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Results for “conditional 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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At least 595 records · Page 33
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NASA NTRS1977

An empirical model for inverted-velocity-profile jet noise prediction

It is known that the noise generated by inverted-velocity-profile coaxial (without center plug) and coannular (with center plug) nozzles should be modeled as the combined contributions of various source regions and noise generation mechanisms. In this paper, an empirical noise-prediction model is described which considers the noise generated by two jet-mixing regions and two potential regions of shock/turbulence interaction. Results calculated from the empirical model are compared with model-scale experimental data for static and simulated flight conditions. These comparisons are made for cases where both streams are subsonic, where the outer stream is supersonic with the inner stream subsonic, and where both streams are supersonic. The cases considered cover a range of inner-to-outer-stream area ratios and include both coaxial and coannular nozzles. It is shown that the model gives reasonable predictions of absolute noise spectra and even better predictions of incremental changes.

Stone, J. R.↗
NASA NTRS1978

Correlation of predicted and measured thermal stresses on a truss-type aircraft structure

A test structure representing a portion of a hypersonic vehicle was instrumented with strain gages and thermocouples. This test structure was then subjected to laboratory heating representative of supersonic and hypersonic flight conditions. A finite element computer model of this structure was developed using several types of elements with the NASA structural analysis (NASTRAN) computer program. Temperature inputs from the test were used to generate predicted model thermal stresses and these were correlated with the test measurements.

Jenkins, J. M.↗
NASA NTRS1995

Interpretation of Dynamics Explorer far UV images of the quiet time thermosphere

A selected set of far ultra violet images of Earth have been analyzed quanitatively to establish their validity for studying thermospheric weather. The set of images chosen for the study was restricted to mostly geomagnetically quiet conditions in order to obtain a baseline understanding of the relationship between the observations and thermospheric phenomenology. The images included low to modrerate solar activity levels. A new model was developed to generate global dayglow images using first principles methods. The mass Spectrometer/incoherent scatter (MSIS-86) model was used to predict the thermospheric concentrations. The analyses of thermospheric images observed in the 123 to 160-nm nominal passband show that the spectral composition for observations on the projected earth disk is dominated by O I 130.4-nm radiation (85-90%), with concentrations from O I 135.6-nm and N2 Lyman-Birge-Hopefield (LBH) bands of about 5-8% each. The synthetic images reproduce the global features of the observed images rather well. Differences between the model and the data are attributed to real atmospheric effects, such as atomic oxygen depletions which are not well reproduced by the MSIS model when geomagnetic activity is elevated. The absoute values recorded were 38-54% higher than predicted. We attribute this discrepancy to low values of the solar extreme ultraviolet irradiances used in the model. Images obtained in the 136 to 165-nm nominal passband are a factor of 2.7 greater than the model. The excess signal observed is most likely due to a long wavelength tail in the instrument sensitivity which allowed Rayleigh scattered sunlight between 180and 250nm to be detected. The understanding of the Dynamics Explorer (DE 1) images gained by this study provides the basis for future work on the global response of the theremosphere to geomagnetic forcing.

Meier, R. R.↗
NASA NTRS2010

Acoustic-Modal Testing of the Ares I Launch Abort System Attitude Control Motor Valve

The Attitude Control Motor (ACM) is being developed for use in the Launch Abort System (LAS) of NASA's Ares I launch vehicle. The ACM consists of a small solid rocket motor and eight actuated pintle valves that directionally allocate.thrust_- 1t.has-been- predicted-that significant unsteady. pressure.fluctuations.will.exist. inside the-valves during operation. The dominant frequencies of these oscillations correspond to the lowest several acoustic natural frequencies of the individual valves. An acoustic finite element model of the fluid volume inside the valve has been critical to the prediction of these frequencies and their associated mode shapes. This work describes an effort to experimentally validate the acoustic finite model of the valve with an acoustic modal test. The modal test involved instrumenting a flight-like valve with six microphones and then exciting the enclosed air with a loudspeaker. The loudspeaker was configured to deliver broadband noise at relatively high sound pressure levels. The aquired microphone signals were post-processed and compared to results generated from the acoustic finite element model. Initial comparisons between the test data and the model results revealed that additional model refinement was necessary. Specifically, the model was updated to implement a complex impedance boundary condition at the entrance to the valve supply tube. This boundary condition models the frequency-dependent impedance that an acoustic wave will encounter as it reaches the end of the supply tube. Upon invoking this boundary condition, significantly improved agreement between the test data and the model was realized.

