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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 1,747 records · Page 97
All sourcesNASA NTRSDOE OSTI
NASA NTRS1982

An application of distributed processing - In the TDRSS ground communication fault isolation and monitoring system

A computer interface has been designed for the Fault Isolation and Monitoring System, a system providing qualitative and quantitative assessment of the communications between the Tracking and Data Relay Satellite System Ground Segment and NASA's Network Control Center. The functional requirements for the interface and the design philosophy are discussed, as are the methods and protocol used to implement the operator support software required to configure the monitoring system and to display and report the results of the analysis. The system relies on the use of intelligent terminals, with the human interface removed from the host computer.

Waddell, R. L., Jr.↗
DOE OSTI2025

Design-to-Deployment Continuum Platform for Microscopes and Computing Ecosystems

Science ecosystems with networked computing systems and physical instruments are increasingly being deployed with a goal to achieve the productivity promised by AI-supported remote automation. In support of these efforts, the virtual infrastructure twins (VITs) have been successfully utilized to develop the orchestration codes for these ecosystems without requiring physical access to expensive instruments, such as electron microscopes. Currently, the utility of such a VIT is severely limited by the computing capacity and capability of the computing system used as its host. Furthermore, codes developed on the VIT typically need to be transferred and refactored for production use, particularly, on high-performance systems with accelerators. In response, we develop a design-to-deployment continuum platform wherein a VIT runs natively on the ecosystem's own computing system, and thereby facilitates the continual in-situ testing and transition of codes for production use. Here, we describe the development and testing of software for remote microscope steering and GPU-based image reconstruction using this platform on a multi-GPU computing system networked to Nion microscopes. We demonstrate a continual transition of steering and reconstruction codes developed under VIT platform to production ecosystem deployment.

Al-Najjar, Anees [Oak Ridge National Laboratory (O↗
DOE OSTI2024

Graph neural networks for CO 2 solubility predictions in Deep Eutectic Solvents

Deep Eutectic Solvents (DESs) are a promising class of solvents for CO 2 capture. DESs are complex mixtures that can be designed to optimize CO solubility and overall capture process efficiency. However, the vast design landscape of DES mixtures makes experimental investigation prohibitive; as such, there is a need for computational models that can quickly and efficiently navigate the design space and inform data collection efforts. In this work, we propose Graph Neural Network (GNN) models for predicting CO 2 solubility for DESs; the GNN leverages a mixture graph representation that captures the molecular structure of the DES components as well as their intermolecular interactions. Here, we compare the GNN framework against alternative architectures (neural networks, graph convolution networks, and random forests) and data representations (molecular fingerprints, sigma profiles, and graphs). We show that the proposed approach offers superior predictive performance; specifically, we show that solubility can be predicted reliably directly from molecular structure (without the need of using sigma profiles as proposed in previous studies). This result is important, as obtaining sigma profiles requires expensive density functional theory computations. We also explored the ability of GNNs to predict solubility for new DES mixtures and operating conditions. We found that the model extrapolates across temperature reliably. However, we also found deficiencies in the ability of the model to predict solubility for DES mixtures, pressures, and molar ratio not included in the training sets; we show that this is due to an inherent lack of chemical diversity in datasets available in the literature. The proposed computational capabilities can thus help navigate the design space of DES and inform data collection efforts. Our models, data, and benchmarks are shared as Python code implemented in Jupyter notebooks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

SoS-SDQN: System of Systems Software-defined Quantum Networking

Quantum networks are needed for quantum domain applications that may run across different network deployments - point-to-point, multi-node networks, and possibly across inter-domain quantum networks, like the quantum internet. Unlike classical computing networks, quantum networks involve heterogeneous systems nodes that currently require local and manual control. This needs a unified control approach to help them integrate, work seamlessly, and have global knowledge. Software-defined Networking (SDN) has been successfully leveraged in classical networking for seamless and software-driven control of network infrastructure and packet switching, but not in management of quantum network applications across heterogeneous systems. In this paper, we review the current state of the art and present early developments of a System-of-Systems Software-defined Quantum Networking architecture (SoS-SDQN), a generic architecture that supports software-driven quantum network experiments across heterogeneous quantum systems. The architecture modifies the generic SDN to address the domain requirements of quantum networks and proposes a multilevel SDQN to provide a global view of network status, running applications, and control of incorporated heterogeneous quantum systems. We also design and implement a SoS SouthBound Quantum Interface (SoS-SBQI), a quantum infrastructure protocol that abstracts automation of applications across the network.

