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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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DOE OSTI2024

Microwave-Assisted Heating for Gasification

Compared to traditional heating, microwave-assisted heating enhances catalyst activity, selectivity and stability. These features are essential in gasification to improve the process outcome while mitigating the generation undesirable by-products such as char. To study the effect of microwaves on fluidized bed reactors, the electromagnetics module of COMSOL and MFiX are coupled using file input/output wherein external calls are made to COMSOL from MFiX. The particle bed heating rates and temperature distributions are investigateed in the case of a monodisperse bed of magnetite particles subjected to single mode transverse electric and magnetic fields in reacting and non-reacting conditions. The effect of inlet gas velocity on the heating rate of particle is also explored in this work.

Koneru, Rahul Babu↗

DOE OSTI2024

Automating the Analysis of Large Language Models Responses through Zero-Shot Question Answering

Recent advancements in Large Language Models (LLMs) have shown significant potential in various applications, yet their evaluation, particularly in zero-shot question answering scenarios, remains a challenging task. In this study, our objective was to explore precision metrics for Large Language Models (LLM) and design and implement a software pipeline to automatically evaluate LLMs' outputs under zero-shot question answering. Zero-shot question answering involves a model providing answers to questions about topics it hasn't seen during training. It leverages the principles of zero-shot learning by relying on semantic understanding and generalization from related knowledge. The data used was metadata from medical databases on congenital heart disease. We explored eleven LLM metrics and selected three for our evaluation: BLEU, BERTScore, and MoverScore. BLEU calculates a score based on the overlap of n-grams (contiguous sequences of n items, typically words) between the machine-generated translation and the reference translations. Higher BLEU scores indicate better correspondence between the machine-generated and human-generated translations. BERTScore is a metric used to evaluate the quality of machine-generated text by measuring the similarity of token embeddings produced by BERT (Bidirectional Encoder Representations from Transformers) between the generated text and reference text. MoverScore is a metric that quantifies the dissimilarity between the distributions of word embeddings from machine-generated text and reference text, emphasizing semantic similarity over exact token overlap. We also introduced HBKI, a composite metric summarizing these approaches. We tested five models —GPT-3, Llama-2, Gemini 1.5 Pro, Solar 10.7B, and Mixtral-8x7b. Our software pipeline, designed and implemented using Object-Oriented Programming principles, allows users to customize the selection and extraction of features for topics of interest in their own research. Our results show that MoverScore delivered the most precise evaluation of the LLM's outputs, while Mixtral-8x7b achieved the best overall performance in extracting metadata from the databases.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

PowerModel-AI: A First On-the-Fly Machine-Learning Predictor for AC Power Flow Solutions

The real-time creation of machine-learning models via active or on-the-fly learning has attracted considerable interest across various scientific and engineering disciplines. These algorithms enable machines to build models autonomously while remaining operational. Through a series of query strategies, the machine can evaluate whether newly encountered data fall outside the scope of the existing training set. In this study, we introduce PowerModel-AI, an end-to-end machine learning software designed to accurately predict AC power flow solutions. We present detailed justifications for our model design choices and demonstrate that selecting the right input features effectively captures load flow decoupling inherent in power flow equations. Our approach incorporates on-the-fly learning, where power flow calculations are initiated only when the machine detects a need to improve the dataset in regions where the model’s suboptimal performance is based on specific criteria. Otherwise, the existing model is used for power flow predictions. This study includes analyses of five Texas A&M synthetic power grid cases, encompassing the 14-, 30-, 37-, 200-, and 500-bus systems. The training and test datasets were generated using PowerModels.jl, an open-source power flow solver/optimizer developed at Los Alamos National Laboratory, NM, USA.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS1975

Advanced supersonic propulsion system technology study, phase 2

Variable cycle engines were identified, based on the mixed-flow low-bypass-ratio augmented turbofan cycle, which has shown excellent range capability in the AST airplane. The best mixed-flow augmented turbofan engine was selected based on range in the AST Baseline Airplane. Selected variable cycle engine features were added to this best conventional baseline engine, and the Dual-Cycle VCE and Double-Bypass VCE were defined. The conventional mixed-flow turbofan and the Double-Bypass VCE were on the subjects of engine preliminary design studies to determine mechanical feasibility, confirm weight and dimensional estimates, and identify the necessary technology considered not yet available. Critical engine components were studied and incorporated into the variable cycle engine design.

