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NASA NTRSDate not supplied

A Data-Driven Reduced Order Model of an Isolated Rotor

There are numerous conceptual design stage rotorcraft analysis tasks which demand a high-fidelity and low cost method for rotor load distribution predictions. Considering Urban Air Mobility (UAM) vehicles aim to operate in close proximity to buildings and with unique rotor configurations, there is a significant challenge in quickly and accurately modeling rotors operating in complex, turbulent flow fields. One potential path for deriving a high-fidelity, low cost rotor model is with data-driven surrogate modeling. In this study, an initial investigation is taken to apply a proper orthogonal decomposition (POD) based reduced order model (ROM) for the purpose of pressure distribution prediction. In this study, a POD ROM was derived to produce distributed pressure predictions on rotor blades subjected to topology change due to variation in twist and taper ratio. Rotor twist was varied between 0◦, 10◦, 20◦, and 30◦ while taper ratio was varied between 1.0, 0.9, 0.8, and 0.7. All rotors consisted of a single blade. The POD ROM was validated for three demonstration cases; a high thrust rotor in hover, a low thrust rotor in hover, and a rotor in forward flight with a flight speed of M = 0.1. Results showed highly accurate distributed load predictions could be achieved at minimal computational cost. Computational cost for hovering blade surface pressure modeling was reduced from 12 hours on 440 cores to 10−5 seconds on a single core. For blade in forward flight cost was reduced from 20 hours on 440 cores to 0.6 seconds on a single core. For cases of high thrust and low thrust rotors, POD ROM was used to undergo a design optimization of the rotor such that figure of merit was maximized. Total optimization time for each case was 1 minute.

Data-Driven↗

NASA NTRS2017

Initial Results of Heuristic Guided Orbit Selection for a Low Frequency Radio Interferometric Spacecraft Constellation

A constellation of radio telescope spacecraft can leverage interferometry to accurately image distant objects throughout the universe, but mission design must balance among many interrelated constraints. In particular, the number of craft and the selection of time-varying orbital parameters play a pivotal role in determining what interferometric baselines are feasible with respect to different targets, and thus drives the breadth and quality of data available to the constellation. The large combinatorial orbit configuration space and competing concerns present a challenging problem that is not well addressed by traditional mission design processes. This paper describes application of automated optimization methods to help direct mission design effort to the most promising dynamic constellation geometries: those that achieve broad interferometric coverage but remain cost-effective and resilient to failures. Several automatic heuristic-driven optimization algorithms representing complementary search strategies were created to explore among concrete constellation configuration plans. Evaluation of each candidate constellation plan was accelerated by efficiently combining precomputed caches of orbital and interferometric data. Results indicate that leveraging automated optimization for constellation mission design is both practical and illuminating: generated solutions provided both evidence for existing design intuitions as well as fresh insights into novel configurations.

Hernandez, Sonia↗

NASA NTRS2021

Machine Learning the COSMO Model for Predicting Thermodynamics of Electrolyte Mixtures

Bottom-up design of electrolyte mixtures for battery systems requires predicting macro thermodynamic properties from molecular constituents. For instance, molten salt electrolyte batteries require conditions far above room temperature to operate. Therefore, discovering mixtures with increasingly lower eutectic melting points is desirable. A model that can approximate chemical activity is a valuable tool to search through the vast compositional design space. Machine learning can predict properties of materials such as vibrational free energies, electronic energy gaps, and thermal conductivities. Moreover, they can learn physical models such as interatomic potentials. The COSMO-SAC model uses theory and empirical parameterization to predict liquid-vapor and liquid-solid properties using first-principles calculations. However, obtaining activity coefficients required for parameterizing the COSMO-SAC model is costly and limited to a select chemical space. In this work, we explored if machine learning methods could improve the COSMO-SAC model and bridge density functional theory calculations to liquid phase thermodynamic properties. Our data-driven approach uses existing databases for sigma-profiles of organic solvents and reconciles their methodological differences via ensemble averaging. First, an optimal machine learning model is constructed for each dataset. Our machine learning algorithms use the sigma-profile as an input feature to predict binary mixtures' activity coefficients using multi-output regression. Each dataset uses different choices of functionals, methods, and basis sets. Therefore, our ensemble model attempts to predict corrected activity coefficients given the combination of all the model outputs. The activity coefficients used for training are generated using the COSMO-SAC model. This approach enables the extraction of meaningful information from the existing datasets to improve the COSMO-SAC model for obtaining thermodynamic properties of electrolyte mixtures. With the liquid phase activities, we can identify electrolyte mixtures that meet desired phase equilibria conditions.

