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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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NASA NTRS1985

The application of artificial intelligence techniques to large distributed networks

Data accessibility and transfer of information, including the land resources information system pilot, are structured as large computer information networks. These pilot efforts include the reduction of the difficulty to find and use data, reducing processing costs, and minimize incompatibility between data sources. Artificial Intelligence (AI) techniques were suggested to achieve these goals. The applicability of certain AI techniques are explored in the context of distributed problem solving systems and the pilot land data system (PLDS). The topics discussed include: PLDS and its data processing requirements, expert systems and PLDS, distributed problem solving systems, AI problem solving paradigms, query processing, and distributed data bases.

Dubyah, R.↗

NASA NTRS2005

Evaluation of a Tool for Airborne-Managed In-Trail Approach Spacing

The Advanced Terminal Area Approach Spacing (ATAAS) tool uses Automatic Dependent Surveillance-Broadcast aircraft state data to compute a speed command for an ATAAS-equipped aircraft to follow and obtain a required time interval behind another aircraft. The ATAAS tool and candidate operational procedures were tested in a high-fidelity, full mission simulator with active airline subject pilots flying an arrival scenario to obtain pilot perceptions of acceptability and workload for the concept. The aircraft consistently achieved the target spacing interval within 1 s when the ATAAS speed guidance was autothrottle-coupled and a slightly greater (4 - 5 s) but consistent interval with pilot-controlled speed changes. The subject pilots rated the ATAAS workload as similar to one with standard procedures for a nominal Instrument Landing System (ILS) approach. They also rated highly various procedural aspects (including amount of head-down time required). Eyetracker data showed only slight changes in instrument scan patterns for ATAAS versus standard ILS procedures.

Oseguera-Lohr, Rosa M.↗

NASA NTRSDate not supplied

Icing Physics Studies Using the 3D SIDRM Test Article: Aerodynamic and Supercooled Liquid Icing Analysis

In-flight icing is an important safety issue and is a factor that affects aircraft design and performance. Newer regulations are driving a need for improvements in airframe and engine icing simulation capability. Experimental data is required for development of icing physics models and simulation validation. To that end, this paper discusses analyzed data from icing tests conducted with a 3D test article in 2022 at the NASA Icing Research Tunnel. The test article, whose geometry is representative of an inter-compressor duct and strut region of a turbofan engine, has been designed and constructed to study the physics of supercooled water icing and ice crystal icing. The surfaces of the Simulated Inter-compressor Duct Research Model (SIDRM) can be heated to simulate the warm surfaces of the turbofan inter-compressor duct. The test article is instrumented with pressure taps, heaters, heat flux gauges, and thermocouples, while a 3D laser scanner, cameras, and a scale to measure ice mass were utilized to characterize the icing behavior. The aim of these tests was to generate ice accretions on the SIDRM test article under well-characterized supercooled liquid icing and ice crystal icing conditions. This paper discusses measurements related to aerodynamic tests and supercooled liquid icing tests that were conducted. Aerodynamic measurements were analyzed and compared to computational simulations and were found to be in good agreement for the range of airspeeds (50 to 230 knots) and angles of attack (0 to 4°) tested. Various parametric sweeps were conducted during the supercooled liquid icing portion of the test entry (cloud median volumetric diameter ranged from 15 to 90 µm, total air temperature from -3 to -17 °C, angle of attack from 0 to 4°, and accretion time from 5 to 20 min). These sweeps were performed to measure that parameter’s impact on ice accretion size, location, characteristics (such as glaze/rime ice and shedding behavior), and test article surface temperature. Analysis of the test data showed that clouds composed of larger drops, colder air temperatures, smaller angles of attack, and longer spray times were the primary parameters that resulted in accretions with greater ice mass. Test article angle of attack and cloud droplet size influenced the location of ice accretion as these two parameters directly impact collection efficiency. With respect to icing characteristics, total air temperature dictated icing type, and smaller cloud drop size along with warmer air temperatures resulted in greater amounts of ice shedding. Surface temperature increased during ice accretion from the release of latent (fusion) heat, where total air temperature and cloud drop size impacted the amount of surface temperature change. The icing measurements collected during the SIDRM tests will be used to develop and validate 3D computational engine icing tools, such as GlennICE, that predictively assesses the onset and growth of ice. One of the goals of the sponsoring NASA project is to develop simulation models and tools that can assist in the design and certification of engines for flight in icing conditions in a cost‑effective way.

