Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “DATA ARCHITECTURE”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,207 records · Page 67
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Small-RPS Enabled Mars Rover Concept

Both the MER and the Mars Pathfinder rovers operated on Mars in an energy-limited mode, since the solar panels generated power during daylight hours only. At other times the rovers relied on power stored in batteries. In comparison, Radioisotope Power Systems (RPS) offer a power-enabled paradigm, where power can be generated for long mission durations (measured in years), independently from the Sun, and on a continuous basis. A study was performed at PL to assess the feasibility of a small-RPS enabled MER-class rover concept and any associated advantages of its mission on Mars. The rover concept relied on design heritage from MER with two significant changes. First, the solar panels were replaced with two single GPHS module based small-RPSs. Second, the Mossbauer spectroscope was substituted with a laser Raman spectroscope, in order to move towards MEPAG defined astrobiology driven science goals. The highest power requirements were contributed to mobility and telecommunication type operating modes, hence influencing power system sizing. The resulting hybrid power system included two small-RPSs and two batteries. Each small-RPS was assumed to generate 50We of power or 62OWh/sol of energy (BOL), comparable to that of MER. The two 8Ah batteries were considered available during peak power usage. Mission architecture, power trades, science instruments, data, communication, thermal and radiation environments, mobility, mass issues were also addressed. The study demonstrated that a new set of RPS-enabled rover missions could be envisioned for Mars exploration within the next decade, targeting astrobiology oriented science objectives, while powered by 2 to 4 GPHS modules.

Mars exploration↗
NASA NTRS2007

Cooperative Collision Avoidance Technology Demonstration Data Analysis Report

This report details the National Aeronautics and Space Administration (NASA) Access 5 Project Office Cooperative Collision Avoidance (CCA) Technology Demonstration for unmanned aircraft systems (UAS) conducted from 21 to 28 September 2005. The test platform chosen for the demonstration was the Proteus Optionally Piloted Vehicle operated by Scaled Composites, LLC, flown out of the Mojave Airport, Mojave, CA. A single intruder aircraft, a NASA Gulf stream III, was used during the demonstration to execute a series of near-collision encounter scenarios. Both aircraft were equipped with Traffic Alert and Collision Avoidance System-II (TCAS-II) and Automatic Dependent Surveillance Broadcast (ADS-B) systems. The objective of this demonstration was to collect flight data to support validation efforts for the Access 5 CCA Work Package Performance Simulation and Systems Integration Laboratory (SIL). Correlation of the flight data with results obtained from the performance simulation serves as the basis for the simulation validation. A similar effort uses the flight data to validate the SIL architecture that contains the same sensor hardware that was used during the flight demonstration.

Source record↗
NASA NTRS2006

Architecture for Control of the K9 Rover

Software featuring a multilevel architecture is used to control the hardware on the K9 Rover, which is a mobile robot used in research on robots for scientific exploration and autonomous operation in general. The software consists of five types of modules: Device Drivers - These modules, at the lowest level of the architecture, directly control motors, cameras, data buses, and other hardware devices. Resource Managers - Each of these modules controls several device drivers. Resource managers can be commanded by either a remote operator or the pilot or conditional-executive modules described below. Behaviors and Data Processors - These modules perform computations for such functions as planning paths, avoiding obstacles, visual tracking, and stereoscopy. These modules can be commanded only by the pilot. Pilot - The pilot receives a possibly complex command from the remote operator or the conditional executive, then decomposes the command into (1) more-specific commands to the resource managers and (2) requests for information from the behaviors and data processors. Conditional Executive - This highest-level module interprets a command plan sent by the remote operator, determines whether resources required for execution of the plan are available, monitors execution, and, if necessary, selects an alternate branch of the plan.

Bresina, John L.↗
NASA NTRS2010

Kepler Science Operations Center Architecture

We give an overview of the operational concepts and architecture of the Kepler Science Data Pipeline. Designed, developed, operated, and maintained by the Science Operations Center (SOC) at NASA Ames Research Center, the Kepler Science Data Pipeline is central element of the Kepler Ground Data System. The SOC charter is to analyze stellar photometric data from the Kepler spacecraft and report results to the Kepler Science Office for further analysis. We describe how this is accomplished via the Kepler Science Data Pipeline, including the hardware infrastructure, scientific algorithms, and operational procedures. The SOC consists of an office at Ames Research Center, software development and operations departments, and a data center that hosts the computers required to perform data analysis. We discuss the high-performance, parallel computing software modules of the Kepler Science Data Pipeline that perform transit photometry, pixel-level calibration, systematic error-correction, attitude determination, stellar target management, and instrument characterization. We explain how data processing environments are divided to support operational processing and test needs. We explain the operational timelines for data processing and the data constructs that flow into the Kepler Science Data Pipeline.

