SEARCH · Engineering Papers

Results for “multi-”

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.

SourceResearch connectionFrom yearThrough year

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

At least 1,513 records · Page 84

All sources NASA NTRS DOE OSTI

NASA NTRS2006

Deep impact sequence planning using multi-mission adaptable planning tools with integrated spacecraft models

The Deep Impact mission was ambitious and challenging. JPL's well proven, easily adaptable multi-mission sequence planning tools combined with integrated spacecraft subsystem models enabled a small operations team to develop, validate, and execute extremely complex sequence-based activities within very short development times. This paper focuses on the core planning tool used in the mission, APGEN. It shows how the multi-mission design and adaptability of APGEN made it possible to model spacecraft subsystems as well as ground assets throughout the lifecycle of the Deep Impact project, starting with models of initial, high-level mission objectives, and culminating in detailed predictions of spacecraft behavior during mission-critical activities.

Seybold, Calina C.↗

NASA NTRS1995

A Multi-Mission Space Avionics Architecture

As the budget for the space industry is dwindling, a reusable avionics architecture applicable across multiple missions is urgently needed to reduce the development and production costs of flight projects. This paper presents a multi-mission avionics architecture which employs interface standards extensively, sot hat avionics systems can be built rapidly by assembling subsystems and instruments in a plug-and-play manner. The key feature of this architecture is a "backbone" standard parallel bus which can accommodate various standard interfaces through a repertoire of I/O modules. Hence, specific ;mission requirements can be met by selecting and integrating the appropriate subsystems and instruments into the system. This architecture can also adopt different "backbone" buses, implement redundant processors, and support distributed processing. An example of the architecture using the MOPS6000 from the Southwest Research Institute is also described. Experiments will be done in the Flight System Testbed (FST) of the Jet Propulsion Laboratory to benchmark the MOPS6000 and to demonstrate its multi-mission capability.

Avionics↗

NASA NTRS1997

Test/Model Correlation for Mars Pathfinder Multi-Body System Drop Tests

A multi-body dynamics computer model was used extensively for the Mars Pathfinder mission to simulate its innovative and uncoventional atmospheric entry, descent and landing approach. To verify the accuracy and validity of this model was vital to the success of this mission, two multi-body system drop tests were performed and augmented by an extensive model correlation effort.

Mars↗

NASA NTRS2013

Multi-color QWIP FPAs for hyperspectral thermal emission instruments

Infrared focal plane arrays (FPAs) covering broad mid- and long-IR spectral ranges are the central parts of the spectroscopic and imaging instruments in several Earth and planetary science missions. To be implemented in the space instrument these FPAs need to be large-format, uniform, reproducible, low-cost, low 1/f noise, and radiation hard. Quantum Well Infrared Photodetectors (QWIPs), which possess all needed characteristics, have a great potential for implementation in the space instruments. However a standard QWIP has only a relatively narrow spectral coverage. A multi-color QWIP, which is compromised of two or more detector stacks, can to be used to cover the broad spectral range of interest. We will discuss our recent work on development of multi-color QWIP for Hyperspectral Thermal Emission Spectrometer instruments. We developed QWIP compromising of two stacks centered at 9 and 10.5 μm, and featuring 9 grating regions optimized to maximize the responsivity in the individual subbands across the 7.5-12 μm spectral range. The demonstrated 1024x1024 QWIP FPA exhibited excellent performance with operability exceeding 99% and noise equivalent differential temperature of less than 15 mK across the entire 7.5-12 μm spectral range.

Gunapala, Sarath D.↗

NASA NTRS2016

Cross-correlation and image alignment for multi-band IR sensors

We present the development of a cross-‐correlation algorithm for correlating objects in the long wave, mid wave and short wave Infrared sensor arrays. The goal is to align the images in the multi-‐ sensor suite by correlating multiple key features in the images. Due to the wavelength differences, the object appears very differently in the sensor images even the sensors focus on the same object. In order to perform accurate correlation of the same object in the multi-‐band images, we perform image processing on the images so that the features of the object become similar to each other. Fourier domain band pass filters are used to enhance the images. Mexican Hat and Gaussian Derivative Wavelets are used to further enhance the features of the object. A Python based QT graphical user interface has been implemented to carry out the process. We show reliable results of the cross-‐correlation of the objects in multiple band videos.

