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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A comparison of machine learning methods to classify radioactive elements using prompt-gamma-ray neutron activation data

The detection of illicit radiological materials is critical to establishing a robust second line of defence in nuclear security. Neutron-capture prompt-gamma activation analysis (PGAA) can be used to detect multiple radioactive materials across the entire Periodic Table. However, long detection times and a high rate of false positives pose a significant hindrance in the deployment of PGAA-based systems to identify the presence of illicit substances in nuclear forensics. In the present work, six different machine-learning algorithms were developed to classify radioactive elements based on the PGAA energy spectra. The model performance was evaluated using standard classification metrics and trend curves with an emphasis on comparing the effectiveness of algorithms that are best suited for classifying imbalanced datasets. We analyse the classification performance based on Precision, Recall, F1-score, Specificity, Confusion matrix, ROC-AUC curves, and Geometric Mean Score (GMS) measures. The tree-based algorithms (Decision Trees, Random Forest and AdaBoost) have consistently outperformed Support Vector Machine and K-Nearest Neighbours. Based on the results presented, AdaBoost is the preferred classifier to analyse data containing PGAA spectral information due to the high recall and minimal false negatives reported in the minority class.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2014

Ghost Imaging of Space Objects

would like to summarize that our "Ghost Imaging of Space Objects" NIAC research effort was successful. As anticipated, its main return has been the newly acquired knowledge and the understanding of the pathways that lead from this knowledge to its applications in observational astronomy. During this research we came across several fundamental insights that were not expected at the beginning. We came to realize that the ghost imaging of dark objects using background thermal light can be treated as a special case of Hanbury Brown and Twiss intensity interferometry in combination with the Babinet's principle for higher-order observables. Extensive recent work performed in this field by other research groups worldwide might seem to detract from the conceptual originality and novelty of our approach; but at the same time it serves as an encouraging indication that the chosen approach is acknowledged in the broader science community as promising. This newly found synergy, acknowledged in the CTA newsletter from May 2014 reporting on the Workshop on Hanbury Brown and Twiss interferometry in Nice, makes us confident that our published and otherwise disseminated results will be integrated into a larger-scale on-going research effort aimed at performing astronomy observations of both bright and dark objects with unparalleled resolution. While we are satisfied with acceptance of our results by the international research community as a contribution to the field of intensity interferometry as well as to the on-going mission-oriented projects, we also have been aiming at receiving support to continue and advance this research at JPL. Several proposals have been submitted in pursuit of this goal. Three of them are still under consideration, and we expect to learn about the funding decisions soon. These new projects would leverage the results of this NIAC study and extend it along well-defined directions that have been discussed above. Shifting the main paradigm of our approach towards intensity interferometry entailed another important realization, that the actual imaging of dark objects, in a sense of mapping the column optical density distribution, is possible by using known numerical techniques, such as the Gerchberg-Saxton approach. This insight was also unanticipated in the beginning and caused a shift of the research focus from the initial plan. While this focus shift has been well-justified and fruitful, it has left several issues unexplored. Some of these issues, too, are included in the future research proposals.

Optics↗
NASA NTRS1995

Decision process for effective system reuse

The speed of growth in high technology differs for software, hardware and firmware. Hardware innovations come in leaps, as opposed to the gradual improvements in software and firmware technologies. This inhibits the full utilization of the hardware advances and reduces the cost benefit ratio of high technology ventures. The Microelectronics Systems Branch has committed its resources to use the look ahead technique whereby the technology is researched in its use, current development track and its future capabilities. This knowledge is used to meet the requirements of the space program not only in the nineties but also through 2005. This paper illustrates Analytical Hierarchy Process techniques used effectively and very successfully to support projects reusing systems with predicted enhancements.