Middour, Christopher↗
NASA NTRS2013

Feeding People's Curiosity: Leveraging the Cloud for Automatic Dissemination of Mars Images

Smartphones and tablets have made wireless computing ubiquitous, and users expect instant, on-demand access to information. The Mars Science Laboratory (MSL) operations software suite, MSL InterfaCE (MSLICE), employs a different back-end image processing architecture compared to that of the Mars Exploration Rovers (MER) in order to better satisfy modern consumer-driven usage patterns and to offer greater server-side flexibility. Cloud services are a centerpiece of the server-side architecture that allows new image data to be delivered automatically to both scientists using MSLICE and the general public through the MSL website (http://mars.jpl.nasa.gov/msl/).

Cloud Computing↗
NASA NTRS2014

Evolving the NASA Near Earth Network for the Next Generation of Human Space Flight

The purpose of this paper is to present the planned development and evolution of the NASA Near Earth Network (NEN) launch communications services in support of the next generation of human space flight programs. Following the final space shuttle mission in 2011, the two NEN launch communications stations were decommissioned. Today, NASA is developing the next generation of human space flight systems focused on exploration missions beyond low-earth orbit, and supporting the emerging market for commercial crew and cargo human space flight services. The NEN is leading a major initiative to develop a modern high data rate launch communications ground architecture with support from the Kennedy Space Center Ground Systems Development and Operations Program and in partnership with the U.S. Air Force (USAF) Eastern Range. This initiative, the NEN Launch Communications Stations (LCS) development project, successfully completed its System Requirements Review in November 2013. This paper provides an overview of the LCS project and a summary of its progress. The LCS ground architecture, concept of operations, and driving requirements to support the new heavy-lift Space Launch System and Orion Multi-Purpose Crew Vehicle for Exploration Mission-1 are presented. Finally, potential future extensions to the ground architecture beyond EM-1 are discussed.

Launch Communications↗
NASA NTRS2017

Mission Assurance: A Model-Based Approach

The purpose of Safety and Mission Assurance (SMA) at NASA’s Jet Propulsion Laboratory is to ensure mission safety and success. As the complexity of technical systems increases, the tools, processes, and infrastructure used to facilitate SMA activities must evolve. A transition from a document-centric approach to a data-driven, model-based architecture is critical in allowing SMA efforts to grapple with complex system designs. This work describes how a sample subset of existing SMA processes can be executed in a model-based environment, while also describing ways in which a model-based architecture can further augment SMA efforts. One key aspect is supporting the complex interplay between the different SMA processes and the systems engineering effort to provide additional insight into the system from an SMA perspective. The proposed architecture for model-based mission assurance provides an avenue for assuring mission safety and success in increasingly complex systems.

Schreiner, Samuel S.↗
NASA NTRS2016

Euclid Near Infrared Spectro Photometer instrument concept and first test results obtained for different breadboards models at the end of phase C

The Euclid mission objective is to understand why the expansion of the Universe is accelerating through by mapping the geometry of the dark Universe by investigating the distance-redshift relationship and tracing the evolution of cosmic structures. The Euclid project is part of ESA's Cosmic Vision program with its launch planned for 2020 (ref [1]). The NISP (Near Infrared Spectrometer and Photometer) is one of the two Euclid instruments and is operating in the near-IR spectral region (900- 2000nm) as a photometer and spectrometer. The instrument is composed of: - a cold (135K) optomechanical subsystem consisting of a Silicon carbide structure, an optical assembly (corrector and camera lens), a filter wheel mechanism, a grism wheel mechanism, a calibration unit and a thermal control system - a detection subsystem based on a mosaic of 16 HAWAII2RG cooled to 95K with their front-end readout electronic cooled to 140K, integrated on a mechanical focal plane structure made with molybdenum and aluminum. The detection subsystem is mounted on the optomechanical subsystem structure - a warm electronic subsystem (280K) composed of a data processing / detector control unit and of an instrument control unit that interfaces with the spacecraft via a 1553 bus for command and control and via Spacewire links for science data This presentation describes the architecture of the instrument at the end of the phase C (Detailed Design Review), the expected performance, the technological key challenges and preliminary test results obtained for different NISP subsystem breadboards and for the NISP Structural and Thermal model (STM).