Torres, Gilbert↗

NASA NTRS2017

Intelligent multi-spectral IR image segmentation

We present a neural network based multi-‐spectral image segmentation method. A neural network is trained on the selected features of both the objects and background in the longwave (LW) Infrared (IR) images. Multiple iterations of training are performed until the accuracy of the segmentation reaches satisfactory level. The segmentation boundary of the LW image is used to segment the midwave (MW) and shortwave (SW) IR images. A second neural network detects the local discontinuities and refines the accuracy of the local boundaries. The neural net based segmentation method is compared with Wavelet-‐threshold and Grab-‐Cut methods. Test results have shown increased accuracy and robustness of this segmentation scheme for multi-‐spectral IR images.

Torres, Gilbert↗

NASA NTRS2018

Multi-Angle Imager for Aerosols Thermal Control System

The Multi-Angle Imager for Aerosols (MAIA) Thermal Control System is a NASA funded instrument that will collect data to help characterize airborne particulate matter over a number of population centers across the globe using multi-angle spectropolarimetric imagery. The data collected by MAIA will facilitate assessments of the impacts of different types of particulate matter on adverse health outcomes. MAIA is a hosted payload meant to operate in a near-circular sun-synchronous polar orbit, with a mean altitude between 600 km and 850 km. The nominal on-orbit mission design lifetime is three years. Temperature control of the MAIA instrument is accomplished with a combination of passive radiators and heaters. The focal plane module (FPM) is cooled to ≤ 235K with a disc shaped radiator that faces the anti-sun side of the sun-synchronous orbit. The temperature of the MAIA cameras and associated electronics is controlled with a cylindrical shaped radiator that projects a near constant area in the nadir direction as the cameras rotate. A noteworthy feature of the MAIA thermal control system design is the novel, low cost, rotationally articulating thermal strap used to transfer heat from the FPMs to their associated FPM Radiator. The strap spans one of the axes of rotation, sweeping out an arc of approximately 60° as the instrument operates. A prototype of the articulating thermal strap was life tested to 260,000 cycles with no signs of significant degradation. An overview of the MAIA thermal control system baseline design is presented, with focus on its novel aspects, including life testing of the prototype articulating thermal strap. In addition, a discussion of the considerations involved in designing a thermal control system for a hosted instrument is provided.

Rosas, Rogelio↗

NASA NTRS2018

Optical and Mechanical Designs of the Multi-band SWIR Receiver for the Lunar Flashlight CubeSat Mission

Mapping and quantifying lunar water ice addresses one of NASA’s Strategic Knowledge Gaps to understand the lunar resource potential for future human exploration of the Moon. Lunar Flashlight is an innovative NASA CubeSat mission dedicated to mapping water ice in the permanently-shadowed and occasionally-sunlit regions in the vicinity of the lunar South Pole. Lunar Flashlight will acquire these measurements from lunar orbit using a multi-band laser reflectometer composed of an optical receiver aligned with four lasers emitting different wavelengths in the shortwave infrared spectral region between 1 µm and 2 µm. The receiver measures the laser radiance reflected from the lunar surface in each spectral band and continuum/absorption reflectance band ratios are then analyzed to quantify water ice concentration in the illuminated spot. The receiver utilizes a 70×70-mm, aluminum, off-axis paraboloidal mirror with a focal length of 70 mm, which collects the incoming light onto a single, 2 mm diameter InGaAs detector with a cutoff wavelength of 2.4 µm. We present the optical and mechanical designs of the receiver, including its optimization for rejection of solar stray-light from outside its intended field of view. This highly mass- and volume-constrained instrument payload will demonstrate several firsts, including being one of the first instruments onboard a CubeSat performing science measurements beyond low Earth orbit and the first planetary mission to use multi-band active reflectometry from orbit.

Sellar, R. Glenn↗

NASA NTRS2018

Review of Multi-Agent Algorithms for Collective Behavior: a Structural Taxonomy

In this paper, we review multi-agent collective behavior algorithms in the literature and classify them according to their underlying mathematical structure. For each mathematical technique, we identify the multi-agent coordination tasks it can be applied to, and we analyze its scalability, bandwidth use, and demonstrated maturity. We highlight how versatile techniques such as artiﬁcial potential functions can be used for applications ranging from low-level position control to high-level coordination and task allocation, we discuss possible reasons for the slow adoption of complex distributed coordination algorithms in the ﬁeld, and we highlight areas for further research and development.