Thermodynamics↗

NASA NTRS1985

The Accuracy of Radio Interferometric Measurements of Earth Rotation

The accuracy of very long base interferometry earth rotation (UT1) measurements is examined by intercomparing TEMPO and POLARIS data for 1982 and the first half of 1983. None of these data are simultaneous, and so a proper intercomparison requires accounting for the scatter introduced by the rapid, unpredictable, UT1 variations driven by exchanges of angular momentum with the atmosphere. A statistical model of these variations, based on meteorological estimates of the Atmospheric Angular Momentum is derived, and the optimal linear (Kalman) smoother for this model is constructed. The scatter between smoothed and independent raw data is consistent with the residual formal errors, which do not depend upon the actual scatter of the UT1 data. This represents the first time that an accurate prediction of the scatter between UT1 data sets were possible.

Eubanks, T. M.↗

NASA NTRS2002

Analysis of Salinity Intrusion in the San Francisco Bay-Delta using a GA- Optimized Neural Net, and Application of the Model to Prediction in the Elkhorn Slough Habitat

The San Francisco Bay Delta is a large hydrodynamic complex that incorporates the Sacramento and San Joaquin Estuaries, the Burman Marsh, and the San Francisco Bay proper. Competition exists for the use of this extensive water system both from the fisheries industry, the agricultural industry, and from the marine and estuarine animal species within the Delta. As tidal fluctuations occur, more saline water pushes upstream allowing fish to migrate beyond the Burman Marsh for breeding and habitat occupation. However, the agriculture industry does not want extensive salinity intrusion to impact water quality for human and plant consumption. The balance is regulated by pumping stations located alone the estuaries and reservoirs whereby flushing of fresh water keeps the saline intrusion at bay. The pumping schedule is driven by data collected at various locations within the Bay Delta and by numerical models that predict the salinity intrusion as part of a larger model of the system. The Interagency Ecological Program (IEP) for the San Francisco Bay/Sacramento-San Joaquin Estuary collects, monitors, and archives the data, and the Department of Water Resources provides a numerical model simulation (DSM2) from which predictions are made that drive the pumping schedule. A problem with this procedure is that the numerical simulation takes roughly 16 hours to complete a C:~ prediction. We have created a neural net, optimized with a genetic algorithm, that takes as input the archived data from multiple stations and predicts stage, salinity, and flow at the Carquinez Straits (at the downstream end of the Burman Marsh). This model seems to be robust in its predictions and operates much faster than the current numerical DSM2 model. Because the system is strongly tidal driven, we used both Principal Component Analysis and Fast Fourier Transforms to discover dominant features within the IEP data. We then filtered out the dominant tidal forcing to discover non-primary tidal effects, and used this to enhance the neural network by mapping input-output relationships in a more efficient manner. Furthermore, the neural network implicitly incorporates both the hydrodynamic and water quality models into a single predictive system. Although our model has not yet been enhanced to demonstrate improve pumping schedules, it has the possibility to support better decision-making procedures that may then be implemented by State agencies if desired. Our intention is now to use this model in the smaller Elkhorn Slough complex near Monterey Bay where no such hydrodynamic model currently exists. At the Elkhorn Slough, we are fusing the neural net model of tidally-driven flow with in situ flow data and airborne and satellite remote sensation data. These further constrain the behavior of the model in predicting the longer-term health and future of this vital estuary.

Thompson, David E.↗

NASA NTRS1986

Preliminary control law and hardware designs for a ride quality augmentation system for commuter aircraft. Phase 2

The continued investigation of the design of Ride Quality Augmentation Systems (RQAS) for commuter aircraft is described. The purpose of these RQAS is the reduction of the vertical and lateral acceleration response of the aircraft due to atmospheric turbulence by the application of active control. The current investigations include the refinement of the sample data feedback control laws based on the control-rate-weighting and output-weighting optimal control design techniqes. These control designs were evaluated using aircraft time simulations driven by Dryden spectra turbulence. Fixed gain controllers were tested throughout the aircrft operating envelope. The preliminary design of the hardware modifications necessary to implement and test the RQAS on a commuter aircraft is included. These include a separate surface elevator and the flap modifications to provide both direct lift and roll control. A preliminary failure mode investigation was made for the proposed configuration. The results indicate that vertical acceleration reductions of 45% and lateral reductions of more than 50% are possible. A fixed gain controller appears to be feasible with only minor response degradation.