Allan, R. D.↗

NASA NTRS1978

Spacesuit mobility joints

Joints for use in interconnecting adjacent segments of an hermetically sealed spacesuit which have low torques, low leakage and a high degree of reliability are described. Each of the joints is a special purpose joint characterized by substantially constant volume and low torque characteristics. Linkages which restrain the joint from longitudinal distension and a flexible, substantially impermeable diaphragm of tubular configuration spanning the distance between pivotally supported annuli are featured. The diaphragms of selected joints include rolling convolutions for balancing the joints, while various joints include wedge-shaped sections which enhance the range of motion for the joints.

Vykukal, H. C.↗

NASA NTRS1984

A search variability in the UV spectrum of Pi Aquarii and Fe 3 shell lines of Be stars

Several short U1 and U2 observations of Be stars are obtained with the Copernicus satellite. Pi Aquarii (B1 IV-Ve) is observed with the U1 and U2 spectrometers. These scans are compared with earlier observations. Variations in the strengths and profiles of selected shell and photospheric features are examined. In order to study possible changes in the temperature of the circumstellar envelope, features covering a wide range in ionization are observed. Included in the observing program are lines of O VI, N V, Si IV, Si III, S III, Fe III, and N I.

Source record↗

NASA NTRS1985

Thematic Mapper Data Quality and Performance Assessment in Renewable Resources/agriculture/remote Sensing

Analysis of the early thematic mapper (TM) data indicate the TM sensor and associated ground processing are performing equal to the high expectations and within advertised specifications. The overall TM system with improved resolution, together with additional and more optimumly placed spectral bands shows much promise for benefits in future analysis activities. By selecting man-made features of known dimensions (e.g., highways, airfields, buildings, and isolated water bodies), an assessment was made of the TM performance relative to the specified 30-meter (98-foot) resolution. The increase of spatial resolution of TM (30 m) over MSS (80 M) appears to be significant not only in resolving spectrally distinct classes that were previously undefinable but also in distinguishing within-field variability. An Important result of the early TM evaluation and pre-TM analyses was the development of an integrated system to receive LANDSAT-4 TM (as well as MSS) data and analyze the data via various approaches.

Bizzell, R. M.↗

NASA NTRS1987

Implementation and flight-test of a multi-mode rotorcraft flight-control system for single-pilot use in poor visibility

A flight investigation was conducted to evaluate a multi-mode flight control system designed according to the most recent recommendations for handling qualities criteria for new military helicopters. The modes and capabilities that were included in the system are those considered necessary to permit divided-attention (single-pilot) lowspeed and hover operations near the ground in poor visibility conditions. Design features included mode-selection and mode-blending logic, the use of an automatic position-hold mode that employed precision measurements of aircraft position, and a hover display which permitted manually-controlled hover flight tasks in simulated instrument conditions. Pilot evaluations of the system were conducted using a multi-segment evaluation task. Pilot comments concerning the use of the system are provided, and flight-test data are presented to show system performance.

Hindson, William S.↗

NASA NTRS1992

Polar cloud and surface classification using AVHRR imagery - An intercomparison of methods

Six Advanced Very High-Resolution Radiometer local area coverage (AVHRR LAC) arctic scenes are classified into ten classes. Three different classifiers are examined: (1) the traditional stepwise discriminant analysis (SDA) method; (2) the feed-forward back-propagation (FFBP) neural network; and (3) the probabilistic neural network (PNN). More than 200 spectral and textural measures are computed. These are reduced to 20 features using sequential forward selection. Theoretical accuracy of the classifiers is determined using the bootstrap approach. Overall accuracy is 85.6 percent, 87.6 percent, and 87.0 percent for the SDA, FFBP, and PNN classifiers, respectively, with standard deviations of approximately 1 percent.