Mirchandani, Chandru↗
DOE OSTI2022

Big Data Analytics for Long-Term Meteorological Observations at Hanford Site

A growing number of physical objects with embedded sensors with typically high volume and frequently updated data sets has accentuated the need to develop methodologies to extract useful information from big data for supporting decision making. This study applies a suite of data analytics and core principles of data science to characterize near real-time meteorological data with a focus on extreme weather events. To highlight the applicability of this work and make it more accessible from a risk management perspective, a foundation for a software platform with an intuitive Graphical User Interface (GUI) was developed to access and analyze data from a decommissioned nuclear production complex operated by the U.S. Department of Energy (DOE, Richland, USA). Exploratory data analysis (EDA), involving classical non-parametric statistics, and machine learning (ML) techniques, were used to develop statistical summaries and learn characteristic features of key weather patterns and signatures. The new approach and GUI provide key insights into using big data and ML to assist site operation related to safety management strategies for extreme weather events. Specifically, this work offers a practical guide to analyzing long-term meteorological data and highlights the integration of ML and classical statistics to applied risk and decision science.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2009

Satellite Proving Ground for the GOES-R Geostationary Lightning Mapper (GLM)

The key mission of the Satellite Proving Ground is to demonstrate new satellite observing data, products and capabilities in the operational environment to be ready on Day 1 to use the GOES-R suite of measurements. Algorithms, tools, and techniques must be tested, validated, and assessed by end users for their utility before they are finalized and incorporated into forecast operations. The GOES-R Proving Ground for the Geostationary Lightning Mapper (GLM) focuses on evaluating how the infusion of the new technology, algorithms, decision aids, or tailored products integrate with other available tools (weather radar and ground strike networks; nowcasting systems, mesoscale analysis, and numerical weather prediction models) in the hands of the forecaster responsible for issuing forecasts and warning products. Additionally, the testing concept fosters operation and development staff interactions which will improve training materials and support documentation development. Real-time proxy total lightning data from regional VHF lightning mapping arrays (LMA) in Northern Alabama, Central Oklahoma, Cape Canaveral Florida, and the Washington, DC Greater Metropolitan Area are the cornerstone for the GLM Proving Ground. The proxy data will simulate the 8 km Event, Group and Flash data that will be generated by GLM. Tailored products such as total flash density at 1-2 minute intervals will be provided for display in AWIPS-2 to select NWS forecast offices and national centers such as the Storm Prediction Center. Additional temporal / spatial combinations are being investigated in coordination with operational needs and case-study proxy data and prototype visualizations may also be generated from the NASA heritage Lightning Imaging Sensor and Optical Transient Detector data. End users will provide feedback on the utility of products in their operational environment, identify use cases and spatial/temporal scales of interest, and provide feedback to the developers for adjusted or new products.

Goodman, Steven J.↗
DOE OSTI2023

Explainable machine learning model for multi-step forecasting of reservoir inflow with uncertainty quantification

We propose an explainable machine learning (ML) model with uncertainty quantification (UQ) to improve multi-step reservoir inflow forecasting. Traditional ML methods have challenges in forecasting inflows multiple days ahead, and lack explainability and UQ. To address these limitations, we introduce an encoder–decoder long short-term memory (ED-LSTM) network for multi-step forecasting, employ the SHapley Additive exPlanation (SHAP) technique for understanding the influence of hydrometeorological factors on inflow prediction, and develop a novel UQ method for prediction trustworthiness. We apply these methods to forecast 7-day inflow in snow-dominant and rain-driven reservoirs. The results demonstrate the effectiveness of the ED-LSTM model, with high forecasting accuracy for short lead times. Our UQ method provides reliable uncertainty estimates, covering 90% of data with a 90% confidence level. The SHAP analysis reveals the importance of historical inflow and precipitation as influential factors. These findings and methods may support reservoir operators in optimizing water resources management decisions.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2005