Davis, D. J.↗

NASA NTRS2019

Numerical Investigation and Optimization of a Flushwall Injector for Scramjet Applications at Hypervelocity Flow Conditions

An investigation utilizing Reynolds-averaged simulations (RAS) was performed in order to demonstrate the use of design and analysis of computer experiments (DACE) methods in Sandia’s DAKOTA software package for surrogate modeling and optimization. These methods were applied to a flow- path fueled with an interdigitated flushwall injector suitable for scramjet applications at hyper- velocity conditions and ascending along a constant dynamic pressure flight trajectory. The flight Mach number, duct height, spanwise width, and injection angle were the design variables selected to maximize two objective functions: the thrust potential and combustion efficiency. Because the RAS of this case are computationally expensive, surrogate models are used for optimization. To build a surrogate model a RAS database is created. The sequence of the design variables comprising the database were generated using a Latin hypercube sampling (LHS) method. A methodology was also developed to automatically build geometries and generate structured grids for each design point. The ensuing RAS analysis generated the simulation database from which the two objective functions were computed using a one-dimensionalization (1D) of the three-dimensional simulation data. The data were fitted using four surrogate models: an artificial neural network (ANN), a cubic polynomial, a quadratic polynomial, and a Kriging model. Variance-based decomposition showed that both objective functions were primarily driven by changes in the duct height. Multiobjective design optimization was performed for all four surrogate models via a genetic algorithm method. Optimal solutions were obtained at the upper and lower bounds of the flight Mach number range. The Kriging model predicted an optimal solution set that exhibited high values for both objective functions. Additionally, three challenge points were selected to assess the designs on the Pareto fronts. Further sampling among the designs of the Pareto fronts may be required to lower the surrogate model errors and perform more accurate surrogate-model-based optimization.

Shenoy, Rajiv R.↗

NASA NTRS2002

Investigation and Development of Data-Driven D-Region Model for HF Systems Impacts

Space Environment Corporation (SEC) and RP Consultants (RPC) are to develop and validate a weather-capable D region model for making High Frequency (HF) absorption predictions in support of the HF communications and radar communities. The weather-capable model will assimilate solar and earth space observations from NASA satellites. The model will account for solar-induced impacts on HF absorption, including X-rays, Solar Proton Events (SPE's), and auroral precipitation. The work plan includes: I . Optimize D-region model to quickly obtain ion and electron densities for proper HF absorption calculations. 2. Develop indices-driven modules for D-region ionization sources for low, mid, & high latitudes including X-rays, cosmic rays, auroral precipitation, & solar protons. (Note: solar spectrum & auroral modules already exist). 3. Setup low-cost monitors of existing HF beacons and add one single-frequency beacon. 4. Use PENEX HF-link database with HF monitor data to validate D-region/HF absorption model using climatological ionization drivers. 5. Develop algorithms to assimilate NASA satellite data of solar, interplanetary, and auroral observations into ionization source modules. 6. Use PENEX HF-link & HF-beacon data for skill score comparison of assimilation versus climatological D-region/HF absorption model. Only some satellites are available for the PENEX time period, thus, HF-beacon data is necessary. 7. Use HF beacon monitors to develop HF-link data assimilation algorithms for regional improvement to the D-region/HF absorption model.

Eccles, J. V.↗

NASA NTRS2019

Numerical Investigation and Optimization of a Flushwall Injector for Scramjet Applications at Hypervelocity Flow Conditions

An investigation utilizing Reynolds-averaged simulations (RAS) was performed in order to find optimal designs for an interdigitated flushwall injector suitable for scramjet applications at hypervelocity conditions. The flight Mach number, duct height, spanwise width, and injection angle were the design variables selected to maximize two objective functions: the thrust potential and combustion efficiency. A Latin hypercube sampling design-of-experiments method was used to select design points for RAS. A methodology was developed that automated building geometries and generating grids for each design. The ensuing RAS analysis generated the performance database from which the two objective functions of interest were computed using a one-dimensional performance utility. The data were fitted using four surrogate models: an artificial neural network (ANN) model, a cubic polynomial, a quadratic polynomial, and a Kriging model. Variance-based decomposition showed that both objective functions were primarily driven by changes in the duct height. Multiobjective design optimization was performed for all four surrogate models via a genetic algorithm method. Optimal solutions were obtained at the upper and lower bounds of the flight Mach number range. The Kriging model obtained an optimal solution set that predicted high values for both objective functions. Additionally, three challenge points were selected to assess the designs on the Pareto fronts. Further sampling among the designs of the Pareto fronts are required in order to lower the errors and perform more accurate surrogate-based optimization. sed optimization.

