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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Communicating Metrics of Land Surface Temperature Variability Using Multi-sensor Machine Learning

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. Further, time series clustering approaches are examined with the objective of identifying key indicators of change in diurnal cycling and extremes on a continental scale. Communicating 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 Duffy↗
NASA NTRSDate not supplied

Holistic and Pragmatic Standards Processes Enable Interdisciplinary Science

Open, interdisciplinary science inevitably relies heavily on standards. Standards are those often unseen agreements that we take for granted when systems and processes are working fine. Yet standards work is perpetual, laborious, and sometimes contentious, especially for standards to work across diverse disciplines. Standards development, maintenance, and implementation is a complex, ongoing socio-technical process. NASA has developed a progressively open science policy and strategy that calls for the establishment of a data standards process reaching across the five diverse divisions of the Science Mission Directorate. This is a delicate exercise. We, therefore, seek to apply a holistic yet pragmatic approach to developing and maintaining a standards process. We adopt an ecological philosophy that focuses on the interactions within the data ecosystem and how standards facilitate those interactions. We couple high-level analysis with on the ground experimentation. We began by 1) mapping information ecosystem components (e.g. data centers, missions, services, protocols, users), 2)establishing how the components interact (e.g. sharing (meta)data, funding, personnel exchange), and 3) modelling system dynamics (e.g. creation of products from multiple data centers, redundant processes, shared services). The goal is to apply understanding of the ecosystem to real world applications (e.g. planning a new mission, implementing new policy requirements, improving process efficiency, etc.). We have also conducted studies of historical standardization efforts, documenting lessons learned and cautionary tales. We then contrast this more abstract work with real examples. We reviewed and assessed multiple existing standards development processes both within and external to NASA. We now work to implement an initial test process which can be further optimized. We seek to define a consistent approach for assigning persistent identifiers for research objects, especially for the purposes of citation. The experience from this relatively ‘simple’ test case adds a pragmatic perspective on how researchers and engineers actually work. This presentation will review the details of this methodology, our initial findings, and how they might apply to other interdisciplinary standardization efforts.

Mark A Parsons↗
NASA NTRSDate not supplied

Multidisciplinary Design Optimization and Analysis of an Open Rotor Stage: Part 1

Successful design of open rotor propulsors requires effective analysis across multiple disciplines, including aerodynamics, acoustics, and structures. A viable design must not only be efficient but must also produce an acceptable level of noise and meet all static and dynamic structural requirements. For design and optimization, this is especially challenging because running high fidelity analyses is resource-intensive, and optimizing a design may require many thousands of cases to be analyzed. For this reason, the NASA team has applied design methodology that utilizes low-cost aerodynamic methods, machine learning models, and high-fidelity analyses when necessary. This includes standard two-dimensional methods such as throughflow analysis and quasi-3D blade-to-blade CFD, as well as some newly developed methods. Optimization using 3D CFD is necessary to maximize performance, and this is considered as well. All optimizations are carried out subject to structural constraints evaluated using finite element analysis. Doing this accurately requires a robust trunnion design, capable of pitching the blade between cruise and takeoff conditions while maintaining acceptable factor of safety. Hot to cold analysis must also be applied in order to correctly determine the as-manufactured shape. For acoustics, the unsteady pressures on the blade surfaces must be predicted, and this can be done either through full-annulus unsteady CFD or through a nonlinear harmonic method (NLH). NLH can run much faster, allowing some acoustic considerations to be made earlier in the design process. The design process is ongoing, and this presentation will review the current status and planned next steps. This part of the talk will focus on aerodynamic performance and be followed by a talk on structures and acoustics.

Design↗
NASA NTRSDate not supplied

DeepONet-Assisted Optimization of Surface Topography for Transition Delay in a Mach 4.5 Boundary Layer

We use deep learning, an ensemble variational technique (EnVar), and direct numerical simulations(DNS) to design an optimal topography for a two-dimensional roughness element that delays the on-set of laminar-turbulent transition in a Mach 4.5 flat-plate boundary layer. Deep operator networks (DeepONets), which have the known ability to learn complex nonlinear operators within dynamical systems, are used for machine learning. For the baseline configuration of a smooth flat plate, the second-mode waves at the DNS inflow cause a quick nonlinear breakdown of the high-speed boundary layer within the computational domain. Results reported in the present study validate the ability of DeepONets to model the transition delay via a given topography of the roughness element. The computing cost to optimize the rough-ness element for minimal skin-friction drag is substantially lowered by the DeepONets-based reduced-order model. In comparison to the baseline method of EnVar optimization based on DNS alone, the DeepONets-based EnVar optimizer is able to delay transition past the outflow boundary of the computational domain while utilizing almost 5–6 times fewer DNS.