NASA Tech Briefs, June 2005

Topics covered include: Apparatus Characterizes Transient Voltages in Real Time; Measuring Humidity in Sealed Glass Encasements; Adaptable System for Vehicle Health and Usage Monitoring; Miniature Focusing Time-of-Flight Mass Spectrometer; Cryogenic High-Sensitivity Magnetometer; Wheel Electrometer System; Carbon-Nanotube Conductive Layers for Thin-Film Solar Cells; Patch Antenna Fed via Unequal-Crossed-Arm Aperture; LC Circuits for Diagnosing Embedded Piezoelectric Devices; Nanowire Thermoelectric Devices; Code for Analyzing and Designing Spacecraft Power System Radiators; Decision Support for Emergency Operations Centers; NASA Records Database; Real-Time Principal- Component Analysis; Fuzzy/Neural Software Estimates Costs of Rocket- Engine Tests; Multicomponent, Rare-Earth-Doped Thermal-Barrier Coatings; Reactive Additives for Phenylethynyl-Containing Resins; Improved Gear Shapes for Face Worm Gear Drives; Alternative Way of Shifting Mass to Move a Spherical Robot; Parylene C as a Sacrificial Material for Microfabrication; In Situ Electrochemical Deposition of Microscopic Wires; Improved Method of Manufacturing SiC Devices; Microwave Treatment of Prostate Cancer and Hyperplasia; Ferroelectric Devices Emit Charged Particles and Radiation; Dusty-Plasma Particle Accelerator; Frozen-Plug Technique for Liquid-Oxygen Plumbing; Shock Waves in a Bose-Einstein Condensate; Progress on a Multichannel, Dual-Mixer Stability Analyzer; Development of Carbon- Nanotube/Polymer Composites; Thermal Imaging of Earth for Accurate Pointing of Deep-Space Antennas; Modifications of a Composite-Material Combustion Chamber; Modeling and Diagnostic Software for Liquefying- Fuel Rockets; and Spacecraft Antenna Clusters for High EIRP.

Source record↗
NASA NTRS2015

Characterization of Orbital Debris Via Hyper-Velocity Ground-Based Tests

To replicate a hyper-velocity fragmentation event using modern-day spacecraft materials and construction techniques to better improve the existing DoD and NASA breakup models. DebriSat is intended to be representative of modern LEO satellites.Major design decisions were reviewed and approved by Aerospace subject matter experts from different disciplines. DebriSat includes 7 major subsystems. Attitude determination and control system (ADCS), command and data handling (C&DH), electrical power system (EPS), payload, propulsion, telemetry tracking and command (TT&C), and thermal management. To reduce cost, most components are emulated based on existing design of flight hardware and fabricated with the same materials. A key laboratory-based test, Satellite Orbital debris Characterization Impact Test (SOCIT), supporting the development of the DoD and NASA satellite breakup models was conducted at AEDC in 1992 .Breakup models based on SOCIT have supported many applications and matched on-orbit events reasonably well over the years.

Cowardin, Heather↗
NASA NTRS2022

Identifying Shocked Feldspar on Mars Using Perseverance Spectroscopic Instruments: Implications for Geochronology Studies on Returned Samples

The Perseverance rover (Mars 2020) mission, the first step in NASA’s Mars Sample Return (MSR) program, will select samples for caching based on their potential to improve understanding Mars’ astrobiological, geological, geochemical, and climatic evolution. Geochronologic analyses will be among the key measurements planned for returned samples. Assessing a sample’s shock history will be critical because shock metamorphism could influence apparent sample age. Shock effects in one Mars-relevant mineral class, plagioclase feldspar, have been well- documented using various spectroscopy techniques (thermal infrared reflectance, emission, and transmission spectroscopy, Raman, and luminescence). A subset of these data will be obtained with the SuperCam and SHERLOC (Scanning Habitable Environments with Raman & Luminescence for Organics & Chemicals) instruments onboard Perseverance to inform caching decisions for MSR. Here, we review shock indicators in plagioclase feldspar as revealed in Raman, luminescence, and IR spectroscopy lab data, with an emphasis on Raman spectroscopy. We consider how this information may inform caching decisions for selecting optimal samples for geochronology measurements. We then identify challenges and make recommendations for both in situ measurements performed with SuperCam and SHERLOC and for supporting lab studies to enhance the success of geochronologic analyses after return to Earth.

mars↗
DOE OSTI2021

Physics-Infused AI/ML Based Digital-Twin Framework for Flow-Induced-Vibration Damage Prediction in a Nuclear Reactor Heat Exchanger