Maciaszek, Thierry↗
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↗
NASA NTRSDate not supplied

NASA Kennedy Space Center Swamp Works 10th Anniversary: Innovative Research & Technology Development Summary

Kennedy Space Center’s (KSC) Swamp Works, provides government and commercial space ventures with the technologies required for working and living on the surfaces of the Moon or other planets and bodies in our solar system. The Swamp Works team establishes rapid, innovative and cost-effective exploration mission solutions through leveraging of partnerships across NASA, industry and academia. Concepts start small and build up fast, with lean development processes and a hands-on approach. Testing is performed in early stages to drive design improvements and progressively increase Technology Readiness Levels (TRL). Swamp Works provides concepts, architecture studies and trades, designs, data, technology development, technology demonstration hardware, flight hardware, testing, flight support and knowledge in support of the development of surface systems. It consists of several teams with associated laboratories and test capabilities. The Granular Mechanics and Regolith Operations (GMRO) Laboratory and the Electrostatics and Surface Physics Laboratory (ESPL) are co-located in the Engineering Development Lab (EDL) facility high bay. The Applied Chemistry Lab (ACL) is in an adjacent facility and other KSC labs are being influenced by the innovation methods pioneered at the Swamp Works. The Swamp Works was founded in January 2013 by a group of scientists and engineers at KSC with the over-arching vision of expanding humanity and civilization into the solar system by the use of space resources via advanced technology. Ultimately, this will create a solar system economy that will improve the human condition due to the abundance of energy and resources. This paper will summarize the projects and technology development that have been performed by the Swamp Works to celebrate its 10th Anniversary of innovation success.

Robotic Mining↗
NASA NTRS2023

NASA Kennedy Space Center Swamp Works: Innovation Methods for Advanced Research & Technology Development

Kennedy Space Center’s (KSC) Swamp Works, provides government and commercial space ventures with the technologies required for working and living on the surfaces of the Moon or other planets and bodies in our solar system. The Swamp Works team establishes agile, innovative and cost effective exploration mission solutions through leveraging of partnerships across NASA, industry and academia. Concepts start small and build up efficiently, with lean development processes and a hands on approach. Testing is performed in early stages to drive design improvements and progressively increase Technology Readiness Levels (TRL). Swamp Works provides concepts, architecture studies and trades, designs, data, technology development, technology demonstration hardware, flight hardware, testing, flight support and knowledge in support of the development of surface systems. This paper will summarize the state of the art in innovation methods, and it will compare the various other methods to how innovation is accomplished at the NASA KSC Swamp Works. Since 2013, Swamp Works has developed a wide variety of technologies, and has demonstrated viable methods for innovation while also collaborating with other government agencies, industry and academia to support space exploration. These methods focus on agile design, test, build iterations where each cycle of prototyping results in improved knowledge and designs. Failures are tole rated in the early stages of these iterations, since the overall development cycle is shorter more efficient and cost effective by retiring risk early in the cycle. Momentum is maintained and the development teams remain highly motivated by the high level of team work, technical excellence, engagement and creative freedom.

Robotic Mining↗
NASA NTRSDate not supplied

Swamp Works Technology Development 10th Anniversary: 2013-2023

Kennedy Space Center’s (KSC) Swamp Works, provides government and commercial space ventures with the technologies required for working and living on the surfaces of the Moon or other planets and bodies in our solar system. The Swamp Works team establishes efficient, innovative and cost-effective exploration mission solutions through leveraging of partnerships across NASA, industry and academia. Concepts start small and build up momentum, with lean development processes and a hands-on approach. Testing is performed in early stages to drive design improvements and progressively increase Technology Readiness Levels (TRL). Swamp Works provides concepts, architecture studies and trades, designs, data, technology development, technology demonstration hardware, flight hardware, testing, flight support and knowledge in support of the development of surface systems. It consists of several teams with associated laboratories and test capabilities. The Granular Mechanics and Regolith Operations (GMRO) Laboratory and the Electrostatics and Surface Physics Laboratory (ESPL) are co-located in the Engineering Development Lab (EDL) facility high bay. The Applied Chemistry Lab (ACL) is in an adjacent facility and other KSC labs are being influenced by the innovation methods pioneered at the Swamp Works. The Swamp Works was founded in January 2013 by a group of scientists and engineers at KSC with the over-arching vision of expanding humanity and civilization into the solar system by the use of space resources via advanced technology. Ultimately, this will create a solar system economy that will improve the human condition due to the abundance of energy and resources. This presentation will summarize the projects and technology development that have been performed by the Swamp Works to celebrate its 10th Anniversary of innovation success.

