Results for “workflow” — Page 19

NASA NTRSDate not supplied

Supporting Exploration Missions by Enabling Exploration Mission System Software

Future exploration missions will consist of a multitude of data sources, systems, and operators collaborating to complete mission objectives. Presently, NASA is instantiating the contractual mechanisms, such as the xEVAS and HLS contracts, to produce these mission assets. Architectural planning is also underway to establish the networking protocols and infrastructure to digitally create and connect mission elements, such as LunaNET. However, without new horizontally integrated data systems, these advancements will be limited in their ability to get mission data appropriately integrated into the plan, train, fly, explore workflow of the operations workforce. Here we describe several mission system software development efforts underway that are designed to support human spaceflight missions. We describe the current iterations of a suite of tools to support EVA procedure authoring and execution, for both ISS and Artemis missions, as well as a software solution to establish and interact with mission context and data products. These tools have been developed iteratively and continue to be tested in several NASA facilities such as the Neutral Buoyancy Lab (NBL), Artemis field testing, and in present-day International Space Station (ISS) operations on orbit. Our solutions demonstrate how software development can be aligned with ongoing operations development activities to discover the features that best support future human spaceflight missions.

EVA Mission System Software↗

NASA NTRSDate not supplied

Comparison of Aerodynamic Analysis Tools Applied to a Propeller-Blown Wing

This paper describes initial results from an ongoing activity comparing two aerodynamic analysis tools: OVERFLOW, a Navier-Stokes flow solver, and FlightStream, a viscous surface-vorticity, unstructured panel method flow solver. Both tools are applied to analyses of an unblown wing and a propeller-blown wing, and then comparisons are performed between the predicted aerodynamic loads and moments from each tool. Computational performance and efficiencies of each tool and their usability in conceptual and preliminary air vehicle design are also discussed. A description of the workflow, underlying theoretical foundations, and best practices are provided. In comparing FlightStream and OVERFLOW, reasonable agreement between predicted performance is observed for flow regimes of interest at high angle of attack prior to stall, including blown wing aero-propulsive effects on lift augmentation and stall delay. In the post-stall regions, FlightStream appears to underestimate the effects of turbulent flow separation that contribute heavily to drag. However, as expected for a high-fidelity, time-accurate solver, the setup and computation time are substantially greater for OVERFLOW than for FlightStream.

Vivek Ahuja↗

NASA NTRSDate not supplied

From the Knowledge-based Digital Platform (KbDP) Concept for Advanced Air Mobility Research to a Preliminary Prototype

Advanced Air Mobility (AAM) encompasses a range of innovative operational and technological changes to aviation (electric aircraft, increasingly automated aircraft, increasingly automated airspace operations, etc.) that are transforming aviation’s role in everyday movement of people and goods. There are multiple associated concepts and use cases for AAM, all interrelated, including small Unmanned Aircraft System (UAS) Traffic Management (UTM), Upper-Class E Traffic Management (ETM), Extensible Traffic Management (xTM), Regional Air Mobility (RAM), and Urban Air Mobility (UAM). These AAM operations must integrate with traditional Air Traffic Management (ATM) operations, as well as non-aviation modes of transportation and logistics. National Aeronautics and Space Administration (NASA) is spearheading an innovative digital engineering approach to integrate, communicate, and facilitate the research of multi-modal transportation systems. The Knowledge-based Digital Platform (KbDP) is a concept being developed that ties the workflows of Project Managers (PM), Principal Investigators (PI), and System Engineers together across organizational boundaries. It does so through the management of an information database defined by mathematical, data science, and system engineering principles. Machine Learning (ML) algorithms play a key role in this concept by extracting meaningful knowledge from the information database, which the human user leverages to greatly improve the efficiency and effectiveness of their research. Expected benefits of this concept include improved technology transfers from research to production, improved research portfolio investments, and research outcomes that are more integrated with all aspects of the multi-modal transportation problem. The preliminary KbDP prototype has been realized using UAM as a pathfinder use case and developed by a team of system engineer, software developer, data scientist, and interns.

