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NASA NTRS2018

Laser Tracker Utilization Methodology in Measuring Truth Trajectories for INS Testing on 6 Degree of Freedom Table at the Marshall Space Flight Center's Contact Dynamics Simulation Laboratory with Lessons Learned

When performing Inertial Navigation System (INS) testing at the Marshall Space Flight Center's (MSFC) Contact Dynamics Simulation Laboratory (CDSL) early in 2017, a Leica Geosystems AT901 Laser Tracker system (LLT) measured the twist & sway trajectories as generated by the 6 Degree Of Freedom (6DOF) Table in the CDSL. These LLT measured trajectories were used in the INS software model validation effort. Several challenges were identified and overcome during the preparation for the INS testing, as well as numerous lessons learned. These challenges included determining the position and attitude of the LLT with respect to an INS-shared coordinate frame using surveyed monument locations in the CDSL and the accompanying mathematical transformation, accurately measuring the spatial relationship between the INS and a 6DOF tracking probe due to lack of INS visibility from the LLT location, obtaining the data from the LLT during a test, determining how to process the results for comparison with INS data in time and frequency domains, and using a sensitivity analysis of the results to verify the quality of the results. While many of these challenges were identified and overcome before or during testing, a significant lesson on test set-up was not learned until later in the data analysis process. It was found that a combination of trajectory-dependent gimbal locking and environmental noise introduced non-negligible noise in the angular measurements of the LLT that spanned the evaluated frequency spectrum. The lessons learned in this experiment may be useful for others performing INS testing in similar testing facilities.

Leggett, Jared O.↗

NASA NTRS2015

On the Comparison of the Long Penetration Mode (LPM) Supersonic Counterflowing Jet to the Supersonic Screech Jet

Classic tonal screech noise created by under-expanded supersonic jets; Long Penetration Mode (LPM) supersonic phenomenon -Under-expanded counter-flowing jet in supersonic free stream -Demonstrated in several wind tunnel tests -Modeled in several computational fluid dynamics (CFD) simulations; Discussion of LPM acoustics feedback and fluid interactions -Analogous to the aero-acoustics interactions seen in screech jets; Lessons Learned: Applying certain methodologies to LPM -Developed and successfully demonstrated in the study of screech jets -Discussion of mechanically induced excitation in fluid oscillators in general; Conclusions -Large body of work done on jet screech, other aero-acoustic phenomenacan have direct application to the study and applications of LPM cold flow jets

Farr, Rebecca A.↗

NASA NTRS2020

Ultrafast Melting at Metal-Semiconductor Interfaces: A Molecular Dynamics Study

Quantifying the thermodynamics of interfacial melting is important for several applications such as laser machining and designing radiation hardened electronic devices. Due to the short time and length scales that are involved in the process, molecular simulations provide an ideal path for understating the interfacial melting phenomena. In this work we investigate tungsten (W) – silicon carbide (SiC) interfaces which are relevant in modern high-power electronic devices. Classical molecular dynamics simulations along with the two-temperature model (TTM) were used to study the radiation induced melting of the interface while a neural network-based machine learning framework was used to track the transient re-crystallization process. The damage endured by the semiconductor at the interface following the radiation event is discussed in detail while the re-crystallization of the metal is described in terms of the classical nucleation theory.

Ashwin Ravichandran↗

NASA NTRSDate not supplied

LEO Sensor to GEO Sensor Algorithm Transfer Models for Land Surface Temperature

Land surface temperature (LST) is a key climate observable used to detect changes in the Earth’s surface energy budget that influence carbon and water cycles. Land surface temperature exhibits strong diurnal variability, which geostationary satellites can observe at scale thanks to their temporal resolution. Due to anthropogenic climate and land use changes, the surface energy balance has been considerably modified and may be described by changes in diurnal temperature range and extremes. Using high performance computing and datasets from the NASA Earth Exchange, we exploit co-located, co-temporal observations from low-earth orbit (LEO) and geostationary (GEO) sensors to develop a deep learning-based method for LEO-to-GEO algorithm emulation. Our model is trained to predict MODIS Terra LST from GOES-16 thermal bands and achieves validation error <2K. Application of the model to unseen times of day (observed by MODIS Aqua) and a new GEO sensor (Himawari-8) observing an unseen spatial domain, demonstrate the generalization of the deep learning model across space, time and spectra. Communicating diurnal LST variability observed by geostationary satellites can have impacts in multiple disciplines, from understanding of snow, vegetation and soil dynamics, to recognizing trends in heat events relevant to human health.

