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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Design of a Modular Monolithic Implicit Solver for Multi-Physics Applications

The design of a modular multi-physics high-order space-time finite-element framework is presented together with its extension to allow monolithic coupling of different physics. One of the main objectives of the framework is to perform efficient high- fidelity simulations of capsule/parachute systems. This problem requires simulating multiple physics including, but not limited to, the compressible Navier-Stokes equations, the dynamics of a moving body with mesh deformations and adaptation, the linear shell equations, non-re effective boundary conditions and wall modeling. The solver is based on high-order space-time - finite element methods. Continuous, discontinuous and C1-discontinuous Galerkin methods are implemented, allowing one to discretize various physical models. Tangent and adjoint sensitivity analysis are also targeted in order to conduct gradient-based optimization, error estimation, mesh adaptation, and flow control, adding another layer of complexity to the framework. The decisions made to tackle these challenges are presented. The discussion focuses first on the "single-physics" solver and later on its extension to the monolithic coupling of different physics. The implementation of different physics modules, relevant to the capsule/parachute system, are also presented. Finally, examples of coupled computations are presented, paving the way to the simulation of the full capsule/parachute system.

Carton De Wiart, Corentin↗
NASA NTRSDate not supplied

Tracking the Hunga Tonga-Hunga Ha’apai Eruption Stratospheric Aerosol and Trace Gas Plumes Using Machine Learning

On January 15, 2022, the Hunga Tonga-Hunga Ha’apai (hereafter, Hunga Tonga) submarine volcano had an explosive eruption that thrusted ash, gases, and water vapor through the troposphere into the stratosphere and mesosphere. Previous studies manually tracked the aerosol and trace gas plumes over time across different positions in the southern hemisphere. Using data retrieved from low earth orbiting satellite instruments (e.g., OMPS, OMI, and CALIPSO), this research demonstrates how open-source machine learning (ML) models, like Meta’s Segment Anything Model (SAM), with prompt engineering can perform automatic plume tracking following the Hunga Tonga eruption. This extensible methodology, and modular data processing and modeling pipeline using NASA Earthdata and Openscapes, establishes a framework for systematically and rapidly studying extreme events, including volcanic eruptions and large-scale wildfires. By combining advanced machine learning techniques, such as SAM’s zero-shot learning, with large volumes of remote sensing data, this work demonstrates how AI and open science can accelerate research and generate actionable results. The tools and technologies presented here can help translate earth science to action from NASA’s current and future Earth observing satellite missions (e.g., the Atmosphere Observing System (AOS)), and assist researchers and stakeholders in understanding, mapping, and responding to natural disasters and extreme events in a changing world.

David M. Giles↗
NASA NTRS2017

SpaceCubeX: A Framework for Evaluating Hybrid Multi-Core CPU FPGA DSP Architectures

The SpaceCubeX project is motivated by the need for high performance, modular, and scalable on-board processing to help scientists answer critical 21st century questions about global climate change, air quality, ocean health, and ecosystem dynamics, while adding new capabilities such as low-latency data products for extreme event warnings. These goals translate into on-board processing throughput requirements that are on the order of 100-1,000 more than those of previous Earth Science missions for standard processing, compression, storage, and downlink operations. To study possible future architectures to achieve these performance requirements, the SpaceCubeX project provides an evolvable testbed and framework that enables a focused design space exploration of candidate hybrid CPU/FPGA/DSP processing architectures. The framework includes ArchGen, an architecture generator tool populated with candidate architecture components, performance models, and IP cores, that allows an end user to specify the type, number, and connectivity of a hybrid architecture. The framework requires minimal extensions to integrate new processors, such as the anticipated High Performance Spaceflight Computer (HPSC), reducing time to initiate benchmarking by months. To evaluate the framework, we leverage a wide suite of high performance embedded computing benchmarks and Earth science scenarios to ensure robust architecture characterization. We report on our projects Year 1 efforts and demonstrate the capabilities across four simulation testbed models, a baseline SpaceCube 2.0 system, a dual ARM A9 processor system, a hybrid quad ARM A53 and FPGA system, and a hybrid quad ARM A53 and DSP system.

