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NASA NTRS2019

Enabling Real-time Multi-messenger Astrophysics Discoveries with Deep Learning

Multi-messenger astrophysics is a fast-growing, interdisciplinary field that combines data, which vary in volume and speed of data processing, from many different instruments that probe the Universe using different cosmic messengers: electromagnetic waves, cosmic rays, gravitational waves and neutrinos. In this Expert Recommendation, we review the key challenges of real-time observations of gravitational wave sources and their electromagnetic and astroparticle counterparts, and make a number of recommendations to maximize their potential for scientific discovery. These recommendations refer to the design of scalable and computationally efficient machine learning algorithms; the cyber-infrastructure to numerically simulate astrophysical sources, and to process and interpret multi-messenger astrophysics data; the management of gravitational wave detections to trigger real-time alerts for electromagnetic and astroparticle follow-ups; a vision to harness future developments of machine learning and cyber-infrastructure resources to cope with the big-data requirements; and the need to build a community of experts to realize the goals of multi-messenger astrophysics.

E A Huerta↗

NASA NTRS1993

Design and application of electromechanical actuators for deep space missions

The annual report Design and Application of Electromechanical Actuators for Deep Space Missions is presented. The reporting period is 16 Aug. 1992 to 15 Aug. 1993. However, the primary focus will be work performed since submission of our semi-annual progress report in Feb. 1993. Substantial progress was made. We currently feel confident in providing guidelines for motor and control strategy selection in electromechanical actuators to be used in thrust vector control (TVC) applications. A small portion was presented in the semi-annual report. At this point, we have implemented highly detailed simulations of various motor/drive systems. The primary motor candidates were the brushless dc machine, permanent magnet synchronous machine, and the induction machine. The primary control implementations were pulse width modulation and hysteresis current control. Each of the two control strategies were applied to each of the three motor choices. With either pulse width modulation or hysteresis current control, the induction machine was always vector controlled. A standard test position command sequence for system performance evaluation is defined. Currently, we are gathering all of the necessary data for formal presentation of the results. Briefly stated for TVC application, we feel that the brushless dc machine operating under PWM current control is the best option. Substantial details on the topic, with supporting simulation results, will be provided later, in the form of a technical paper prepared for submission and also in the next progress report with more detail than allowed for paper publication.

Haskew, Tim A.↗

NASA NTRS1992

Dynamic simulation of compressor and gas turbine performance

Dynamic simulation of compressor and gas turbine performance is useful in a variety of design, analysis, and test applications. Mathematical models employing estimated or experimentally-derived component characteristics have been widely used, especially for near-design-point studies of acceleration and deceleration transients and control strategies. With recent progress in simulation methods and computational power, it has become possible to build models with detail at the stage level, and with fundamental fluid mechanics input. Such models are more useful because of the ability to study the details of inter-component flow property behavior during machine transients, even including stall and surge. Dynamic performance simulation models are discussed with emphasis on the fundamental principles of the models and the methods used to represent component and stage flow characteristics. Results of several simulations of the dynamic behavior of multistage compressors are shown with comparisons to experimental data. Possibilities for advanced computational techniques for near-real-time simulations of compressors and gas turbines are reviewed.

Walter F O'Brien↗

NASA NTRS1997

Automating the Process of Optimization in Spacecraft Design

Spacecraft design optimization is a difficult problem, due to the complexity of optimization cost surfaces, and human expertise in optimization that is necessary in order to achieve good results. In this paper, we propose the use of a set of generic, metaheuristic optimization algorithms (e.g., generic algorithms, simulated annealing), which is configured for a particular optimization problem by an adaptive problem solver based on artificial intelligence and machine learning techniques. We describe work in progress on OASIS, a system for adaptive problem solving based on these principles.