Machine Learning↗
NASA NTRS1979

The application of model-referenced adaptive control to robotic manipulators

The achievement of quality dynamic performance in manipulator systems is difficult using conventional control methods because of both the inherent geometric nonlinearities of these systems and the dependence of the system dynamics on the characteristics of manipulated objects. A model-referenced adaptive control law is developed for maintaining uniformly good performance over a wide range of motions and payloads. The effectiveness of the approach is demonstrated in several simulations and the system stability as a function of input is investigated. Also developed is a 'learning signal' approach designed to minimize initial transients arising from abrupt changes in the inertial payload.

Dubowsky, S.↗
NASA NTRS1992

Adding dynamic rules to self-organizing fuzzy systems

This paper develops a Dynamic Self-Organizing Fuzzy System (DSOFS) capable of adding, removing, and/or adapting the fuzzy rules and the fuzzy reference sets. The DSOFS background consists of a self-organizing neural structure with neuron relocation features which will develop a map of the input-output behavior. The relocation algorithm extends the topological ordering concept. Fuzzy rules (neurons) are dynamically added or released while the neural structure learns the pattern. The DSOFS advantages are the automatic synthesis and the possibility of parallel implementation. A high adaptation speed and a reduced number of neurons is needed in order to keep errors under some limits. The computer simulation results are presented in a nonlinear systems modelling application.

Buhusi, Catalin V.↗
NASA NTRS2015

Space Launch System Liftoff and Transition Aerodynamic Characterization in the NASA Langley 14- by 22-Foot Subsonic Wind Tunnel

A 1.75% scale force and moment model of the Space Launch System was tested in the NASA Langley Research Center 14- by 22-Foot Subsonic Wind Tunnel to quantify the aerodynamic forces that will be experienced by the launch vehicle during its liftoff and transition to ascent flight. The test consisted of two parts: the first was dedicated to measuring forces and moments for the entire range of angles of attack (0deg to 90deg) and roll angles (0 deg. to 360 deg.). The second was designed to measure the aerodynamic effects of the liftoff tower on the launch vehicle for ground winds from all azimuthal directions (0 deg. to 360 deg.), and vehicle liftoff height ratios from 0 to 0.94. This wind tunnel model also included a set of 154 surface static pressure ports. Details on the experimental setup, and results from both parts of testing are presented, along with a description of how the wind tunnel data was analyzed and post-processed in order to develop an aerodynamic database. Finally, lessons learned from experiencing significant dynamics in the mid-range angles of attack due to steady asymmetric vortex shedding are presented.

Pinier, Jeremy T.↗
NASA NTRS2015

Coupled Facility/Payload Vibration Modeling Improvements

A major phase of aerospace hardware verification is vibration testing. The standard approach for such testing is to use a shaker to induce loads into the payload. In preparation for vibration testing at NASA/GSFC there is an analysis to assess the responses of the payload. A new method of modeling the test is presented that takes into account dynamic interactions between the facility and the payload. This dynamic interaction has affected testing in the past, but been ignored or adjusted for during testing. By modeling the combination of the facility and test article (payload) it is possible to improve the prediction of hardware responses. Many aerospace test facilities work in similar way to those at NASA Goddard Space Flight Center. Lessons learned here should be applicable to other test facilities with similar setups.

Finite Element Analysis↗
NASA NTRSDate not supplied

DeepONet-Assisted Optimization of Surface Topography for Transition Delay in A Mach 4.5 Boundary Layer

We use deep learning, an ensemble variationaltechnique (EnVar), and direct numerical simulations(DNS) to design an optimal topography for a two-dimensional roughness element that delays the on-set of laminar-turbulent transition in a Mach 4.5 flat-plate boundary layer. Deep operator networks (Deep-ONets), which have the known ability to learn com-plex nonlinear operators within dynamical systems,are used for machine learning. For the baseline config-uration of a smooth flat plate, the second-mode wavesat the DNS inflow cause a quick nonlinear breakdownof the high-speed boundary layer within the computa-tional domain. Results reported in the present studyvalidate the ability of DeepONets to model the tran-sition delay via a given topography of the roughnesselement. The computing cost to optimize the rough-ness element for minimal skin-friction drag is substan-tially lowered by the DeepONets-based reduced-ordermodel. In comparison to the baseline method of EnVaroptimization based on DNS alone, the DeepONets-based EnVar optimizer is able to delay transition pastthe outflow boundary of the computational domainwhile utilizing almost 5–6 times fewer DNS.

