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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 1,369 records · Page 76
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Web-based Decision Support System Tools: the Soil and Water Assessment Tool Online Visualization and Analyses (SWATOnline) and NASA Earth Observation Data Downloading and Reformatting Tool (NASAaccess)

The current influx of climate related information required scientists to communicate their findings to decision makers in governments, disaster preparedness organizations, and the general public. The Soil and Water Assessment Tool (SWAT) is a powerful modelling tool that allows scientists to simulate many of the physical processes involved in the water cycle. This article presents the design, methods and development efforts to overcome some of the limitations of the previously developed SWAT visualization software programs by creating a set of modular web applications that can be duplicated, customized, and run. Moreover, this article features a web application development tool for climate data retrieval. The NASAaccess fetches, extracts and reformats climate data from the National Aeronautics and Space Administration servers and outputs data compatible with hydrological models. This work has the potential to increase the SWAT's model impact on non-technically trained stakeholders and decision makers charged with water and climate management.

Tethys↗
DOE OSTI2020

High Performance Computing Systems Tools, Visualization, and Management

High Performance Computing (HPC) systems are complex setups of servers, storage devices, network switches, and cables that are specifically designed to accommodate hundreds of users running highly computationally intensive applications at a time. These applications require numerous softwares, licenses, and various levels of storage as well. All of these resources must be monitored and managed by HPC administrators, which presents a daunting task. In this project, I created numerous software tools as part of an HPC Visualization and Management system, which is now used by HPC administrators on a daily basis.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2018

Generic Control Allocation Toolbox for Preliminary Vehicle Design

This paper describes the development and application of a Generic Control Allocation Toolbox developed at NASA Langley Research Center (LGCAT) intended to aid engineers during the preliminary design phase of an aerospace vehicle. The static controllability space in the forms of a Theoretical Attainable Moment Set, Φ, or Theoretical Attainable Force Set, φ is difficult to visualize for modern vehicles with multiple types of redundant control effectors. The objective of LGCAT is to provide system engineers and designers early in the vehicle design phase with quick insights on how control effector parameters such as quantity, sizing, location, orientation, redundancy, etc., affect the overall controllability and other performance metrics. Having such information in hand allows system engineers to make more informed decisions on overall mission objectives such as performance vs. reliability vs. cost, etc. early in a vehicle design phase and reduce the number of iterations necessary in the design and analysis cycles. LGCAT can accept a variety of control effector types including aerodynamic surfaces, rotors, thrust vector control (TVC) engines, and reaction control systems (RCS). LGCAT is MATLAB based, user friendly, and is capable of performing the analysis in the Graphical User Interface (GUI) or script mode. Current add-on features include interfacing with engineering level codes such as Vehicle Sketch Pad (VSP) and generating the corresponding Φ and φ for an arbitrary vehicle design. These capabilities potentially make LGCAT an integral part of the preliminary design phase for any vehicle.

Pei, Jing↗
NASA NTRS1994

Microfabricated Hydrogen Sensor Technology for Aerospace and Commercial Applications

Leaks on the Space Shuttle while on the Launch Pad have generated interest in hydrogen leak monitoring technology. An effective leak monitoring system requires reliable hydrogen sensors, hardware, and software to monitor the sensors. The system should process the sensor outputs and provide real-time leak monitoring information to the operator. This paper discusses the progress in developing such a complete leak monitoring system. Advanced microfabricated hydrogen sensors are being fabricated at Case Western Reserve University (CWRU) and tested at NASA Lewis Research Center (LeRC) and Gencorp Aerojet (Aerojet). Changes in the hydrogen concentrations are detected using a PdAg on silicon Schottky diode structure. Sensor temperature control is achieved with a temperature sensor and heater fabricated onto the sensor chip. Results of the characterization of these sensors are presented. These sensors can detect low concentrations of hydrogen in inert environments with high sensitivity and quick response time. Aerojet is developing the hardware and software for a multipoint leak monitoring system designed to provide leak source and magnitude information in real time. The monitoring system processes data from the hydrogen sensors and presents the operator with a visual indication of the leak location and magnitude. Work has commenced on integrating the NASA LeRC-CWRU hydrogen sensors with the Aerojet designed monitoring system. Although the leak monitoring system was designed for hydrogen propulsion systems, the possible applications of this monitoring system are wide ranged. Possible commercialization of the system will also be discussed.