This report summarizes some of the ongoing work related to the development of an expert-elicitation-digital-twin framework for real time damage state prediction in heat exchanger components of a nuclear reactor. The framework is targeted towards predicting damage associated with coupled low cycle fatigue (associated with regular heat-up, cool-down and power operation transients) and high cycle fatigue (associated with flow induced vibration transients). The overall framework will be based on a NoSQL based database, physics-infused-geometry-dependent virtual-sensor data, different AI/ML techniques-based data-driven-predictive-model applications (Apps) and real-time plant sensor measurements available through few existing sensors. Towards this overall goal, this report updates some of the ongoing work, such as on implementation of a NoSQL Database (such as MongoDB), FE based heat transfer analysis of a heat exchanger (e.g. of a PWR steam generator) for generating geometry-dependent virtual sensor data and evaluation of various AI/ML models such as based on multivariate linear regression, ensembled decision-tree based Random-Forest and Gradient-Boosting regression and high-dimensional-kernel-function-transformation based Support-Vector-Machine regression models. The AI/ML models were evaluated for predicting multi-time-series thermal states at thousands of 3D point-clouds

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2012

The Invasive Species Forecasting System (ISFS): An iRODS-Based, Cloud-Enabled Decision Support System for Invasive Species Habitat Suitability Modeling

The Invasive Species Forecasting System (ISFS) is an online decision support system that allows users to load point occurrence field sample data for a plant species of interest and quickly generate habitat suitability maps for geographic regions of interest, such as a national park, monument, forest, or refuge. Target customers for ISFS are natural resource managers and decision makers who have a need for scientifically valid, model- based predictions of the habitat suitability of plant species of management concern. In a joint project involving NASA and the Maryland Department of Natural Resources, ISFS has been used to model the potential distribution of Wavyleaf Basketgrass in Maryland's Chesapeake Bay Watershed. Maximum entropy techniques are used to generate predictive maps using predictor datasets derived from remotely sensed data and climate simulation outputs. The workflow to run a model is implemented in an iRODS microservice using a custom ISFS file driver that clips and re-projects data to geographic regions of interest, then shells out to perform MaxEnt processing on the input data. When the model completes, all output files and maps from the model run are registered in iRODS and made accessible to the user. The ISFS user interface is a web browser that uses the iRODS PHP client to interact with the ISFS/iRODS- server. ISFS is designed to reside in a VMware virtual machine running SLES 11 and iRODS 3.0. The ISFS virtual machine is hosted in a VMware vSphere private cloud infrastructure to deliver the online service.

Gill, Roger↗
NASA NTRS2015

DebriSat: The New Hypervelocity Impact Test for Satellite Breakup Fragment Characterization

To replicate a hyper-velocity fragmentation event using modern-day spacecraft materials and construction techniques to better improve the existing DoD and NASA breakup models: DebriSat is intended to be representative of modern LEO satellites. Major design decisions were reviewed and approved by Aerospace subject matter experts from different disciplines. DebriSat includes 7 major subsystems. Attitude determination and control system (ADCS), command and data handling (C&DH), electrical power system (EPS), payload, propulsion, telemetry tracking and command (TT&C), and thermal management. To reduce cost, most components are emulated based on existing design of flight hardware and fabricated with the same materials. center dotA key laboratory-based test, Satellite Orbital debris Characterization Impact Test (SOCIT), supporting the development of the DoD and NASA satellite breakup models was conducted at AEDC in 1992. Breakup models based on SOCIT have supported many applications and matched on-orbit events reasonably well over the years.

Cowardin, Heather↗
DOE OSTI2024

Visualization Within the Department of Energy: NREL IEEE VIS Application Spotlight

This presentation highlights the role of advanced visualization techniques at the National Renewable Energy Laboratory (NREL) in supporting cutting-edge research across diverse energy domains. From immersive analytics and uncertainty visualization to high-resolution and real-time data analysis, NREL's visualization capabilities enable scientists to explore complex datasets more effectively. These tools are critical for advancing research in materials science, renewable energy technologies, biofuels, electric vehicle infrastructure, energy efficiency - from industrial processes to entire communities - and then bringing these innovations to practice through energy systems integration. NREL's visualization tools drive innovation across renewable energy and grid modernization efforts by providing deeper insights and improving decision-making.

grid modernization↗
NASA NTRS1992

Integration and use of Microgravity Research Facility: Lessons learned by the crystals by vapor transport experiment and Space Experiments Facility programs