Swamp Works↗
NASA NTRS2023

Swamp Works Technology Development 10th Anniversary: 2013-2023 - Innovative Research & Technology Development Summary

Kennedy Space Center’s (KSC) Swamp Works, provides government and commercial space ventures with the technologies required for working and living on the surfaces of the Moon or other planets and bodies in our solar system. The Swamp Works team establishes rapid, innovative and cost-effective exploration mission solutions through leveraging of partnerships across NASA, industry and academia. Concepts start small and build up efficiently, with lean development processes and a hands-on approach. Testing is performed in early stages to drive design improvements and progressively increase Technology Readiness Levels (TRL). Swamp Works provides concepts, architecture studies and trades, designs, data, technology development, technology demonstration hardware, flight hardware, testing, flight support and knowledge in support of the development of surface systems. It consists of several teams with associated laboratories and test capabilities. The Granular Mechanics and Regolith Operations (GMRO) Laboratory and the Electrostatics and Surface Physics Laboratory (ESPL) are co-located in the Engineering Development Lab (EDL) facility high bay. The Applied Chemistry Lab (ACL) is in an adjacent facility and other KSC labs are being influenced by the innovation methods pioneered at the Swamp Works. The Swamp Works was founded in January 2013 by a group of scientists and engineers at KSC with the over-arching vision of expanding humanity and civilization into the solar system by the use of space resources via advanced technology. Ultimately, this will create a solar system economy that will improve the human condition due to the abundance of energy and resources. This presentation will summarize the projects and technology development that have been performed by the Swamp Works to celebrate its 10th Anniversary of innovation success.

Swamp Works↗
DOE OSTI2013

Development of an object-oriented ORIGEN for advanced nuclear fuel modeling applications

The ORIGEN package serves as the core depletion and decay calculation module within the SCALE code system. A recent major re-factor to the ORIGEN code architecture as part of an overall modernization of the SCALE code system has both greatly enhanced its maintainability as well as afforded several new capabilities useful for incorporating depletion analysis into other code frameworks. This paper will present an overview of the improved ORIGEN code architecture (including the methods and data structures introduced) as well as current and potential future applications utilizing the new ORIGEN framework. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

On-Detector Machine Learning for Beam-Induced Background Rejection at a 10 TeV Muon Collider

A 10 TeV Muon Collider is a compelling candidate for a future energy-frontier facility, offering unprecedented opportunities to explore the fundamental laws of particle physics. Muon decays in the collider ring produce intense beam-induced background (BIB) that can overwhelm detector occupancy and exceed readout bandwidth constraints. We investigate the potential of on-detector Machine Learning for BIB rejection in the vertex detector, exploiting pixel cluster shapes to distinguish background from collision products. We study three classes of lightweight neural-network architectures, and evaluate their implementation feasibility using high-level synthesis. Selected architectures achieve 88 to 90% data reduction at 99% signal efficiency, while requiring hardware resources compatible with potential ASIC implementation. These results demonstrate the potential of performing substantial BIB rejection directly in the pixel readout, providing a strategy for meeting the tracker readout requirements at a future Muon Collider.

Abadjiev, Daniel [Chicago U.]↗
DOE OSTI2024

Integrating multi-modal remote sensing, deep learning, and attention mechanisms for yield prediction in plant breeding experiments