Hunter, Gary W.↗
DOE OSTI2021

Multiomics Data Collection, Visualization, and Utilization for Guiding Metabolic Engineering

Biology has changed radically in the past two decades, growing from a purely descriptive science into also a design science. The availability of tools that enable the precise modification of cells, as well as the ability to collect large amounts of multimodal data, open the possibility of sophisticated bioengineering to produce fuels, specialty and commodity chemicals, materials, and other renewable bioproducts. However, despite new tools and exponentially increasing data volumes, synthetic biology cannot yet fulfill its true potential due to our inability to predict the behavior of biological systems. Here, we showcase a set of computational tools that, combined, provide the ability to store, visualize, and leverage multiomics data to predict the outcome of bioengineering efforts. We show how to upload, visualize, and output multiomics data, as well as strain information, into online repositories for several isoprenol-producing strain designs. We then use these data to train machine learning algorithms that recommend new strain designs that are correctly predicted to improve isoprenol production by 23%. This demonstration is done by using synthetic data, as provided by a novel library, that can produce credible multiomics data for testing algorithms and computational tools. In short, this paper provides a step-by-step tutorial to leverage these computational tools to improve production in bioengineered strains.

09 BIOMASS FUELS↗
NASA NTRS2022

FPP: A Modeling Language for F Prime

We present F Prime Prime (FPP), a new open-source modeling language for F Prime. F Prime is an open-source flight software framework developed at JPL and deployed, among other places, on the Mars helicopter Ingenuity. FPP provides a convenient way to model the architectural elements of an F Prime application, e.g., components, ports, and their connections. It has a succinct and readable syntax, a well- defined semantics, and robust error checking and reporting. The FPP tool suite, written in Scala, analyzes FPP models, reports errors, and translates correct FPP models to a combination of XML and C++. Existing F Prime tools translate the XML to a partial implementation in C++, to be completed by the developers. The model elements have clean interfaces and are highly reusable. An accompanying visualization tool constructs diagrams of components and connections that FSW developers can use to understand and communicate their designs, for ex- ample at reviews. We discuss the design and implementation of FPP and the integration of FPP into F Prime. We also discuss our experience using FPP to construct F Prime models. Finally, we discuss our plans for future work, including improved code generation, improved visualization, and more advanced analysis capabilities.

Starch, Michael D.↗
DOE OSTI2017

Modeling DNP3 Traffic Characteristics of Field Devices in SCADA Systems of the Smart Grid

In the generation, transmission, and distribution sectors of the smart grid, intelligence of field devices is realized by programmable logic controllers (PLCs). Many smart-grid subsystems are essentially cyber-physical energy systems (CPES): For instance, the power system process (i.e., the physical part) within a substation is monitored and controlled by a SCADA network with hosts running miscellaneous applications (i.e., the cyber part). To study the interactions between the cyber and physical components of a CPES, several co-simulation platforms have been proposed. However, the network simulators/emulators of these platforms do not include a detailed traffic model that takes into account the impacts of the execution model of PLCs on traffic characteristics. As a result, network traces generated by co-simulation only reveal the impacts of the physical process on the contents of the traffic generated by SCADA hosts, whereas the distinction between PLCs and computing nodes (e.g., a hardened computer running a process visualization application) has been overlooked. To generate realistic network traces using co-simulation for the design and evaluation of applications relying on accurate traffic profiles, it is necessary to establish a traffic model for PLCs. In this work, we propose a parameterized model for PLCs that can be incorporated into existing co-simulation platforms. We focus on the DNP3 subsystem of slave PLCs, which automates the processing of packets from the DNP3 master. To validate our approach, we extract model parameters from both the configuration and network traces of real PLCs. Simulated network traces are generated and compared against those from PLCs. Our evaluation shows that our proposed model captures the essential traffic characteristics of DNP3 slave PLCs, which can be used to extend existing co-simulation platforms and gain further insights into the behaviors of CPES.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