Kate Marie Duffy↗

NASA NTRSDate not supplied

ICME for NASA Aerospace Applications: Batteries for Electric Aviation

NASA’s approach to computational materials modeling is detailed in the NASA Vision 2040 Roadmap for Multiscale Modeling and Simulation of Materials and Systems. This report is in the spirit of national initiatives such as the Material Genome Initiative (MGI), Integrated Computational Materials Engineering (ICME), and others. We utilize a combination of fundamental modeling, computational high-throughput screening, and data science methods, e.g., machine learning, are used to find innovative solutions to NASA or national technology challenges. Applications of interest are wide ranging from advanced alloys to batteries to coatings, among others. In this talk, we present three examples for recent work related to NASA applications. First, doping advanced sulfur battery cathodes with selenium boosts electrical conductivity important for electric aircraft applications. First principles calculations will be discussed that result in compositional design maps for these materials. Second, development of icephobic coatings is important to mitigate safety hazards associated with icing for aircraft. Molecular dynamics simulations are reported for ice-surface interfaces to understand adhesion mechanisms and help screen optimal ice-phobic coatings. Third, shape memory alloys have numerous applications as actuators, superelastic materials, etc. for aerospace. We report machine learning models that predict martensitic transition temperatures across a broad swath of compositional space.

John Lawson↗

NASA NTRS2001

Using Model-Based Reasoning for Autonomous Instrument Operation - Lessons Learned From IMAGE/LENA

Model-based reasoning has been applied as an autonomous control strategy on the Low Energy Neutral Atom (LENA) instrument currently flying on board the Imager for Magnetosphere-to-Aurora Global Exploration (IMAGE) spacecraft. Explicit models of instrument subsystem responses have been constructed and are used to dynamically adapt the instrument to the spacecraft's environment. These functions are cast as part of a Virtual Principal Investigator (VPI) that autonomously monitors and controls the instrument. In the VPI's current implementation, LENA's command uplink volume has been decreased significantly from its previous volume; typically, no uplinks are required for operations. This work demonstrates that a model-based approach can be used to enhance science instrument effectiveness. The components of LENA are common in space science instrumentation, and lessons learned by modeling this system may be applied to other instruments. Future work involves the extension of these methods to cover more aspects of LENA operation and the generalization to other space science instrumentation.

Johnson, Michael A.↗

NASA NTRS1995

Attitude determination using an adaptive multiple model filtering Scheme

Attitude determination has been considered as a permanent topic of active research and perhaps remaining as a forever-lasting interest for spacecraft system designers. Its role is to provide a reference for controls such as pointing the directional antennas or solar panels, stabilizing the spacecraft or maneuvering the spacecraft to a new orbit. Least Square Estimation (LSE) technique was utilized to provide attitude determination for the Nimbus 6 and G. Despite its poor performance (estimation accuracy consideration), LSE was considered as an effective and practical approach to meet the urgent need and requirement back in the 70's. One reason for this poor performance associated with the LSE scheme is the lack of dynamic filtering or 'compensation'. In other words, the scheme is based totally on the measurements and no attempts were made to model the dynamic equations of motion of the spacecraft. We propose an adaptive filtering approach which employs a bank of Kalman filters to perform robust attitude estimation. The proposed approach, whose architecture is depicted, is essentially based on the latest proof on the interactive multiple model design framework to handle the unknown of the system noise characteristics or statistics. The concept fundamentally employs a bank of Kalman filter or submodel, instead of using fixed values for the system noise statistics for each submodel (per operating condition) as the traditional multiple model approach does, we use an on-line dynamic system noise identifier to 'identify' the system noise level (statistics) and update the filter noise statistics using 'live' information from the sensor model. The advanced noise identifier, whose architecture is also shown, is implemented using an advanced system identifier. To insure the robust performance for the proposed advanced system identifier, it is also further reinforced by a learning system which is implemented (in the outer loop) using neural networks to identify other unknown quantities such as spacecraft dynamics parameters, gyro biases, dynamic disturbances, or environment variations.