Systems Engineering↗

NASA NTRSDate not supplied

Application of Framework for Estimating Performance and Associated Uncertainty for Modified Aircraft Configurations Using NASA's X-57 Maxwell

A framework to estimate the performance and associated uncertainty of modified configurations of certified aircraft is applied to the X-57 Maxwell aircraft. In previous theoretical studies, the framework was shown to predict performance and uncertainty bounds accurately. The X-57 Maxwell is an experimental aircraft designed to demonstrate the benefits of distributed electric propulsion through a series of four incremental modifications to a Tecnam P2006T aircraft. The available models and data are first shown to be within the application domain of the framework. We then apply the framework to two X-57 Maxwell modifications. We compare the estimated performance and associated uncertainties against the airworthiness criteria. The results indicate that the framework is a promising tool for the certification by analysis workflow. We expect the framework to reduce and supplement the flight testing required to show compliance to airworthiness certification criteria for a modified configuration.

Uncertainty Quantification↗

NASA NTRSDate not supplied

Application of Framework for Estimating Performance and Associated Uncertainty for Modified Aircraft Configurations Using NASA's X-57 Maxwell

A framework to estimate the performance and associated uncertainty of modified configurations of certified aircraft is applied to the X-57 Maxwell aircraft. In previous theoretical studies, the framework was shown to predict performance and uncertainty bounds accurately. The X-57 Maxwell is an experimental aircraft designed to demonstrate the benefits of distributed electric propulsion through a series of four incremental modifications to a Tecnam P2006T aircraft. The available models and data are first shown to be within the application domain of the framework. We then apply the framework to two X-57 Maxwell modifications. We compare the estimated performance and associated uncertainties against the airworthiness criteria. The results indicate that the framework is a promising tool for the certification by analysis workflow. We expect the framework to reduce and supplement the flight testing required to show compliance to airworthiness certification criteria for a modified configuration.

Uncertainty Quantification↗

NASA NTRSDate not supplied

Hierarchical screening for Li-based solid electrolytes using fast, interpretable machine-learned potentials

Li-based solid-state electrolyte materials enable safer, all-solid-state batteries but the computational search for candidates with favorable stability and Li-ion conductivity is challenging due to the size of the search space and the cost of evaluating transport properties with ab initio methods. The prohibitive cost of high-throughput screening with DFT has lead to the development of surrogate models using geometric analysis, empirical potentials, and descriptors for ionic transport. Here, I will discuss a hierarchical screening approach for identifying promising materials using a combination of density functional theory, bond-valence methods, and machine learning potentials generated with the Ultra-Fast Force Fields (UF3) framework. We show how the inexpensive bond-valence method can be used to guide the generation of training samples for machine learning, in addition to filtering candidates. Finally, we apply the hierarchical workflow to screen for ionic conductivity across a database of Li-containing compounds.

Materials discovery↗

NASA NTRSDate not supplied

Evaluating SAR Radiometric Terrain Correction Solutions: Optimal products for applied users

Operational applications of Synthetic Aperture Radar (SAR) are under development around the world, driven by the regularly-acquired, free-and-open source C-band SAR observations provided by ESA’s Sentinel-1 sensor constellation since 2014. Groups like SERVIR, a joint NASA and USAID initiative, are at the forefront of remote sensing applications for societal benefit. A takeaway from SERVIR’s experience is the need for appropriately geocoded and fully calibrated SAR data that is ready to use for a range of ecosystems-related applications. Radiometric Terrain Corrected (RTC) data are key entry-level products for multiple applications that range from ecosystems to hazards. This work fills a gap in current research by evaluating several RTCs produced by open-source software solutions (SNAP-7 and ISCE-2), the gold standard commercial software (GAMMA), a Google Earth Engine (GEE) based workflow, and the uncorrected GRD products currently available in GEE. RTCs were analyzed for geolocation quality, absolute radiometric calibration, and fidelity of the radiometric terrain flattening over ten sites representing varied terrains. In addition, a time series analysis was conducted over two locations. Overall, no significant differences for radiometric calibration were found across RTC products. However, all RTCs performed better than uncorrected GRD products. The main differences between products were found in geolocation quality. These results not only demonstrate the need for the uptake and distribution of RTC products for ecosystems applications, but demonstrate the ability to do so with open source methods, adding value to developing affordable operational applications.

