requirements, moving parts, and so forth directly affect the choices for the spacecraft structural configuration and its subsystem design and component selection. The conceptual design process, which translates mission requirements into early spacecraft concepts, must be tolerant of frequent changes in the payload complement and resource requirements. A computer data base was designed and implemented for the purposes of containing the payload characteristics pertinent for spacecraft conceptual design, tracking the evolution of these payloads over time, and enabling the integration of the payload data with engineering analysis programs for improving the efficiency in producing spacecraft designs. In-house tools were used for constructing the data base and for performing the actual integration with an existing program for optimizing payload mass locations on the spacecraft.

Rowell, Lawrence F.↗

Developing multi-gene CRISPRa/i programs to accelerate DBTL cycles in ABF hosts engineered for chemical production

This project developed and implemented a modular CRISPR activation and interference (CRISPRa/i) platform to accelerate strain optimization and pathway development for industrially relevant microbial hosts. By integrating multiplexed transcriptional perturbation tools with data-driven Design–Build–Test–Learn (DBTL) workflows, the team achieved reductions in cycle time and enhanced production of industrial aromatics, particularly 4-aminocinnamic acid (4-ACA), in Pseudomonas putida. Key accomplishments included: ● Development of a robust, tunable CRISPRa/i system in P. putida that enabled efficient multi-target gene regulation via guide RNA (gRNA) programs ● Completion of two full DBTL cycles, guided by machine learning (ML) models trained on transcriptomic and performance data, reducing engineering time by over 30% ● Optimization of multi-gene regulatory programs to balance expression of host and pathway modules, improve 4-ACA titers, and resolve metabolic bottlenecks ● Demonstration of system portability through a limited proof-of-concept extension in Acinetobacter baylyi, underscoring the generalizability of the approach ● Evaluation of strain performance on lignocellulosic biomass-derived substrates, demonstrating the feasibility of converting renewable carbon into aromatic building blocks These results illustrate the feasibility of applying ML-guided CRISPRa/i perturbation strategies to accelerate strain development in complex microbial systems. The resulting tools and datasets contribute to DOE objectives by improving platform predictability, reducing development costs, and enabling broader access to sustainable, economically viable bioproduction technologies.

09 BIOMASS FUELS↗

High-Altitude Flight Cooling Investigation of a Radial Air-Cooled Engine

An investigation of the cooling of an 18-cylinder, twin-row, radial, air-cooled engine in a high-performance pursuit airplane has been conducted for variable engine and flight conditions at altitudes ranging from 5000 to 35,000 feet in order to provide a basis for predicting high-altitude cooling performance from sea-level or low altitude experimental results. The engine cooling data obtained were analyzed by the usual NACA cooling-correlation method wherein cylinder-head and cylinder-barrel temperatures are related to the pertinent engine and cooling-air variables. A theoretical analysis was made of the effect on engine cooling of the change of density of the cooling air across the engine (the compressibility effect), which becomes of increasing importance as altitude is increased. Good agreement was obtained between the results of the theoretical analysis and the experimental data.

Manganiello, Eugene J↗

Exploration Clinical Decision Support System: Medical Data Architecture

The Exploration Clinical Decision Support (ECDS) System project is intended to enhance the Exploration Medical Capability (ExMC) Element for extended duration, deep-space mission planning in HRP. A major development guideline is the Risk of "Adverse Health Outcomes & Decrements in Performance due to Limitations of In-flight Medical Conditions". ECDS attempts to mitigate that Risk by providing crew-specific health information, actionable insight, crew guidance and advice based on computational algorithmic analysis. The availability of inflight health diagnostic computational methods has been identified as an essential capability for human exploration missions. Inflight electronic health data sources are often heterogeneous, and thus may be isolated or not examined as an aggregate whole. The ECDS System objective provides both a data architecture that collects and manages disparate health data, and an active knowledge system that analyzes health evidence to deliver case-specific advice. A single, cohesive space-ready decision support capability that considers all exploration clinical measurements is not commercially available at present. Hence, this Task is a newly coordinated development effort by which ECDS and its supporting data infrastructure will demonstrate the feasibility of intelligent data mining and predictive modeling as a biomedical diagnostic support mechanism on manned exploration missions. The initial step towards ground and flight demonstrations has been the research and development of both image and clinical text-based computer-aided patient diagnosis. Human anatomical images displaying abnormal/pathological features have been annotated using controlled terminology templates, marked-up, and then stored in compliance with the AIM standard. These images have been filtered and disease characterized based on machine learning of semantic and quantitative feature vectors. The next phase will evaluate disease treatment response via quantitative linear dimension biomarkers that enable image content-based retrieval and criteria assessment. In addition, a data mining engine (DME) is applied to cross-sectional adult surveys for predicting occurrence of renal calculi, ranked by statistical significance of demographics and specific food ingestion. In addition to this precursor space flight algorithm training, the DME will utilize a feature-engineering capability for unstructured clinical text classification health discovery. The ECDS backbone is a proposed multi-tier modular architecture providing data messaging protocols, storage, management and real-time patient data access. Technology demonstrations and success metrics will be finalized in FY16.

