TPSAS-NF1676L-12076-DND
AirSTAR is the collection of Ground Support Systems, Subscale Flight Vehicles and Operations Expertise required for conducting remotely piloted flight-dynamics and controls research.
David Cox↗
SEARCH · Engineering Papers
Results for “Expertise”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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TPSAS-NF1676L-17243-DND
This presentation captures ERA Project's effort in using integrated cost-schedule-risk analysis tool, identifying and capturing explicit relationships between program funds, schedule, and discrete risks. The project team merged a myriad of cost and schedule data for timely, objective, uncertainty analysis of the ITD's budget and schedule. The project team conducted detailed schedule uncertainty analysis showing schedule activities and how their interrelationships were impacted, representing an impressive advance beyond routine Gantt charts. Furthermore, the ERA Project collected discrete risk events including their impacts to costs and/or schedule and interwove these risks into cost and schedule logic networks, providing the foundation to conduct cost and schedule analyses. The team integrated this data into the analysis model, providing probabilistic results that were then used by the ERA Project's senior management for decision-making purposes. The ERA Project management team, through technical and pathfinder expertise in integrating cost, schedule, risk, and technical content, consistently provided risk-informed recommendations to Implementing Centers and ISRP senior management which ultimately ensured the successful ARMD Key Decision Point (KDP) review of the ERA Phase 2 ITD Portfolio and increased the likelihood of achieving technical objectives within cost and schedule constraints.
Gaudy M Bezos-O'Connor↗
Virtual Project Management Challenge
Technical knowledge is essential to the work done by NASA practitioners. So it may be surprising to learn that the most ingenious and elegant solutions to project challenges often arise not from a specific technical discipline perspective, but from a systems-focused mindset. While technical discipline expertise is a critical component of project success, sometimes the simplest resolution to a problem involves a systems-wide solution. Because of this, encouraging a systems mentality among project team members can be a powerful tool in mitigating risk and eliminating threats. Join us for an exploration of the critical ways in which a systems engineering and integration perspective can enhance project management efforts. Presenter Bo Bejmuk is the former Space Shuttle Orbiter Program Director and manager of Sea Launch Program operations and development for Boeing. He recently served as Chairman of the Standing Review Board for the NASA Constellation Program. Moderator Ramien Pierre is from NASA's Academy for Program/Project and Engineering Leadership (APPEL).
Bo Bejmuk↗
Unsupervised Anomaly Detection in High-Dimensional Flight Data Using Convolutional Variational Auto-Encoder
The modern National Airspace System (NAS) is an extremely safe system. The industry has experienced a steady decrease in fatalities over the years. This can be contributed to both improved flight critical systems with redundant hardware and software protections as well as an increased focus on active monitoring and response to real time and historically identified vulnerabilities by implementing more resilient procedures and protocols. The main practice for identifying vulnerabilities in operations leverages domain expertise using knowledge about how the system should behave with the expected tolerances to known safety margins. This approach works well when the system has a well-defined operating condition. However, the operations in the NAS can be highly complex with various nuances that render it difficult to clearly pre-define all known safety vulnerabilities. With the advancement of data science and machine learning techniques, the potential to automatically identify emerging vulnerabilities in the observed operations has become more practical in recent years. The state-of-the-art anomaly detection approaches in aerospace data usually rely on supervised or semi-supervised learning. However, in many real-world problems such as flight safety creating labels for the data requires huge amount of efforts and is largely expensive. As a result, in this article, we develop a Convolutional Variational Auto-Encoder (CVAE), an unsupervised learning approach for anomaly detection in high-dimensional heterogeneous time-series data. We validate performance of CVAE compared to the state-of-the-art supervised learning approach (as an upper bound) as well as an supervised clustering based on K-Means (as a lower bound) on Yahoo!'s benchmark time series anomaly detection data. Finally, we showcase performance of CVAE on a case study of identifying anomalies in the first 60 seconds of commercial flights' take-offs using Flight Operational Quality Assurance (FOQA) data.
Milad Memarzadeh↗
NASA SBIR Fuels Cloud Instruments, Business Growth
Anasphere’s SBIR contracts with NASA in 2005, 2011, and 2013 resulted in the development of a lightweight cloud water content sensor that has enabled significant business growth for the small company. Not only has the company successfully deployed the innovation in both NASA and DOE-sponsored programs, but the instruments have been sold globally and catalyzed the expansion of the company’s manufacturing relationships—bringing in valuable expertise while maintaining lean operations. Its success has also led to further SBIR work with NASA, the most recent of which is supporting development of a universal cloud water content sensor that Anasphere expects will widen industry adoption of the technology even further.