supercooled liquid icing↗

NASA NTRS1975

Sequential filtering applied to the determination of tracking station locations

The extended sequential filter has been applied to the problem of dynamically determining the geocentric coordinates of two laser satellite tracking stations. This filter provides significant advantages over the classical batch methods through (1) fewer iterations required for convergence, (2) wider radius of convergence, and (3) availability of the parameter estimate evolution. Processing the data sequentially readily identifies the data arcs required to minimize the effects of geopotential model errors. By means of the Smithsonian standard earth 2 and the Goddard earth model 1 geopotentials to reduce laser range observations of the Beacon Explorer-C satellite, it is demonstrated that a two-pass arc is optimal for estimating the height of one station and all coordinates of the second station while minimizing the effect of geopotential model error. These two-pass estimates are in good agreement with other determinations that utilize considerably more data as well as different satellites.

Schutz, B. E.↗

NASA NTRS1985

Advanced local area network concepts

Development of a good model of the data traffic requirements for Local Area Networks (LANs) onboard the Space Station is the driving problem in this work. A parameterized workload model is under development. An analysis contract has been started specifically to capture the distributed processing requirements for the Space Station and then to develop a top level model to simulate how various processing scenarios can handle the workload and what data communication patterns result. A summary of the Local Area Network Extendsible Simulator 2 Requirements Specification and excerpts from a grant report on the topological design of fiber optic local area networks with application to Expressnet are given.

Grant, Terry↗

NASA NTRSDate not supplied

Analysis of Ice Mass Growth Over Time on the CRM65 Midspan Hybrid Model

The Aeronautics Research Mission Directorate at NASA is developing and applying tools to enable future technologies towards sustainable flight. Aircraft icing has been identified as a potential barrier to entry into service for innovative designs necessitating improvements to computational ice accretion tools. NASA is developing the Glenn Icing Computational Environment (GlennICE) to address deficiencies in the computational modeling capabilities of previously developed ice accretion solvers. To benchmark and improve the ability to model highly three-dimensional ice accretion, high quality validation data against experimental data is required. The CRM65 Midspan Hybrid geometry was previously tested at the NASA Icing Research Tunnel to generate experimental data for swept wing geometries typical for commercial transport aircraft. As a part of a 2018 icing test campaign, experimental data characterizing the relationship between ice accretion time and ice mass growth was obtained and can be leveraged for use in validation of computational tools. The desire for computational ice accretion solvers to predict ice shapes profiles accreted experimentally has often overshadowed the comparison to the mass and bulk volume of ice accreted. To address this deficiency, an analysis is presented in which GlennICE is applied to simulations of the CRM65 Midspan Hybrid model tested in the NASA Icing Research Tunnel. Results from the computational fluid dynamics simulations compared favorably to the experimental pressure coefficient data, thus validating the modeling setup. The experimental data showed excellent repeatability for the 15.0 minute accretion time. The comparisons between the experimental and computational ice mass over time showed good agreement up to 10.0 minutes after which the ice mass was underpredicted. The experimental ice mass was largely linear with some nonlinear data. The bulk volume of ice accreted experimentally compared well to GlennICE for the scanned ice shapes and mean combined cross section ice shapes, but was underpredicted for the maximum combined cross section ice shapes at longer accretion times. The experimental minimum combined cross section, mean combined cross section, and maximum combined cross section profiles when compared to GlennICE show good agreement for the mean combined cross section up to 15.0 minutes. The analyses show that with a single-shot method, GlennICE currently underpredicts the ice mass for longer accretion times, is not able to match the bulk volume of the maximum combined cross section due to dominating scallop features, and future work is required to generate a more generalized ice bulk density model.