InSituNet: Deep Image Synthesis for Parameter Space Exploration of Ensemble Simulations

We propose InSituNet, a deep learning based surrogate model to support parameter space exploration for ensemble simulations that are visualized in situ. In situ visualization, generating visualizations at simulation time, is becoming prevalent in handling large-scale simulations because of the I/O and storage constraints. However, in situ visualization approaches limit the flexibility of post-hoc exploration because the raw simulation data are no longer available. Although multiple image-based approaches have been proposed to mitigate this limitation, those approaches lack the ability to explore the simulation parameters. Our approach allows flexible exploration of parameter space for large-scale ensemble simulations by taking advantage of the recent advances in deep learning. Specifically, we design InSituNet as a convolutional regression model to learn the mapping from the simulation and visualization parameters to the visualization results. With the trained model, users can generate new images for different simulation parameters under various visualization settings, which enables in-depth analysis of the underlying ensemble simulations. We demonstrate the effectiveness of InSituNet in combustion, cosmology, and ocean simulations through quantitative and qualitative evaluations.

deep learning↗
DOE OSTI2018

Hierarchical visualization of materials space with graph convolutional neural networks

The combination of high throughput computation and machine learning has led to a new paradigm in materials design by allowing for the direct screening of vast portions of structural, chemical, and property spaces. The use of these powerful techniques leads to the generation of enormous amounts of data, which in turn calls for new techniques to efficiently explore and visualize the materials space to help identify underlying patterns. In this work, we develop a unified framework to hierarchically visualize the compositional and structural similarities between materials in an arbitrary material space with representations learned from different layers of graph convolutional neural networks. We demonstrate the potential for such a visualization approach by showing that patterns emerge automatically that reflect similarities at different scales in three representative classes of materials: perovskites, elemental boron, and general inorganic crystals, covering material spaces of different compositions, structures, and both. For perovskites, elemental similarities are learned that reflects multiple aspects of atom properties. For elemental boron, structural motifs emerge automatically showing characteristic boron local environments. For inorganic crystals, the similarity and stability of local coordination environments are shown combining different center and neighbor atoms. The method could help transition to a data-centered exploration of materials space in automated materials design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

OptiBench: An Optimization Benchmark Tool for Renewable Energy Problems

We propose a benchmark framework and visualization tool, OptiBench, for analyzing the performance of state-of-the-art optimization solvers across a variety of optimization problems in renewable energy research. Our framework is designed from the ground up in the Julia programming language and enables analysis at scale on high performance computing (HPC) systems. Our visualization tool allows effortless evaluation of optimization solver performance, robustness, and accuracy through intuitive plots, e.g., performance profiles, heat maps, and distribution plots. We have tested three benchmark suites relevant to the modeling of renewable energy systems, viz., CUTEst, PGLib-OPF, and WaterTAP water treatment optimization problems. We illustrate benchmarking of CUTEst using OptiBench on the National Renewable Energy Laboratory's (NREL) HPC Kestrel. Our findings indicate that MA57 HSL linear solver demonstrated the best overall performance for an experimental IPOPT implementation. Our work is ongoing and we intend to add support for more optimization solvers and benchmark test suites in the future.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

OptiBench: An Optimization Benchmark Tool for Renewable Energy Problems

We propose a benchmark framework and visualization tool, OptiBench, for analyzing the performance of state-of-the-art optimization solvers across a variety of optimization problems in renewable energy research. Our framework is designed from the ground up in the Julia programming language and enables analysis at scale on high performance computing (HPC) systems. Our visualization tool allows effortless evaluation of optimization solver performance, robustness, and accuracy through intuitive plots, e.g., performance profiles, heat maps, and distribution plots. We have tested three benchmark suites relevant to the modeling of renewable energy systems, viz., CUTEst, PGLib-OPF, and WaterTAP water treatment optimization problems. We illustrate benchmarking of CUTEst using OptiBench on the National Laboratory of the Rockies's (NLR) HPC Kestrel. Our findings indicate that MA57 HSL linear solver demonstrated the best overall performance for an experimental IPOPT implementation. Our work is ongoing and we intend to add support for more optimization solvers and benchmark test suites in the future.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2010