Helen Blue Parache↗

NASA NTRSDate not supplied

Feasibility Study of Distributed Decision-Making on the Edge for Urban Air Mobility

The Concept of Operations for Urban Air Mobility (UAM) put forward by FAA, NASA, and several industry stakeholders acknowledges the diversity and complexity in UAM operations and, thereby, envisions a federated architecture for UAM management. In this architecture, the decision-making is distributed to a set of service providers who collectively manage the shared airspace usage by different stakeholders. This notionally brings autonomy closer to the UAM businesses and encourages to explore the feasibility of decision making on the very edge, which is the topic of the presented research. This paper reports research conducted on the hypothesis based on which the residual compute capability onboard smart unmanned aerial systems (UASs) is utilized to build situational awareness and resolve conflicts by passive and active coordination among multiple UASs, thereby implementing a layer of distributed autonomy in UAM. Key features of the edge-computing approach involve inter-UAS information exchange, independent assessment of own flight and environmental conditions, and estimation of other UASs’ flight preferences, incorporating machine learning techniques in the last two. Parallel computing on portable graphics processing unit (GPU) enables the machine learning workflow on the edge. A custom-built 3D simulator is used to evaluate the efficacy of the distributed decision-making on the edge. Each edge node, representing a smart UAS, connects to the simulator from a remote location and independently controls the behavior of the corresponding virtual asset in the simulator, analogous to participants in an online multi-player game. The presented edge-computing-based distributed decision-making framework is envisioned to pave the way for collective mobility of autonomous air vehicles in the future shared airspace, while allowing the inclusion of the business preferences of the UAS operators within allowed regulatory limits.

Edge computing↗

NASA NTRSDate not supplied

Feasibility Study of Distributed Decision-Making on the Edge for Urban Air Mobility

The Concept of Operations for Urban Air Mobility (UAM) put forward by FAA, NASA, and several industry stakeholders acknowledges the diversity and complexity in UAM operations and, thereby, envisions a federated architecture for UAM management. In this architecture, the decision-making is distributed to a set of service providers who collectively manage the shared airspace usage by different stakeholders. This notionally brings autonomy closer to the UAM businesses and encourages to explore the feasibility of decision making on the very edge, which is the topic of the presented research. This paper reports research conducted on the hypothesis based on which the residual compute capability onboard smart unmanned aerial systems (UASs) is utilized to build situational awareness and resolve conflicts by passive and active coordination among multiple UASs, thereby implementing a layer of distributed autonomy in UAM. Key features of the edge-computing approach involve inter-UAS information exchange, independent assessment of own flight and environmental conditions, and estimation of other UASs’ flight preferences, incorporating machine learning techniques in the last two. Parallel computing on portable graphics processing unit (GPU) enables the machine learning workflow on the edge. A custom-built 3D simulator is used to evaluate the efficacy of the distributed decision-making on the edge. Each edge node, representing a smart UAS, connects to the simulator from a remote location and independently controls the behavior of the corresponding virtual asset in the simulator, analogous to participants in an online multi-player game. The presented edge-computing-based distributed decision-making framework is envisioned to pave the way for collective mobility of autonomous air vehicles in the future shared airspace, while allowing the inclusion of the business preferences of the UAS operators within allowed regulatory limits.