optimization↗

NASA NTRSDate not supplied

Dense Feature Tracking of Atmospheric Winds with Deep Optical Flow

Atmospheric winds are a key physical phenomenon impacting natural hazards, energy transport, ocean currents, large-scale circulation, and ecosystem fluxes. Observing winds is a complex process and presents a large gap in NASA’s Earth Observation System. Atmospheric motion vectors (AMVs) aim to fill this gap by making numerical estimates of cloud movement between sequences of multi-spectral satellite images, tracking clouds and water vapor. Recent imaging hardware and software advancements have enabled the use of numerical optical flow techniques to produce accurate and dense vector fields outperforming traditional methods. This work presents WindFlow as the first machine learning based system for feature tracking atmospheric motion using optical flow. Due to the lack of large-scale satellite-based observations, we leverage high-resolution numerical simulations from NASA's GEOS-5 Nature Run to perform supervised learning and transfer to satellite images. We demonstrate that our approach using deep learning based optical flow scales to ultra-high-resolution images of size 2881x5760 with less than 1 m/s bias and 2.5 m/s average error. Four network and learning architectures are compared and it is found that recurrent all-pairs field transforms (RAFT) produces the lowest errors on all metrics for wind speed and direction. Results on held out numerical outputs shows RAFT's good performance in each of the spatial, temporal, and physical dimensions. A comparison between WindFlow and an operational AMV product against rawinsonde observations show that RAFT transfers across simulations and thermal infrared satellite observations. This work shows that machine learning based optical flow is an efficient approach to generating robust feature tracking for AMVs consistently over large regions.

Atmospheric winds↗

NASA NTRS2024

Machine Learning for the Prediction of Local Asteroid Damages

Risk assessment studies of local asteroid hazards traditionally simulate the physics of meteors with engineering models tailored to analyze tens-of-millions of scenarios. However, these simplified approaches still need to solve time-dependent ODEs to model the entry process and the resulting ground damage. With a computational cost of O(0.01 CPU.s) per scenario, simulating these large numbers of potential entry conditions in risk assessment studies can take several days on local computers. To improve computational efficiency, we propose in this paper an orthogonal approach based on machine learning models to predict the size of damaged areas given a list of entry parameters. We train 5 machine learning methods and compare the predictions to the outputs of the PAIR model, first only with primitive entry condition variables, and then with more advanced features. We find that complex models like neural networks are well-suited to estimate blast hazards, while simpler linear models can accurately assess thermal damage. For both types of hazards, the radii of damaged areas can be predicted with around 10% average errors and a coefficient of determination (R2) of 0.99. The CPU time is decreased by a factor O(10 3 ) compared to the PAIR model, which enables the simulation of millions of scenarios in minutes, on a local computer. We then use the same machine learning approaches for a classification task where the models are trained to predict if an asteroid will produce a given level of damage. Results show that complex models like the gradient boosting classifier and the neural network can perform this task with 98% accuracy. Beyond surrogate models, we finally incorporate the machine learning algorithms to the state-of-the-art Shapley sensitivity analysis and present a ranking of the entry parameters based on their contributions to ground damages.

SMD↗

NASA NTRS1992

Meso-beta scale numerical simulation studies of terrain-induced jet streak mass/momentum perturbations

Work performed during the report period is summarized. The first numerical experiment which was performed on the North Carolina Supercomputer Center's CRAY-YMP machine during the second half of FY92 involved a 36 hour simulation of the CCOPE case study. This first coarse-mesh simulation employed the GMASS model with a 178 x 108 x 32 matrix of grid points spaced approximately 24 km apart. The initial data was comprised of the global 2.5 x 2.5 degree analyses as well as all available North American rawinsonde data valid at 0000 UTC 11 July 1981. Highly-smoothed LFM-derived terrain data were utilized so as to determine the mesoscale response of the three-dimensional atmosphere to weak terrain forcing prior to including the observed highly complex terrain of the northern Rocky Mountain region. It was felt that the model should be run with a spectrum of terrain geometries, ranging from observed complex terrain to no terrain at all, to determine how crucial the terrain was in forcing the mesoscale phenomena. Both convection and stratiform (stable) precipitation were not allowed in this simulation so that their relative importance could be determined by inclusion in forth-coming simulations. A full suite of planetary boundary layer forcing was allowed in the simulation, including surface sensible and latent heat fluxes employing the Blakadar PBL formulation. The details of this simulation, which in many ways could be considered the control simulation, including the important synoptic-scale, meso-alpha scale, and meso-beta scale circulations is described. These results are compared to the observations diagnosed by Koch and his colleagues as well as hypotheses set forth in the project proposal for terrain-influences upon the jet stream and their role in the generation of mesoscale wave phenomenon. The fundamental goal of the analyses being the discrimination among background geostrophic adjustment, terrain influences, and shearing instability in the initiation and maintainance of mesoscale internal wave phenomena. Based upon these findings, FY93 plans are discussed. A review of linear theory and theoretical modeling of a geostrophic zonal wind anomaly is included.