Shenoy, Rajiv R.↗

NASA NTRSDate not supplied

James Webb Space Telescope Navigation Optimization Challenges

This paper details the orbit determination, solar radiation pressure (SRP) modeling, and station-keeping maneuver planning for the NASA James Webb Space Telescope during the routine science phase of the mission. The complexities of SRP modeling driven by the vehicle’s large area, attitude profile, and attitude constraints for maneuver execution entail unique challenges for spaceflight navigation. The techniques utilized to combine predictive attitude and maneuver targeting modeling were refined using the experiences and data accumulated during and following the commissioning phase. The Navigation Team at NASA Goddard Space Flight Center’s Flight Dynamics Facility responded to these challenges and implemented methods for trajectory optimization and improving maneuver efficiency.

Flight Dynamics↗

NASA NTRSDate not supplied

James Webb Space Telescope Navigation Optimization Challenges

Flight Dynamics↗

NASA NTRS2001

Properties of the Water Column and Bottom Derived from AVIRIS Data

Using AVIRIS data as an example, we show in this study that the optical properties of the water column and bottom of a large, shallow area can be adequately retrieved using a model-driven optimization technique. The simultaneously derived properties include bottom depth, bottom albedo, and water absorption and backscattering coefficients, which in turn could be used to derive concentrations of chlorophyll, dissolved organic matter, and suspended sediments. The derived bottom depths were compared with a bathymetry chart and a boat survey and were found to agree very well. Also, the derived bottom-albedo image shows clear spatial patterns, with end members consistent with sand and seagrass. The image of absorption and backscattering coefficients indicates that the water is quite horizontally mixed. These results suggest that the model and approach used work very well for the retrieval of sub-surface properties of shallow-water environments even for rather turbid environments like Tampa Bay, Florida.

Lee, Zhong-Ping↗

NASA NTRS2013

Science Opportunity Analyzer (SOA) Version 8

SOA allows scientists to plan spacecraft observations. It facilitates the identification of geometrically interesting times in a spacecraft s orbit that a user can use to plan observations or instrument-driven spacecraft maneuvers. These observations can then be visualized multiple ways in both two- and three-dimensional views. When observations have been optimized within a spacecraft's flight rules, the resulting plans can be output for use by other JPL uplink tools. Now in its eighth major version, SOA improves on these capabilities in a modern and integrated fashion. SOA consists of five major functions: Opportunity Search, Visualization, Observation Design, Constraint Checking, and Data Output. Opportunity Search is a GUI-driven interface to existing search engines that can be used to identify times when a spacecraft is in a specific geometrical relationship with other bodies in the solar system. This function can be used for advanced mission planning as well as for making last-minute adjustments to mission sequences in response to trajectory modifications. Visualization is a key aspect of SOA. The user can view observation opportunities in either a 3D representation or as a 2D map projection. Observation Design allows the user to orient the spacecraft and visualize the projection of the instrument field of view for that orientation using the same views as Opportunity Search. Constraint Checking is provided to validate various geometrical and physical aspects of an observation design. The user has the ability to easily create custom rules or to use official project-generated flight rules. This capability may also allow scientists to easily assess the cost to science if flight rule changes occur. Data Output allows the user to compute ancillary data related to an observation or to a given position of the spacecraft along its trajectory. The data can be saved as a tab-delimited text file or viewed as a graph. SOA combines science planning functionality unique to both JPL and the sponsoring spacecraft. SOA is able to ingest JPL SPICE Kernels that are used to drive the tool and its computations. A Percy search engine is then included that identifies interesting time periods for the user to build observations. When observations are then built, flight-like orientation algorithms replicate spacecraft dynamics to closely simulate the flight spacecraft s dynamics. SOA v8 represents large steps forward from SOA v7 in terms of quality, reliability, maintainability, efficiency, and user experience. A tailored agile development environment has been built around SOA that provides automated unit testing, continuous build and integration, a consolidated Web-based code and documentation storage environment, modern Java enhancements, and a focus on usability