Alnajjar, Anees [ORNL] (ORCID:0000000237101601)↗
NASA NTRS2006

Adaptive Inner-Loop Rover Control

Adaptive control technology is developed for the inner-loop speed and steering control of the MAX Rover. MAX, a CMU developed rover, is a compact low-cost 4-wheel drive, 4-wheel steer (double Ackerman), high-clearance agile durable chassis, outfitted with sensors and electronics that make it ideally suited for supporting research relevant to intelligent teleoperation and as a low-cost autonomous robotic test bed and appliance. The design consists of a feedback linearization based controller with a proportional - integral (PI) feedback that is augmented by an online adaptive neural network. The adaptation law has guaranteed stability properties for safe operation. The control design is retrofit in nature so that it fits inside the outer-loop path planning algorithms. Successful hardware implementation of the controller is illustrated for several scenarios consisting of actuator failures and modeling errors in the nominal design.

Kulkarni, Nilesh↗
NASA NTRS2014

Modular Pulsed Plasma Electric Propulsion System for Cubesats

Current capabilities of CubeSats must be improved in order to perform more ambitious missions. Electric propulsion systems will play a key role due to their large specific impulse. Compared to other propulsion alternatives, their simplicity allows an easier miniaturization and manufacturing of autonomous modules into the nano and pico-satellite platform. Pulsed Plasma Thrusters (PPTs) appear as one of the most promising technologies for the near term. The utilization of solid and non-volatile propellants, their low power requirements and their proven reliability in the large scale make them great candidates for rapid implementation. The main challenges are the integration and miniaturization of all the electronic circuitry into a printed circuit board (PCB) that can satisfy the strict requirements that CubeSats present. NASA Ames and the George Washington University have demonstrated functionality and control of three discrete Micro-Cathode Arc Thrusters (CAT) using a bench top configuration that was compatible with the ARC PhoneSat Bus. This demonstration was successfully conducted in a vaccum chamber at the ARC Environmental Test Laboratory. A new effort will integrate a low power Plasma Processing Unit and two plasma thrusters onto a single printed circuit board that will utilize less than 13 U of Bus volume. The target design will be optimized for the accommodation into the PhoneSatEDISON Demonstration of SmallSatellite Networks (EDSN) bus as it uses the same software interface application, which was demonstrated in the previous task. This paper describes the design, integration and architecture of the proposed propulsion subsystem for a planned Technology Demonstration Mission. In addition, a general review of the Pulsed Plasma technology available for CubeSats is presented in order to assess the necessary challenges to overcome further development.

PCB↗
NASA NTRS2009

The Deep Impact Network Experiment Operations Center

Delay/Disruption Tolerant Networking (DTN) promises solutions in solving space communications challenges arising from disconnections as orbiters lose line-of-sight with landers, long propagation delays over interplanetary links, and other phenomena. DTN has been identified as the basis for the future NASA space communications network backbone, and international standardization is progressing through both the Consultative Committee for Space Data Systems (CCSDS) and the Internet Engineering Task Force (IETF). JPL has developed an implementation of the DTN architecture, called the Interplanetary Overlay Network (ION). ION is specifically implemented for space use, including design for use in a real-time operating system environment and high processing efficiency. In order to raise the Technology Readiness Level of ION, the first deep space flight demonstration of DTN is underway, using the Deep Impact (DI) spacecraft. Called the Deep Impact Network (DINET), operations are planned for Fall 2008. An essential component of the DINET project is the Experiment Operations Center (EOC), which will generate and receive the test communications traffic as well as "out-of-DTN band" command and control of the DTN experiment, store DTN flight test information in a database, provide display systems for monitoring DTN operations status and statistics (e.g., bundle throughput), and support query and analyses of the data collected. This paper describes the DINET EOC and its value in the DTN flight experiment and potential for further DTN testing.

flight experiment↗
DOE OSTI2021

Power System Event Identification Based on Deep Neural Network With Information Loading