Davis, R. Benjamin↗
NASA NTRS2013

On the Impact Angle of Hurricane Sandy's New Jersey Landfall

Hurricane Sandy's track crossed the New Jersey coastline at an angle closer to perpendicular than any previous hurricane in the historic record, one of the factors contributing to recordsetting peak-water levels in parts of New Jersey and New York. To estimate the occurrence rate of Sandy-like tracks, we use a stochastic model built on historical hurricane data from the entire North Atlantic to generate a large sample of synthetic hurricanes. From this synthetic set we calculate that under long-term average climate conditions, a hurricane of Sandy's intensity or greater (category 1+) makes NJ landfall at an angle at least as close to perpendicular as Sandy's at an average annual rate of 0.0014 yr-1 (95% confidence range 0.0007 to 0.0023); i.e., a return period of 714 years (95% confidence range 435 to 1429).

hurricanes↗
NASA NTRSDate not supplied

Systems Health Management and Prognostics Approaches for Electric Aircrafts

As more and more electric vehicles emerge in our daily operation progressively, a very critical challenge lies in the prediction of remaining driving flying time/distance for the flying vehicles. This information is important, particularly in the case of auto vehicles, because such vehicles can become self-aware, autonomously compute its own capabilities, and identify how to best plan and successfully complete vehicular missions safely. In case of electric aircrafts, computing the remaining flying time is also safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision-making. A systematic prediction framework is implemented to identify all possible sources of uncertainty, quantify each of them individually, and mathematically estimate their combined effect on the system-level quantity of interest, in this case, the remaining flying time/distance of the unmanned aircraft. Note - This presentation contains all previously published information.

Systems Health Managent↗
NASA NTRSDate not supplied

Hybrid Approaches to Systems Health Management and Prognostics

To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision making. In principle, data driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for an effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Systems Health Management↗
NASA NTRSDate not supplied

Health Monitoring and Prognostics for Electric Aircrafts

As more and more electric vehicles emerge in our daily operation progressively, a very critical challenge lies in the prediction of remaining driving flying time/distance for the flying vehicles. This information is important, particularly in the case of auto vehicles, because such vehicles can become self-aware, autonomously compute its own capabilities, and identify how to best plan and successfully complete vehicular missions safely. In case of electric aircrafts, computing the remaining flying time is also safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision-making. A systematic prediction framework is implemented to identify all possible sources of uncertainty, quantify each of them individually, and mathematically estimate their combined effect on the system-level quantity of interest, in this case, the remaining flying time/distance of the unmanned aircraft. Note - This presentation contains all previously approved and published information.

Systems Health Managent↗
NASA NTRS2023

Turbulence Model Validation Through Joint Experimental /Computational Studies of Separated Flow Over A Three-Dimensional Tapered Bump: Part I - Experimental Investigation

The results of a three-year experimental campaign aimed at comprehensively documenting the separated flow over a three-dimensional bump are presented with the purpose of generating a benchmark experimental database useful in validating computational fluid dynamics (CFD) flow simulations. The bump model geometry was designed to provide well-defined and repeatable smooth-body flow separation conditions that were suitable for both experiments and simulations. The bump had a Gaussian streamwise profile with a constant height equal to 8.5% of its width over the central 60% of the test section width. The remaining 40% were outboard spanwise portions that gradually taper to zero using an error function profile to minimize side-wall boundary layer effects. The model was immersed in a turbulent boundary layer that was developed on a suspended flat plate in the Notre Dame Mach 0.6 Wind Tunnel. In order to document the effect of the incoming boundary layer thickness on the flow separation, the bump model could be located at two streamwise positions. The mean velocity and turbulence intensity of the wind tunnel freestream flow field and approaching turbulent boundary layer were fully documented. The measurements of the flow separation region included surface visualization, wall shear stress using oil-film interferometry, mean and dynamic surface pressure, and planar and stereoscopic particle image velocimetry. The experiments were conducted over a range of Mach numbers from 0.05 to 0.2 corresponding to a range of Reynolds numbers based on the test section spanwise dimension (0.914m) of 1.0 × 10 6 ≤ Re L = U ∞ L/ν ≤ 4.0 × 10 6 . The bulk of the results are presented for the higher Mach number conditions of 0.1 and 0.2 with Re L = 2.0 × 10 6 and 4.0 × 10 6 , respectively. Extensive uncertainty analysis of the data was performed. The data is archived in the NASA Langley Turbulence Modeling Resource website at https://turbmodels.larc.nasa.gov/Other_exp_Data/speedbump_sep_exp.html. In addition to the experiments, a computational effort was made in parallel by the CFD group at Boeing Research & Technology highlighting the usefulness of the data set, which is outlined in the accompanying CFD report.