Biomedical support↗

Computing Properties Of Chemical Mixtures At Equilibrium

Scientists and engineers need data on chemical equilibrium compositions to calculate theoretical thermodynamic properties of chemical systems. Information essential in design and analysis of such equipment as compressors, turbines, nozzles, engines, shock tubes, heat exchangers, and chemical-processing equipment. CET93 is general program that calculates chemical equilibrium compositions and properties of mixtures for any chemical system for which thermodynamic data are available. Includes thermodynamic data for more than 1,300 gaseous and condensed species and thermal-transport data for 151 gases. Written in FORTRAN 77.

Mcbride, B. J.↗

Advanced Health Management Algorithms for Crew Exploration Applications

Achieving the goals of the President's Vision for Exploration will require new and innovative ways to achieve reliability increases of key systems and sub-systems. The most prominent approach used in current systems is to maintain hardware redundancy. This imposes constraints to the system and utilizes weight that could be used for payload for extended lunar, Martian, or other deep space missions. A technique to improve reliability while reducing the system weight and constraints is through the use of an Advanced Health Management System (AHMS). This system contains diagnostic algorithms and decision logic to mitigate or minimize the impact of system anomalies on propulsion system performance throughout the powered flight regime. The purposes of the AHMS are to increase the probability of successfully placing the vehicle into the intended orbit (Earth, Lunar, or Martian escape trajectory), increase the probability of being able to safely execute an abort after it has developed anomalous performance during launch or ascent phases of the mission, and to minimize or mitigate anomalies during the cruise portion of the mission. This is accomplished by improving the knowledge of the state of the propulsion system operation at any given turbomachinery vibration protection logic and an overall system analysis algorithm that utilizes an underlying physical model and a wide array of engine system operational parameters to detect and mitigate predefined engine anomalies. These algorithms are generic enough to be utilized on any propulsion system yet can be easily tailored to each application by changing input data and engine specific parameters. The key to the advancement of such a system is the verification of the algorithms. These algorithms will be validated through the use of a database of nominal and anomalous performance from a large propulsion system where data exists for catastrophic and noncatastrophic propulsion sytem failures.

Davidson, Matt↗

Leveraging large language models to address data scarcity in machine learning for graphene synthesis

Machine learning in experimental materials science faces significant challenges due to the scarcity of data, which are costly and time-consuming to generate, particularly when relying on in-house experiments. Literature data mining offers a potential solution but introduces issues like mixed data quality, inconsistent formats, and non-uniform reporting of synthesis parameters, resulting in partially missing and heterogeneous features across the dataset. Here, we propose data imputation and feature engineering methods that employ pre-trained large language models (LLMs) to enhance machine learning performance on scarce, heterogeneous datasets, demonstrated on graphene CVD synthesis data and the ML-HydPARK hydrogen storage dataset. GPT models perform data imputation via tailored prompting and semantic normalization of inconsistently reported features through embeddings, for example, to harmonize the complex nomenclature of CVD substrates. Beyond yielding more diverse and richer feature representations than traditional methods such as K-nearest neighbors (KNN) and Multivariate Imputation by Chained Equations (MICE), LLM-based data imputation is evaluated against dataset characteristics and prompting strategies. We vary the level of autonomy granted to the LLM, from generic prompting that leverages pre-trained knowledge for autonomous data generation to data-informed prompting that constrains outputs using target-specific information, and demonstrate which level of autonomy yields superior imputation performance across datasets and feature types. The proposed data engineering methods markedly improve downstream performance; for example, in graphene layer number classification using a support vector machine (SVM), binary accuracy increases from 39% to 65% and ternary accuracy from 52% to 72%. Fine-tuning experiments on both datasets show that combining our proposed LLM-based data imputation and feature encoding methods with numerical machine learning predictors outperforms standalone fine-tuned LLM predictors in data-scarce settings. The proposed strategies emphasize data enhancement techniques rather than refining learning architectures or regularizing loss functions, offering a broadly applicable framework for improving machine learning performance on scarce, inhomogeneous datasets.