Lam, Quang↗

NASA NTRS1989

Computer network environment planning and analysis

The GSFC Computer Network Environment provides a broadband RF cable between campus buildings and ethernet spines in buildings for the interlinking of Local Area Networks (LANs). This system provides terminal and computer linkage among host and user systems thereby providing E-mail services, file exchange capability, and certain distributed computing opportunities. The Environment is designed to be transparent and supports multiple protocols. Networking at Goddard has a short history and has been under coordinated control of a Network Steering Committee for slightly more than two years; network growth has been rapid with more than 1500 nodes currently addressed and greater expansion expected. A new RF cable system with a different topology is being installed during summer 1989; consideration of a fiber optics system for the future will begin soon. Summmer study was directed toward Network Steering Committee operation and planning plus consideration of Center Network Environment analysis and modeling. Biweekly Steering Committee meetings were attended to learn the background of the network and the concerns of those managing it. Suggestions for historical data gathering have been made to support future planning and modeling. Data Systems Dynamic Simulator, a simulation package developed at NASA and maintained at GSFC was studied as a possible modeling tool for the network environment. A modeling concept based on a hierarchical model was hypothesized for further development. Such a model would allow input of newly updated parameters and would provide an estimation of the behavior of the network.

Dalphin, John F.↗

NASA NTRSDate not supplied

Lessons Learned and Best Practices for Utilizing a Generalized Composite Impact Model

A material model which incorporates several key capabilities which have been identified as lacking in currently available composite impact models has been developed. The material model utilizes experimentally based tabulated input to define the evolution of plasticity and damage as opposed to specifying discrete input parameters (such as modulus and strength). It has been implemented into the commercially available transient dynamic finite element code LS-DYNA® as MAT_213. The model can simulate the nonlinear deformation, damage and failure that take place in a composite under dynamic loading conditions. As MAT_213 is now being used by a general user community that did not participate in the model development process, a number of issues have been identified that caused uncertainty in assembling a MAT_213 input deck and conducting a MAT_213 analysis. The overall goal of this effort is to develop a quantified set of lessons learned and best practices which will permit a new user to conduct useful MAT_213 simulations without needing to have detailed expert knowledge of the material model and its theoretical underpinnings.

Micromechanics↗

NASA NTRS2017

Machine Learning Approaches to Increasing Value of Spaceflight Omics Databases

The number of spaceflight bioscience mission opportunities is too small to allow all relevant biological and environmental parameters to be experimentally identified. Simulated spaceflight experiments in ground-based facilities (GBFs), such as clinostats, are each suitable only for particular investigations -- a rotating-wall vessel may be 'simulated microgravity' for cell differentiation (hours), but not DNA repair (seconds) -- and introduce confounding stimuli, such as motor vibration and fluid shear effects. This uncertainty over which biological mechanisms respond to a given form of simulated space radiation or gravity, as well as its side effects, limits our ability to baseline spaceflight data and validate mission science. Machine learning techniques autonomously identify relevant and interdependent factors in a data set given the set of desired metrics to be evaluated: to automatically identify related studies, compare data from related studies, or determine linkages between types of data in the same study. System-of-systems (SoS) machine learning models have the ability to deal with both sparse and heterogeneous data, such as that provided by the small and diverse number of space biosciences flight missions; however, they require appropriate user-defined metrics for any given data set. Although machine learning in bioinformatics is rapidly expanding, the need to combine spaceflight/GBF mission parameters with omics data is unique. This work characterizes the basic requirements for implementing the SoS approach through the System Map (SM) technique, a composite of a dynamic Bayesian network and Gaussian mixture model, in real-world repositories such as the GeneLab Data System and Life Sciences Data Archive. The three primary steps are metadata management for experimental description using open-source ontologies, defining similarity and consistency metrics, and generating testing and validation data sets. Such approaches to spaceflight and GBF omics data may soon enable unique insight into which measured phenomena correlate to biological mechanisms that are truly affected by spaceflight conditions; which are most likely to be confounded by other variables; and which are insufficiently characterized, significantly increasing existing and future science return from ISS and spaceflight missions.

Gentry, Diana↗

NASA NTRS1988

The organization of an autonomous learning system

The organization of systems that learn from experience is examined, human beings and animals being prime examples of such systems. How is their information processing organized. They build an internal model of the world and base their actions on the model. The model is dynamic and predictive, and it includes the systems' own actions and their effects. In modeling such systems, a large pattern of features represents a moment of the system's experience. Some of the features are provided by the system's senses, some control the system's motors, and the rest have no immediate external significance. A sequence of such patterns then represents the system's experience over time. By storing such sequences appropriately in memory, the system builds a world model based on experience. In addition to the essential function of memory, fundamental roles are played by a sensory system that makes raw information about the world suitable for memory storage and by a motor system that affects the world. The relation of sensory and motor systems to the memory is discussed, together with how favorable actions can be learned and unfavorable actions can be avoided. Results in classical learning theory are explained in terms of the model, more advanced forms of learning are discussed, and the relevance of the model to the frame problem of robotics is examined.