High-Fidelity Roadway Modeling and Simulation

Roads are an essential feature in our daily lives. With the advances in computing technologies, 2D and 3D road models are employed in many applications, such as computer games and virtual environments. Traditional road models were generated by professional artists manually using modeling software tools such as Maya and 3ds Max. This approach requires both highly specialized and sophisticated skills and massive manual labor. Automatic road generation based on procedural modeling can create road models using specially designed computer algorithms or procedures, reducing the tedious manual editing needed for road modeling dramatically. But most existing procedural modeling methods for road generation put emphasis on the visual effects of the generated roads, not the geometrical and architectural fidelity. This limitation seriously restricts the applicability of the generated road models. To address this problem, this paper proposes a high-fidelity roadway generation method that takes into account road design principles practiced by civil engineering professionals, and as a result, the generated roads can support not only general applications such as games and simulations in which roads are used as 3D assets, but also demanding civil engineering applications, which requires accurate geometrical models of roads. The inputs to the proposed method include road specifications, civil engineering road design rules, terrain information, and surrounding environment. Then the proposed method generates in real time 3D roads that have both high visual and geometrical fidelities. This paper discusses in details the procedures that convert 2D roads specified in shape files into 3D roads and civil engineering road design principles. The proposed method can be used in many applications that have stringent requirements on high precision 3D models, such as driving simulations and road design prototyping. Preliminary results demonstrate the effectiveness of the proposed method.

Wang, Jie↗
DOE OSTI2022

CALORIE: A Constraint Language and Optimizing Runtime for Exascale Power Management (Final Report)

This final technical report summarizes the key accomplishments on the CALORIE project, a DOE Early Career award received by PI Henry Hoffmann at the University of Chicago. CALORIE’s main goal was to create principled methodologies, tools, and practices to help scientists and high-performance computing (HPC) operators maximize the performance and insights obtained from scientific computing applications in the face of exascale power constraints. The project accomplished these goals by completing the following objectives: • Designing a language for describing application goals (including constraints and objectives) and system capabilities. The application goals will include things like which simulations or simulations plus in situ analysis will be run together, what the power constraints are, and what requirements there are for in situ analysis (for example, a desired frame rate for visualization). The system capabilities include all components that can be adjusted to tradeoff power and performance. • Designing a runtime system that takes specified goals and capabilities and dynamically determines what capabilities to use to meet the goals. This runtime adapts to changes in goals, application behavior, or available capabilities to automatically maintain the goals despite unexpected disturbances. • Developing a foundational understanding of how power constraints affect the problem of scheduling applications in large-scale systems. This report provides an overview of the accomplishments related to each of these key objectives.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2005

Development of Ionic Liquid Monopropellants for In-Space Propulsion

A family of new, low toxicity, high energy monopropellants is currently being evaluated at NASA Marshall Space Flight Center for in-space rocket engine applications such as reaction control engines. These ionic liquid monopropellants, developed in recent years by the Air Force Research Laboratory, could offer system simplification, less in-flight thermal management, and reduced handling precautions, while increasing propellant energy density as compared to traditional storable in-space propellants such as hydrazine and nitrogen tetroxide. However, challenges exist in identifying ignition schemes for these ionic liquid monopropellants, which are known to burn at much hotter combustion temperatures compared to traditional monopropellants such as hydrazine. The high temperature combustion of these new monopropellants make the use of typical ignition catalyst beds prohibitive since the catalyst cannot withstand the elevated temperatures. Current research efforts are focused on monopropellant ignition and burn rate characterization, parameters that are important in the fundamental understanding of the monopropellant behavior and the eventual design of a thruster. Laboratory studies will be conducted using alternative ignition techniques such as laser-induced spark ignition and hot wire ignition. Ignition delay, defined as the time between the introduction of the ignition source and the first sign of light emission from a developing flame kernel, will be measured using Schlieren visualization. An optically-accessible liquid monopropellant burner, shown schematically in Figure 1 and similar in design to apparatuses used by other researchers to study solid and liquid monopropellants, will be used to determine propellant burn rate as a function of pressure and initial propellant temperature. The burn rate will be measured via high speed imaging through the chamber s windows.