Pavone, Marco↗

NASA NTRSDate not supplied

Human Capabilities Assessments for Autonomous Missions: A Multi-Team Research Effort to Reduce Risk in the Human-System Integration Architecture for Future Deep-Space Missions

In future exploration missions beyond low earth-orbit, crew will have to execute complex operations and respond to off-nominal events, without real-time support from Mission Control. It is anticipated that increased reliance on automated systems, including human-centric vehicle and information architecture, will need to be designed to support the crew; increased risk to performance, health, and safety may occur if these are not implemented appropriately. The Human Factors and Behavioral Performance Element (HFBP) in the NASA Human Research Program supports research to characterize and mitigate such human health and performance risks, including the Risk of Adverse Outcome Due to Inadequate Human Systems Integration Architecture (HSIA). The HSIA risk addresses the integration of onboard capability and the crew roles and responsibilities necessary to enable the crew to respond effectively and efficiently in the increasingly autonomous mission operations environment. In 2017, HFBP released the “Human Capabilities Assessments for Autonomous Missions” (HCAAM) research topic to address HSIA related questions. HCAAM is a major NASA research effort that has assembled a multidisciplinary team from seven institutions to work closely with design and engineering efforts on research towards developing and refining human performance standards, guidelines and automation tools. The scientific focus is on quantitative assessment of human capabilities relevant to future deep-space missions during which earth/spacecraft communication is so delayed and intermittent that the crew must be able to function autonomously. The integrated strategy of the HCAAM project characterizes human capabilities and limitations related to potential performance decrements during long duration exploration mission spaceflight as relevant to both routine and complex task performance; defines system characteristics that reduce the likelihood or impact of potential decrements in human performance capabilities; performs integrated assessment of intelligent system responses within the context of an operational environment with relevant NASA tools, systems, and data structures in order to determine positive or negative interactions and validate recommended approaches; and proposes specific updates to existing standards and guidelines for inclusion in NASA handbooks for the design of future spacecraft intelligent systems that provide crew performance assessment/feedback, and to also serve as decision-support aids for the onboard crew (i.e., NASA-STD-3001, and NASA/SP-Human Integration Design Handbook (HIDH)). The scientific research vectors being addressed by the seven HCAAM teams include: - crew task performance (accuracy, efficiency) (crew + automation) - crew performance (accuracy, efficiency) - crew Situation Awareness - procedure design and multi-modal enhancement - concurrent tasking (mixed manual + some level of autonomy) - task handover - crew self-planning and time-lining - task design - trust in automation, real-time calibration - human multi-sensory feedback and guidance - human trust in on-board software-based intelligent assistants - virtual assistants The presentation will highlight plans and progress made in each of these research areas as well as the methods by which surrogate astronaut crews in the NASA JSC HERA spaceflight analog facility will function as human test subjects for all of the HCAAM research projects.

HCAAM VNSCOR↗

NASA NTRS2019

Data Analysis Challenges for Multi-Messenger Astrophysics

Recent multi-messenger observations of gravitational-wave and high-energy neutrino sources together with electromagnetic signatures have opened new ways of observing the Universe. These promise a future in which physics and astronomy will be advanced by combining observations and data from across the electromagnetic spectrum with gravitational waves and neutrinos. We consider the challenges the field is facing in fully utilizing data for multi-messenger astrophysics. Such data come from heterogeneous detector networks and standards, and their analysis is often time-critical to guide further observations. In this area, science capabilities depend on the interplay among observation, theory and computational/modeling work. Advances in data science and computing present additional opportunities and considerations in analyzing such data. We invited ADASS participants to a Birds of a Feather session to engage in discussion on the challenges and opportunities in data analysis for multimessenger astrophysics.