Kanerva, Pentti↗

NASA NTRS1998

Spatial Characteristics of the Unsteady Differential Pressures on 16 percent F/A-18 Vertical Tails

Buffeting is an aeroelastic phenomenon which plagues high performance aircraft at high angles of attack. For the F/A-18 at high angles of attack, vortices emanating from wing/fuselage leading edge extensions burst, immersing the vertical tails in their turbulent wake. The resulting buffeting of the vertical tails is a concern from fatigue and inspection points of view. Previous flight and wind-tunnel investigations to determine the buffet loads on the tail did not provide a complete description of the spatial characteristics of the unsteady differential pressures. Consequently, the unsteady differential pressures were considered to be fully correlated in the analyses of buffet and buffeting. The use of fully correlated pressures in estimating the generalized aerodynamic forces for the analysis of buffeting yielded responses that exceeded those measured in flight and in the wind tunnel. To learn more about the spatial characteristics of the unsteady differential pressures, an available 16%, sting-mounted, F-18 wind-tunnel model was modified and tested in the Transonic Dynamics Tunnel (TDT) at the NASA Langley Research Center as part of the ACROBAT (Actively Controlled Response Of Buffet-Affected Tails) program. Surface pressures were measured at high angles of attack on flexible and rigid tails. Cross-correlation and cross-spectral analyses of the pressure time histories indicate that the unsteady differential pressures are not fully correlated. In fact, the unsteady differential pressure resemble a wave that travels along the tail. At constant angle of attack, the pressure correlation varies with flight speed.

Moses, Robert W.↗

NASA NTRSDate not supplied

Discriminative Methodology using the Unscented Transform in Special Euclidean Group SE(3) for Autonomous GN&C

Kalman filtering techniques usually compare a known dynamic model with some process noise to an updated, noisy measurement. Both sources of noise are generally assumed to be Gaussian and are filtered to obtain a more accurate estimate of the state of the system of interest. However, these methods assume that a relatively accurate dynamical model already exists. By including discriminative methodology to interpret differences in the predictive model and the measured states over time, an incorrect model can be corrected overtime, allowing a spacecraft to learn about its environment and adapt to changing conditions. In this paper, a discriminative unscented Kalman filter (DUKF) is introduced on the special Euclidean group SE(3) formulation framework. A spacecraft is initialized near a small body with no knowledge of its parameters, and using several consecutive conditional models the environment is studied and the filter is updated. The case study of a spacecraft in a slightly elliptical, inclined, relatively tight orbit around the irregular moon Phobos is simulated and discussed. The accuracy of this approach is quantifying the differences between the true state, the UKF with a poor model, and the DUKF.

GN&C↗

NASA NTRS1993

Wing design for a civil tiltrotor transport aircraft: A preliminary study

A preliminary study was conducted on the design of the wing-box structure for a civil tiltrotor transport aircraft. The wing structural weight is to be minimized subject to structural and aeroelastic constraints. The composite wing-box structure is composed of skin, stringers, ribs, and spars. The design variables include skin ply thicknesses and orientations and spar cap and stringer cross-sectional areas. With the total task defined, an initial study was conducted to learn more about the intricate dynamic and aeroelastic characteristics of the tiltrotor aircraft and their roles in the wing design. Also, some work was done on the wing finite-element modeling (via PATRAN) which would be used in structural analysis and optimization. Initial studies indicate that in order to limit the wing/rotor aeroelastic and dynamic interactions in the preliminary design, the cruise speed, rotor system, and wing geometric attributes must all be held fixed.

Rais-Rohani, Masoud↗

NASA NTRSDate not supplied

Operational Modal Analysis of the Artemis I Dynamic Rollout Test and Wet Dress Rehearsal