Flow separation↗
NASA NTRSDate not supplied

Silver Electrolysis for Disinfection of Spacecraft Potable Water: 2024 Update

Anodic dissolution of silver electrodes, or “silver electrolysis,” is being investigated as a means of introducing biocidal silver into potable water on exploration spacecraft. This paper provides an update on the effort to implement this technology into a spacecraft potable water system. Previous papers reported on the feasibility of the technology for this application, strategies to prevent a potential fault condition termed “electrode bridging,” results from a preliminary investigation into the cathode reaction, and preliminary multiphysics modeling of the reactor. Since then, work has begun on the design of a next-generation silver electrolysis reactor prototype that will incorporate improvements identified in previous testing and package the reactor in a more flight-like configuration. This development effort has included additional testing to optimize the reactor design for prevention of electrode bridging, further investigation into the cathode reaction (including the use of a dissolved hydrogen sensor), and an assessment of the feasibility of using the reactor in other applications, such as for microbial shock and preparation for system dormancy.

silver biocide↗
NASA NTRSDate not supplied

Silver Electrolysis for Disinfection of Spacecraft Potable Water: 2024 Update

Anodic dissolution of silver electrodes, or “silver electrolysis,” is being investigated as a means of introducing biocidal silver into potable water on exploration spacecraft. This paper provides an update on the effort to implement this technology into a spacecraft potable water system. Previous papers reported on the feasibility of the technology for this application, strategies to prevent a potential fault condition termed “electrode bridging,” results from a preliminary investigation into the cathode reaction, and preliminary multiphysics modeling of the reactor. Since then, work has begun on the design of a next-generation silver electrolysis reactor prototype that will incorporate improvements identified in previous testing and package the reactor in a more flight-like configuration. This development effort has included additional testing to optimize the reactor design for prevention of electrode bridging, further investigation into the cathode reaction (including the use of a dissolved hydrogen sensor), and an assessment of the feasibility of using the reactor in other applications, such as for microbial shock and preparation for system dormancy.

silver biocide↗
NASA NTRS1987

Stress-intensity factor calculations using the boundary force method

The Boundary Force Method (BFM) was formulated for the three fundamental problems of elasticity: the stress boundary value problem, the displacement boundary value problem, and the mixed boundary value problem. Because the BFM is a form of an indirect boundary element method, only the boundaries of the region of interest are modeled. The elasticity solution for the stress distribution due to concentrated forces and a moment applied at an arbitrary point in a cracked infinite plate is used as the fundamental solution. Thus, unlike other boundary element methods, here the crack face need not be modeled as part of the boundary. The formulation of the BFM is described and the accuracy of the method is established by analyzing a center-cracked specimen subjected to mixed boundary conditions and a three-hole cracked configuration subjected to traction boundary conditions. The results obtained are in good agreement with accepted numerical solutions. The method is then used to generate stress-intensity solutions for two common cracked configurations: an edge crack emanating from a semi-elliptical notch, and an edge crack emanating from a V-notch. The BFM is a versatile technique that can be used to obtain very accurate stress intensity factors for complex crack configurations subjected to stress, displacement, or mixed boundary conditions. The method requires a minimal amount of modeling effort.

Tan, P. W.↗
NASA NTRS1980

Satellite power system (SPS) magnetron tube assessment study

Taks performed to extend the data base and to define a technology development program for the magnetron directional amplifier for the SPS are reviewed. These include: (1) demonstrating the tracking of phase and amplitude of the microwave output to phase and amplitude references; (2) expanding the range of power over which the directional amplifier will operate; (3)recognizing the importance of amplitude control in overall system design and in simplifying power conditioning; (4) developing a preliminary design for the overall architecture of the power module; (5) demonstrating magnetron starting using the amplitude control system; (6) mathematically modelling and performing a computerized study of the pyrolytic graphite radiating fin; (7) defining the mass of the magnetic circuit for the SPS tube; (8) noise measurement; (9) achieving harmonic suppression by notch reflection filters; (10) estimating the mass of the transmitting antenna; (11) developing a magnetron package with power generation, phase control, and power condition functions; and (12) projecting magnetron package characteristics.