Icing↗

NASA NTRSDate not supplied

Analysis of Ice Mass Growth Over Time on the CRM65 Midspan Hybrid Model

Icing↗

DOE OSTI2025

OLCF’s Advanced Computing Ecosystem (ACE): FY25 Update for Ongoing Efforts

The advent of widespread use of artificial intelligence (AI) and machine learning (ML) models in science, coupled with fast data production rates of scientific instruments strain the traditional batch-oriented high-performance computing (HPC) environment. As scientific exploration continues to require more data and faster processing and analysis, new emerging technologies and capabilities to enable cross-facility and time-sensitive workflows are required for seamless integration of HPC and experimental facilities. The Advanced Computing Ecosystem (ACE) is a strategic initiative within the Oak Ridge Leadership Computing Facility (OLCF) established in 2024 to support the development of cutting-edge technologies to advance computational research and infrastructure at OLCF and across the Department of Energy (DOE). Several DOE initiatives are spearheading the evolution of the scientific landscape by blurring facility boundaries and connecting the user facilities to advance scientific capabilities and ensure energy dominance. The DOE Integrated Research Infrastructure (IRI) program is one example that is laying a foundation to support complex cross-facility workflows. The IRI program aims to integrate diverse computational resources, data infrastructures, and scientific instruments to facilitate collaboration and accelerate scientific discovery. The Interconnected Science Ecosystem (INTERSECT) initiative at Oak Ridge National Laboratory (ORNL) is another example that aims to revolutionize scientific research through AI-driven, interconnected autonomous laboratories and research facilities. Finally, the American Science Cloud (AmSC), recently announced in the “One Big Beautiful Bill”, aims to leverage prior infrastructure efforts of the IRI and automation and AI efforts of INTERSECT (and others) to build a federated, AI-augmented AmSC platform to unify the DOE’s computing, experimental, and data resources to catalyze scientific innovation.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2016

Bluetooth(Registered Trademark) Heart Rate Monitors for Spaceflight

Heart rate monitoring is required during exercise for crewmembers aboard the International Space Station (ISS) and will be for future exploration missions. The cardiovascular system must be sufficiently stressed throughout a mission to maintain the ability to perform nominal and contingency/emergency tasks. High quality heart rate data is required to accurately determine the intensity of exercise performed by the crewmembers and show maintenance of VO2max. The quality of the data collected on ISS is subject to multiple limitations and is insufficient to meet current requirements. PURPOSE: To evaluate the performance of commercially available Bluetooth® heart rate monitors (BT_HRM) and their ability to provide high quality heart rate data to monitor crew health on board ISS and during future exploration missions. METHODS: Nineteen subjects completed 30 data collection sessions of various intensities on the treadmill and/or cycle. Subjects wore several BT_HRM technologies for each testing session. One electrode-based chest strap (CS) was worn, while one or more optical sensors (OS) was worn. Subjects were instrumented with a 12-lead ECG to compare the heart rate data from the Bluetooth sensors. Each BT_RHM data set was time matched to the ECG data and a +/-5bpm threshold was applied to the difference between the two data sets. Percent error was calculated based on the number of data points outside the threshold and the total number of data points. REULTS: The electrode-based chest straps performed better than the optical sensors. The best performing CS was CS1 (1.6%error), followed by CS4 (3.3%error), CS3 (6.4%error), and CS2 (9.2%error). The OS resulted in 10.4% error for OS1 and 14.9% error for OS2. CONCLUSIONS: The highest quality data came from CS1, unfortunately it has been discontinued by the manufacturer. The optical sensors have not been ruled out for use, but more investigation is needed to determine how to get the best quality data. CS2 will be used in an ISS Bluetooth validation study, because it simultaneously transmits Magnetic Pulse which is integrated with existing exercise hardware on ISS. The simultaneous data streams allow for beat to beat comparison between the current ISS standard and CS2.Upon Bluetooth(Registered Trademark) validation aboard ISS, down select of a new BT_HRM for operational use will be made.