Witoff, Robert J.↗

NASA NTRS1995

Mississippi State University Center for Air Sea Technology FY95 Research Program

The Mississippi State University (MSU) Center for Air Sea Technology (CAST) evolved from the Institute for Naval Oceanography's (INO) Experimental Center for Mesoscale Ocean Prediction (ECMOP) which was started in 1989. MSU CAST subsequently began operation on 1 October 1992 under an Office of Naval Research (ONR) two-year grant which ended on 30 September 1994. In FY95 MSU CAST was successful in obtaining five additional research grants from ONR, as well as several other research contracts from the Naval Oceanographic Office via NASA, the Naval Research Laboratory, the Army Corps of Engineers, and private industry. In the past, MSU CAST technical research and development has produced tools, systems, techniques, and procedures that improve efficiency and overcome deficiency for both the operational and research communities residing with the Department of Defense, private industry, and university ocean modeling community. We continued this effort with the following thrust areas: to develop advanced methodologies and tools for model evaluation, validation and visualization, both oceanographic and atmospheric; to develop a system-level capability for conducting temporally and ; spatially scaled ocean simulations driven by or are responsive to ocean models, and take into consideration coupling to atmospheric models; to continue the existing oceanographic/atmospheric data management task with emphasis on distributed databases in a network environment, with database optimization and standardization, including use of Mosaic and World Wide Web (WWW) access; and to implement a high performance parallel computing technology for CAST ocean models

Yeske, Lanny↗

NASA NTRSDate not supplied

Natural Language Processing Analysis of Notices to Airmen for Air Traffic Management Optimization

With new emerging technologies in the field of NLP, we explore their applications to digitize and analyze heritage Air Traffic Management (ATM) documents for planning and optimizing airspace operations. Specifically, this research focuses on harvesting semi-structured or un-structured information contained in Notices to Airmen (NOTAMs). Using NLP and other advanced data analytics, we will construct a data-driven framework which facilitates finding language patterns and the use of pretrained language models for classification and extraction of useful airspace constraints and restrictions. These may lead to tools that assist airspace users in understanding the constraints more efficiently, contributing to better route planning and safer execution. This paper explores three workflows entailing different NLP tasks. First, unsupervised techniques like word embedding and topic modeling are used for pattern finding and document classification. Second, a dataset is created by extracting information from the semi-structured NOTAM format as metadata for categorizing, visualizing, and extracting key entities driving NOTAM content. Third, modern pre-built deep learning based transformer models such as BERT, RoBERTa, and XLNet are evaluated on the question answering task, an even more robust approach to information extraction, as well as their respective fine-tuning tasks. In this work we include various performance metrics for the trained models to evaluate both accuracy and precision and we show that the models can be generalized for their respective tasks. The research work developed shows promise in uncovering trends in digital NOTAMs in the NAS and also offers a new framework for digitizing and inferring insights from free-form legacy NOTAMs, that are yet to be digitized.

Natural Language Processing↗

NASA NTRSDate not supplied

Natural Language Processing (NLP) Analysis of NOTAMs for Air Traffic Management Optimization

Natural Language Processing↗

NASA NTRSDate not supplied

Use NASA Earthdata in the Cloud

With the impending arrival of new, high-data-volume Earth observation missions, NASA’s ability to effectively ingest, process, and archive large amounts of data requires the most cost-effective, flexible, and scalable data-management architectures and technologies. To meet these demands, NASA's Earth Science Data Systems (ESDS) Program is implementing a strategic vision to develop and operate multiple components of NASA's Earth Observing System Data and Information System (EOSDIS) in a commercial cloud environment. As more earth observing data is moved into the cloud, NASA EOSDIS wants to share our reasoning for moving data into the commercial cloud, as well as demonstrate how end-users can take advantage of both data in the cloud and cloud-deployed services. This presentation explains the enterprise reasons for moving data into the cloud, specifically the scalability of cloud systems, the flexibility to pivot to the most optimal language/system/architecture, and the reduced redundancy of a common architecture. Then we primarily focus on the user-driven reasons for moving to the cloud. Equal access to high performance computing helps all parties, especially users that don't have access to their own on-premise computing infrastructure. Rapid deployment allows users to spin up their own systems without needing the extensive platform previously required for large scale development. Cost effectiveness allows users a wide breadth of computing options, from super computer ability to small scale development, which can help underserved communities. Finally we link to internal resources that users can utilize to begin their cloud based development journey without having to be an expert in the field.

Nicholas Doty↗

NASA NTRS1991

Hypersonic waverider analysis - A comparison of numerical and experimental results

In this study two waverider configurations are analyzed numerically and experimentally. The geometries are designed using an inverse design and optimization code with the optimization procedure driven by maximized L/D and limited by geometric constraints. Euler and Navier-Stokes numerical simulations are performed with a partially flux-split finite-difference code developed at NASA Ames, F3D. Experimental data is obtained at NASA Langley in the Unitary Plan Wind Tunnel (UPWT) and the 20-Inch Mach 6 wind tunnel for flow visualization, force/moment, and surface pressure measurements at both on- and off-design conditions. Euler simulations are compared with analytic results obtained from conical flow theory, and Navier-Stokes simulations are compared with experimental results both qualitatively and quantitatively for on- and off-design conditions.

Jones, K. D.↗