Online power system event identification and classification are crucial to enhancing the reliability of transmission systems. In this study, we develop a deep neural network (DNN) based approach to identify and classify power system events by leveraging real-world measurements from hundreds of phasor measurement units (PMUs) and labels from thousands of events. Two innovative designs are embedded into the baseline model built on convolutional neural networks (CNNs) to improve the event classification accuracy. First, we propose a graph signal processing based PMU sorting algorithm to improve the learning efficiency of CNNs. Second, we deploy information loading based regularization to strike the right balance between memorization and generalization for the DNN. Numerical results based on real-world dataset from the Eastern Interconnection of the U.S power transmission grid show that the combination of PMU based sorting and the information loading based regularization techniques help the proposed DNN approach achieve highly accurate event identification and classification results.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Flavor Classification in ICARUS Using Convolutional Visual Networks

In this work, we adapt the Convolutional Visual Network (CVN) approach [1] to the ICARUS detector by incorporating TPC stitching methods inspired by NuGraph[8]. The stitching technique used here is unique to ICARUS, designed specifically to handle its distinct detector segmentations. We then retrain the network using ICARUS-specific data. This study underscores the flexibility of deep learning models in high-energy physics and the importance of accounting for detector-specific features when transferring machine learning techniques between experiments. This poster presents the methods, classification performance, and insights gained from applying CVN to ICARUS data.

Wieler, Felipe [Tech. Fed. Parana U.]↗
DOE OSTI2021

Atmospheric Radiation Measurement (ARM) User Facility 2020 Decadal Vision

The Atmospheric Radiation Measurement (ARM) user facility was established in 1989 by the U.S Department of Energy (DOE) Office of Biological and Environmental Research (BER) to provide an observational basis for studying the Earth’s climate. ARM began collecting observations in 1992 and was designated a user facility in 2003. The facility includes a network of extensively instrumented long-term fixed-location observatories and mobile facilities. The ARM facility also includes an aerial component to augment these ground-based measurements. Because of the diversity of in situ and remotely obtained observations, ARM’s data management infrastructure is designed to collect, process, and deliver data to the research community.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

A hybrid Penman-Monteith and machine learning model for simulating evapotranspiration and its components

Integrating physical processes with machine learning has advanced evapotranspiration (ET) simulation, yet most hybrid models fail to partition total ET into its components: soil evaporation (E) and vegetation transpiration (T). This study introduces Residual Neural Network–Penman–Monteith (RNN-PM), a novel hybrid dual-source ET model designed to overcome this limitation. The model synergizes the physically-based Penman–Monteith framework with three specialized residual neural networks trained to estimate key conductance parameters (canopy conductance, soil surface conductance, and aerodynamic conductance). Furthermore this explicit parameterization allows for the direct partitioning of total ET. Validation at National Ecological Observatory Network (NEON) flux sites using high-frequency partitioned E and T shows that RNN-PM reliably reproduces ET and the transpiration fraction (T/ET). For ET, the model achieves an average Kling–Gupta efficiency (KGE) of 0.89 and a root-mean-square error (RMSE) of 0.55 mm/day; for T/ET, the KGE is 0.87 with an RMSE of 0.06. Furthermore, RNN-PM demonstrates robust generalization, accurately simulating ET and its components well beyond the initial training dataset, even under extreme climatic conditions. This study extended the analysis by comparing the RNN-PM model with seven established dual-source ET models. The results indicate that RNN-PM outperforms both conventional machine learning models and purely physical process-based models in simulating ET components in most cases. Among the purely physical process-based dual-source models, those based on surface temperature decomposition showed improved performance as the leaf area index (LAI) decreased when evaluated against high-frequency ET component datasets. In contrast, the performance of conductance-based dual-source models declined with decreasing LAI. Although purely machine learning-based models can produce relatively accurate simulations of ET components, they often exhibit limited generalization capability, an issue that the RNN-PM model effectively overcomes. Ultimately, the RNN-PM model represents a significant advance in simulating ET components, offering a novel and scalable approach for improving the representation of land–atmosphere interactions in Earth system models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

AI-Enhanced Co-Design for Next-Generation Microelectronics: Innovating Innovation [Workshop Report]