Peter S Shawhan↗

NASA NTRS2020

Integrated Multi-satellite Evaluation for the Global Precipitation Measurement: Impact of Precipitation Types on Spaceborne Precipitation Estimation

An integrated multi-sensor assessment is proposed as a novel approach to advance satellite precipitation validation in order to provide users and algorithm developers with an assessment adequately coping with the varying performances of merged satellite precipitation estimates. Gridded precipitation rates retrieved from space sensors with quasi-global coverage feed numerous applications ranging from water budget studies to forecasting natural hazards caused by extreme events. Characterizing the error structure of satellite precipitation products is recognized as a major issue for the usefulness of these estimates. The Global Precipitation Measurement (GPM) mission aims at unifying precipitation measurements from a constellation of low-earth orbiting (LEO) sensors with various capabilities to detect, classify and quantify precipitation. They are used in combination with geostationary observations to provide gridded precipitation accumulations. The GPM Core Observatory satellite serves as a calibration reference for consistent precipitation retrieval algorithms across the constellation. The propagation of QPE uncertainty from LEO active/passive microwave (PMW) precipitation estimates to gridded QPE is addressed in this study, by focusing on the impact of precipitation typology on QPE from the Level-2 GPM Core Observatory Dual-frequency Precipitation Radar (DPR) to the Microwave Imager (GMI) to Level-3 IMERG precipitation over the Conterminous U.S. A high-resolution surface precipitation used as a consistent reference across scales is derived from the ground radar-based Multi-Radar/MultiSensor. While the error structure of the DPR, GMI and subsequent IMERG is complex because of the interaction of various error factors, systematic biases related to precipitation typology are consistently quantified across products. These biases display similar features across Level-2 and Level-3, highlighting the need to better resolve precipitation typology from space and the room for improvement in globalscale precipitation estimates. The integrated analysis and framework proposed herein applies more generally to precipitation estimates from sensors and error sources affecting low-earth orbiting satellites and derived gridded products.

Pierre-Emmanuel Kirstette↗

NASA NTRS2021

A Recursive Multi-step Machine Learning Approach for Airport Configuration Prediction

Airport configuration selection is a complex decision-making process that involves several operational and human factors. In this paper we propose a novel recursive multi-step machine learning (ML) approach to predict airport configuration. The multi-step approach guarantees stability of the predicted configuration by taking as input the configuration predicted at the previous time step. The features of the proposed model include weather data, future arrival and departure counts and current configuration. Due to the importance of arrival and departure counts in predicting the airport configuration, arrival counts are calculated using landing time predictions selected from physics-based landing time predictions available in FAA System Wide Information Management data feeds for each flight. The selection rules were developed and refined to select the most accurate time for different phases of flight. The proposed model predicts the airport configurations up to 6 hours ahead. In this paper we show the predictive performance of the proposed model for six major US airports, including Charlotte Douglas International Airport (CLT), Dallas/Fort Worth International Airport (DFW), John F. Kennedy International Airport (JFK), Newark Liberty International Airport (EWR), LaGuardia Airport (LGA) and Dallas Love Field Airport (DAL). We trained and evaluated models on 2019 and 2020 data in order to study the effect of the pandemic and how changes in traffic patterns affected the performance of the proposed model. Results are compared with a baseline assuming no airport configuration changes. In our results for DFW, we obtained a prediction accuracy of 89.3% for 3 hours ahead prediction, and 82.8% for 6 hours ahead when applied on 2019 data.

machine learning↗

NASA NTRS2021

A Recursive Multi-step Machine Learning Approach for Airport Configuration Prediction

machine learning↗

NASA NTRS2021

Climate Change Impacts and Adaptation for Dryland Farming Systems in Zimbabwe: A Stakeholder-Driven Integrated Multi-Model Assessment

Decision makers need accurate information to address climate variability and change and accelerate transformation to sustainability. A stakeholder-driven, science-based multi-model approach has been developed and used by the Agricultural Model Intercomparison and Improvement Project (AgMIP) to generate actionable information for adaptation planning processes. For a range of mid-century climate projections—likely to be hotter, drier, and more variable—contrasting future socio-economic scenarios (Representative Agricultural Pathways, RAPs) were co-developed with stakeholders to portray a sustainable development scenario and a rapid economic growth pathway. The unique characteristic of this application is the integration of a multi-modeling approach with stakeholder engagement to co-develop scenarios and adaptation strategies. Distribution of outcomes were simulated with climate, crop, livestock, and economic impact assessment models for smallholder crop livestock farmers in a typical dryland agro-ecological zone in Zimbabwe, characterized by low and erratic rainfall and nutrient depleted soils. Results showed that in Nkayi District, Western Zimbabwe, climate change would threaten most of the farms, and, in particular, those with large cattle herds due to feed shortages. Adaptation strategies that showed the most promise included diversification using legume production, soil fertility improvement, and investment in conducive market environments. The switch to more legumes in the farming systems reduced the vulnerability of the very poor as well as the more resourced farmers. Overall, the sustainable development scenario consistently addressed institutional failures and motivated productivity-enhancing, environmentally sound technologies and inclusive development approaches. This yielded more favorable outcomes than investment in quick economic wins from commercializing agriculture.