The Crystals by Vapor Transport Experiment (CVTE) and Space Experiments Facility (SEF) are materials processing facilities designed and built for use on the Space Shuttle mid deck. The CVTE was built as a commercial facility owned by the Boeing Company. The SEF was built under contract to the UAH Center for Commercial Development of Space (CCDS). Both facilities include up to three furnaces capable of reaching 850 C minimum, stand-alone electronics and software, and independent cooling control. In addition, the CVTE includes a dedicated stowage locker for cameras, a laptop computer, and other ancillary equipment. Both systems are designed to fly in a Middeck Accommodations Rack (MAR), though the SEF is currently being integrated into a Spacehab rack. The CVTE hardware includes two transparent furnaces capable of achieving temperatures in the 850 to 870 C range. The transparent feature allows scientists/astronauts to directly observe and affect crystal growth both on the ground and in space. Cameras mounted to the rack provide photodocumentation of the crystal growth. The basic design of the furnace allows for modification to accommodate techniques other than vapor crystal growth. Early in the CVTE program, the decision was made to assign a principal scientist to develop the experiment plan, affect the hardware/software design, run the ground and flight research effort, and interface with the scientific community. The principal scientist is responsible to the program manager and is a critical member of the engineering development team. As a result of this decision, the hardware/experiment requirements were established in such a way as to balance the engineering and science demands on the equipment. Program schedules for hardware development, experiment definition and material selection, flight operations development and crew training, both ground support and astronauts, were all planned and carried out with the understanding that the success of the program science was as important as the hardware functionality. How the CVTE payload was designed and what it is capable of, the philosophy of including the scientists in design and operations decisions, and the lessons learned during the integration process are descussed.

Heizer, Barbara L.↗
NASA NTRS2020

Searching for Hyperspectral Optical Proxies to Aid Chesapeake Bay Resource Managers in the Detection of Poor Water Quality

Shellfish aquaculture is a growing industry in the Chesapeake Bay. As population grows near the coast, extreme weather events cause a greater volume of pollutant runoff from impervious surfaces and agricultural lands. Resource managers who monitor shellfish beds need reliable information on a variety of water quality indicators at higher frequency than is possible through field monitoring programs and at a higher level of detail than current satellite products can provide. Although many factors causing degraded water quality that can impact human health are not currently discernable by traditional multispectral techniques, hyperspectral imagery offers a new opportunity to detect phytoplankton communities associated with harmful algal blooms and biotoxin production. Together with resource managers in their routine monitoring of sites around the bay from small boats, we have been exploring remotely sensed optical proxies for the detection of harmful algal blooms and sewage. Early warning by remote sensing could guide sampling and improve the efficiency of shellfish bed closures, ultimately improving health outcomes for humans and animals. An extensive network of routine sampling by Chesapeake Bay Program managers makes this is an ideal location to develop and test future satellite data products to support management decisions. Next generation hyperspectral measurements from the future Plankton Aerosol Cloud ocean Ecosystem (PACE) mission at nearly daily frequency, combined with the potential of higher spatial resolution from the Surface Biology and Geology (SBG) observing system recommended in the recent Decadal Survey, along with high frequency observations from the newly selected Geostationary Littoral Imaging and Monitoring Radiometer (GLIMR) Earth Venture Instrument make this a critical time for defining the needs of the aquaculture and resource management community to save lives, time, and money.

Schollaert Uz, Stephanie↗
DOE OSTI2025

Microreactor Optimization Using Simulation And Economics (mouse)