Buxton, Roxanne E.↗

NASA NTRS2016

Bluetooth Heart Rate Monitors For Spaceflight

Heart rate monitoring is required for crewmembers during exercise aboard the International Space Station (ISS) and will be for future exploration missions. The cardiovascular system must be sufficiently stressed throughout a mission to maintain the ability to perform nominal and contingency/emergency tasks. High quality heart rate data are required to accurately determine the intensity of exercise performed by the crewmembers and show maintenance of VO2max. The quality of the data collected on ISS is subject to multiple limitations and is insufficient to meet current requirements. PURPOSE: To evaluate the performance of commercially available Bluetooth heart rate monitors (BT_HRM) and their ability to provide high quality heart rate data to monitor crew health aboard the ISS and during future exploration missions. METHODS: Nineteen subjects completed 30 data collection sessions of various intensities on the treadmill and/or cycle. Subjects wore several BT_HRM technologies for each testing session. One electrode-based chest strap (CS) was worn, while one or more optical sensors (OS) were worn. Subjects were instrumented with a 12-lead ECG to compare the heart rate data from the Bluetooth sensors. Each BT_HRM data set was time matched to the ECG data and a +/-5bpm threshold was applied to the difference between the 2 data sets. Percent error was calculated based on the number of data points outside the threshold and the total number of data points. RESULTS: The electrode-based chest straps performed better than the optical sensors. The best performing CS was CS1 (1.6% error), followed by CS4 (3.3% error), CS3 (6.4% error), and CS2 (9.2% error). The OS resulted in 10.4% error for OS1 and 14.9% error for OS2. CONCLUSIONS: The highest quality data came from CS1, but unfortunately it has been discontinued by the manufacturer. The optical sensors have not been ruled out for use, but more investigation is needed to determine how to obtain the best quality data. CS2 will be used in an ISS Bluetooth validation study, because it simultaneously transmits magnetic pulse that is integrated with existing exercise hardware on ISS. The simultaneous data streams allow for beat-to-beat comparison between the current ISS standard and CS2. Upon Bluetooth validation aboard ISS, the research team will down select a new BT_HRM for operational use.

Buxton, R. E.↗

NASA NTRS1991

Subsystems component definitions summary program

A computer program, the Subsystems Component Definitions Summary (SUBCOMDEF), was developed to provide a quick and efficient means of summarizing large quantities of subsystems component data in terms of weight, volume, resupply, and power. The program was validated using Space Station Freedom Program Definition Requirements Document data for the internal and external thermal control subsystem. Once all component descriptions, unit weights and volumes, resupply, and power data are input, the user may obtain a summary report of user-specified portions of the subsystem or of the entire subsystem as a whole. Any combination or all of the parameters of wet and dry weight, wet and dry volume, resupply weight and volume, and power may be displayed. The user may vary the resupply period according to individual mission requirements, as well as the number of hours per day power consuming components operate. Uses of this program are not limited only to subsystem component summaries. Any applications that require quick, efficient, and accurate weight, volume, resupply, or power summaries would be well suited to take advantage of SUBCOMDEF's capabilities.

Scott, A. Don↗

DOE OSTI2025

Data and scripts from: “Denoising autoencoder for reconstructing sensor observation data and predicting evapotranspiration: noisy and missing values repair and uncertainty quantification”