Machine Learning↗
NASA NTRS1983

Identification and stochastic control of helicopter dynamic modes

A general treatment of parameter identification and stochastic control for use on helicopter dynamic systems is presented. Rotor dynamic models, including specific applications to rotor blade flapping and the helicopter ground resonance problem are emphasized. Dynamic systems which are governed by periodic coefficients as well as constant coefficient models are addressed. The dynamic systems are modeled by linear state variable equations which are used in the identification and stochastic control formulation. The pure identification problem as well as the stochastic control problem which includes combined identification and control for dynamic systems is addressed. The stochastic control problem includes the effect of parameter uncertainty on the solution and the concept of learning and how this is affected by the control's duel effect. The identification formulation requires algorithms suitable for on line use and thus recursive identification algorithms are considered. The applications presented use the recursive extended kalman filter for parameter identification which has excellent convergence for systems without process noise.

Molusis, J. A.↗
NASA NTRS1996

Overview of HATP Experimental Aerodynamics Data for the Baseline F/A-18 Configuration

Determining the baseline aerodynamics of the F/A-18 was one of the major objectives of the High-Angle-of-Attack Technology Program (HATP). This paper will review the key data bases that have contributed to our knowledge of the baseline aerodynamics and the improvements in test techniques that have resulted from the experimental program. Photographs are given highlighting the forebody and leading-edge-extension (LEX) vortices. Other data representing the impact of Mach and Reynolds numbers on the forebody and LEX vortices will also be detailed. The level of agreement between different tunnels and between tunnels and flight will be illustrated using pressures, forces, and moments measured on a 0.06-scale model tested in the Langley 7- by 10-Foot High Speed Tunnel, a 0.16-scale model in the Langley 30- by 60-Foot Tunnel, a full-scale vehicle in the Ames 80- by 120-Foot Wind Tunnel, and the flight F/A-18 High Alpha Research Vehicle (HARV). Next, creative use of wind tunnel resources that accelerated the validation of the computational fluid dynamics (CFD) codes will be described. Lastly, lessons learned, deliverables, and program conclusions are presented.

Hall, Robert M.↗
NASA NTRS1989

Development of an intelligent interface for adding spatial objects to a knowledge-based geographic information system

Earth Scientists lack adequate tools for quantifying complex relationships between existing data layers and studying and modeling the dynamic interactions of these data layers. There is a need for an earth systems tool to manipulate multi-layered, heterogeneous data sets that are spatially indexed, such as sensor imagery and maps, easily and intelligently in a single system. The system can access and manipulate data from multiple sensor sources, maps, and from a learned object hierarchy using an advanced knowledge-based geographical information system. A prototype Knowledge-Based Geographic Information System (KBGIS) was recently constructed. Many of the system internals are well developed, but the system lacks an adequate user interface. A methodology is described for developing an intelligent user interface and extending KBGIS to interconnect with existing NASA systems, such as imagery from the Land Analysis System (LAS), atmospheric data in Common Data Format (CDF), and visualization of complex data with the National Space Science Data Center Graphics System. This would allow NASA to quickly explore the utility of such a system, given the ability to transfer data in and out of KBGIS easily. The use and maintenance of the object hierarchies as polymorphic data types brings, to data management, a while new set of problems and issues, few of which have been explored above the prototype level.

Campbell, William J.↗
NASA NTRS1999

A New Way of Testing Large Thin Membrane, Optical Modal Testing of the 1/10th Scale Model of the NASA NAGT Sunshield

The Next Generation Space Telescope design includes a deployed sunshield to thermally insulate the telescope, keeping vital detector temperatures below 60 degrees Kelvin. The sunshield consists of four struts supporting four thin film membranes. Since it would be very difficult to test the full-scale sunshield on the ground due to its size and the 1-g environment, it is important to have accurate analytical models of the sunshield to analyze its affect on the observatory. A 1/10th-scale model of the sunshield has been manufactured in order to test the analytical modeling techniques used to predict the dynamic behavior of such large, lightweight structures. A modal survey test of the scale model has been performed in a vacuum environment, using a laser vibrometer to measure the mode shapes of the membranes. The presentation describes the setup and test procedures related to these tests and presents some of the experimental results obtained and the lessons learned.