Blevins, John A.↗
NASA NTRS2016

Localization from Visual Landmarks on a Free-Flying Robot

We present the localization approach for Astrobee, a new free-flying robot designed to navigate autonomously on the International Space Station (ISS). Astrobee will accommodate a variety of payloads and enable guest scientists to run experiments in zero-g, as well as assist astronauts and ground controllers. Astrobee will replace the SPHERES robots which currently operate on the ISS, whose use of fixed ultrasonic beacons for localization limits them to work in a 2 meter cube. Astrobee localizes with monocular vision and an IMU, without any environmental modifications. Visual features detected on a pre-built map, optical flow information, and IMU readings are all integrated into an extended Kalman filter (EKF) to estimate the robot pose. We introduce several modifications to the filter to make it more robust to noise, and extensively evaluate the localization algorithm.

Coltin, Brian↗
NASA NTRS1993

The effects of spatially displaced visual feedback on remote manipulator performance

The results of this evaluation have important implications for the arrangement of remote manipulation worksites and the design of workstations for telerobot operations. This study clearly illustrates the deleterious effects that can accompany the performance of remote manipulator tasks when viewing conditions are less than optimal. Future evaluations should emphasize telerobot camera locations and the use of image/graphical enhancement techniques in an attempt to lessen the adverse effects of displaced visual feedback. An important finding in this evaluation is the extent to which results from previously performed direct manipulation studies can be generalized to remote manipulation studies. Even though the results obtained were very similar to those of the direct manipulation evaluations, there were differences as well. This evaluation has demonstrated that generalizations to remote manipulation applications based upon the results of direct manipulation studies are quite useful, but they should be made cautiously.

Smith, Randy L.↗
NASA NTRS1991

Flow visualization and computational studies of a reverse flow circular combustor

The development of a reverse-flow circular combustor is described in terms of the visualization techniques that are employed to optimize the flow and mixing of fuel and air. The Fluent computer program is used in conjunction with flow visualization studies to investigate the performance of the combustor. The combustor has multiple mini-combustion zones (MCZs) and fuel injection nozzles along its circular geometry, and the fuel injection design is modeled to determine temperature distribution, including the performance at the combustor exit. The yellow dye smoke used in the visualization demonstrated desirable flow patterns with rapid circulation patterns within the MCZs and extensive interaction between the channel flow and the MCZs. The results are similar to those of CFD models, and the main conclusions are that fuel atomization can be further optimized and that the fuel injector nozzle can control swirls, fuel spray angle, and jet penetration.

Ghorashi, B.↗
NASA NTRS1992

Development of a truss joint for robotic assembly of space structures

This report presents the results of a detailed study of mechanical fasteners which were designed to facilitate robotic assembly of structures. Design requirements for robotic structural assembly were developed, taking into account structural properties and overall system design, and four candidate fasteners were designed to meet them. These fasteners were built and evaluated in the laboratory, and the Hammer-Head joint was chosen as superior overall. It had a high reliability of fastening under misalignments of 2.54 mm (0.1 in) and 3 deg, the highest end fixity (2.18), the simplest end effector, an integral capture guide, good visual verification, and the lightest weight (782 g, 1.72 lb). The study found that a good design should incorporate chamfers sliding on chamfers, cylinders sliding on chamfers, and hard surface finishes on sliding surfaces. The study also comments on robot flexibility, sag, hysteresis, thermal expansion, and friction which were observed during the testing.

Parma, George F.↗