In April 5-7, 2022, Sandia National Laboratories hosted a second virtual workshop to further explore the potential for developing AI-enhanced co-design for microelectronics (AICoM). This second piece in an ongoing workshop series again brought together two themes. The first theme, co-design for next generation microelectronics, was drawn from the 2018 Department of Energy Office of Science (DOE SC) “Basic Research Needs for Microelectronics” (BRN) report (DOE/SC, 2018, 2021), which called for a “fundamental rethinking” of the traditional design approach to microelectronics, in which subject matter experts (SMEs) in each microelectronics discipline (materials, devices, circuits, algorithms, etc.) work near-independently. Instead, the BRN called for a non-hierarchical, egalitarian vision of co-design, wherein “each scientific discipline informs and engages the others” in “parallel but intimately networked efforts to create radically new capabilities.” The second theme, exploiting and advancing artificial intelligence (AI) to support co-design for microelectronics, acknowledges the continuing breakthroughs in AI that are currently enhancing and accelerating solutions to traditional design problems in materials synthesis and processing, circuit design, and electronic design automation (EDA).

42 ENGINEERING↗
NASA NTRS2017

Implementation of (O-)CGR in The ONE

Routing in Delay-/Disruption-Tolerant Networking (DTN) requires specific solutions as link impairments prevent the use of ordinary Internet algorithms, based on a timely dissemination of network topology information. Among DTN routing algorithms there is a dichotomy between opportunistic and deterministic (scheduled) solutions. The former are numerous and apply to terrestrial environments; CGR is the most widely supported algorithm designed for scheduled connectivity, and it is usually applied to space networks. However, in an attempt to provide a unified approach, an opportunistic variant of CGR, Opportunistic CGR (OCGR) has been recently proposed by some of the authors. Performance evaluations are normally carried out for opportunistic solutions by means of simulators, such as The ONE considered in this paper. CGR by contrast is more often studied by means of small testbeds. As the simulation approach could be complementary for CGR, and essential for OCGR, the authors have recently ported both of them into The ONE, by developing and releasing as free software a specific additional package. The aim of this paper is to show the rationale of this choice and discuss the many challenges that needed to be tackled to achieve this primary goal.

Tempesta, G.↗
NASA NTRS2015

Mini-Stamp as a Micro-Display for At-A-Glance Subsystem Information for DSN Links

Operators of the Deep Space Network (DSN) attend to numerous tasks with the overall goal of providing continuous support for the world's deep space missions. This high-stakes operations environment requires operators to understand the state of the Deep Space Network and predict what will happen next. Under the Follow-the-Sun initiative which requires remote operations of the highly complex telecommunications equipment, operators will need to remain aware of the state of the entire network rather than just their own facility, and transitioning fluidly between periods of low activity and periods of high demand. I designed a micro-display for operators to see, at a glance, the state of a Deep Space Network support including its subsystems. Using in-depth user-centered and participatory design techniques to identify information requirements, I designed what I called a Postage Stamp (NTR-49720) for individual operators to be able to maintain awareness of their own assigned supports. However, under Follow the Sun, operators must remain aware of all supports. The area occupied by the Postage Stamp must shrink to allow operators to see the state of the entire system, e.g., via a Big Board posted prominently in the operations room. Micro-displays are tools for mental model re-alignment, helping operators to keep their mental models of how the system works and behaves aligned with the changing state of the complex system. Data-driven micro-displays such as the Postage Stamp and Mini-Stamp display information about the system in a consistent way. Like a traffic light, the format of the micro-display never changes: the operator always knows where to look to find a specific piece of information. The Mini-Stamp always looks like the Mini-Stamp, and all of its data fields always lie in the same place on the micro-display. Real-time data flows through the Mini-Stamp to provide information to the operator.

