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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 1,135 records · Page 63

All sources NASA NTRS DOE OSTI

DOE OSTI2022

Inference, Prediction, & Entropy-Rate Estimation of Continuous-Time, Discrete-Event Processes

Inferring models, predicting the future, and estimating the entropy rate of discrete-time, discrete-event processes is well-worn ground. However, a much broader class of discrete-event processes operates in continuous-time. Here, we provide new methods for inferring, predicting, and estimating them. The methods rely on an extension of Bayesian structural inference that takes advantage of neural network’s universal approximation power. Based on experiments with complex synthetic data, the methods are competitive with the state-of-the-art for prediction and entropy-rate estimation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRS1972

System/360 Computer Assisted Network Scheduling (CANS) System

Computer assisted scheduling techniques that produce conflict-free and efficient schedules have been developed and implemented to meet needs of the Manned Space Flight Network. CANS system provides effective management of resources in complex scheduling environment. System is automated resource scheduling, controlling, planning, information storage and retrieval tool.

Brewer, A. C.↗

NASA NTRS1982

Rotational modulation of the chromospheric activity in the young solar-type star, X-1 Orionis

The IUE satellite was used to observe one of the youngest G stars (GO V) for which Duncan (1981) derives an age of 6 x 10 to the 8th power years from the Li abundance. Rotational modulation was looked for in the emission flux in the chromospheric and transition region lines of this star. Variations in the Ca 11 K-lines profile were studied with the CHF telescope at Mauna Kea. Results show that the same modulation of the emission flux of Ca 11 due to stellar rotation is present in the transition region feature of C IV and probably of He II. For other UV lines the modulation is not apparent, due to a more complex surface distribution of the active areas or supergranulation network, or a shorter lifetime of the conditions which give rise to these features, or to the uncertainities in the measured line strengths. The Mg II emission flux is constant to within + or - 3.4% implying a rather uniform distribution of Mg II emission areas. The Ca II emission not only shows a measurable variation in intensity but also variations in detailed line profile shape when observed at high resolution.

Boesgaard, A. M.↗

NASA NTRS1990

Environmental projects, volume 10. Environmental assessment: New 34-meter antenna at Apollo site

The Goldstone Deep Space Communications Complex (GDSCC) is part of NASA's Deep Space Network (DSN), one of the world's largest and most sensitive scientific telecommunications and radio navigation networks. A detailed description of the GDSCC is presented. At present the Venus Station has an unused 9-meter antenna and a 26-meter (85 ft) antenna known as DSS-13. Construction of a new 34-meter (111.5 ft) antenna at the Venus site is under way to replace the present DSS-13 26-meter antenna. The proposed construction at the Apollo Site of a new, high efficiency, 34-meter, multifrequency beam waveguide-type antenna to replace the aging, 20-year old, DSS-12 34-meter antenna located at the Echo Site is analyzed. This new 34-meter antenna, to be constructed at the Apollo Site and to be known as DSS-18, will be of a design similar to the new DSS-13 34-meter antenna now being constructed at the Venus Site. When the new 34-meter antenna is completed and operational at the Apollo Site (planned for 1993), the old DSS-12 34-meter antenna at the Echo Site will be decommissioned, dismantled, and removed.

Source record↗

NASA NTRS1990

Environmental projects. Volume 12: Friable asbestos abatement, GDSCC

The Goldstone Deep Space Communications Complex (GDSCC) is part of the NASA Deep Space Network, one of the world's largest and most sensitive scientific telecommunications and radio navigation networks. Activities at the GDSCC are carried out in support of six large parabolic dish antennas. These activities may give rise to a variety of environmental hazards, particularly the danger of exposure of GDSCC personnel to asbestos fibers that have been shown to be responsible for such serious ailments as asbestosis, lung cancer, and mesothelioma. Asbestos-containing materials (ACM's) were used in the construction of many of the approximately 100 buildings and structures that were built at the GDSCC during a 30-year period from the 1950s through 1980s. The friable asbestos-abatement program at the GDSCC is presented which consists of text, illustrations, and tables that describe the friable asbestos abatement carried out at the GDSCC from December 21, 1988 through May 11, 1989.