Edge computing↗

NASA NTRSDate not supplied

Comparison of Aerodynamic Analysis Tools Applied to a Propeller-Blown Wing

This paper describes initial results from an ongoing activity comparing two aerodynamic analysis tools: OVERFLOW, a Navier-Stokes flow solver, and FlightStream, a viscous surface-vorticity, unstructured panel method flow solver. Both tools are applied to analyses of an unblown wing and a propeller-blown wing, and then comparisons are performed between the predicted aerodynamic loads and moments from each tool. Computational performance and efficiencies of each tool and their usability in conceptual and preliminary air vehicle design are also discussed. A description of the workflow, underlying theoretical foundations, and best practices are provided. In comparing FlightStream and OVERFLOW, reasonable agreement between predicted performance is observed for flow regimes of interest at high angle of attack prior to stall, including blown wing aero-propulsive effects on lift augmentation and stall delay. In the post-stall regions, FlightStream appears to underestimate the effects of turbulent flow separation that contribute heavily to drag. However, as expected for a high-fidelity, time-accurate solver, the setup and computation time are substantially greater for OVERFLOW than for FlightStream.

Vivek Ahuja↗

NASA NTRSDate not supplied

A Design Framework for Thick and Thin Tow-Steered Composites Using Mechanics of Structure Genome

The design of tow-steered composites is one of the most popular and promising topics under the class of variable stiffness structures. In this work, the authors propose a new design framework and tool for the optimization of tow-steered composites. Mechanics of structure genome (MSG) provides accurate computation of plate/shell section properties including thick and highly curved structures. The computed section properties can be directly used in commercial computer-aided engineering tools. Tow-steered composites are modeled through a design framework wherein tow paths are parameterized in a general way and projected onto a finite element mesh, and MSG calculates the local shell/plate properties. The result is a complete workflow from design parameter input to structural performance evaluation. An open-source optimization software package is used to enable constrained optimization of structural stiffness for the broad tow-steered composite design space. Numerical examples are provided to demonstrate the capabilities of this tool and the promise and potential of tow-steered designs that optimize structural performance while satisfying manufacturing constraints.

Su Tian↗

NASA NTRS2023

Thermomechanical Property Prediction of Amorphous and Crystal PEKK via Molecular Dynamics

Traditionally, advanced aerospace composites have been manufactured using thermoset resins. However, recently, thermoplastics have been investigated for use in the manufacturing of composite materials due to their unique manufacturing characteristics. Thermoplastic resins can be reshaped and formed, along with the added benefit of being recyclable, which thermoset resin cannot. Thermoplastic materials undergo a crystallization process during manufacturing which affects the percent crystallinity of the material. The crystallization needs to be understood better to maximize the potential of thermoplastic resins. PEKK is a thermoplastic material with good chemical, thermal, and mechanical loading resistance. PEKK is also a material NASA is interested in for developing new bonded joint technology. The crystalline microstructure of PEKK is at the micrometer length scale, and it is of interest to model the effects of the crystallinity structure on PEKK’s bulk properties. Molecular dynamics (MD) is a simulation tool that allows for property-structure relationships between atomistic structure and nanometer-length portions of a material. This makes MD a useful tool for developing the structure-property relationship of PEKK. However, the micrometer length scale of PEKK’s crystal structure is too large for MD. Thus, a hybrid approach to modeling PEKK’s microstructure is proposed in this work where MD models are built of both the amorphous and crystalline phases of PEKK. The engineering material properties can be obtained using MD at the nanometer length scale. A micromechanics approach can then generate the micrometer length scale of the crystallinity and the effective properties can be homogenized. The objective of this paper is to show the MD model workflow and the MD-predicted properties of PEKK. The properties can then be homogenized with different crystalline percentages to build design graphs that can be used to tailor PEKK for specific composite applications.

poly ether ketone ketone↗

NASA NTRSDate not supplied

Updates in Developing a Prototype Science Pipeline and Full-Volume, Global Hyperspectral Synthetic Data Sets for NASA’s Earth System Observatory’s Upcoming Surface, Biology and Geology Mission