Welch, R. M.↗

NASA NTRS1993

A new clustering algorithm applicable to multispectral and polarimetric SAR images

We describe an application of a scale-space clustering algorithm to the classification of a multispectral and polarimetric SAR image of an agricultural site. After the initial polarimetric and radiometric calibration and noise cancellation, we extracted a 12-dimensional feature vector for each pixel from the scattering matrix. The clustering algorithm was able to partition a set of unlabeled feature vectors from 13 selected sites, each site corresponding to a distinct crop, into 13 clusters without any supervision. The cluster parameters were then used to classify the whole image. The classification map is much less noisy and more accurate than those obtained by hierarchical rules. Starting with every point as a cluster, the algorithm works by melting the system to produce a tree of clusters in the scale space. It can cluster data in any multidimensional space and is insensitive to variability in cluster densities, sizes and ellipsoidal shapes. This algorithm, more powerful than existing ones, may be useful for remote sensing for land use.

Wong, Yiu-Fai↗

NASA NTRS1994

La Chalupa-30: Lessons learned from a 30-day subsea mission analogue

The Behavior and Performance Laboratory (BPL) utilizes space mission analogs to study issues such as the psychological health and well-being, team characteristics, and task performance of crew members on long-duration missions. The analog used in this investigation was an underwater habitat named La Chalupa, which was selected for its similar features to a space station environment. The primary objectives of the La Chalupa-30 investigation were to evaluate the efficiency of several methods for collecting data in remote environments and to assess aspects of living and working under isolated and confined conditions.

Vanderark, Steve↗

NASA NTRS1994

Design of a High Thermal Gradient Bridgman Furnace

The Advanced Automated Directional Solidification Furnace (AADSF) is a Bridgman-Stockbarger microgravity processing facility, designed and manifested to first fly aboard the second United States Microgravity Payload (USMP-2) Space Shuttle mission. The AADSF was principally designed to produce high axial thermal gradients, and is particularly suitable for metals solidification experiments, including non-dilute alloys. To accommodate a wider range of experimental conditions, the AADSF is equipped with a reconfigurable gradient zone. The overall design of the AADSF and the relationship between gradient zone design and furnace performance are described. Parametric thermal analysis was performed and used to select gradient zone design features that fulfill the high thermal gradient requirements of the USMP-2 experiment. The thermal model and analytical procedure, and parametric results leading to the first flight gradient zone configuration, are presented. Performance for the USMP-2 flight experiment is also predicted, and analysis results are compared to test data.

LeCroy, J. E.↗

NASA NTRS2001

MNASA as a Test for Carbon Fiber Thermal Barrier Development

A carbon fiber rope thermal barrier is being evaluated as a replacement for the conventional room temperature vulcanizing (RTV) thermal barrier that is currently used to protect o-rings in Reusable Solid Rocket Motor (RSRM) nozzle joints. Performance requirements include its ability to cool any incoming, hot propellant gases that fill and pressurize the nozzle joints, filter slag and particulates, and to perform adequately in various joint assembly conditions as well as dynamic flight motion. Modified National Aeronautics and Space Administration (MNASA) motors, with their inherent and unique ability to replicate select RSRM internal environment features, were an integral step in the development path leading to full scale RSRM static test demonstration of the carbon fiber rope (CFR) joint concept. These 1/4 scale RSRM motors serve to bridge the gap between the other classes of subscale test motors (extremely small and moderate duration, or small scale and short duration) and the critical asset RSRM static test motors. A series of MNASA tests have been used to demonstrate carbon fiber rope performance and have provided rationale for implementation into a full-scale static motor and flight qualification.

Bauer, Paul↗

NASA NTRS2003

Evaluation of Genetic Algorithm Concepts Using Model Problems: Multi-Objective Optimization - Part 2

A genetic algorithm approach suitable for solving multi-objective optimization problems is described and evaluated using a series of simple model problems. Several new features including a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. Results indicate that the genetic algorithm optimization approach is flexible in application and extremely reliable, providing optimal results for all optimization problems attempted. The binning algorithm generally provides pareto front quality enhancements and moderate convergence efficiency improvements for most of the model problems. The gene-space transformation procedure provides a large convergence efficiency enhancement for problems with non-convoluted pareto fronts and a degradation in efficiency for problems with convoluted pareto fronts. The most difficult problems --multi-mode search spaces with a large number of genes and convoluted pareto fronts-- require a large number of function evaluations for GA convergence, but always converge.