Hybrid Flight Architectures↗
NASA NTRS2002

SMEX-Lite Modular Solar Array Architecture

For the most part, Goddard solar arrays have been custom designs that are unique to each mission. The solar panel design has been frozen prior to issuing an RFP for their procurement. There has typically been 6-9 months between RFP release and contract award, followed by an additional 24 months for performance of the contract. For Small Explorer (SMEX) missions, with three years between mission definition and launch, this has been a significant problem. The SMEX solar panels have been sufficiently small that the contract performance period has been reduced to 12-15 months. The bulk of this time is used up in the final design definition and fabrication of flight solar cell assemblies. Even so, it has been virtually impossible to have the spacecraft design at a level of maturity sufficient to freeze the solar panel geometry and release the RFP in time to avoid schedule problems with integrating the solar panels to the spacecraft. With that in mind, the SMEX-Lite project team developed a modular architecture for the assembly of solar arrays to greatly reduce the cost and schedule associated with the development of a mission- specific solar array. In the modular architecture, solar cells are fabricated onto small substrate panels. This modular panel (approximately 8.5" x 17" in this case) becomes the building block for constructing solar arrays for multiple missions with varying power requirements and geometrical arrangements. The mechanical framework that holds these modules together as a solar array is the only mission-unique design, changing in size and shape as required for each mission. There are several advantages to this approach. First, the typical solar array development cycle requires a mission unique design, procurement, and qualification including a custom qualification panel. With the modular architecture, a single qualification of the SMEX-Lite modules and the associated mechanical framework in a typical configuration provided a qualification by similarity to multiple missions. It then becomes possible to procure solar array modules in advance of mission definition and respond quickly and inexpensively to a selected mission's unique requirements. The solar array modular architecture allows the procurement of solar array modules before the array geometry has been frozen. This reduces the effect of procurement lead-time on the mission integration and test flow by as much as 50%. Second, by spreading the non-recurring costs over multiple missions, the cost per unit area is also reduced. In the case of the SMEX-Lite procurement, this reduction was by about one third of the cost per unit area compared to previous SMEX mission-unique procurements. Third, the modular architecture greatly facilitates the infusion of new solar cell technologies into flight programs as these technologies become available. New solar cell technologies need only be fabricated onto a standard-sized module to be incorporated into the next available mission. The modular solar array can be flown in a mixed configuration with some new and some standard cell technologies. Since each module has its own wiring terminals, the array can be arranged as desired electrically with little impact to cost and schedule. The solar array modular architecture does impose some additional constraints on systems and subsystem engineers. First, they must work with discrete solar array modules rather than size the array to fit exactly within an available envelope. The array area is constrained to an integer multiple of the module area. Second, the modular design is optimized for space radiation and thermal environments not greatly different from a typical SMEX LEO environment. For example, a mission with a highly elliptical orbit (e.g., Polar, SMEX/FAST) would require thicker coverglasses to protect the solar cells from the more intense radiation environment.

Lyons, John↗
NASA NTRS1975

Flange design for large-scale modular assembly jigs

Technique incorporates weld-free method for securing flanges to projecting ends of unmachined box-beam framework so flanged structure may be reused without modification. One such framework may be readily assembled to another by simply matching flanges together and passing connecting members between preformed holes in structures.

Gilman, M. M.↗
NASA NTRS2012

Fault Mitigation Schemes for Future Spaceflight Multicore Processors

Future planetary exploration missions demand significant advances in on-board computing capabilities over current avionics architectures based on a single-core processing element. The state-of-the-art multi-core processor provides much promise in meeting such challenges while introducing new fault tolerance problems when applied to space missions. Software-based schemes are being presented in this paper that can achieve system-level fault mitigation beyond that provided by radiation-hard-by-design (RHBD). For mission and time critical applications such as the Terrain Relative Navigation (TRN) for planetary or small body navigation, and landing, a range of fault tolerance methods can be adapted by the application. The software methods being investigated include Error Correction Code (ECC) for data packet routing between cores, virtual network routing, Triple Modular Redundancy (TMR), and Algorithm-Based Fault Tolerance (ABFT). A robust fault tolerance framework that provides fail-operational behavior under hard real-time constraints and graceful degradation will be demonstrated using TRN executing on a commercial Tilera(R) processor with simulated fault injections.

software based↗
NASA NTRS1976

Flanged major modular assembly jig

Weldless methods and means are described for securing flanges to the projecting ends of an unmachined box beam framework in such a manner that the flanged structure may be reused without modification. And one framework may be readily assembled to another by simply matching the flanges together and passing connecting members between performed holes in the structures.