The Surface Biology and Geology (SBG) mission recently passed mission confirmation review and has entered phase A – design and development. SBG will acquire high resolution solar-reflected spectroscopy and thermal infrared observations at a data rate of ~2.5 TB/day and generate products at ~40 TB/day. Given that the per-day volume is greater than NASA’s total extant airborne hyperspectral data collection, collecting, processing, disseminating, and exploiting the SBG data present new challenges. To meet these challenges, we have developed a prototype science pipeline and a full-volume global hyperspectral synthetic data set to help prepare for SBG’s flight (see poster GC42D-0730). Our science pipeline is based on the science processing technology developed for NASA’s Kepler and TESS planet-hunting missions. The pipeline infrastructure, Ziggy, provides a scalable architecture for robust, repeatable, and replicable science and application products that can be run on a range of systems from a laptop to the cloud or a supercomputer. Ziggy is compliant with NASA Procedural Requirement (NPR) 7150.2C, is at a technical readiness level (TRL) of 7 and has been released to github.com/nasa/ziggy. We integrated Ziggy with EO-1/Hyperion workflows to build a prototype pipeline and ingested the 17-year mission archive that provides globally sampled visible through shortwave infrared spectra that are representative of SBG data types and volumes. We fully implemented the first stage and processed the entire 55 TB Hyperion data set from the raw data (Level 0) to top-of-the-atmosphere radiance (Level 1R). We are currently evaluating the ISOFIT atmospheric correction module to convert the L1R data to surface reflectance (Level 2) before reprocessing the full data set to L2. Crosschecks are being performed with RadCalNet as well as with coincident observations by AVIRIS. We are also investigating modern methods for georectifying the Hyperion scenes. Finally, we describe an analysis of the cost to conduct forward processing and reprocessing campaigns for SBG on HECC with dedicated compute and storage resources using the resurrected Hyperion pipeline as a proxy for full-volume SBG data. The analysis demonstrates that SBG L0 data can be processed to L2 on HECC with full reprocessing campaigns every two years for ~$2.6M over a 7-year lifespan. Moreover, 69% of the system capacity would be available for other activities, possibly enabling future open-source science activities, including algorithm development, L3+ processing, .etc.

ESD↗

NASA NTRS2022

Supporting Responsible Machine Learning in Heliophysics

Over the last decade, Heliophysics researchers have increasingly adopted a variety of machine learning methods such as artificial neural networks, decision trees, and clustering algorithms into their workflow. Adoption of these advanced data science methods had quickly outpaced institutional response, but many professional organizations such as the European Commission, the National Aeronautics and Space Administration (NASA), and the American Geophysical Union have now issued (or will soon issue) standards for artificial intelligence and machine learning that will impact scientific research. These standards add further (necessary) burdens on the individual researcher who must now prepare the public release of data and code in addition to traditional paper writing. Support for these is not reflected in the current state of institutional support, community practices, or governance systems. We examine here some of these principles and how our institutions and community can promote their successful adoption within the Heliophysics discipline.

Machine learning↗

NASA NTRS2023

From Natural Language Requirements to the Verification of Programmable Logic Controllers: Integrating FRET into PLCverif

PLCverif is an actively developed project at CERN, enabling the formal verification of Programmable Logic Controller (PLC) programs in critical systems. In this paper, we present our work on improving the formal requirements specification experience in PLCverif through the use of natural language. To this end, we integrate NASA’s FRET, a formal requirement elicitation and authoring tool, into PLCverif. FRET is used to specify formal requirements in structured natural language, which automatically translates into temporal logic formulae. FRET’s output is then directly used by PLCverif for verification purposes. We discuss practical challenges that PLCverif users face when authoring requirements and the FRET features that help alleviate these problems. We present the new requirement formalization workflow and report our experience using it on two critical CERN case studies.