Climate change adaptation↗

NASA NTRS2021

Orion Meteoroid and Debris Analysis with a Single Kevlar® Layer Enhancement to the Propulsion Tank Multi-Layer Insulation: Comparison of the Observed Data and the Adjusted Ballistic Models

In a continual mass optimization effort for the Orion vehicle by NASA and its prime contractor Lockheed Martin Space, along with the European Space Agency (ESA) and its contractors Airbus and Thales Alenia Space-Italy (TAS-I), a series of shield characterization shots have been performed to quantify the ballistic enhancement of a single-layer of Kevlar® in multi-layer insulation (MLI). This enhanced MLI is part of Orion’s propulsion subsystem, and it is planned to replace a double-layer Kevlar® configuration starting at the Artemis IV mission. This effort has included fifty-two shield characterization shots for five different shield configurations over the four Orion propellant storage vessels. These storage vessels are located in the European Service Module (ESM). This shield characterization has been managed by the Hypervelocity Impact Technology (HVIT) group in NASA Astromaterials Research and Exploration Sciences at Johnson Space Center (JSC) for the NASA Engineering and Safety Center (NESC) and the Multi-Purpose Crew Vehicle (MPCV) program office in coordination with Lockheed Martin Space and Thales Alenia Space-Italy (TAS-I). The shield characterization has used the two-stage, light-gas-gun at the Remote Hypervelocity Test Laboratory (RHTL) of NASA JSC White Sands Test Facility (WSTF) for acceleration of projectiles to representative orbital impact speeds. From this shield characterization effort, a revised ballistic limiting equation (BLE) has been developed for each of the five representative shielding configurations of the ESM pressure vessels. This document provides a brief description of the shield configurations considered for the Orion program and the shield characterization findings. These results are compared to the assumed BLE for each configuration going into the Artemis missions that utilize this version of ballistic enhanced MLI. The models are integrated into a fourteen day High Lunar Orbit (HLO) mission as a representative reliability prediction impact for Orion, and it has been found that the predicted risk of a loss-of-crew (LOC) event is actually a little less than half of the assumed value that launched this design modification opportunity.

Joshua E Miller↗

NASA NTRSDate not supplied

Multi-Flight Common Routes

Flights often experience large delays when they are routed around weather. Multi-flight common routes advisories provide delay recovery by suggesting time-saving re-routes for groups of flights whose current weather-avoidance routes have become outdated because the weather has dissipated and/or moved away. The multi-flight common routes tool provides time-saving route change advisories taking into account flight plans, wind fields, and the spatio-temporal evolution of predicted convective weather. A graphical user interface enables these advisories to be easily modified by a traffic manager for possible operational implementation.

ATD-3↗

NASA NTRSDate not supplied

Towards the Development of a Multi-Agent Cognitive Networking System for the Lunar Environment

This paper details the development of a multi-agent cognitive system intended to optimize networking performance in the lunar environment. NASA’s current concept of the future of lunar communication, LunaNet, outlines a complex network of networks. Challenges such as scalability, interoperability and reliability must first be addressed to successfully fulfill this vision. Machine intelligence can greatly reduce the reliance on human operators and enable efficient operations for tasks such as scheduling and network management. The application of machine learning, artificial intelligence, and other automated decision-making techniques can be used to allow network nodes to intelligently sense and adapt to changes in the environment such as link disruptions, new nodes joining the network, and support for a diverse range of protocols. Cognitive networking seeks to evolve these technologies into an autonomous system with improved science data return, reliability, and scalability. In this paper, we study three main areas a means to further develop cognitive networking capabilities: networking and flight software development, analysis of wireless data for modeling and simulation, and development of algorithms for a multi-agent system.

Rachel Dudukovich↗