This data package includes data and scripts from the manuscript “Denoising autoencoder for reconstructing sensor observation data and predicting evapotranspiration: noisy and missing values repair and uncertainty quantification”.The study addressed common challenges faced in environmental sensing and modeling, including uncertain input data, missing sensor observations, and high-dimensional datasets with interrelated but redundant variables. Point-scaled meteorological and soil sensor observations were perturbed with noises and missing values, and denoising autoencoder (DAE) neural networks were developed to reconstruct the perturbed data and further predict evapotranspiration. This study concluded that (1) the reconstruction quality of each variable depends on its cross-correlation and alignment to the underlying data structure, (2) uncertainties from the models were overall stronger than those from the data corruption, and (3) there was a tradeoff between reducing bias and reducing variance when evaluating the uncertainty of the machine learning models.This package includes:(1) Four ipython scripts (.ipynb): “DAE_train.ipynb” trains and evaluates DAE neural networks, “DAE_predict.ipynb” makes predictions from the trained DAE models, “ET_train.ipynb” trains and evaluates ET prediction neural networks, and “ET_predict.ipynb” makes predictions from trained ET models.(2) One python file (.py): “methods.py” includes all user-defined functions and python codes used in the ipython scripts.(3) A “sub_models” folder that includes five trained DAE neural networks (in pytorch format, .pt), which could be used to ingest input data before being fed to the downstream ET models in ‘ET_train.ipynb” or ‘ET_predict.ipynb’.(4) Two data files (.csv). Daily meteorological, vegetation, and soil data is in “df_data.csv”, where “df_meta.csv” contains the location and time information of “df_data.csv”. Each row (index) in “df_meta.csv” corresponds to each row in “df_data.csv”. These data files are formatted to follow the data structure requirements and be directly used in the ipython scripts, and they have been shuffled chronologically to train machine learning models. The meteorological and soil data was collected using point sensors between 2019-2023 at(4.a) Three shrub-dominated field sites in East River, Colorado (named “ph1”, “ph2” and “sg5” in “df_meta.csv”, where “ph1” and “ph2” were located at PumpHouse Hillslopes, and “sg5” was at Snodgrass Mountain meadow) and(4.b) One outdoor, mesoscale, and herbaceous-dominated experiment in Berkeley, California (named “tb” in “df_meta.csv”, short for Smartsoils Testbed at Lawrence Berkeley National Lab).- See "df_data_dd.csv" and "df_meta_dd.csv" for variable descriptions and the Methods section for additional data processing steps. See "flmd.csv" and "README.txt" for brief file descriptions.- All ipython scripts and python files are written in and require PYTHON language software.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1990

Knowledge structure representation and automated updates in intelligent information management systems

A continuing effort to apply rapid prototyping and Artificial Intelligence techniques to problems associated with projected Space Station-era information management systems is examined. In particular, timely updating of the various databases and knowledge structures within the proposed intelligent information management system (IIMS) is critical to support decision making processes. Because of the significantly large amounts of data entering the IIMS on a daily basis, information updates will need to be automatically performed with some systems requiring that data be incorporated and made available to users within a few hours. Meeting these demands depends first, on the design and implementation of information structures that are easily modified and expanded, and second, on the incorporation of intelligent automated update techniques that will allow meaningful information relationships to be established. Potential techniques are studied for developing such an automated update capability and IIMS update requirements are examined in light of results obtained from the IIMS prototyping effort.

Corey, Stephen↗

NASA NTRS1973

DORMAN computer program (study 2.5). Volume 1: Executive summary

The DORCA Applications study has been directed at development of a data bank management computer program identified as DORMAN. Because of the size of the DORCA data files and the manipulations required on that data to support analyses with the DORCA program, automated data techniques to replace time-consuming manual input generation are required. The Dynamic Operations Requirements and Cost Analysis (DORCA) program was developed for use by NASA in planning future space programs. Both programs are designed for implementation on the UNIVAC 1108 computing system. The purpose of this Executive Summary Report is to define for the NASA management the basic functions of the DORMAN program and its capabilities.

Stricker, L. T.↗

NASA NTRSDate not supplied

Distributed Lunar Data Platform with Advanced Machine Learning Capabilities in Support of Lunar Science and Exploration

The United States 2020 Space Policy directive declares that NASA, in cooperation with private industry, will “extend human economic activity into deep space by establishing a permanent human presence on the Moon”. This goal will require advanced data management, as well as analysis, modeling and representation of lunar information in order to prepare for Artemis human missions, lunar science investigations and exploration. To meet this requirement, we conceptualize and present an implementation strategy for a distributed platform for lunar data retrieval, inferencing and analysis, which will be based on federated learning and the NASA Celestial Mapping System (CMS). In addition to demonstrating the imperative of enabling lunar-borne data to remain in-situ but still accessible, this presentation will also include examples of how third parties could contribute both datasets and new functionality into this platform using an AI-based data import pipeline and a plug-in architecture respectively.