Source record↗
NASA NTRS2001

Effects of Traveling Magnetic Field on Dynamics of Solidification

TMF is based on imposing a controlled phase-shift in a train of electromagnets, forming a stack. Thus, the induced magnetic field can be considered to be travelling along the axis of the stack. The coupling of this traveling wave with an electrically conducting fluid results in a basic flow in a form of a single axisymmetric roll. The magnitude and direction of this flow can be remotely controlled. Furthermore, it is possible to localize the effect of this force field though activating only a number of the magnets. This force field generated in the fluid can, in principle, be used to control and modify convection in the molten material. For example, it can be used to enhance convective mixing in the melt, and thereby modify the interface shape, and macrosegregation. Alternatively, it can be used to counteract thermal and/or solutal buoyancy forces. High frequency TMF can be used in containerless processing techniques, such as float zoning, to affect the very edge of the fluid so that Marangoni flow can be counter balanced. The proposed program consists of basic fundamentals and applications. Our goal in conducting the following experiments and analyses is to establish the validity of TMF as a new tool for solidification processes. Due to its low power consumption and simplicity of design, this tool may find wide spread use in a variety of space experiments. The proposed ground based experiments are intended to establish the advantages and limitations of employing this technique. In the fundamentals component of the proposed program, we will use theoretical tools and experiments with mercury to establish the fundamental aspects of TMF-induced convection through a detailed comparison of theoretical predictions and experimental measurements of flow field. In this work, we will conduct a detailed parametric study involving the effects of magnetic field strength, frequency, wave vector, and the fluid geometry. The applications component of this work will be focused on investigating the effect of TMF on the following solidification and pre-directional solidification processes: (1) Bridgman growth of Ga:Ge with the goal of counteracting the buoyancy-driven convection; (2) Mixing of Pb-Ga and Pb-Sn alloys with the aim of initiating and maintaining a uniform melt prior to solidification processing; and (3) Float Zone growth with the aim of identifying, through simulations and model experiments, conditions needed to counteract Marangoni flow in a microgravity environment. The proposed research has strong relevance to microgravity research and the objectives of the NRA. TMF can provide a unique and accurate mechanism for generation and control of desirable flow patterns for microgravity research. These attributes have significant relevance to 1) Alloy mixing prior to solidification in a microgravity environment. TMF can provide this mixing with a low level of power consumption; (2) TMF can offset the deleterious effects of Marangoni convection in microgravity containerless processing. Thus, TMF can be instrumental in further understanding this phenomena; (3) Generation of controlled flows will allow the investigation of the effect of these flows on growth morphology and growth kinetics; and (4) On Earth, TMF has the potential to significantly counter-balance thermosolutal convection, thereby creating conditions similar to those obtained in microgravity. Once demonstrated, this new tool for use in solidification has the strong potential to find applications in a host of microgravity material research projects.

Mazuruk, Konstantin↗
NASA NTRSDate not supplied

Using XR for Improving Scientific Discovery With Numerical Weather Models

Earth science (ES) digital twins will help us understand the complex interactions and interrelationships that make up our Earth system and the impacts of earth science phenomena on it. Our work addresses two underdeveloped areas in current ES digital twin work: improving the understanding and interaction with ES model outputs by using Virtual and Mixed Reality (XR) tools and improving the non-intuitive mapping of continuous ES natural phenomena to gridded reference frames in current numerical models. Traditionally, scientists working on ES view and analyze the results of calculated or measured observables with static 1-dimensional (1D), 2D or 3D plots displayed on flat computer screens or paper. Using such limited mediums, it can be very difficult to identify, track and understand the evolution of key features due to poor viewing angles and the nature of flat computer screens. In addition, numerical models, such as the NASA Goddard Earth Observing System (GEOS) ES model, are almost exclusively formulated, visualized and analyzed in an Eulerian reference frame with fixed grid points in space and time. However, ES phenomena such as convective clouds, hurricanes and wildfire smoke plumes are visualized and analyzed in a Lagrangian reference frame: therefore it is often difficult and unnatural to understand these phenomena in relation to each other, visualized either in an Eulerian or Lagrangian context. In 3D visualizations, data generally takes one of three forms: gridded (e.g., voxelized) data, where space is divided into regions; point clouds, where data is represented as a set of points; and meshes, where objects are rendered as surfaces composed of small polygons (usually triangles). A gridded, Eulerian reference frame has been the default representation for the 2D visual analysis of atmospheric data in part because the numerical methods used to generate atmospheric model data in the first place use a gridded approach, with equations defining the relationships between the physical variables in each of a grid's cells across successive timesteps. In our work, we are particularly interested in data from GEOS. Another reason why gridded representations tend to be used for visualizing data from such models is because trajectories are difficult to interpret from representations on 2D surfaces, due to line-of-sight ambiguity. Instead of a fixed grid from GEOS, we embed a trajectory model to simulate particles' movement throughout a GEOS run. We then ingest these particle trajectories as animated point clouds with a NASA open source XR toolkit, the Mixed Reality Exploration Toolkit (MRET), and merge GEOS data with ES phenomena data onto one combined visualization that the user can intuitively interact with. Efficient rendering of arbitrarily large point clouds is an ongoing challenge being addressed by the computer science community, with the GPU-based optimizations and efficient GPU memory utilization a common theme of recent advances, especially for XR, where sustained high frame rate is mandatory to save the user from suffering due to simulation sickness. In this work, we describe and evaluate our progress in choosing and implementing appropriate methods for rendering arbitrarily large point clouds within MRET for XR. While tracking the XR headset enables the immersion of a user within a 3D scene of a data visualization, tracking of XR handheld controllers or user’s hands enables us to implement intuitive user interactions with the visualized datasets. Conventional tools require a user working with an ES visualization to conduct many interactions to commit their intended selections or manipulations with a visualized dataset; for example to specify a set of points in 3D space. Doing so in a 2D flat screen interface has traditionally required specifying a set of points in three distinct 2D coordinate systems (XY, XZ, and YZ), which is cumbersome. In other scientific domains, it has been shown that specifying or selecting a location or volume in XR using handheld controllers or tracked hands allows for greater speed and accuracy. We anticipate the same will hold true for atmospheric data, and we will share initial results of measuring the utility of such an interface. Notably, as the data being visualized is generated by GEOS as a prediction based on initial conditions, an intended application of our tool is to serve as part of an iterative feedback loop. Through XR, a scientist will review and manipulate a GEOS model run, modifying the conditions as needed to do subsequent runs of GEOS. Thereby, XR-based improvements to speed and accuracy of 3D tagging of points minimizes the effort required by both the scientist and the computer cluster conducting the necessary calculations.