Holloway, Alexandra↗
NASA NTRS2006

Ikhana: A NASA UAS Supporting Long Duration Earth Science Missions

NASA's Ikhana unmanned aerial vehicle (UAV) is a General Atomics MQ-9 Predator-B modified to support the conduct of Earth science missions for the NASA Science Mission Directorate through partnerships, other government agencies and universities. Ikhana, a Native American word meaning 'intelligence', can carry over 2000 lbs of atmospheric and remote sensing instruments in the payload bay and external pods. The aircraft is capable of mission durations in excess of 24 hours at altitudes above 40,000 ft. Redundant flight control, avionics, power, and network systems increase the system reliability and allow easier access to public airspace. The aircraft is remotely piloted from a mobile ground control station (GCS) using both C-band line-of-sight and Ku-band over-the-horizon satellite datalinks. NASA's GCS has been modified to support on-site science monitoring, or the downlink data can be networked to remote sites. All ground support systems are designed to be deployable to support global Eart science investigations. On-board support capabilities include an instrumentation system and an Airborne Research Test System (ARTS). The ARTS can host research algorithms that will autonomously command and control on-board sensors, perform sensor health monitoring, conduct data analysis, and request changes to the flight plan to maximize data collection. The ARTS also has the ability to host algorithms that will autonomously control the aircraft trajectory based on sensor needs, (e.g. precision trajectory for repeat pass interferometry) or to optimize mission objectives (e.g. search for specific atmospheric conditions). Standard on-board networks will collect science data for recording and for inclusion in the aircraft's high bandwidth downlink. The Ikhana project will complete GCS development, science support systems integration, external pod integration and flight clearance, and operations crew training in early 2007. A large-area remote sensing mission is currently scheduled for the Summer 2007.

Cobleigh, Brent R.↗
NASA NTRS1991

Compact reactor design automation

A conceptual compact reactor design automation experiment was performed using the real-time expert system G2. The purpose of this experiment was to investigate the utility of an expert system in design; in particular, reactor design. The experiment consisted of the automation and integration of two design phases: reactor neutronic design and fuel pin design. The utility of this approach is shown using simple examples of formulating rules to ensure design parameter consistency between the two design phases. The ability of G2 to communicate with external programs even across networks provides the system with the capability of supplementing the knowledge processing features with conventional canned programs with possible applications for realistic iterative design tools.

Nassersharif, Bahram↗
NASA NTRS1993

Developing a space network interface simulator: The NTS approach

This paper describes the approach used to redevelop the Network Control Center (NCC) Test System (NTS), a hardware and software facility designed to make testing of the NCC Data System (NCCDS) software efficient, effective, and as rigorous as possible prior to operational use. The NTS transmits and receives network message traffic in real-time. Data transfer rates and message content are strictly controlled and are identical to that of the operational systems. NTS minimizes the need for costly and time-consuming testing with the actual external entities (e.g., the Hubble Space Telescope (HST) Payload Operations Control Center (POCC) and the White Sands Ground Terminal). Discussed are activities associated with the development of the NTS, lessons learned throughout the project's lifecycle, and resulting productivity and quality increases.

Hendrzak, Gary E.↗
NASA NTRS2009

Hybrid NN/SVM Computational System for Optimizing Designs

A computational method and system based on a hybrid of an artificial neural network (NN) and a support vector machine (SVM) (see figure) has been conceived as a means of maximizing or minimizing an objective function, optionally subject to one or more constraints. Such maximization or minimization could be performed, for example, to optimize solve a data-regression or data-classification problem or to optimize a design associated with a response function. A response function can be considered as a subset of a response surface, which is a surface in a vector space of design and performance parameters. A typical example of a design problem that the method and system can be used to solve is that of an airfoil, for which a response function could be the spatial distribution of pressure over the airfoil. In this example, the response surface would describe the pressure distribution as a function of the operating conditions and the geometric parameters of the airfoil. The use of NNs to analyze physical objects in order to optimize their responses under specified physical conditions is well known. NN analysis is suitable for multidimensional interpolation of data that lack structure and enables the representation and optimization of a succession of numerical solutions of increasing complexity or increasing fidelity to the real world. NN analysis is especially useful in helping to satisfy multiple design objectives. Feedforward NNs can be used to make estimates based on nonlinear mathematical models. One difficulty associated with use of a feedforward NN arises from the need for nonlinear optimization to determine connection weights among input, intermediate, and output variables. It can be very expensive to train an NN in cases in which it is necessary to model large amounts of information. Less widely known (in comparison with NNs) are support vector machines (SVMs), which were originally applied in statistical learning theory. In terms that are necessarily oversimplified to fit the scope of this article, an SVM can be characterized as an algorithm that (1) effects a nonlinear mapping of input vectors into a higher-dimensional feature space and (2) involves a dual formulation of governing equations and constraints. One advantageous feature of the SVM approach is that an objective function (which one seeks to minimize to obtain coefficients that define an SVM mathematical model) is convex, so that unlike in the cases of many NN models, any local minimum of an SVM model is also a global minimum.

Rai, Man Mohan↗