Thermal model of a 75 watt
Planar radioisotope thermoelectric generator system for NASA deep space missions and integrated spacecraft power supply
Parker, A. J., Jr.↗
SEARCH · Engineering Papers
Results for “Deep generative models”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Underplating, anatexis and assimilation of metacarbonate: A possible source for large CO2 fluxes in the deep crust
Recent models for granulite petrogenesis have involved infiltrative streaming of CO2 derived from deep-seated sources, removal of H2O in locally generated silicate melts, or various combinations of these two processes. All these models require a heat source of some type to generate the high crustal temperatures associated with anatexis and granulite-grade metamorphism, and this most likely takes the form of mantle-derived basaltic magma that is either underplated or intruded into the lower continental crust. Huppert and Sparks have recently shown that such underplating is likely to cause rapid, very large-scale melting of the overlying crust (roof rock) over time scales of only a few hundred years. This model is readily applicable to the granulite terrane of Southern India where metacarbonates occur within the deeper parts of the section including the amphibolite-granulite transition zone. Furthermore, it obviates the need to remove low melt fractions from deep crustal rocks as a principal dehydration mechanism; compaction theory has shown this to be a very sluggish process that is unlikely to be important over typical geological time scales. It also avoids any mechanism involving the subduction of large volumes of sedimentary carbonate into the mantle to provide a CO2 source.
Wickham, S. M.↗
GRACE gravity model: assssment in terms of deep ocean currents from hydrography and from the ECCO ocean model
Here we assess the new generation of gravity models, derived from GRACE data. The differences between a global geoid model (one from GRACE data and one the well-known EGM-96), minus a Mean Sea Surface derived from over a decade of altimetric data are compared to hydrographic data from the Levitus compilation and to the ECCO numerical ocean model, which assimilates altimetry and other data.
oceans ocean currents El Nino TOPEX Jason seawinds↗
Diurnal Forcing of Planetary Atmospheres
This project investigated and developed models which will assist in developing an understanding of the diurnal forcings in the atmospheres of Mars, Venus, Earth and Jupiter. The differences in the temperatures during the day, and night, generate very deep diurnal boundary layers and strong slope-related winds. A general circulation model (GCM) was adapted for computation of Martian atmospheric dynamics, to simulate the global transport of these atmospheric tracers. The model was applied to the study of the Martian seasonal water cycle. The principles have been applied to venus. The region of study is the upper cloud level, and above rather than the area close to the surface. The model was also applied in efforts to study the evolution of volcanic clouds in the Earth's stratosphere. In particular this was used to model the dispersion of sulfuric acid particles in the atmosphere. A brief study of the application of the tidal theory to the planet Jupiter has been undertaken. The goal of the study is to deduce the source of the tidal dissipation which is thought to have led to the orbital resonances amongst the Galilean satellites and considerable heating of the interiors of Io, Europa, and Ganymede.
Houben, Howard C.↗
A Dynamo Simulation Generating Saturn-Like Small Magnetic Dipole Tilts
Abstract Among planetary dynamos, the magnetic field of Saturn stands out in its exceptional level of axisymmetry. One of its peculiar features is that the magnetic dipole mode is tilted with respect to the planetary rotation axis by only ≈0.007° or less. Numerical dynamo simulations performed in this context have had great difficulty in producing such small dipole tilt angles without introducing ad hoc ingredients such as a latitudinally varying heat flux pattern in the outer layers or stably stratified layers (SSLs). Here we present a numerical dynamo simulation that generates a highly axisymmetric dynamo with a dipole tilt of about ≈0.0008° on average. The model consists of a deep dynamo layer and an overlying low-conductivity layer but without any SSLs. We highlight a novel mechanism where strong differential rotation generated in the atmospheric layer penetrates into the dynamo region, helping to maintain a very small magnetic dipole tilt. Plain Language Summary Saturn's dipole-dominant magnetic field exhibits a very peculiar feature: the dipole component of the planetary magnetic field is tilted by less than ≈0.007° with respect to the planetary spin axis. Numerical simulations performed in this context suggest that if a spatial heat-flux variation is imposed, along with a stably stratified region, on top of an active dynamo layer, then small dipole tilt values can be realized. Here we present a model where extremely small dipole tilt values can be achieved without these ad hoc ingredients. Our simulations demonstrate that dynamo theory allows extremely small dipole tilt values in a relatively simple model configuration.