Chemical vapor deposition↗

Propulsion Health Monitoring of a Turbine Engine Disk Using Spin Test Data

This paper considers data collected from an experimental study using high frequency capacitive sensor technology to capture blade tip clearance and tip timing measurements in a rotating turbine engine-like-disk-to predict the disk faults and assess its structural integrity. The experimental results collected at a range of rotational speeds from tests conducted at the NASA Glenn Research Center s Rotordynamics Laboratory are evaluated using multiple data-driven anomaly detection techniques to identify abnormalities in the disk. Further, this study presents a select evaluation of an online health monitoring scheme of a rotating disk using high caliber sensors and test the capability of the in-house spin system.

Abdul-Aziz, Ali↗

A method to estimate weight and dimensions of aircraft gas turbine engines. Volume 1: Method of analysis

Weight and envelope dimensions of aircraft gas turbine engines are estimated within plus or minus 5% to 10% using a computer method based on correlations of component weight and design features of 29 data base engines. Rotating components are estimated by a preliminary design procedure where blade geometry, operating conditions, material properties, shaft speed, hub-tip ratio, etc., are the primary independent variables used. The development and justification of the method selected, the various methods of analysis, the use of the program, and a description of the input/output data are discussed.

Pera, R. J.↗

Aerospike Engine Post-Test Diagnostic System Delivered to Rocketdyne

The NASA Glenn Research Center at Lewis Field, in cooperation with Rocketdyne, has designed, developed, and implemented an automated Post-Test Diagnostic System (PTDS) for the X-33 linear aerospike engine. The PTDS was developed to reduce analysis time and to increase the accuracy and repeatability of rocket engine ground test fire and flight data analysis. This diagnostic system provides a fast, consistent, first-pass data analysis, thereby aiding engineers who are responsible for detecting and diagnosing engine anomalies from sensor data. It uses analytical methods modeled after the analysis strategies used by engineers. Glenn delivered the first version of PTDS in September of 1998 to support testing of the engine s power pack assembly. The system was used to analyze all 17 power pack tests and assisted Rocketdyne engineers in troubleshooting both data acquisition and test article anomalies. The engine version of PTDS, which was delivered in June of 1999, will support all single-engine, dual-engine, and flight firings of the aerospike engine.

Meyer, Claudia M.↗

Discerning Trends in Performance Across Multiple Events

Mass Data is a computer program that enables rapid, easy discernment of trends in performance data across multiple flights and ground tests. The program can perform Fourier analysis and other functions for the purposes of frequency analysis and trending of all variables. These functions facilitate identification of past use of diagnosed systems and of anomalies in such systems, and enable rapid assessment of related current problems. Many variables, for computation of which it is usually necessary to perform extensive manual manipulation of raw downlist data, are automatically computed and made available to all users, regularly eliminating the need for what would otherwise be an extensive amount of engineering analysis. Data from flight, ground test, and simulation are preprocessed and stored in one central location for instantaneous access and comparison for diagnostic and trending purposes. Rules are created so that an event log is created for every flight, making it easy to locate information on similar maneuvers across many flights. The same rules can be created for test sets and simulations, and are searchable, so that information on like events is easily accessible.

Slater, Simon↗

Design and Test of a Liquid Oxygen / Liquid Methane Thruster with Cold Helium Pressurization Heat Exchanger