Bruce R Cogan↗
An Overview of Research in Nondestructive Evaluation at the National Aeronautics and Space Administration’s Langley Research Center
The primary focus of the nondestructive evaluation (NDE) effort at Langley Research Center (LaRC) is to pioneer advances in NDE and structural health monitoring (SHM) through fundamental scientific research, technology development, and transferring these to the scientific and aerospace communities. This research supports a variety of NASA missions in space operations, exploration and aeronautics. The work presented will focus on the development of advanced inspection systems to detect critical flaws in both metallic and non-metallic materials and structures for aerospace applications. The emphasis is on increasing the performance of inspection systems to provide defect detection and quantitative characterization of the material state. LaRC NDE utilizes expertise in computational ultrasonic, thermal and eddy current simulations to reduce measured responses to size and locations of flaws, establish technique limitations and optimize performance. This presentation will highlight research into five strategic areas: (1) The development of novel solutions using state-of-the-art NDE technologies such as eddy current, ultrasound, thermography and x-ray computed tomography; (2) Research in the development of unique NDE technologies such as terahertz, wavefield imaging and nonlinear ultrasound; (3) The application of both computational and analytic models to NDE problems; (4) The development of advanced analytic methods to interpret large complex data sets; and (5) Advanced methods of applying and interrogating sensors for structural health monitoring.
K Elliott Cramer↗
Automation of the Uncertainty Quantification Process Based on Probability Boxes with DAKOTA
To date, while the use of CFD is prevalent, very few efforts have been undertaken that truly attempt to document all (or even most) of the sources of uncertainty in the simulations. Instead, the current state-of-the-art relies heavily on the experience of the CFD practitioner to estimate the uncertainty associated with their simulations through simple sensitivity studies or subject matter expertise. This practice will have to be replaced with a formal uncertainty quantification (UQ) process if CFD is to play an expanded role in the design research and engineering community, test and evaluation community, and ultimately certification for flight. This is especially true for hypersonic air-breathing propulsion systems due to the environment, scale, and duration limitations of ground test facilities. Accounting for uncertainties in a formal manner is a tedious process. Moreover, the typical CFD practitioner is not likely to be familiar with formal UQ methods. Hence, a major obstacle that has prevented the adoption of UQ methods for engineering design and development work is the lack of a tool set to automate most (if not all) of the UQ workflow. Towards this end, the SANDIA package DAKOTA (which has been developed to drive both UQ and optimization processes) will be tightly wrapped around the VULCAN-CFD code to automate the uncertainty quantification process. The automated process will be applied to an isolator turbulence model validation exercise that has previously been documented using a manual approach to the UQ process. Hence, the focus of this paper will be documenting the level to which automation can hide the UQ process details from the CFD practitioner rather than the UQ method itself.
CFD↗
An Overview of Research in Nondestructive Evaluation at the National Aeronautics and Space Administration’s Langley Research Center
The primary focus of the nondestructive evaluation (NDE) effort at Langley Research Center (LaRC) is to pioneer advances in NDE and structural health monitoring (SHM) through fundamental scientific research, technology development, and transferring these to the scientific and aerospace communities. This research supports a variety of NASA missions in space operations, exploration and aeronautics. The work presented will focus on the development of advanced inspection systems to detect critical flaws in both metallic and non-metallic materials and structures for aerospace applications. The emphasis is on increasing the performance of inspection systems to provide defect detection and quantitative characterization of the material state. LaRC NDE utilizes expertise in computational ultrasonic, thermal and eddy current simulations to reduce measured responses to size and locations of flaws, establish technique limitations and optimize performance. This presentation will highlight research into five strategic areas: (1) The development of novel solutions using state-of-the-art NDE technologies such as eddy current, ultrasound, thermography and x-ray computed tomography; (2) Research in the development of unique NDE technologies such as terahertz, wavefield imaging and nonlinear ultrasound; (3) The application of both computational and analytic models to NDE problems; (4) The development of advanced analytic methods to interpret large complex data sets; and (5) Advanced methods of applying and interrogating sensors for structural health monitoring.