Lin, Yuh-Lang↗

NASA NTRS1980

The role of the real-time simulation facility, SIMFAC, in the design, development and performance verification of the Shuttle Remote Manipulator System (SRMS) with man-in-the-loop

The SIMFAC has played a vital role in the design, development, and performance verification of the shuttle remote manipulator system (SRMS) to be installed in the space shuttle orbiter. The facility provides for realistic man-in-the-loop operation of the SRMS by an operator in the operator complex, a flightlike crew station patterned after the orbiter aft flight deck with all necessary man machine interface elements, including SRMS displays and controls and simulated out-of-the-window and CCTV scenes. The characteristics of the manipulator system, including arm and joint servo dynamics and control algorithms, are simulated by a comprehensive mathematical model within the simulation subsystem of the facility. Major studies carried out using SIMFAC include: SRMS parameter sensitivity evaluations; the development, evaluation, and verification of operating procedures; and malfunction simulation and analysis of malfunction performance. Among the most important and comprehensive man-in-the-loop simulations carried out to date on SIMFAC are those which support SRMS performance verification and certification when the SRMS is part of the integrated orbiter-manipulator system.

Mccllough, J. R.↗

NASA NTRSDate not supplied

A Machine Learning Model for Solar Sail Shape Reconstruction Using Flight Data

Solar sail deformation leads to disturbance torques from solar radiation pressure, driving performance requirements for momentum management systems. For the Solar Cruiser technology demonstrator mission, we have developed a model leveraging neural network-based machine learning to derive sail shape characteristics. The model uses torque and attitude telemetry simulated from a reduced-order tensor model of the deformed sail mesh over a characterization sequence. The machine learning model predicts sail boom deflection with comparable accuracy to that of an onboard context camera. This model can discover sail shape with no additional mass or data downlink requirements, allowing for validation of sail force modeling assumptions using in flight data. The results from the project hold promise for the further implementation of machine learning techniques in solar sail telemetry analysis and control.

solar sail↗

NASA NTRSDate not supplied

A Machine Learning Model for Solar Sail Shape Reconstruction Using Flight Data

solar sail↗

NASA NTRSDate not supplied

Comparison of Entry Descent and Landing Aerodynamic Databases with Uncertainty Quantification Developed Using Machine Learning Techniques

When developing the aerodynamic databases for use in trajectory simulations, it is important to develop a system of metrics to qualify which aerodynamic models are best to use. Since aerodynamics are just one input into trajectory simulations, the results of these simulations do not reflect on the quality of the aerodynamic database used. This means that aerodynamic database comparisons must be done offline. While traditional metrics that focus on mean/nominal predictions are a good first step, more robust estimates of the prediction interval become important as more focused uncertainty models are developed. We explore the limitations of evaluating aerodynamic models based purely on nominal-centered response surfaces. Before elaborating and evaluating metrics based on distributed models, the value of evaluating prediction interval and confidence interval are discussed to conclude that prediction intervals are more relevant to the use of trajectory analysis. Several metrics to evaluate the prediction interval are introduced with a focus on the standard calibration metric. Finally, we compare candidate models using both mean and distributed metrics. A finalized candidate model developed using state of the art machine learning methods is compared to a baseline model developed using traditional aerodynamic database modeling techniques.

Aerodynamic Database↗

NASA NTRS1987

The cortex transform - Rapid computation of simulated neural images

With a goal of providing means for accelerating the image processing, machine vision, and testing of human vision models, an image transform was designed, which makes it possible to map an image into a set of images that vary in resolution and orientation. Each pixel in the output may be regarded as the simulated response of a neuron in human visual cortex. The transform is amenable to a number of shortcuts that greatly reduce the amount of computation.

Watson, Andrew B.↗

NASA NTRS1967

Man-computer roles in space navigation and guidance, phase I

Estimated man-machine requirement computations for space navigation and guidance

COMPUTER SIMULATION↗

NASA NTRS1979

Lewis Research Center studies of multiple large wind turbine generators on a utility network

A NASA-Lewis program to study the anticipated performance of a wind turbine generator farm on an electric utility network is surveyed. The paper describes the approach of the Lewis Wind Energy Project Office to developing analysis capabilities in the area of wind turbine generator-utility network computer simulations. Attention is given to areas such as, the Lewis Purdue hybrid simulation, an independent stability study, DOE multiunit plant study, and the WEST simulator. Also covered are the Lewis mod-2 simulation including analog simulation of a two wind turbine system and comparison with Boeing simulation results, and gust response of a two machine model. Finally future work to be done is noted and it is concluded that the study shows little interaction between the generators and between the generators and the bus.