Microreactor Optimization Using Simulation and Economics (MOUSE) is a tool that integrates both nuclear microreactor design and reactor economics to provide comprehensive evaluations and optimizations. This tool enables stakeholders to explore the interplay between technical and economic variables, guiding them towards effective and competitive microreactor solutions. For the reactor core simulations, MOUSE leverages the OpenMC Monte Carlo Particle Transport Code to perform detailed core simulations for various microreactor designs. The included OpenMC models are 2D core designs of a Liquid Metal Thermal Microreactor (LMTR), a Gas-Cooled TRISO-Fueled Microreactor (GCMR), and a Heat Pipe Microreactor. Beyond core design, MOUSE includes simplified calculations for: - Calculating the masses of heat exchangers within the system. - Mechanical power of pumps. - Estimating the area occupied by various buildings within the nuclear plant. For the economic analysis, MOUSE provides detailed bottom-up cost estimates, encompassing a wide range of costs including preconstruction costs, direct costs, indirect costs, training costs, financial costs, operation & maintenance (O&M) costs, and fuel costs. These cost estimations are developed using data from the MARVEL project and additional literature sources, enabling the calculation of total capital costs and levelized cost of energy for both first-of-a-kind and nth-of-a-kind microreactors. MOUSE also enables analysis of the cost drivers and competitiveness in the electricity market. MOUSE allows users to modify a wide array of technical and economic parameters to evaluate different scenarios and their impacts. Examples of these parameters include: Fuels, coolants, or reflector materials Enrichment levels Control drum materials and geometry Fuel pin geometry and materials Moderator pin geometry and materials Reactor core and reflector dimensions Packing factor for the TRISO particles Nuclear reactor power and reactor burnup Number of sensors Shielding thickness Reactor vessel and guard vessel dimensions Operational staff requirements Number of emergency shutdowns Levelization period Interest rate Construction duration Since MOUSE is powered by the WATTS toolkit, it supports optimization studies, parametric analyses, and uncertainty calculations/propagation. The optimization techniques enable users to identify optimal design and economic configurations. The parametric analysis tools allow users to explore the sensitivity of various parameters, while uncertainty propagation helps quantify the impact of uncertainties on overall performance and cost. User Interface and Workflow: Currently, MOUSE is a command-line-based tool. Users can input various reactor design or economic parameters, modify the designs, run simulations, and visualize results through comprehensive data visualization and reporting capabilities. The typical workflow involves setting up the reactor model, defining economic parameters, running simulations, and analyzing the results to make informed decisions. By combining advanced design calculations with detailed economic modeling, MOUSE provides a robust framework for optimizing nuclear microreactor technologies, enhancing their competitiveness, and guiding stakeholders towards innovative and cost-effective solutions.

Hanna, Botros [Idaho National Laboratory (INL), Id↗
NASA NTRS1993

Selection criteria and facilitation training for the study of groupware

Computer support for planning and decision making groups is a growing trend in the 90s. Groupware is a name often applied to group software and has been defined as 'computer-based systems that support groups engaged in a common task (or goal) and that provide an interface to a shared environment'. Unlike most single-user software, groupware assists user groups in their collaboration, coordination, and communication efforts. This paper focuses on groupware to support the meeting process. These systems are often called group decision support systems (GDSS), electronic meeting systems (EMS), or group support systems (GSS). The term 'meeting support groupware' is used here to include any computer-based system to support meetings. In order to understand this technology, one must first understand groups, what they do and the problems they face, and groupware, a wide range of technology to support group work. Guidelines for selecting groups for study as part of an overall research plan are provided in this document. These were taken from the literature and from persons for whom the information in this paper was targeted. Also, guidelines for facilitation training are discussed. Familiarity with known and accepted techniques are the principle duties of the facilitator and any form of training must include practice in using these techniques.

Robichaux, Barry P.↗
NASA NTRSDate not supplied

Machine Learning Approach for Aircraft Performance Model Parameter Estimation for Trajectory Prediction Applications

Inaccurate prediction of aircraft trajectory by ground-based decision support tools (DST) is a major concern in air traffic management (ATM). Aircraft trajectory prediction tools rely on a simplified point-mass aircraft performance model (APM) to make their predictions. Even though the performance coefficients and weight of an aircraft are a vital part of the APM’s predictions and accuracy, these coefficients are proprietary in nature and therefore, unavailable to DSTs. Current ATM research focuses on improving the estimate of some APM parameters by freezing all other coefficients. This simplified approach introduces unwanted sources of bias and negatively impacts the accuracy of the performance model. In this paper, we apply machine learning (ML) techniques for the simultaneous prediction of three key APM parameters (two drag coefficients and the initial aircraft weight). To accomplish this, we employ an ordinary differential equation (ODE) fitting approach to generate optimized APM parameter labels customized to each individual flight record. Subsequently, we train ML models to capture the relationship between the historical data and the optimized APM parameters. Two different ML model solutions are applied and APM coefficients are predicted for unseen flights. The results indicate that the ML models are able to capture the relationship between APM parameters and flight-related features with good accuracy.

trajectory prediction, machine learning, aircraft ↗