NDE↗
A Framework for Assessment of Autonomy Challenges in Air Traffic Management
Traditionally, air traffic management services have been provided by air traffic controllers and managers stationed in ground facilities, employed or contracted by the public sector, and supported by automation. These centralized, human-centric air traffic management services do not scale to accommodate increasing demands from conventional and new entrant operations for access to the national airspace system. One transformation that provides much needed scalability is increasing the level of autonomy of air traffic management by enabling edge agents of the system, including vehicles, operators, and third-party service suppliers, to collectively self-manage independently from the centralized service providers and enabling the automation to also take on more independent traffic management responsibility from the human agents. This paper identifies challenges to increasing the level of autonomy of air traffic management services. It describes a framework to enable a systematic identification of these challenges. The framework consists of a functional breakdown of air traffic management services and several dimensions characterizing different autonomy scales. The autonomy dimensions include the automation level between human and machine agents, the locus of control between centralized and distributed edge agents, cognitive activities for autonomous situation awareness and decision making, intelligence levels ranging from skill-based to expertise-based autonomous behavior, and uncertainty levels of the dynamics and environment in which autonomous agents operate. Several challenges are identified and categorized using the different dimensions of the autonomy framework.
automation, autonomy framework, collective autonom↗
Scalable Traffic Management for Emergency Response Operations (STEReO)
STEReO brings together several technologies in Unmanned Aircraft Systems (UAS) Traffic Management (UTM), Autonomy, Communications, Human Factors, and Domain Expertise & Tools, aimed at providing scalability and flexibility, as well as operational resiliency to dynamic changes during a disaster event. Some of the concepts STEReO explores are: collaborative tools to ingest remote sensing information and distribute a common mission operating picture, apply ad-hoc communication networks to facilitate timely information sharing and communication of changes, vehicle-to-vehicle and onboard autonomy technologies ensure the safety and resiliency of operations, and apply NASA’s UAS traffic management system (UTM) as a public safety UAS Service Supplier (USS) to access and coordinate use of the airspace by both manned and unmanned operations. The potential benefits of STEReO include: standardized, cross-platform communication means increased interoperability and ease of cooperation/collaboration, increased situation awareness and common operating picture allow for earlier detection and decision making, and scalable to size and complexity of environment, operations, and mission objectives. This presentation gives an informational overview of the STEReO project to attendees of the AIAA Aviation conference.
emergency response operations↗
Strategic Perspectives on the Future of Systems Engineering at NASA
NASA’s Model-Based Systems Engineering (MBSE) Infusion and Modernization Initiative (MIAMI) chartered a strategy group comprising early to mid-career NASA subject matter experts with diverse experiences to look into the future of systems engineering at NASA. The purpose of the group was to provide a vision for the future state of systems engineering practices and to develop a strategic plan to enable the evolution of the art up to 20 years in the future. The group used a design thinking approach to gather ideas and obtained insight into current engineering processes and domain outlook by interviewing engineers of varying expertise and experiences who had worked on teams of different sizes for missions large and small. The group built a roadmap to highlight future needs, projected capabilities, and technology and competency gaps and developed a strategic plan to ensure the expedient introduction of these capabilities. The resulting strategic plan recommends capability development and workforce strategies and provides guidance for Agency-wide SE policy. Artifacts, details, and raw data from the strategy team’s work are contained in NASA/TM-20205002911/SUPPL, Strategic Perspectives on the Future of Systems Engineering at NASA: Supplemental Information: Appendixes A to K.
Anupa R Bajwa↗
Automatic Generation of Guard-Stable Floating-Point Code
In floating-point programs, test instability occurs when the control flow of a conditional statement diverges from its ideal execution under real arithmetic. This phenomenon is caused by the presence of round-off errors in floating-point computations. Writing programs that correctly handle test instability often require expertise on finite precision computations and rounding errors. This paper presents a fully automatic tool chain that generates and formally verifies a test-stable floating-point C program from its functional specification in real arithmetic. The generated program is instrumented to soundly detect when unstable tests may occur and, in these cases, to issue a warning. The proposed approach combines the PRECiSA floating-point static analyzer, the Frama-C software verification suite, and the PVS theorem prover.