Holst, Terry L.↗

NASA NTRS2003

How simple is software defect detection?

This study benchmarks several FSS techniques and reports several studies where a large set metrics were reduced to a handful with little loss of detection accuracy. This result raises the possibility that software defect detection may be much simpler than previously believed.

feature subset selection software fault model soft↗

NASA NTRS2006

Real Time On-line Space Research Laboratory Environment Monitoring with Off-line Trend and Prediction Analysis

One of the responsibilities of the NASA Glenn Principal Investigator Microgravity Services is to support NASA sponsored investigators in the area of reduced-gravity acceleration data analysis, interpretation and the monitoring of the reduced-gravity environment on-board various carriers. With the International Space Station currently operational, a significant amount of acceleration data is being down-linked and processed on ground for both the space station onboard environment characterization (and verification) and scientific experiments. Therefore, to help principal investigator teams monitor the acceleration level on-board the International Space Station to avoid undesirable impact on their experiment, when possible, the NASA Glenn Principal Investigator Microgravity Services developed an artificial intelligence monitoring system, which detects in near real time any change in the environment susceptible to affect onboard experiments. The main objective of the monitoring system is to help research teams identify the vibratory disturbances that are active at any instant of time onboard the International Space Station that might impact the environment in which their experiment is being conducted. The monitoring system allows any space research scientist, at any location and at any time, to see the current acceleration level on-board the Space Station via the World Wide Web. From the NASA Glenn s Exploration Systems Division web site, research scientists can see in near real time the active disturbances, such as pumps, fans, compressor, crew exercise, re-boost, extra-vehicular activity, etc., and decide whether or not to continue operating or stopping (or making note of such activity for later correlation with science results) their experiments based on the g-level associated with that specific event. A dynamic graphical display accessible via the World Wide Web shows the status of all the vibratory disturbance activities with their degree of confidence as well as their g-level contribution to the environment. The system can detect both known and unknown vibratory disturbance activities. It can also perform trend analysis and prediction by analyzing past data over many Increments of the space station for selected disturbance activities. This feature can be used to monitor the health of onboard mechanical systems to detect and prevent potential system failure as well as for use by research scientists during their science results analysis. Examples of both real time on-line vibratory disturbance detection and off-line trend analysis are presented in this paper. Several soft computing techniques such as Kohonen s Self-Organizing Feature Map, Learning Vector Quantization, Back-Propagation Neural Networks, and Fuzzy Logic were used to design the system.

Jules, Kenol↗

NASA NTRS2006

Fault-tolerant communication channel structures

Systems and techniques for implementing fault-tolerant communication channels and features in communication systems. Selected commercial-off-the-shelf devices can be integrated in such systems to reduce the cost.

Alkalai, Leon↗

NASA NTRS2019

Using Machine-Learning to Dynamically Generate Operationally Acceptable Strategic Reroute Options

The newly developed Trajectory Option Set (TOS), a preference-weighted set of alternative routes submitted by flight operators, is a capability in the U.S. traffic flow management system that enables automated trajectory negotiation between flight operators and Air Navigation Service Providers. The objective of this paper is to describe and demonstrate an approach for automatically generating pre-departure and airborne TOSs that have a high probability of operational acceptance. The approach uses hierarchical clustering of historical route data to identify route candidates. The probability of operational acceptance is then estimated using predictors trained on historical flight plan amendment data using supervised machine learning algorithms, allowing the routes with highest probability of operational acceptance to be selected for the TOS. Features used describe historical route usage, difference in flight time and downstream demand to capacity imbalance. A random forest was found to be the best performing algorithm for learning operational acceptability, with a model accuracy of 0.96. The approach is demonstrated for an historical pre-departure flight from Dallas/Fort Worth International Airport to Newark Liberty International Airport.

Evans, Antony↗