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NASA NTRS2004

Transformations in Air Transportation Systems For the 21st Century

Globally, our transportation systems face increasingly discomforting realities: certain of the legacy air and ground infrastructures of the 20th century will not satisfy our 21st century mobility needs. The consequence of inaction is diminished quality of life and economic opportunity for those nations unable to transform from the 20th to 21st century systems. Clearly, new thinking is required regarding business models that cater to consumers value of time, airspace architectures that enable those new business models, and technology strategies for innovating at the system-of-networks level. This lecture proposes a structured way of thinking about transformation from the legacy systems of the 20th century toward new systems for the 21st century. The comparison and contrast between the legacy systems of the 20th century and the transformed systems of the 21st century provides insights into the structure of transformation of air transportation. Where the legacy systems tend to be analog (versus digital), centralized (versus distributed), and scheduled (versus on-demand) for example, transformed 21st century systems become capable of scalability through technological, business, and policy innovations. Where air mobility in our legacy systems of the 20th century brought economic opportunity and quality of life to large service markets, transformed air mobility of the 21st century becomes more equitable available to ever-thinner and widely distributed populations. Several technological developments in the traditional aircraft disciplines as well as in communication, navigation, surveillance and information systems create new foundations for 21st thinking about air transportation. One of the technological developments of importance arises from complexity science and modern network theory. Scale-free (i.e., scalable) networks represent a promising concept space for modeling airspace system architectures, and for assessing network performance in terms of robustness, resilience, and other metrics. The lecture offers an air transportation system topology and a scale-free network linkage graphic as framework for transportation system innovation. Successful outcomes of innovation in air transportation could lay the foundations for new paradigms for aircraft and their operating capabilities, air transportation system topologies, and airspace architectures and procedural concepts. These new paradigms could support scalable alternatives for the expansion of future air mobility to more consumers in more parts of the world.

Holmes, Bruce J.↗

NASA NTRS2012

Gene Expression Measurement Module (GEMM) - A Fully Automated, Miniaturized Instrument for Measuring Gene Expression in Space

The capability to measure gene expression on board spacecraft opens the door to a large number of high-value experiments on the influence of the space environment on biological systems. For example, measurements of gene expression will help us to understand adaptation of terrestrial life to conditions beyond the planet of origin, identify deleterious effects of the space environment on a wide range of organisms from microbes to humans, develop effective countermeasures against these effects, and determine the metabolic bases of microbial pathogenicity and drug resistance. These and other applications hold significant potential for discoveries in space biology, biotechnology, and medicine. Supported by funding from the NASA Astrobiology Science and Technology Instrument Development Program, we are developing a fully automated, miniaturized, integrated fluidic system for small spacecraft capable of in-situ measurement of expression of several hundreds of microbial genes from multiple samples. The instrument will be capable of (1) lysing cell walls of bacteria sampled from cultures grown in space, (2) extracting and purifying RNA released from cells, (3) hybridizing the RNA on a microarray and (4) providing readout of the microarray signal, all in a single microfluidics cartridge. The device is suitable for deployment on nanosatellite platforms developed by NASA Ames' Small Spacecraft Division. To meet space and other technical constraints imposed by these platforms, a number of technical innovations are being implemented. The integration and end-to-end technological and biological validation of the instrument are carried out using as a model the photosynthetic bacterium Synechococcus elongatus, known for its remarkable metabolic diversity and resilience to adverse conditions. Each step in the measurement process-lysis, nucleic acid extraction, purification, and hybridization to an array-is assessed through comparison of the results obtained using the instrument with those from standard laboratory protocols. Once developed, the system can be used with minor modifications for multiple experiments on different platforms in space, including extension to higher organisms and microbial monitoring. A proposed version of GEMM that is capable of handling both microbial and tissue samples on the International Space Station will be briefly summarized.