Gilman, M. M.↗
NASA NTRS2012

A Framework for Probabilistic Evaluation of Interval Management Tolerance in the Terminal Radar Control Area

Projections of future traffic in the national airspace show that most of the hub airports and their attendant airspace will need to undergo significant redevelopment and redesign in order to accommodate any significant increase in traffic volume. Even though closely spaced parallel approaches increase throughput into a given airport, controller workload in oversubscribed metroplexes is further taxed by these approaches that require stringent monitoring in a saturated environment. The interval management (IM) concept in the TRACON area is designed to shift some of the operational burden from the control tower to the flight deck, placing the flight crew in charge of implementing the required speed changes to maintain a relative spacing interval. The interval management tolerance is a measure of the allowable deviation from the desired spacing interval for the IM aircraft (and its target aircraft). For this complex task, Formal Methods can help to ensure better design and system implementation. In this paper, we propose a probabilistic framework to quantify the uncertainty and performance associated with the major components of the IM tolerance. The analytical basis for this framework may be used to formalize both correctness and probabilistic system safety claims in a modular fashion at the algorithmic level in a way compatible with several Formal Methods tools.

Hercencia-Zapana, Heber↗
NASA NTRSDate not supplied

Standalone Hazard Evaluation and Refinement From Instrument Findings (S.H.E.R.I.F.)

The Standalone Hazard Evaluation From Instrument Findings or SHERIF is a set of novel algorithms and associated framework designed to support the generation of Digital Elevation Maps (DEMs) from multiple LiDAR scans and perform Hazard Detection (HD) and Safe Site Selection with minimal input from other onboard systems. SHERIF can employ several techniques to perform Point Cloud registration(PCR) on disparate LiDAR scans of a planetary surface to generate a DEM which evolves over the course of a trajectory, with a flow designed to enable robustness. The framework also supports a variety of Hazard Detection and Safe Site Selection algorithms which can be run on the evolving DEM being produced. SHERIF features a robust and modular construction, allowing the user a high degree of flexibility in selecting and implementing whichever PCR and HD/SSL algorithms they may prefer, while maintaining the data products and sensor independence of the core SHERIF framework. SHERIF was recently tested in a hardware in the loop simulation at NASA JSC.

Entry Descent and Landing Guidance Navigation Cont↗
NASA NTRSDate not supplied

Standalone Hazard Evaluation and Refinement From Instrument Findings (S.H.E.R.I.F.)

The Standalone Hazard Evaluation From Instrument Findings (SHERIF) system is a set of novel algorithms and associated framework designed to support the generation of Digital Elevation Maps (DEMs) from multiple LiDAR scans and perform Hazard Detection (HD) and Safe Site Identification (SSI) with no dependencies on other onboard systems. SHERIF can employ several techniques to perform robust 3D keypoint extraction and Point Cloud registration (PCR) on disparate LiDAR scans of a planetary surface to generate a DEM which evolves over the course of a trajectory. The framework also supports a variety of Hazard Detection and Safe Site Identification algorithms which can be applied to the evolving DEM being produced. SHERIF features a robust and modular construction, allowing the user a high degree of flexibility in selecting and implementing whichever keypoint identificaiton, PCR and HD/SSI algorithms they may prefer, while maintaining the data products and sensor independence of the core SHERIF framework. SHERIF was recently evaluated via simulation and hardware-in-the-loop experimental testing at NASA Johnson Space Center.

hazard detection↗
NASA NTRSDate not supplied

Standalone Hazard Evaluation and Refinement From Instrument Findings (S.H.E.R.I.F.)

The Standalone Hazard Evaluation From Instrument Findings (SHERIF) system is a set of novel algorithms and associated framework designed to support the generation of Digital Elevation Maps (DEMs) from multiple LiDAR scans and perform Hazard Detection (HD) and Safe Site Identification (SSI) with no dependencies on other onboard systems. SHERIF can employ several techniques to perform robust 3D keypoint extraction and Point Cloud registration (PCR) on disparate LiDAR scans of a planetary surface to generate a DEM which evolves over the course of a trajectory. The framework also supports a variety of Hazard Detection and Safe Site Identification algorithms which can be applied to the evolving DEM being produced. SHERIF features a robust and modular construction, allowing the user a high degree of flexibility in selecting and implementing whichever keypoint identificaiton, PCR and HD/SSI algorithms they may prefer, while maintaining the data products and sensor independence of the core SHERIF framework. SHERIF was recently evaluated via simulation and hardware-in-the-loop experimental testing at NASA Johnson Space Center.