Rakesh Kumar Yadav↗
Simulator of Space Communication Networks
Multimission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE) is a suite of software tools that simulates the behaviors of communication networks to be used in space exploration, and predict the performance of established and emerging space communication protocols and services. MACHETE consists of four general software systems: (1) a system for kinematic modeling of planetary and spacecraft motions; (2) a system for characterizing the engineering impact on the bandwidth and reliability of deep-space and in-situ communication links; (3) a system for generating traffic loads and modeling of protocol behaviors and state machines; and (4) a system of user-interface for performance metric visualizations. The kinematic-modeling system makes it possible to characterize space link connectivity effects, including occultations and signal losses arising from dynamic slant-range changes and antenna radiation patterns. The link-engineering system also accounts for antenna radiation patterns and other phenomena, including modulations, data rates, coding, noise, and multipath fading. The protocol system utilizes information from the kinematic-modeling and link-engineering systems to simulate operational scenarios of space missions and evaluate overall network performance. In addition, a Communications Effect Server (CES) interface for MACHETE has been developed to facilitate hybrid simulation of space communication networks with actual flight/ground software/hardware embedded in the overall system.
Clare, Loren↗
Deep Learning and Uncertainty Quantification for Climate Resilience
Modeling and monitoring of earth’s processes through physical models and satellite observations at high resolutions is crucial for ensuring society’s ability to adapt to climate change. Deep learning (DL) has been shown to be a valuable tool for generating high resolution data, emulating physical models, and detecting weather patterns which can then be used to inform stakeholders and decision makers. However, both the data and model parameters contain substantial uncertainties that may alter users’ decisions. In this work we present two DL applications on high-resolution climate and satellite datasets using Bayesian neural networks to generate well calibrated uncertainty estimates.
Vandal, Thomas↗
Multi-Agent Motion Planning using Deep Learning for Space Applications
State-of-the-art motion planners cannot scale to a large number of systems. Motion planning for multiple agents is an NP (non-deterministic polynomial-time) hard problem, so the computation time increases exponentially with each addition of agents. This computational demand is a major stumbling block to the motion planner's application to future NASA missions involving the swarm of space vehicles. We applied a deep neural network to transform computationally demanding mathematical motion planning problems into deep learning-based numerical problems. We showed optimal motion trajectories can be accurately replicated using deep learning-based numerical models in several 2D and 3D systems with multiple agents. The deep learning-based numerical model demonstrates superior computational efficiency with plans generated 1000 times faster than the mathematical model counterpart.
Madani, Ramtin↗
Formal methods for test case generation
The invention relates to the use of model checkers to generate efficient test sets for hardware and software systems. The method provides for extending existing tests to reach new coverage targets; searching *to* some or all of the uncovered targets in parallel; searching in parallel *from* some or all of the states reached in previous tests; and slicing the model relative to the current set of coverage targets. The invention provides efficient test case generation and test set formation. Deep regions of the state space can be reached within allotted time and memory. The approach has been applied to use of the model checkers of SRI's SAL system and to model-based designs developed in Stateflow. Stateflow models achieving complete state and transition coverage in a single test case are reported.
Rushby, John↗
An 810 ft/sec soil impact test of a 2-foot diameter model nuclear reactor containment system
A soil impact test was conducted on a 880-pound 2-foot diameter sphere model. The impact area consisted of back filled desert earth and rock. The impact generated a crater 5 feet in diameter by 5 feet deep. It buried itself a total of 15 feet - as measured to the bottom of the model. After impact the containment vessel was pressure checked. No leaks were detected nor cracks observed.