Floating-Point Arithmetic↗
Sustainable Ozone & Aerosol Measurements from a 6U CubeSat: The Stratospheric Aerosol & Gas Experiment (SAGE) IV Pathfinder
The Clean Air Act mandates NASA to monitor atmospheric ozone.Stratospheric aerosols and ozone are Targeted Observables under the 2017 Decadal Survey. NASA LaRC has been the Center of expertise for ozone and stratospheric aerosol science over four decades through the development, launch, and operation of four Stratospheric Aerosol and Gas Experiment (SAGE) instruments.SAGE III is the calibration standard for multiple ozone-measuring instruments. SAGE III continues to be very successful through almost three years of Phase E operations/science data collection for a nominal 3-year mission (launched February 19, 2017).
Michael Drew Obland↗
NASA’s Initial and Sustained Artemis Human Landing Systems
On March 26, 2019, in keeping with President Trump’s Space Policy Directive-1, Vice President Pence charged NASA with landing the first woman and the next man on the South Pole of the Moon by 2024, followed by a sustained presence on and around the Moon by 2028. NASA’s Human Landing System (HLS) Program is responsible for the final mode of transportation in deep space that will carry humans to and from the surface of the Moon, to be designed and developed by American companies for NASA’s Artemis lunar exploration program. This paper examines the approach for Artemis human landing systems for both the initial missions and future sustained missions. While achieving the 2024 goal requires a focus on speed and the use of mature technologies, planning toward sustained operations to and from the lunar surface requires a focus on reliability and reusability. The two approaches, however, are not mutually exclusive, as demonstrated by the HLS prime contractors’ integrated lander system proposals. On April 30, 2020, NASA announced that Blue Origin of Kent, Washington, Dynetics (a Leidos company) of Huntsville, Alabama, and SpaceX of Hawthorne, California, were the awardees for NASA’s Human Landing System contracts under Appendix H of the NextSTEP-2 Broad Agency Announcement. The companies began work in a 10-month base period during which NASA teams worked with the companies to streamline the review of required products and to share the agency’s expertise in human spaceflight systems development. Following the base period, NASA will determine which company or companies will develop the human landers for the initial missions, including the 2024 landing, and which companies will develop landers for future sustained missions toward the end of the decade.
HLS↗
Building Capacity for Policy-makers in a Virtual Setting: Providing Tools to Analyze Wildfire Smoke Plumes and Their Impacts
The NASA DEVELOP Program conducted 10-week long feasibility projects in a remote work setting, including partnering with The Nature Conservancy’s Washington Chapter and the Puget Sound Clean Air Agency to investigate wildfire smoke from 2000 - 2020 in the Pacific Northwest using satellite-derived data. The team engaged with platforms for collaboration both internally with NASA affiliates and externally with community organizations. Working from multiple states, the team members used a variety of software including Google Meet, Microsoft Teams, and Google Earth Engine to foster communication and work with data in a shared virtual environment. Throughout the project, the team learned that executing the project in a distanced work setting made it easier to reach out to scientists across the country for expertise and guidance. To study changes in air quality resulting from wildfire smoke, the team utilized data from NASA’s Fire Information from Resource Management System (FIRMS) and the ESA’s Sentinel-5 TROPOspheric Monitoring Instrument (TROPOMI). The team created a Google Earth Engine web-based tool, “Plume Hazards and Observations of Emissions by Navigating an Interactive eXplorer” (PHOENIX), to visualize changes in pollutants and aerosol optical depth after fire events. The potential relationship between plume height and fire radiative power was evaluated by using NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Aqua and Terra satellites and NASA’s Multi-angle Imaging SpectroRadiometer (MISR) aboard Terra with the MISR INteractive eXplorer (MINX). The PHOENIX smoke assessment tool and science communication infographics will be shared electronically with the partner organizations. Furthermore, the team introduced the partners to MINX and will provide a tailored tutorial that included a recorded video and a written component with a live virtual workshop. These resources build capacity for further research and education on wildfire smoke and air quality within communities.