hazard detection↗
NASA NTRSDate not supplied

Py MILab: Capturing, Analyzing and Storing Test Data

Integrated Computational Materials Engineering (ICME) has recently received widespread attention due to its promises in reducing dependence on physical testing for engineering design by relying on simulation, reducing both time and cost to market for various applications. ICME however requires validated multiscale material models, which is heavily dependent on available test data with full material and test pedigree, including material processing, test and measurement equipment, raw data collection, and analysis methodology and results. Populating searchable information management systems with such rich data sets is often burdensome for data producers, resulting in a lack of findable data for modelers to validate and verify their models. To overcome these cultural barriers to ICME, NASA has developed of various database-integration toolsets that perform both data management activities within the organization’s best practices with additional functionality that relieves the effort of the data producer and promotes adoption of information management system. One such tool currently under development is Py MILab, an automatic framework for automatic capturing, analysis, maintenance, and storage of material test data. Py MILab uses a modular approach for capturing raw data, analyzing the data, and storing the data in a database, interfaced by neutral file structures, to promote plug-and-play capabilities for various analysis types. TMAnalysis is a Python-based tool that performs automatic data reduction and analysis of uniaxial thermomechanical test data. The TMAnalysis toolset can be implemented within the Analysis module of Py MILab, and thus requires a populated neutral file form the Raw Data Module of Py MILab and outputs a Analysis neutral file compatible with the Database Module of Py MILab. TMAnalysis is able to perform automatic segmentation of multistage tests and perform data analysis and reduction, including determination of point-wise properties in tension, compression, and shear, analysis of stress relaxation tests, creep analysis and zone identification, and combination of these stage types for tests with complex loading histories. The TMAnalysis code is accompanied with a graphical user interface (GUI) that allows users to easily analyze test data in bulk, verify the automatic, consistent analysis performed by the backend code, and edit stage segmentation if necessary before producing the output neutral files, ensuring data is properly analyzed and maintained with full traceability.

Data management↗
NASA NTRSDate not supplied

Managing the Digital Thread for Structural Applications With Fit for Purpose Materials

With the increased emphasis on reducing the cost and time to market of new materials, the need for analytical tools that enable the virtual design and optimization of materials throughout their processing - internal structure - property - performance envelope, along with the capturing and storing of the associated material and model information across its lifecycle, has become critical. This need is also fueled by the demands for higher efficiency in material testing; consistency, quality and traceability of data; product design; engineering analysis; as well as control of access to proprietary or sensitive information. Consequently, at NASA Glenn Research Center a robust information management system that manages the digital thread across the full material life (i.e., capture, analysis, maintenance, and dissemination of data) cycle directed at the design of ‘fit-for-purpose materials’ is under development. To this end the Application Table has been incorporated within NASA Glenn Research Center’s ICME Information Management framework within the ANSYS Granta MI tool. The Application Table provides a place where material and structural application information/requirements can be linked to marry the “design-the-material” (structural engineering) and the “design-with-material” (material science) paradigms and thereby enable application-driven design and optimization of materials and structures. In additional several associated toolsets, specifically: AIMAOS (Automated Information Management Across Organizations and Scales), Py MILab, and JARIMIS (Just A Rather Intelligent Material Interrogation System) are also under development to assist in the judicious automation of this process. AIMOAS offers users an interactive graphical user interface for connecting material information management systems with both commercial and in-house simulation tools at various length scales to enable such automation in the handoff across scales and maintenance of material digital twins and the digital thread. Py MILab, is an automatic framework for the capture, analysis, maintenance, and storage of material test data. Py MILab uses a modular approach for capturing raw data, analyzing the data, and storing the data in a database, interfaced by neutral file structures, to promote plug-and-play capabilities for various analysis types. Finally, JARIMIS is an expert system that integrates various materials informatics tools (e.g., MicroNet, Surrogate ML models, ANSYS Granta MI, etc.) to enable inverse design of materials and facilitate the application of machine learning (ML) and data science with human in the loop decision making to rapidly discover and optimize new materials.

Digital Transformation↗
NASA NTRS2023

Python Based Plume Dynamics Estimation Tool (PyPDET) Rapid Plume Strike Analysis for RPOD Maneuvers in Deep Space Operations