Puthoff, R. L.↗
Ocean alkalinity and the Cretaceous/Tertiary boundary
A biogeochemical cycle model resolving ocean carbon and alkalinity content is applied to the Maestrichtian and Danian. The model computes oceanic concentrations and distributions of Ca(2+), Mg(2+), and Sigma-CO2. From these values an atmospheric pCO2 value is calculated, which is used to estimate rates of terrestrial weathering of calcite, dolomite, and calcium and magnesium silicates. Metamorphism of carbonate rocks and the subsequent outgassing of CO2 to the atmosphere are parameterized in terms of carbonate rock reservoir sizes, total land area, and a measure of overall tectonic activity, the sea-floor generation rate. The ocean carbon reservoir computed by the model is used with Deep Sea Drilling Project (DSDP) C-13 data to estimate organic detrital fluxes under a variety of ocean mixing rate assumptions. Using Redfield ratios, the biogenic detrital flux estimate is used to partition the ocean carbon and alkalinity reservoirs between the mixed layer and deep ocean. The calcite flux estimate and carbonate ion concentrations are used to determine the rate of biologically mediated CaCO3 titration. Oceanic productivity was severely limited for approximately 500 kyr following the K/T boundary resulting in significant increases in total ocean alkalinity. As productivity returned to the ocean, excess carbon and alkalinity was removed from the ocean as CaCO3. Model runs indicate that this resulted in a transient imbalance in the other direction. Ocean chemistry returned to near-equilibrium by about 64 mybp.
Caldeira, K. G.↗
Pixel-Based Smoke Detection with Neural Network
Exposure to biomass burning has been linked to respiratory and cardiovascular illnesses in humans. Traditional satellite based visual and multispectral smoke detection methods are not scalable to capabilities of new generations of remote sensing platforms. We develop a scalable, deep learning based detection model capable of identifying smoke pixels using GOES-16 shortwave reflectance data and present a operational web-based tool to visualize smoke predictions.
Ramasubramanian, M.↗
SatNet: A Benchmark for Satellite Scheduling Optimization
Satellites provide essential services such as networking and weather tracking, and the number of near-earth and deep space satellites are expected to grow rapidly in the coming years. Communications with terrestrial ground stations is one of the critical functionalities of any space mission. Satellite scheduling is a problem that has been scientifically investigated since the 1970s. A central aspect of this problem is the need to consider resource contention and satellite visibility constraints as they require line of sight. Due to the combinatorial nature of the problem, prior solutions such as linear programs and evolutionary algorithms require extensive compute capabilities to output a feasible schedule for each scenario. Machine learning based scheduling can provide an alternative solution by training a model with historical data and generating a schedule quickly with model inference. We present SatNet, a benchmark for satellite scheduling optimization based on historical data from the NASA Deep Space Network. We propose formulation of the satellite scheduling problem as a Markov Decision Process and use reinforcement learning (RL) policies to generate schedules. The nature of constraints imposed by SatNet differ from other combinatorial optimization problems such as vehicle routing studied in prior literature. Our initial results indicate that RL is an alternative optimization approach that can generate candidate solutions of comparable quality to existing state-of-the-practice results. However, we also find that RL policies overfit to the training dataset and do not generalize well to new data, thereby necessitating continued research on reusable and generalizable agents.