NASA DEVELOP↗
Algorithmic Detection of Elemental Biosignatures
Machine learning models that classify a sample as indicative or non-indicative of life could play an important role in life-detection missions. Their predictions result from agnostic algorithms and thereby add redundancy to judgements resulting from human expertise. Additionally, their important features can reveal the most informative measurements within the operational constraints of a life-detection mission. The Ladder of Life Detection (Neveu 2018) identifies the need for an understanding of how combinations of multiple biosignatures affect overall confidence. The present work provides a starting point to answer this need, and future work will expand the data types to obtain even more predictive combinations of features. Elemental abundance was chosen as a starting set of features due to its availability in diverse sample types, which are needed to train a generalizable model. A standardized dataset was collected, including 35 non-indicative, e.g., lunar rock, basalt; 19 indicative mixed, e.g., seawater, agricultural soil; 46 indicative non-alive, e.g., coal, chalk; and 10 indicative alive, e.g., biofilm, bacteria. This dataset could be valuable for complementary biosignature research. The samples were standardized to the same limit of detection of a simulated mission scenario. Four classification models were used: k-nearest neighbors (KNN), logistic regression (LR), linear support vector machines (SVM), and Gaussian naïve Bayes (GNB). To obtain feature importances, KNN was run on three principal components of the training data and LR and SVM were run with L1 and L2 regularization. The performances and feature importances of the six model variants on 40:60 train to validation ratios were assessed with Monte Carlo simulations. ROC AUC and mean accuracy scores ranged between 82% - 94%, with sensitivity greater than specificity. For indicative of life predictors, all models had C and Ca as strong and Cl as medium; a majority of models had N, K, and P as medium. For non-indicative of life predictors, all models had Si as strong, and a majority of models had Mg, Al, and Ti as medium. Varied elements were Fe (slightly non-indicative), H (slightly indicative), O (widely varied), Na, Mn, and S. These results serve as a proof of concept and suggest important elemental signals beyond merely the CHNOPS of Earth-based life.
Algorithmic↗
Exploring data-driven modeling of boundary layer transition
Prediction of laminar-turbulent transition in boundary layer flows is an important component of predicting the aerodynamic performance of a number of aerospace configurations. According to the CFD Vision 2030 [1], transition modeling represents acriticalarea in CFD simulation capability that will remain a pacing item for the foreseeable future. The fact thattransition can take placevia either one of a myriad possible paths adds to the challenges inreliable transition predictions, despite a limited knowledge of the relevant input parameters. In the low disturbance environments typical of flight applications, transition is often initiated by small amplitude disturbances in the form of linear instability waves of the laminar boundary layer. These disturbances amplify linearly at first and eventually undergo a sequence of nonlinear interactions that result in transition to turbulence. Because the nonlinear phase is rather rapid, the amplification of boundary layer instabilities is governed by the linearstability theory over a majority of the distance leading up to the onset of transition. Semi-empirical transition correlations based on the linear stability theory have been successful in explaining the observed trends in transition location within a broad class of flows. However, the application of stability theory is highly non-robust and often requires a significant domain expertise. Recent work at the NASA Langley Research Center has beenaimed at bridging the gap between physics based transition analyses such as those based on linear stability theory and practical applications that require transition prediction by users that may not be well versed in transition physics. The applications of deep learning have been at the center of these efforts. This presentation will focus on the progress achieved thus far, highlighting the applications of neural networks to selectedtransition scenarios across a range of Mach numbers and flow configuration, as well as the lessons learnedand remaining challengeswithrespect to the selection of training data and neural networks architectures, hyperparameter tuning, and the physical insights distilled from the otherwise black-box models.
M. R. Malik↗
State of the Profession Considerations: NASA Langley Research Center Capabilities / Technologies for Autonomous In-Space Assembly and Modular Persistent Assets
Successfully implementing OSAM into next generation revolutionary observatories requires integrating expertise and technologies in modular space structures, assembly operations, autonomy, and modeling/simulation. LaRC OSAM technologies/capabilities have been presented to inform the Planetary Science and Astrobiology Decadal Survey community of the robust and mature existing capability to support an OSAM based architecture for their next observatory. LaRC Structures and Assembly capabilities enable; a modular telescope architecture, high-performance structural modules, and robotic assembly techniques. LaRC Autonomy capabilities ensure that the robotic assembly will be accomplished in a safe and robust manner and only require humans in a supervisory role. The LaRC toolbox of Modeling and Simulation capabilities that is calibrated using module-level ground testing, will ensure that the performance of the fully assembled observatory, a very large zero-g system that will never be assembled/tested in a gravity environment, meets all performance requirements when it enters into service. Integrating all three LaRC capabilities and including embedded metrology, will enable servicing, repair, instrument upgrades (and/or replacement) while ensuring a very long lifetime for the observatory and providing a return-on-investment that is substantially greater than the initial cost. Further confidence will be achieved as OSAM technologies are validated in a new LaRC OSAM laboratory that allows large-scale collaborative testing of modular hardware, simulation software and algorithms, and autonomous agents.
Large space structures↗