Gilbert, L. J.↗

NASA NTRS1988

Spur gears: Optimal geometry, methods for generation and Tooth Contact Analysis (TCA) program

The contents of this report include the following: (1) development of optimal geometry for crowned spur gears; (2) methods for their generation; and (3) tooth contact analysis (TCA) computer programs for the analysis of meshing and bearing contact on the crowned spur gears. The method developed for synthesis is used for the determination of the optimal geometry for crowned pinion surface and is directed to reduce the sensitivity of the gears to misalignment, localize the bearing contact, and guarantee the favorable shape and low level of the transmission errors. A new method for the generation of the crowned pinion surface has been proposed. This method is based on application of the tool with a surface of revolution that slightly deviates from a regular cone surface. The tool can be used as a grinding wheel or as a shaver. The crowned pinion surface can also be generated by a generating plane whose motion is provided by an automatic grinding machine controlled by a computer. The TCA program simulates the meshing and bearing contact of the misaligned gears. The transmission errors are also determined.

Litvin, Faydor L.↗

NASA NTRS1987

Optoelectronic analogs of self-programming neural nets - Architecture and methodologies for implementing fast stochastic learning by simulated annealing

Self-organization and learning is a distinctive feature of neural nets and processors that sets them apart from conventional approaches to signal processing. It leads to self-programmability which alleviates the problem of programming complexity in artificial neural nets. In this paper architectures for partitioning an optoelectronic analog of a neural net into distinct layers with prescribed interconnectivity pattern to enable stochastic learning by simulated annealing in the context of a Boltzmann machine are presented. Stochastic learning is of interest because of its relevance to the role of noise in biological neural nets. Practical considerations and methodologies for appreciably accelerating stochastic learning in such a multilayered net are described. These include the use of parallel optical computing of the global energy of the net, the use of fast nonvolatile programmable spatial light modulators to realize fast plasticity, optical generation of random number arrays, and an adaptive noisy thresholding scheme that also makes stochastic learning more biologically plausible. The findings reported predict optoelectronic chips that can be used in the realization of optical learning machines.

Farhat, Nabil H.↗

NASA NTRS1988

Antarctic Planetary Testbed (APT): A facility in the Antarctic for research, planning and simulation of manned planetary missions and to provide a testbed for technological development

The notion of using Antarctica as a planetary analog is not new. Ever since the manned space program gained serious respect in the 1950's, futurists have envisioned manned exploration and ultimate colonization of the moon and other extraterrestrial bodies. In recent years, much attention has been focused on a permanently manned U.S. space station, a manned Lunar outpost and a manned mission to Mars and its vicinity. When such lofty goals are set, it is only prudent to research, plan and rehearse as many aspects of such a mission as possible. The concept of the Antarctic Planetary Testbed (APT) project is intended to be a facility that will provide a location to train and observe potential mission crews under conditions of isolation and severity, attempting to simulate an extraterrestrial environment. Antarctica has been considered as an analog by NASA for Lunar missions and has also been considered by many experts to be an excellent Mars analog. Antarctica contains areas where the environment and terrain are more similar to regions on the Moon and Mars than any other place on Earth. These features offer opportunities for simulations to determine performance capabilities of people and machines in harsh, isolated environments. The initial APT facility, conceived to be operational by the year 1991, will be constructed during the summer months by a crew of approximately twelve. Between six and eight of these people will remain through the winter. As in space, structures and equipment systems will be modular to facilitate efficient transport to the site, assembly, and evolutionary expansion. State of the art waste recovery/recycling systems are also emphasized due to their importance in space.

Ahmadi, Mashid↗

NASA NTRS1992

Earth-based analogs of lunar and planetary facilities

Antarctica contains areas where the environment and terrain are more similar to regions on the Moon and Mars than any other place on Earth. These features offer opportunities for simulations to determine performance capabilities of people and machines in harsh, isolated locales. The Sasakawa International Center for Space Architecture (SICSA) plans to create a facility on Antarctica for research, planning, and demonstrations in support of planetary exploration. The Antarctic Planetary Testbed (APT) will be financed and utilized by public and private organizations throughout the world. Established on a continent owned by no country, it can serve as a model for cooperation between spacefaring nations. APT science and technology programs will expand knowledge about the nature and origin of our solar system, and will support preparations for human settlements beyond Earth that may occur within the first quarter of the next century. The initial APT facility, conceived to be operational by the year 1992, will be constructed during the summer months by a crew of approximately 12. Six to eight of these people will remain through the winter. As in space, structures and equipment systems will be modular to facilitate efficient transport to the site, assembly, and evolutionary expansion. State-of-the-art waste recovery/recycling systems are also emphasized due to their importance in space.

Bell, Larry↗