Wilson, Brian↗
Development of a Data Fusion Methodology for Lineload Aerodynamic Databases for a Launch Vehicle during Liftoff and Transition
The need for databases for the distributed loading on launch vehicles during the early portion of flight necessitates the use of expensive computational flows in regimes where wake effects dominate. While also being expensive, this is a regime that computational tools tend to historically have problems simulating accurately. To help tackle this problem, a method of data fusion to combine computational results to wind tunnel derived force and moment data is developed. Using this method, significant reduction in computational costs and increases in confidence of the final product is possible and has been used to generate several databases for the Space Launch System (SLS) at NASA. While the full details of database generation are not part of this work, the crucial method at its core is developed here. Two SLS geometries are used throughout the work to demonstrate the techniques. These are two of the larger geometries and represent both planned crewed missions to the Moon as well as potential cargo missions to deep space. The method uses principal component analysis (PCA) to generate a reduced ordered model (ROM) to help fill in the full parameter space. Other similar techniques are explored, but were not found to have a significant result on the predictions of the ROM. Because the full number of components are kept to generate the model, this lack of difference is expected. This method is then extended to ensure that predicted surfaces match trusted force and moment data derived from wind tunnel testing. This extension is done by setting up a constrained optimization problem in order to minimize the deviation from the surface resolved computational data while still integrating to the desired values. When generating the constrained optimization problem, a weighting factor to balance these competing needs is introduced. The work compares previously introduced weighting terms from similar work to the proposed terms and shows that the previously used terms do not have as desirable behavior in this flow regime. This method is then expanded by developing a technique to incorporate uncertainty quantification into the developed data fusion methodology. This expansion takes a two pronged approach. One examines transferring the uncertainties in the force and moment database and characterizes how those adjustments change the predicted lineloads. The second looks at model form error and looks how rebuilding the model using slightly different data changes the predictions. These two terms are then combined in order to create an uncertainty model that takes both effects into account. The limitations of the proposed methods is then discussed as well as possible techniques to address these shortcomings.
Launch Vehicles↗
Using Deep Learning to Automate Inference of Meteoroid Pre-Entry Properties
Properly assessing the asteroid threat depends on the knowledge of asteroid pre-entry parameters, such as size, velocity, mass, density, and strength. Although a vast number of possible bodies to study exist, such characterization of asteroid populations is currently limited by substantial costs associated with space rendezvous missions and rare meteorite findings. As asteroids fragment, ablate, and decelerate in the atmosphere, they emit light detectable by ground-based and space-borne instruments. Earth’s atmosphere, thus, becomes an accessible laboratory that enables impactor risk assessments by facilitating inference of the pre-entry parameters. These asteroid pre-entry conditions are typically deduced by modeling the entry and breakup physics that best reproduce the observed light or energy deposition curve. However, this process requires extensive manual trial-and-error of uncertain modeling parameters. Automating meteor modeling and inference would improve property distributions used in risk assessments and enable population characterization as more light curves become more readily available through the presence of space assets and ground-based camera networks. We previously developed a genetic algorithm to automate meteor modeling by using the fragment-cloud model (FCM) to search for the values of the FCM input parameters (e.g., diameter) that generate energy deposition profiles that match the observed one. Now, we apply deep learning to infer asteroid diameter, velocity, and density from observed energy deposition curves. We trained and tested our neural network models with synthetic energy deposition curves modeled using the FCM rubble pile implementation. We present an application of a 1D convolutional neural network and compare its performance to other attempted regressors and machine learning techniques, such as a fully connected neural network and Random Forest regression, to demonstrate its capabilities. We validate our model weights and approach using the Chelyabinsk, Tagish Lake, Benešov, Košice, and Lost City meteors.