A liquid oxygen / liquid methane 2,000 lbf thruster was designed and tested in conjuction with a nozzle heat exchanger for cold helium pressurization. Cold helium pressurization systems offer significant spacecraft vehicle dry mass savings since the pressurant tank size can be reduced as the pressurant density is increased. A heat exchanger can be incorporated into the main engine design to provide expansion of the pressurant supply to the propellant tanks. In order to study the systems integration of a cold-helium pressurization system, a 2,000 lbf thruster with a nozzle heat exchanger was designed for integration into the Project Morpheus vehicle at NASA Johnson Space Center. The testing goals were to demonstrate helium loading and initial conditioning to low temperatures, high-pressure/low temperature storage, expansion through the main engine heat exchanger, and propellant tank injection/pressurization. The helium pressurant tank was an existing 19 inch diameter composite-overwrap tank, and the targert conditions were 4500 psi and -250 F, providing a 2:1 density advantage compared to room tempatrue storage. The thruster design uses like-on-like doublets in the injector pattern largely based on Project Morpheus main engine hertiage data, and the combustion chamber was designed for an ablative chamber. The heat exchanger was installed at the ablative nozzle exit plane. Stand-alone engine testing was conducted at NASA Stennis Space Center, including copper heat-sink chambers and highly-instrumented spoolpieces in order to study engine performance, stability, and wall heat flux. A one-dimensional thermal model of the integrated system was completed. System integration into the Project Morpheus vehicle is complete, and systems demonstrations will follow.

Melcher, John C.↗

Geospatial Data Platform for All

Spatiotemporal data has evolved in scale due to augmented use in cross-domain applications. Simultaneously, there is substantial growth in the availability of Geographic Information Systems (GIS) data provided by the United States Geological Survey (USGS) along with other federal, state, county, or local agencies through open-data portals and public access APIs. However, data availability does not equate with accessibility. Large-scale analyses and applications require robust, performant data management with co-location of data storage and computing. The insufficiency of data management infrastructure compels researchers to adopt ad hoc project- specific GIS data storage solutions (e.g., copying data to High-Performance computer file systems). As an ad hoc storage strategy does not scale, it hampers cross-domain analyses causing difficulty in data reuse and utilizing existing code bases. Furthermore, GIS data is complex and requires expertise to analyze and manipulate due to its intricate data structures and data-specific projection transformations. Despite the challenges, we recognize that derived GIS data products, e.g., satellite or LIDAR-based images, can be used in downstream applications such as AI by domain, but non-GIS experts. To address the data needs and overcome the challenges, we are working towards a GIS Data Platform focused on efficient data storage, data discovery and access, and an API to enable common workflows. We propose a knowledge-graph (KG) approach for data discovery, whereby datasets are semantically linked to higher- level constructs such as projects and research areas. The semantic data links enable researchers to explore datasets in a top-down approach by specifying relevant and meaningful terms (assists in finding hidden data). An advantage is that the nodes and edges in a knowledge graph create built-in semantic documentation. Deeper spatiotemporal connections between data sources can be encoded via Graph Neural Networks (GNN) (Zhang et al., 2021). The KG approach can be extended to integrate the data itself in a Virtual KG (VKG). Our work will derive inspiration from large-scale VKG efforts that have been undertaken or are currently underway as part of the OpenStreetMap project (Ding et al., 2021). For DOE Data Days, we share the proposed geospatial data platform hybrid (cloud/on-prem) architecture, our work-to-date on storing, retrieving, and transforming LiDAR and raster data relevant to two important NREL use-cases, including the Renewable Energy Potential (reV) Model, and present our proposal for a KG based data discovery engine.

data platform↗

Data Driven Fault Detection of Premixer Centerbody Degradation in a Swirl Combustor

This paper introduces a data-driven framework for combustor-focused, performance-based condition monitoring of gas turbines. Commercial condition monitoring systems typically generate huge amounts of data that make efficient onboard monitoring challenging. This paper focuses on quantifying combustor component degradation, using premixer centerbody degradation in a swirl stabilized combustor as a case study. The input for these analyses is acoustic pressure measurements acquired at various locations on the combustor. The diagnosis methodology is based on a classification framework and consists of 3 steps: 1) Data curation, 2) Feature Engineering, and 3) Diagnosis. Data curation ensures good quality of the data that is passed through the algorithm. Feature engineering deals with the extraction of the most informative features, from the most informative sensors, that can accurately capture the introduced fault. To perform diagnosis, the classification model is trained using experimentally acquired data and is then tested on a separate data set. The framework was able to achieve high classification accuracy (>99%) for training size as low as 30% of the total recorded observations. The low number of features required to achieve this accuracy suggests high potential for integration into existing onboard condition monitoring systems.