Artificial Intelligence↗

DOE OSTI2024

Inferring building height from footprint morphology data

As cities continue to grow globally, characterizing the built environment is essential to understanding human populations, projecting energy usage, monitoring urban heat island impacts, preventing environmental degradation, and planning for urban development. Buildings are a key component of the built environment and there is currently a lack of data on building height at the global level. Current methodologies for developing building height models that utilize remote sensing are limited in scale due to the high cost of data acquisition. Other approaches that leverage 2D features are restricted based on the volume of ancillary data necessary to infer height. Here, we find, through a series of experiments covering 74.55 million buildings from the United States, France, and Germany, it is possible, with 95% accuracy, to infer building height within 3 m of the true height using footprint morphology data. Our results show that leveraging individual building footprints can lead to accurate building height predictions while not requiring ancillary data, thus making this method applicable wherever building footprints are available. The finding that it is possible to infer building height from footprint data alone provides researchers a new method to leverage in relation to various applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

ARENA: Asynchronous Reconfigurable Accelerator Ring to Enable Data-Centric Parallel Computing

The next generation HPC and data centers are likely to be reconfigurable and data-centric due to the trend of hardware specialization and the emergence of data-driven applications. In this work, we propose ARENA – an asynchronous reconfigurable accelerator ring architecture as a potential scenario on how the future HPC and data centers will be like. Despite using the coarse-grained reconfigurable arrays (CGRAs) as the substrate platform, our key contribution is not only the CGRA-cluster design itself, but also the ensemble of a new architecture and programming model that enables asynchronous tasking across a cluster of reconfigurable nodes, so as to bring specialized computation to the data rather than the reverse. We presume distributed data storage without asserting any prior knowledge on the data distribution. Hardware specialization occurs at runtime when a task finds the majority of data it requires are available at the present node. In other words, we dynamically generate specialized CGRA accelerators where the data reside. The asynchronous tasking for bringing computation to data is achieved by circulating the task token, which describes the dataflow graphs to be executed for a task, among the CGRA cluster connected by a fast ring network. Evaluations on a set of HPC and data-driven applications across different domains show that ARENA can provide better parallel scalability with reduced data movement (53.9 percent). Compared with contemporary compute-centric parallel models, ARENA can bring on average 4.37× speedup. The synthesized CGRAs and their task-dispatchers only occupy 2.93mm 2 chip area under 45nm process technology and can run at 800MHz with on average 759.8mW power consumption. ARENA also supports the concurrent execution of multi-applications, offering ideal architectural support for future high-performance parallel computing and data analytics systems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Training Ultrasound Image Classification Deep-Learning Algorithms for Pneumothorax Detection Using a Synthetic Tissue Phantom Apparatus

Ultrasound (US) imaging is a critical tool in emergency and military medicine because of its portability and immediate nature. However, proper image interpretation requires skill, limiting its utility in remote applications for conditions such as pneumothorax (PTX) which requires rapid intervention. Artificial intelligence has the potential to automate ultrasound image analysis for various pathophysiological conditions. Training models require large data sets and a means of troubleshooting in real-time for ultrasound integration deployment, and they also require large animal models or clinical testing. Here, we detail the development of a dynamic synthetic tissue phantom model for PTX and its use in training image classification algorithms. The model comprises a synthetic gelatin phantom cast in a custom 3D-printed rib mold and a lung mimicking phantom. When compared to PTX images acquired in swine, images from the phantom were similar in both PTX negative and positive mimicking scenarios. We then used a deep learning image classification algorithm, which we previously developed for shrapnel detection, to accurately predict the presence of PTX in swine images by only training on phantom image sets, highlighting the utility for a tissue phantom for AI applications.

Boice, Emily N. (ORCID:0000000171802842)↗