Tarano, Ana Maria↗
A Framework for Deep Learning Emulation of Numerical Models With a Case Study in Satellite Remote Sensing
Numerical models based on physics represent the state of the art in Earth system modeling and comprise our best tools for generating insights and predictions. Despite rapid growth in computational power, the perceived need for higher model resolutions overwhelms the latest generation computers, reducing the ability of modelers to generate simulations for understanding parameter sensitivities and characterizing variability and uncertainty. Thus, surrogate models are often developed to capture the essential attributes of the full-blown numerical models. Recent successes of machine learning methods, especially deep learning (DL), across many disciplines offer the possibility that complex nonlinear connectionist representations may be able to capture the underlying complex structures and nonlinear processes in Earth systems. A difficult test for DL-based emulation, which refers to function approximation of numerical models, is to understand whether they can be comparable to traditional forms of surrogate models in terms of computational efficiency while simultaneously reproducing model results in a credible manner. A DL emulation that passes this test may be expected to perform even better than simple models with respect to capturing complex processes and spatiotemporal dependencies. Here, we examine, with a case study in satellite-based remote sensing, the hypothesis that DL approaches can credibly represent the simulations from a surrogate model with comparable computational efficiency. Our results are encouraging in that the DL emulation reproduces the results with acceptable accuracy and often even faster performance. We discuss the broader implications of our results in light of the pace of improvements in high-performance implementations of DL and the growing desire for higher resolution simulations in the Earth sciences.
Bayesian Deep Learning↗
Implementing Artificial Thinking Autonomy with Model-Based System Engineering
Complex autonomous systems capable of successfully operating independently under ‘known unknowns’ and harsh conditions require paradigm innovation in modern development strategies. In the field of autonomy, developing a system-of-systems which can ostensibly think for itself in the face of ‘unknown unknowns’ is still a field of ongoing research. Maturing the systems architecting and modeling methodologies for developing henceforth named Thinking Autonomous Systems, which are verified with digital mission simulation, can potentially usher in the next generation of artificial intelligence for space exploration. The concept presented in this paper incorporates multiple Model-Based Systems Engineering and simulation methodologies combined as a new paradigm to design a novel, biomimetic thinking autonomy strategy. Anachronistic concepts from classical Kantian philosophy will be leveraged to inspire architectural designs that could be used for complex distributed systems in deep space. To accomplish this, digital transformation of a document-based implementation plan for Thinking Autonomous Systems, generated by experienced NASA software engineers, is implemented for NASA’s Platform for Autonomous Systems by creating descriptive and executable software models in SysML to prototype real-time operating capabilities. This conceptual implementation has been developed by incorporating model-based digital simulations to theorize how a cyberphysical thinking system would achieve specific strategies without crew reliance, while simultaneously being resilient to all operating conditions and remaining functional when devoid of ground communication. Additionally, ensuring that an autonomous system framework is an ethical Artificial Intelligence requires careful consideration of system behavior and accountability, human factors for teaming with a thinking autonomous system, and comparison to other modern approaches used for implementing true autonomy. This paper presents the first steps in formalizing the metacognition required for instantiating a truly Thinking Autonomous System; the approach described symphonizes autonomy characteristics from classical philosophical into a unified software architecture describing human thought. In the future, the foundational models described in this paper can be further leveraged to help advance research into thinking autonomy requirements for future deep space missions as well as for current near-term applications, i.e., living aboard crewed spacecraft like a NASA Gateway cislunar habitat.
Artificial Thought↗
Advanced Mirror & Modelling Technology Development
The 2020 Decadal technology survey is starting in 2018. Technology on the shelf at that time will help guide selection to future low risk and low cost missions. The Advanced Mirror Technology Development (AMTD) team has identified development priorities based on science goals and engineering requirements for Ultraviolet Optical near‐Infrared (UVOIR) missions in order to contribute to the selection process. One key development identified was lightweight mirror fabrication and testing. A monolithic, stacked, deep core mirror was fused and replicated twice to achieve the desired radius of curvature. It was subsequently successfully polished and tested. A recently awarded second phase to the AMTD project will develop larger mirrors to demonstrate the lateral scaling of the deep core mirror technology. Another key development was rapid modeling for the mirror. One model focused on generating optical and structural model results in minutes instead of months. Many variables could be accounted for regarding the core, face plate and back structure details. A portion of a spacecraft model was also developed. The spacecraft model incorporated direct integration to transform optical path difference to Point Spread Function (PSF) and between PSF to modulation transfer function. The second phase to the project will take the results of the rapid mirror modeler and integrate them into the rapid spacecraft modeler.
Effinger, Michael↗