Ross, Brian↗
NASA NTRS2016

Post-Flight Analysis of GPSR Performance During Orion Exploration Flight Test 1

On 5 December 2014, the first test flight of the Orion Multi-Purpose Crew Vehicle executed a unique and challenging flight profile including an elevated re-entry velocity and steeper flight path angle to envelope lunar re-entry conditions. A new navigation system including a single frequency (L1) GPS receiver was evaluated for use as part of the redundant navigation system required for human space flight. The single frequency receiver was challenged by a highly dynamic flight environment including flight above low Earth orbit, as well as single frequency operation with ionospheric delay present. This paper presents a brief description of the GPS navigation system, an independent analysis of flight telemetry data, and evaluation of the GPSR performance, including evaluation of the ionospheric model employed to supplement the single frequency receiver. Lessons learned and potential improvements will be discussed.

Barker, Lee↗
NASA NTRS2016

Understanding Oceanic Heavy Precipitation Using Scatterometer, Satellite Precipitation, and Reanalysis Products

The primary aim of this study is to understand the heavy precipitation events over Oceanic regions using vector wind retrievals from space based scatterometers in combination with precipitation products from satellite and model reanalysis products. Heavy precipitation over oceans is a less understood phenomenon and this study tries to fill in the gaps which may lead us to a better understanding of heavy precipitation over oceans. Various phenomenon may lead to intense precipitation viz. MJO (Madden-Julian Oscillation), Extratropical cyclones, MCSs (Mesoscale Convective Systems), that occur inside or outside the tropics and if we can decipher the physical mechanisms behind occurrence of heavy precipitation, then it may lead us to a better understanding of such events which further may help us in building more robust weather and climate models. During a heavy precipitation event, scatterometer wind observations may lead us to understand the governing dynamics behind that event near the surface. We hypothesize that scatterometer winds can observe significant changes in the near-surface circulation and that there are global relationships among these quantities. To the degree to which this hypothesis fails, we will learn about the regional behavior of heavy precipitation-producing systems over the ocean. We use a "precipitation feature" (PF) approach to enable statistical analysis of a large database of raining features.

Winds↗
NASA NTRS1979

System parameter adaptation via a learning procedure

The paper considers the problem of designing a learning control system (LCS) that is capable of meeting design requirements over many possible operating conditions of the plant by adjusting, in a prescribed manner, the feedforward and feedback gains of the plant. The approach utilizes the best features of two methods, gain scheduling and adaptive control. The LCS was implemented using two models, one representing the longitudinal dynamics and the other the lateral dynamics of the simulated plant. A block diagram illustrating the functional organization of an LCS is presented.

Mekel, R.↗
NASA NTRS1989

United States Air Force Academy get-away-special flexible beam experiment

The Department of Astronautics at the United States Air Force Academy is currently planning to fly an experiment in a NASA Get-Away-Special (GAS) canister. The experiment was named the flex beam experiment. The primary technical objective of the flex beam experiment is to measure the damping of a thin beam in the vacuum and zero G environment of space. By measuring the damping in space, it is hoped to determine the amount of damping the beam normally experiences due to the gravitational forces present on Earth. This will allow validation of models which predict the dynamics of thin beams in the space environment. The experiment will also allow the Academy to develop and improve its ability to perform experiments within the confines of a NASA GAS canister. Several experiments, of limited technical difficulty, were flown by the Academy. More complex experiments are currently planned and it is hoped to learn techniques with each space shuttle flight.

Bubb, Keith W.↗
NASA NTRS2019

Physically-Informed Artificial Neural Networks for Atomistic Modeling of Materials

A new approach is presented for the development of classical interatomic potentials using physically-informed neural networks (PINN) combined with an analytical bond-order atomic interaction model. Due to the strong physical underpinnings, the PINN potentials demonstrate much better transferability than the existing machine-learning potentials while drastically improving the accuracy in comparison with traditional potentials. PINN potentials can be constructed for both metallic and covalent materials in a unified manner. A number of applications of PINN potentials to large-scale molecular dynamics and Monte Carlo simulations and calculation of thermal and mechanical properties of diverse materials are demonstrated. Some of the specific materials systems include silicon and aluminum, as well as alloys and compounds. Computational aspects of PINN potentials are discussed and future developments in this field are outlined.

J Hickman↗