I worked as a NASA Intern during the Summer 2023 term in the DS-00 division under the supervision of my mentor, Dr. Jonathan Pitt. Our goal was to build on our previous work from 2022 to develop a plume strike estimation tool using a prescribed physics methodology and model plume impingement effects while considering the dynamics of a rendezvous, operations, proximity, and docking (RPOD) maneuver. This tool supports previously configured CFD-DSMC calculations by allowing for rapid analysis of initial designs using a low-fidelity source flow model. Engineers can then use the high-fidelity CFD-DSMC tool to consolidate results as they work towards finalizing a design. This year’s project was focused on developing a software application that other engineers would be using in their analysis. Thus, the user’s experience was considered in the development of this application. Proper documentation, testability, and modularity of the codebase was our priority. For example, the project included auto documentation procedures to start building towards a User Manual, while also including dedicated demonstration cases for more explicit communication of functionality. Also, this project included a framework for testing the source code for future developments. Additionally, care was taken to develop the code using an Object-Oriented Programming approach. Thus, allowing for a modular extensibility of functionality in anticipation of future developments. The core work of this project was developing an algorithm that would transform the visiting vehicle and associated thruster data according to the kinematics described in the jet firing history. It would then calculate the estimated plume strikes on two of the target vehicles and write data accordingly into a VTK file. Summer work is to conclude by developing and presenting a PowerPoint slide deck at the intern exit briefing on August 11 th , 2023. Once the model for simple plume strike calculations is developed and tested there are several avenues to explore to continue development of this tool. These are also discussed in this report.

Plume Impingement↗
NASA NTRS1987

A modular BLSS simulation model

A bioregenerative life support system (BLSS) for extraterrestrial use will be faced with coordination problems more acute than those in any ecosystem found on Earth. A related problem in BLSS design is providing an interface between the various life support processors, one that will allow for their coordination while still allowing for system expansion. A modular model is presented of a BLSS that interfaces system processors only with the material storage reservoirs, allowing those reservoirs to act as the principal buffers in the system and thus minimizing difficulties with processor coordination. The modular nature of the model allows independent development of the detailed submodels that exist within the model framework. Using this model, BLSS dynamics were investigated under normal conditions and under various failure modes. Partial and complete failures of various components, such as the waste processors or the plants themselves, drive transient responses in the model system, allowing the examination of the effectiveness of the system reservoirs as buffers. The results from simulations help to determine control strategies and BLSS design requirements. An evolved version could be used as an interactive control aid in a future BLSS.

Rummel, John D.↗
NASA NTRS1987

A modular BLSS simulation model

The coordination of material flows in earth's biosphere is largely made possible by the buffering effect of huge material reservoirs. Without similarly-sized buffers, a bioregenerative life support system (BLSS) for extraterrestrial use will be faced with coordination problems more acute than those in any ecosystem found on earth. A related problem in BLSS design is providing an interface between the various life-support processors, one that will allow for their coordination while still allowing for system expansion. A modular model is presented of a BLSS that interfaces system processors only with the material storage reservoirs, allowing those reservoirs to act as the principal buffers in the system and thus minimizing difficulties with processor coordination. The modular nature of the model allows independent development of the detailed submodels that exist within the model framework. Using this model, BLSS dynamics were investigated under normal conditions and under various failure modes. Partial and complete failures of various components, such as the waste processor or the plants themselves, drive transient responses in the model system, allowing examination of the effectiveness of the system reservoirs as buffers. The results from simulations of this sort will help to determine control strategies and BLSS design requirements. An evolved version of this model could be used as an interactive control aid in a future BLSS.

Rummel, John D.↗
NASA NTRS1995

T/BEST: Technology Benefit Estimator Select Features and Applications

A Technology Benefit Estimator (T/BEST) system has been developed to provide a formal method to assess advanced aerospace technologies and quantify the benefit contributions for prioritization. An open-ended, modular approach is used to allow for upgrade and insertion of advanced technology modules. T/BEST's software framework, beginner-to-expert operation, interface architecture, and key analysis modules are discussed. In this paper, selected features and applications of T/BEST are demonstrated. Sample cases pertaining to structural analysis of titanium and composite blades are presented. The performance of hot and cold composite fan blades is also discussed. The cost required to manufacture titanium and composite fan blades is estimated.

Generazio, Edward R.↗
NASA NTRS2011

TAMU: A New Space Mission Operations Paradigm

The Transferable, Adaptable, Modular and Upgradeable (TAMU) Flight Production Process (FPP) is a model-centric System of System (SoS) framework which cuts across multiple organizations and their associated facilities, that are, in the most general case, in geographically diverse locations, to develop the architecture and associated workflow processes for a broad range of mission operations. Further, TAMU FPP envisions the simulation, automatic execution and re-planning of orchestrated workflow processes as they become operational. This paper provides the vision for the TAMU FPP paradigm. This includes a complete, coherent technique, process and tool set that result in an infrastructure that can be used for full lifecycle design and decision making during any flight production process. A flight production process is the process of developing all products that are necessary for flight.

Meshkat, Leila↗