Thomas Grubb↗
NASA NTRS1992

The origins of radial fracture systems and associated large lava flows on Venus

Magellan images have revealed the existence of systems of radial fractures on venus that are very similar in form to terrestrial dike swarms such as the Mackenzie swarm in Northern Canada. The association of many of the fracture systems with lava flows, calderas, and volcanic edifices further support the idea of a dike emplacement origin. A global survey of the Magellan images has allowed the location of 300 such fracture systems. Two types of fracture systems are defined. A series of models were developed to simulate the emplacement of dikes on Venus. Observations of fracture lengths and widths were then used to constrain the emplacement conditions. The model results show that the great length and relatively large width of the fractures can only be explained if the dikes that produce them were emplaced in high driving pressure (pressure buffered) conditions. Such conditions imply high rates of melt production, which is consistent with the melt being derived directly from a plume head. We have recently modeled the vertical emplacement of a dike from the top of a mantle plume and calculated the eruption rates such a dike would produce on reaching the surface. This modeling shows that eruption rates of approximately 0.1 cu km/hr can readily be generated by such a dike, consistent with the above results. However, the sensitivity of the model to dike width and therefore driving pressure means that eruption rates from dikes emplaced from the base of the crust or the head of a mantle plume could be orders of magnitude higher than this. Clearly, therefore, the model needs to be refined in order to better constrain eruption conditions. However, it is worth noting here that the initial results do show that even for moderate dike widths, eruption rates could be at least on the order of those estimated for terrestrial flood basalts.

Parfitt, Elisabeth A.↗
NASA NTRS2000

Accretion of Moon and Earth and the emergence of life

The discrepancy between the impact records on the Earth and Moon in the time period, 4.0-3.5 Ga calls for a re-evaluation of the cause and localization of the late lunar bombardment. As one possible explanation, we propose that the time coverage in the ancient rock record is sufficiently fragmentary, so that the effects of giant, sterilizing impacts throughout the inner solar system, caused by marauding asteroids, could have escaped detection in terrestrial and Martian records. Alternatively, the lunar impact record may reflect collisions of the receding Moon with a series of small, original satellites of the Earth and their debris in the time period about 4.0-3.5 Ga. The effects on Earth of such encounters could have been comparatively small. The location of these tellurian moonlets has been estimated to have been in the region around 40 Earth radii. Calculations presented here, indicate that this is the region that the Moon would traverse at 4.0-3.5 Ga, when the heavy and declining lunar bombardment took place. The ultimate time limit for the emergence of life on Earth is determined by the effects of planetary accretion--existing models offer a variety of scenarios, ranging from low average surface temperature at slow accretion of the mantle, to complete melting of the planet followed by protracted cooling. The choice of accretion model affects the habitability of the planet by dictating the early evolution of the atmosphere and hydrosphere. Further exploration of the sedimentary record on Earth and Mars, and of the chemical composition of impact-generated ejecta on the Moon, may determine the choice between the different interpretations of the late lunar bombardment and cast additional light on the time and conditions for the emergence of life.

NASA Discipline Exobiology↗
NASA NTRSDate not supplied

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↗