Source record↗

NASA NTRS1993

Systems engineering for very large systems

Very large integrated systems have always posed special problems for engineers. Whether they are power generation systems, computer networks or space vehicles, whenever there are multiple interfaces, complex technologies or just demanding customers, the challenges are unique. 'Systems engineering' has evolved as a discipline in order to meet these challenges by providing a structured, top-down design and development methodology for the engineer. This paper attempts to define the general class of problems requiring the complete systems engineering treatment and to show how systems engineering can be utilized to improve customer satisfaction and profit ability. Specifically, this work will focus on a design methodology for the largest of systems, not necessarily in terms of physical size, but in terms of complexity and interconnectivity.

Lewkowicz, Paul E.↗

NASA NTRS1997

Fault Analysis of Space Station DC Power Systems-Using Neural Network Adaptive Wavelets to Detect Faults

This paper describes the application of neural network adaptive wavelets for fault diagnosis of space station power system. The method combines wavelet transform with neural network by incorporating daughter wavelets into weights. Therefore, the wavelet transform and neural network training procedure become one stage, which avoids the complex computation of wavelet parameters and makes the procedure more straightforward. The simulation results show that the proposed method is very efficient for the identification of fault locations.

Momoh, James A.↗

NASA NTRS2001

Recent Troposheric Ozone Observations from Satellite and In-Situ Measurements: An Overview

The past 4-5 years have seen an unprecedented increase in tropospheric ozone (and related) observations thanks to focused campaigns, new satellite data, and to innovative approaches to climatology (commercial aircraft sampling, networks). The result has been new insights into the complex chemistry and dynamics affecting tropospheric ozone in the non-urban environment: global pollution, subtropical stratospheric folds, disturbances associated with the 1997 El-Nino. A synthesis based on selected examples of field and satellite data will be presented.

Thompson, A.↗

NASA NTRS2001

New Insights into Tropospheric Ozone from Satellites and Soundings

Thompson, A.↗

NASA NTRS2020

On Development of Three-Dimensional Visualization Capabilities in Glenn Research Center Communication Analysis Suite

With NASA’s upcoming mission to return to the Moon sustainably by 2024 and using that success as a means to step onto the barren world of Mars, it remains more important than ever to conduct research and planning as thoroughly and efficiently as possible. In a mission as complex as landing humans onto another celestial body, a network of orbiting satellites and ground stations must accurately and reliably communicate with each other, enabling crucial data communications throughout the mission. Visualizing this important data communication increases the understanding of the data and can accelerate analyses efforts. The purpose of this software development is to create an interactive visualization with data taken MATLAB® scripts in the GRC Communication Analysis Suite that is easy to understand, can show all necessary data, and display the data accurately. The main types of data to visualize are from the State Propagation, Line of Sight and Dynamic Link Margin scripts. These all show positions and orbits of satellites and ground stations, while the Line of Sight data shows when they have the ability to communicate with each other based on their respective antenna positions and fields of view. Additionally, the Dynamic Link Margin mode color-codes the communication link performance onto the Line of Sight access lines. Visualization requires a graphics language that is easily accessible, has the needed features, and able to easily read data produced by the GRC Communication Analysis Suite MATLAB® scripts. ThreeJS, a graphics library for Web Graphics Library, coded in JavaScript was selected for the visualization. The next part of the software development was to move the data from MATLAB® to the JavaScript. The best way to accomplish this was to implement a MATLAB® function converting the output data of the scripts to a JavaScript Object Notation file. A key part of the development was creating the visualization within JavaScript and ThreeJS to visualize any combination of planets, moons, orbits, satellites, ground stations, line of sight links, and handle future features without changing major parts of the code. The current visualization capability runs directly from MATLAB®, and can dynamically create any scene. This software development currently supports the lunar communications analysis underway by NASA, and can be easily expanded upon in the future to aid any analysis requirements to help plan current and future space missions.