FRET↗

NASA NTRSDate not supplied

A Design Framework for Thick and Thin Tow-Steered Composites Using Mechanics of Structure Genome

The design of tow-steered composites is one of the most popular and promising topics under the class of variable stiffness structures. In this work, the authors propose a new design framework and tool for the optimization of tow-steered composites. Mechanics of structure genome (MSG) provides accurate computation of plate/shell section properties including thick and highly curved structures. The computed section properties can be directly used in commercial computer-aided engineering tools. Tow-steered composites are modeled through a design framework wherein tow paths are parameterized in a general way and projected onto a finite element mesh, and MSG calculates the local shell/plate properties. The result is a complete workflow from design parameter input to structural performance evaluation. An open-source optimization software package is used to enable constrained optimization of structural stiffness for the broad tow-steered composite design space. Numerical examples are provided to demonstrate the capabilities of this tool and the promise and potential of tow-steered designs that optimize structural performance while satisfying manufacturing constraints.

Su Tian↗

NASA NTRSDate not supplied

Implementing JEDI into NASA GMAO’s Real Time Production Suite

NASA’s Global Modeling and Assimilation Office (GMAO) has prepared their first production system involving the Joint Effort for Data assimilation Integration (JEDI) framework. In this system the central analysis, that drives the deterministic forecast, will be provided using JEDI. This talk outlines the phased approach to implementing JEDI into production that GMAO has designed, and how this approach will allow for a careful analysis of the system against the existing data assimilation framework (GSI). In the first phase of implementation the existing data assimilation system will perform certain actions that are still under development in JEDI. These include thinning the observations and producing satellite bias correction coefficients. JEDI is hooked up to the existing workflow so a single line switch can activate whether the existing or JEDI-based analysis is cycled. Outside of the monumental effort to construct JEDI that is ongoing at the Joint Center for Satellite Data Assimilation (JCSDA), GMAO have undertaken two areas of considerable effort. The talk will describe these efforts and highlight the main challenges that have been encountered. The first area of work is to implement the background error model from the existing data assimilation system into JEDI. The second is to validate the observing system in JEDI against the one in GSI, which has involved several new features being added to the observation operators in JEDI. While the longer-term plans involve trying to improve on the GSI in these two areas, GMAO is keen to have JEDI start from a trusted baseline. This is also key to implementing JEDI quickly so other priorities, such as increasing the number of model levels, can be easily worked on in parallel. GMAO is actively working on a framework to shepherd in the next generation coupled data assimilation system and model. As JEDI is implemented for the first time the plan is to ambitiously cycle through implementations, frequently bringing JEDI features to production. Details of these plans will be given in the talk and we will highlight key implementation and product milestones that we hope to achieve, as well as touch on the development environment that we will use to support frequent refreshing of the production system.

JEDI↗

NASA NTRSDate not supplied

Implementation of Combinatorial Optimization Techniques for Automated Fiber Placement Through Thickness Defect Stack-Up Minimization

The Computer Aided Process Planning (CAPP) module was developed to facilitate and accelerate the process planning workflow for Automated Fiber Placement (AFP). CAPP assists process planners in identifying optimal starting point locations and layup strategies for each ply of a laminate. Ply optimization operates on measurement and scoring of geometry-based defects such as gaps, overlaps, angle deviation, and steering. This paper expands on the established framework for analyzing defect stack-up through thickness of a laminate. Four different combinatorial optimization algorithms are implemented and evaluated: (1) genetic algorithm, (2) differential evolution, (3) particle swarm, and (4) greedy search. The algorithms identify the optimal combination of ply-level layup strategies, by scoring potential laminates on defect stacking, using two different objective functions. A final optimization approach is also presented which trades some performance for a large gain in efficiency. These approaches are compared to a randomized combination using a complex tool surface in a virtual case study. The result is a streamlined methodology for comparing different laminate-level manufacturing strategies and minimizing the through thickness defect stack up.

CAPP↗