In both plant breeding and crop management, interpretability plays a crucial role in instilling trust in AI-driven approaches and enabling the provision of actionable insights. The primary objective of this research is to explore and evaluate the potential contributions of deep learning network architectures that employ stacked LSTM for end-of-season maize grain yield prediction. A secondary aim is to expand the capabilities of these networks by adapting them to better accommodate and leverage the multi-modality properties of remote sensing data. In this study, a multi-modal deep learning architecture that assimilates inputs from heterogeneous data streams, including high-resolution hyperspectral imagery, LiDAR point clouds, and environmental data, is proposed to forecast maize crop yields. The architecture includes attention mechanisms that assign varying levels of importance to different modalities and temporal features that, reflect the dynamics of plant growth and environmental interactions. The interpretability of the attention weights is investigated in multi-modal networks that seek to both improve predictions and attribute crop yield outcomes to genetic and environmental variables. This approach also contributes to increased interpretability of the model's predictions. The temporal attention weight distributions highlighted relevant factors and critical growth stages that contribute to the predictions. The results of this study affirm that the attention weights are consistent with recognized biological growth stages, thereby substantiating the network's capability to learn biologically interpretable features. Accuracies of the model's predictions of yield ranged from 0.82-0.93 R 2 ref in this genetics-focused study, further highlighting the potential of attention-based models. Further, this research facilitates understanding of how multi-modality remote sensing aligns with the physiological stages of maize. The proposed architecture shows promise in improving predictions and offering interpretable insights into the factors affecting maize crop yields, while demonstrating the impact of data collection by different modalities through the growing season. By identifying relevant factors and critical growth stages, the model's attention weights provide valuable information that can be used in both plant breeding and crop management. The consistency of attention weights with biological growth stages reinforces the potential of deep learning networks in agricultural applications, particularly in leveraging remote sensing data for yield prediction. To the best of our knowledge, this is the first study that investigates the use of hyperspectral and LiDAR UAV time series data for explaining/interpreting plant growth stages within deep learning networks and forecasting plot-level maize grain yield using late fusion modalities with attention mechanisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Multi-Scale Integrated Monitoring System for Enhancing Methane Emission Detection, Quantification & Prediction

This report details the progress and findings of a comprehensive study on reviewing existing solutions, identifying technology gaps, and formulating an “all-in-one” integrated strategy for developing the next-generation multiscale methane monitoring and modeling platform, conducted under grant number DE-FE0032292. Co-led by Dr. David Ebert, Dr. Binbin Weng, and Dr. Chenghao Wang at the University of Oklahoma, the project’s goal was to develop an integrated approach for building this engineering platform to detect, quantify, and mitigate methane emissions across various temporal scale, spatial scales, and sectors. The planning grant study began with an extensive review of various methane sensing and monitoring technologies and systems, surveying over 100 technology providers globally. This review revealed the prevalence of optical methods over chemical methods in commercially available sensors, with Non-Dispersive Infrared (NDIR), Tunable Diode Laser Absorption Spectroscopy (TDLAS), and Optical Gas Imaging (OGI) cameras being the most prevalent options. A trend towards more advanced optical techniques was observed, driven by increased regulatory focus and technological advancements. The technical evaluation of these sensing technologies provided crucial insights into their capabilities and limitations. The study examined emerging technologies such as Differential Absorption LiDAR (DIAL), which show promise for high-precision and long-range detection. The team then investigated the features and application bandwidth of various sensing platforms, including handheld, fixed/stationary, mobile, aerials, and spaceborne monitors. Pilot field studies were conducted to assess the capabilities of solutions for different emission scenarios. Field work with sensor deployments was conducted at three distinct site types: an oil & gas industry site, a cattle ranching operation, and a waste processing facility. The team also conducted a thorough review of methane flux inverse modeling approaches, focused on physically based methods. These approaches were categorized into simple, intermediate, and advanced methods. A realtime WRF-GHG (Weather Research and Forecasting-Greenhouse Gas) modeling system was developed and applied, incorporating multiple data sources to guide field experiments and inform methane plume detection. The project identified and analyzed numerous categories of methane data sources, including satellite measurements, ground-based sensors, and inventory databases. Key platforms examined include EDGAR, EPA GHGI, NASA TROPOMI, Carbon Mapper, and Climate TRACE, among others. The team proposed an architecture for a comprehensive methane monitoring platform. This system incorporates multi-source data acquisition, advanced data processing and assimilation, interactive visualization tools, and analytical capabilities for emissions forecasting and scenario analysis. The proposed platform aims to provide a user-friendly interface catering to various stakeholders, from researchers to policymakers. The architecture includes sophisticated data ingestion methods, a centralized data warehouse, and advanced analytical tools for data fusion and interpretation. To ensure the relevance and effectiveness of the proposed system, a comprehensive survey was conducted to gather stakeholder input on system requirements. Key findings include a strong need for integrating various data types and formats, a preference for real-time data updates and advanced visualization tools, and a demand for user-friendly interfaces catering to different expertise levels.

03 NATURAL GAS↗