Visualization↗

NASA NTRS2017

A Framework for Extending the Science Traceability Matrix: Application to the Planned Europa Mission

One of the most critical functions of the systems engineering requirements process for a large multi-instrument science-driven space mission is to successfully communicate customer expectations into a comprehensive and traceable science requirements flowdown. These requirements are essential to communicating the constraints on the scope of the science investigations and clarifying how multiple instruments contribute to a given science goal. They also provide insight into how the science goals of the whole mission are affected by design choices. There is little specific guidance available on best practices for developing this science-driven flowdown. A unified Science Traceability Matrix (USTM) contains a significant amount of information that can be leveraged for that purpose, but the USTM was not designed to directly produce a complete science requirements flowdown. Thus, starting with the principles codified in a USTM, the authors propose a framework that directly maps into the requirements flowdown and supports broader systems engineering processes while retaining its meaning to the science team. This Science Traceability and Alignment Framework, or STAF, defines a set of common definitions and valid relationships to structure communication across the project. In addition, STAF populates a network of information that can be useful to support complex mission analysis activities such as fault protection. This work discusses the highest-level implementation of the STAF, the project-domain or P-STAF, which describes an approach to decomposing customer requirements into science requirements. The planned Europa Mission is used as a case study for the implementation of this framework and its potential benefits to a project.

Susca, Sara↗

NASA NTRSDate not supplied

DeepONet-Assisted Optimization of Surface Topography for Transition Delay in a Mach 4.5 Boundary Layer

We use deep learning, an ensemble variational technique (EnVar), and direct numerical simulations(DNS) to design an optimal topography for a two-dimensional roughness element that delays the on-set of laminar-turbulent transition in a Mach 4.5 flat-plate boundary layer. Deep operator networks (DeepONets), which have the known ability to learn complex nonlinear operators within dynamical systems, are used for machine learning. For the baseline configuration of a smooth flat plate, the second-mode waves at the DNS inflow cause a quick nonlinear breakdown of the high-speed boundary layer within the computational domain. Results reported in the present study validate the ability of DeepONets to model the transition delay via a given topography of the roughness element. The computing cost to optimize the rough-ness element for minimal skin-friction drag is substantially lowered by the DeepONets-based reduced-order model. In comparison to the baseline method of EnVar optimization based on DNS alone, the DeepONets-based EnVar optimizer is able to delay transition past the outflow boundary of the computational domain while utilizing almost 5–6 times fewer DNS.

Machine Learning↗

DOE OSTI2021

Robotic Understanding of Spatial Relationships Using Neural-Logic Learning

Understanding spatial relations of objects is critical in many robotic applications such as grasping, manipulation, and obstacle avoidance. Humans can simply reason object's spatial relations from a glimpse of a scene based on prior knowledge of spatial constraints. The proposed method enables a robot to comprehend spatial relationships among objects from RGB-D data. This paper proposed a neural-logic learning framework to learn and reason spatial relations from raw data by following logic rules on spatial constraints. The neural-logic network consists of three blocks: grounding block, spatial logic block, and inference block. The grounding block extracts high-level features from the raw sensory data. The spatial logic blocks can predicate fundamental spatial relations by training a neural network with spatial constraints. The inference block can infer complex spatial relations based on the predicated fundamental spatial relations. Simulations and robotic experiments evaluated the performance of the proposed method.

Wang, Dali↗

DOE OSTI2025

Scalable Bayesian Physics-Informed Kolmogorov-Arnold Networks

Uncertainty quantification (UQ) plays a pivotal role in scientific machine learning, especially when surrogate models are used to approximate complex systems. Although multilayer perceptions (MLPs) are commonly employed as surrogates, they often suffer from overfitting due to their large number of parameters. Kolmogorov-Arnold networks (KANs) offer an alternative solution with fewer parameters. However, gradient-based inference methods, such as Hamiltonian Monte Carlo (HMC), may result in computational inefficiency when applied to KANs, especially for large-scale datasets, due to the high cost of back-propagation. To address these challenges, we propose a novel approach, combining the dropout Tikhonov ensemble Kalman inversion (DTEKI) with Chebyshev KANs. This gradient-free method effectively mitigates overfitting and enhances numerical stability. In addition, we incorporate the active subspace method to reduce the parameter-space dimensionality, allowing us to improve the accuracy of predictions and obtain more reliable uncertainty estimates. Extensive experiments demonstrate the efficacy of our approach in various test cases, including scenarios with large datasets and high noise levels. Our results show that the new method achieves comparable or better accuracy, much higher efficiency as well as stability compared to HMC, in addition to scalability. Moreover, by leveraging the low-dimensional parameter subspace, our method preserves prediction accuracy while substantially reducing further the computational cost.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2008