data driven methods, fault detection, swirl flames↗

Analysis of in-flight acoustic data for a twin-engined turboprop airplane

Acoustic measurements were made on the exterior and interior of a general aviation turboprop airplane during four flight tests. The test conditions were carefully controlled and repeated for each flight in order to determine data variability. For the first three flights the cabin was untreated and for the fourth flight the fuselage was treated with glass fiber batts. On the exterior, measured propeller harmonic sound pressure levels showed typical standard deviations of +1.4 dB, -2.3 dB, and turbulent boundary layer pressure levels, +1.2 dB, -1.6. Propeller harmonic levels in the cabin showed greater variability, with typical standard deviations of +2.0 dB, -4.2 dB. When interior sound pressure levels from different flights with different cabin treatments were used to evaluate insertion loss, the standard deviations were typically plus or minus 6.5 dB. This is due in part to the variability of the sound pressure level measurements, but probably is also influenced by changes in the model characteristics of the cabin. Recommendations are made for the planning and performance of future flight tests to measure interior noise of propeller-driven aircraft, either high-speed advanced turboprop or general aviation propellers.

Wilby, J. F.↗

Numerical analyses of a rocket engine turbine and comparison with air test data

The study presents cold air test data on the Space Shuttle Main Engine High Pressure Fuel Turbopump turbine recently collected at the NASA Marshall Space Flight Center. Overall performance data, static pressures on the first- and second-stage nozzles, and static pressures along with the gas path at the hub and tip are gathered and compared with various (1D, quasi-3D, and 3D viscous) analysis procedures. The results of each level of analysis are compared to test data to demonstrate the range of applicability for each step in the design process of a turbine. One-dimensional performance prediction, quasi-3D loading prediction, 3D wall pressure distribution prediction, and 3D viscous wall pressure distribution prediction are illustrated.

Tran, Ken↗

Neural network approach to Space Shuttle Main Engine health monitoring

A neural network was trained to distinguish anomalies in Space Shuttle Main Engine sensor data from noisy normal steady-state sensor data. Power spectra of successive windows of individual sensor data were presented to a neural network using Kohonen's topological feature map training algorithm. The trained network for each sensor was then tested to determine if it would detect anomalies in the sensor data, and if so, the time at which the anomaly would be detected. Power spectra from a few hundred seconds of actual test data from NASA tests 901-364 and 904-044 were used to test the network. In both cases, the neural network detected the onset of anomalous engine behavior at approximately the same time within each test as the onset times reported by NASA and Rocketdyne experts in their post-test analyses.

Whitehead, Bruce A.↗

Propulsion Flight-Test Fixture

NASA Dryden Flight Research Center s new Propulsion Flight Test Fixture (PFTF), designed in house, is an airborne engine-testing facility that enables engineers to gather flight data on small experimental engines. Without the PFTF, it would be necessary to obtain such data from traditional wind tunnels, ground test stands, or laboratory test rigs. Traditionally, flight testing is reserved for the last phase of engine development. Generally, engines that embody new propulsion concepts are not put into flight environments until their designs are mature: in such cases, either vehicles are designed around the engines or else the engines are mounted in or on missiles. However, a captive carry capability of the PFTF makes it possible to test engines that feature air-breathing designs (for example, designs based on the rocket-based combined cycle) economically in subscale experiments. The discovery of unknowns made evident through flight tests provides valuable information to engine designers early in development, before key design decisions are made, thereby potentially affording large benefits in the long term. This is especially true in the transonic region of flight (from mach 0.9 to around 1.2), where it can be difficult to obtain data from wind tunnels and computational fluid dynamics. In January 2002, flight-envelope expansion to verify the design and capabilities of the PFTF was completed. The PFTF was flown on a specially equipped supersonic F-15B research testbed airplane, mounted on the airplane at a center-line attachment fixture, as shown in Figure 1. NASA s F-15B testbed has been used for several years as a flight-research platform. Equipped with extensive research air-data, video, and other instrumentation systems, the airplane carries externally mounted test articles. Traditionally, the majority of test articles flown have been mounted at the centerline tank-attachment fixture, which is a hard-point (essentially, a standardized weapon-mounting fixture). This hard-point has large weight margins, and, because it is located near the center of gravity of the airplane, the weight of equipment mounted there exerts a minimal effect on the stability and controllability of the airplane. The PFTF (see Figure 2) includes a one-piece aluminum structure that contains space for instrumentation, propellant tanks, and feed-system components. The PFTF also houses a force balance, on which is mounted the subscale engine or other experimental apparatus that is to be the subject of a flight test. The force balance measures a combination of inertial and aerodynamic forces and moments acting on the experimental apparatus.

Palumbo, Nate↗