NASA has developed an expendable heavy lift launch vehicle capability, the Space Launch System (SLS), to support lunar and deep space exploration. The uncrewed Artemis I was the first flight of this new launch vehicle and tested critical systems for the upcoming crewed Artemis II flight to the moon. Accelerations were recorded at a multitude of locations on Artemis, the Mobile Launcher (ML), and the Crawler Transporter (CT)during the rollout of Artemis I from the Vehicle Assembly Building (VAB) to Launch Pad 39B March 2022 and is referred to as the Artemis I Dynamic Rollout Test (DRT). While Artemis I was at Launch Pad 39B, the Wet Dress Rehearsal (WDR) was performed to demonstrate launch readiness and acceleration measurements were also recorded. Finally, Artemis I rolled back from Launch Pad 39B to the VAB in April 2022, where acceleration measurements were also recorded and is referred to as the rollback portion of DRT. Because the forces during rollout and at the launch pad acting on Artemis I, the ML, and the CT are not directly measurable, Operational Modal Analysis (OMA) techniques, instead of traditional Experimental Modal Analysis (EMA) techniques, were used to identify modal characteristics. The OMA analysis of DRT and WDR directly builds upon the lessons learned from the OMA analysis of an earlier rollout of the ML from the VAB. DRT and WDR dynamic characteristics will be used to support SLS Integrated Modal Test finite element model correlation efforts and Exploration Ground System ML and CT finite element model verification and validation, which are part of the Building Block approach the Space Launch System program has implemented. The dynamic characteristics extracted from DRT as well as the rollout acceleration time histories themselves will be used in the development of generic rollout forcing functions that will provide refined estimates of the Artemis IV rollout forces, which will have the heavier and larger SLS Block 1B launch vehicle and Mobile Launcher 2 (ML-2). This paper briefly describes Artemis I, the ML, and the CT physical characteristics, DRT rollout/rollback and WDR data collection, the challenges in implementing OMA techniques due in part to the CT harmonics, and how these challenges were overcome to obtain the Artemis I DRT configuration and WDR configuration modal characteristics.

Apollo↗

NASA NTRSDate not supplied

Operational Modal Analysis of the Artemis I Dynamic Rollout Test and Wet Dress Rehearsal

Apollo↗

NASA NTRSDate not supplied

NASA’s Prototype Spectral Water Inversion Processor and Emulator (SWIPE): Towards Global Coastal and Inland Water Quality and Algal Biodiversity Monitoring

Degradation of Earth’s inland water resources due to anthropogenic perturbations and climate anomalies at both local and global scales continues to place human health at substantial risk. There is now a growing necessity to develop pragmatic approaches that allow timely and effective extrapolation of local processes, to spatially resolved global products, and to promote operational and sustainable resource policy management. This presentation will provide updates on NASA’s prototype open-source aquatic modeling platform, Spectral Water Inversion Processor and Emulator (SWIPE), which is a comprehensive, multi-faceted modeling platform for both forward and inverse modeling of diverse aquatic ecosystems from the benthos to top-of-atmosphere (TOA). SWIPE provides a cohesive application which leverages recent advancements in particle modeling, Big Data analytics, and machine learning to develop a high-fidelity synthetic training ground for sensitivity studies and algorithm development for multispectral or upcoming hyperspectral missions. Some of the prominent features of SWIPE to be discussed include: 1. Advanced hyperspectral modeling of globally diverse algal and non-algal particles using a novel two-layer coated sphere scattering model and radiative transfer modeling, 2. Massive, highly detailed synthetic spectral libraries of Analysis-Ready-Data (ARD) which include spectral libraries of particle microphysics, water biogeophysical and optical properties, as well as surface and TOA reflectances at 1 nm resolution, 3. An ensemble of pre-built analytic, machine learning, and deep learning inversion algorithms for various water quality and biodiversity related retrieval parameters and uncertainty quantification, 4. Sensor-agnostic water quality inversion at wide ranging spatial and spectral resolutions including a codebase for seamless application in the Google Earth Engine and NASA Earth Exchange (NEX) for planetary scale analysis. SWIPE will be a fully open-source platform based in python with comprehensive documentation, tutorials, and options for distributed computing on high performance computing clusters or on single, local machines. Further, we will discuss how we envision SWIPE contributing towards a global analysis of coastal and inland water quality dynamics.

top-of-atmosphere (TOA)↗

NASA NTRS2007

Inner Magnetospheric Electric Fields Derived from IMAGE EUV

The local and global patterns of plasmaspheric plasma transport reflect the influence of electric fields imposed by all sources in the inner magnetosphere. Image sequences of thermal plasma G:istribution obtained from the IMAGE Mission Extreme Ultraviolet Imager can be used to derive plasma motions and, using a magnetic field model, the corresponding electric fields. These motions and fields directly reflect the dynamic coupling of injected plasmasheet plasma and the ionosphere, in addition to solar wind and atmospheric drivers. What is being learned about the morphology of inner magnetospheric electric fields during storm and quite conditions from this new empirical tool will be presented and discussed.

Gallagher, D. L.↗