Distributing Data to Hand-Held Devices in a Wireless Network

ADROIT is a developmental computer program for real-time distribution of complex data streams for display on Web-enabled, portable terminals held by members of an operational team of a spacecraft-command-and-control center who may be located away from the center. Examples of such terminals include personal data assistants, laptop computers, and cellular telephones. ADROIT would make it unnecessary to equip each terminal with platform- specific software for access to the data streams or with software that implements the information-sharing protocol used to deliver telemetry data to clients in the center. ADROIT is a combination of middleware plus software specific to the center. (Middleware enables one application program to communicate with another by performing such functions as conversion, translation, consolidation, and/or integration.) ADROIT translates a data stream (voice, video, or alphanumerical data) from the center into Extensible Markup Language, effectuates a subscription process to determine who gets what data when, and presents the data to each user in real time. Thus, ADROIT is expected to enable distribution of operations and to reduce the cost of operations by reducing the number of persons required to be in the center.

Hodges, Mark↗

DOE OSTI2020

Vision-Guided Automation and Assistance

Robotic automation and assistance is an excellent way to increase efficiency and safety when working in a potentially hazardous workspace. However, many traditional automation approaches rely on the precise placement of items to be manipulated. The scope of such automated tasks is also limited to the manipulation of one type of object since there is no way to distinguish between different objects. The addition of a vision pipeline, where robot vision is used to localize and recognize objects, can allow the robot to perform more complex tasks in a variable workspace. In my vision pipeline, a depth camera is used to generate a 3D point cloud of the workspace. Objects to be manipulated are isolated in the point cloud by filtering out shared surfaces and using Euclidean cluster extraction to group clusters of points. The locations of the objects are then estimated from the point cloud of the isolated objects. The localization of objects with robot vision allows for variable workspaces, reducing the overhead associated with requiring all objects to be precisely placed in the workspace. For object recognition, deep learning is used to train a neural network that classifies objects from the raw point clouds. More complex tasks that require multiple objects to be manipulated differently can be achieved through the vision pipeline. Through its ability to visualize the workspace, robot vision greatly improves efficiency and safety of automated and assistive tasks.

42 ENGINEERING↗

DOE OSTI2023

A Review of Physics-Informed Machine Learning in Fluid Mechanics

Physics-informed machine-learning (PIML) enables the integration of domain knowledge with machine learning (ML) algorithms, which results in higher data efficiency and more stable predictions. This provides opportunities for augmenting—and even replacing—high-fidelity numerical simulations of complex turbulent flows, which are often expensive due to the requirement of high temporal and spatial resolution. In this review, we (i) provide an introduction and historical perspective of ML methods, in particular neural networks (NN), (ii) examine existing PIML applications to fluid mechanics problems, especially in complex high Reynolds number flows, (iii) demonstrate the utility of PIML techniques through a case study, and (iv) discuss the challenges and opportunities of developing PIML for fluid mechanics.

42 ENGINEERING↗

NASA NTRS1999

BIO-Plex Information System Concept

This paper describes a suggested design for an integrated information system for the proposed BIO-Plex (Bioregenerative Planetary Life Support Systems Test Complex) at Johnson Space Center (JSC), including distributed control systems, central control, networks, database servers, personal computers and workstations, applications software, and external communications. The system will have an open commercial computing and networking, architecture. The network will provide automatic real-time transfer of information to database server computers which perform data collection and validation. This information system will support integrated, data sharing applications for everything, from system alarms to management summaries. Most existing complex process control systems have information gaps between the different real time subsystems, between these subsystems and central controller, between the central controller and system level planning and analysis application software, and between the system level applications and management overview reporting. An integrated information system is vitally necessary as the basis for the integration of planning, scheduling, modeling, monitoring, and control, which will allow improved monitoring and control based on timely, accurate and complete data. Data describing the system configuration and the real time processes can be collected, checked and reconciled, analyzed and stored in database servers that can be accessed by all applications. The required technology is available. The only opportunity to design a distributed, nonredundant, integrated system is before it is built. Retrofit is extremely difficult and costly.

Jones, Harry↗