Pohorille, Andrew↗

NASA NTRS2021

Resilient Space Habitat Design Using Safety Controls

Space habitats will involve a complex and tightly coupled combination of hardware, software, and humans, while operating in challenging environments that pose many risks, both known and unknown. It will not be possible to design habitats that are immune to failure, nor will it be possible to foresee all possible failures. Rather than aiming for designs where ―failure is not an option,‖ habitats must be resilient to disruptions. We propose an approach to resilient design for space habitats based on the concept of safety controls from system safety engineering. We model disruptions using a state-and-trigger approach, where the space habitat is in one of three distinct states at each time instance: nominal, hazardous, or accident. We use safety controls as ways of preventing a system from entering or remaining in a hazardous or accident state. We develop a safety control option space for the habitat, from which designers can select the set of safety controls that best meet resilience, performance, and other system goals. The safety control option space is likely to be large, accordingly, we design a database that links safety controls to the applicable states and triggers. We demonstrate our approach on the early design stage of a Martian space habitat.

Safety↗

NASA NTRS2008

Transport Aircraft System Identification from Wind Tunnel Data

Recent studies have been undertaken to investigate and develop aerodynamic models that predict aircraft response in nonlinear unsteady flight regimes for transport configurations. The models retain conventional static and rotary dynamic terms but replace conventional acceleration terms with more general indicial functions. In the Integrated Resilient Aircraft Controls project of the NASA Aviation Safety Program one aspect of the research is to apply these current developments to transport configurations to facilitate development of advanced controls technology. This paper describes initial application of a more general modeling methodology to the NASA Langley Generic Transport Model, a sub-scale flight test vehicle.

Murphy, Patrick C.↗

NASA NTRS2009

NASA Iced Aerodynamics and Controls Current Research

This slide presentation reviews the state of current research in the area of aerodynamics and aircraft control with ice conditions by the Aviation Safety Program, part of the Integrated Resilient Aircraft Controls Project (IRAC). Included in the presentation is a overview of the modeling efforts. The objective of the modeling is to develop experimental and computational methods to model and predict aircraft response during adverse flight conditions, including icing. The Aircraft icing modeling efforts includes the Ice-Contaminated Aerodynamics Modeling, which examines the effects of ice contamination on aircraft aerodynamics, and CFD modeling of ice-contaminated aircraft aerodynamics, and Advanced Ice Accretion Process Modeling which examines the physics of ice accretion, and works on computational modeling of ice accretions. The IRAC testbed, a Generic Transport Model (GTM) and its use in the investigation of the effects of icing on its aerodynamics is also reviewed. This has led to a more thorough understanding and models, both theoretical and empirical of icing physics and ice accretion for airframes, advanced 3D ice accretion prediction codes, CFD methods for iced aerodynamics and better understanding of aircraft iced aerodynamics and its effects on control surface effectiveness.

Addy, Gene↗

NASA NTRSDate not supplied

Enterococci in Space: Adaptation, Antibiotic Resistance, and Clinical Implications

Enterococci are gram-positive bacteria that originated when our ancient animal ancestors emerged from the oceans to live on land, and brought their gut flora with them. Enterococcus faecalis (EF) and Enterococcus faecium, are common human commensals and can harbor multidrug resistance. Both have been previously isolated from the International Space Station (ISS). Likely as a consequence of their evolutionary origins, enterococci show remarkable stress resistance within, but also outside, their human hosts. Their antibiotic resistance, coupled with tolerance to desiccation, starvation, and disinfection, make some EF strains potent pathogens in the built environment (e.g., hospitals), and a potential risk to crew health during space missions. Here we describe our planned flight studies, currently in development. Genomic Enumeration of Antibiotic Resistance in Space (GEARS) will characterize the frequency and genomic identity of antibiotic resistant organisms, including enterococci, on the ISS, and expand our future in-space sequencing-based diagnostic capabilities. Enterococcus Growth Advantage on ISS via Tn-seq (EnteroGAIT) will assess the evolutionary selective pressure of the space environment (microgravity, space radiation) using EF as a model system during a long-duration culture and persistence experiment. Adaptation & Evolution of Resilient Enterococcus in Space (AERES) will study existing and newly identified isolates to characterize the “natural” evolutionary history of EF on Earth and in space to reveal mechanisms of microbial adaption including, possibly, natural selection. Our work to date has identified that EF ISS isolates share virulence factors found in clinical strains and commensals, and revealed limitations of current pathogenicity assessment tools. While ISS isolates were, in some cases, multi-drug resistant, current data suggests these are largely commensal strains. Our work will further refine potential crew health risks and improve understanding of EF adaptation to the built environment, of great relevance on Earth where EF is the second leading cause of hospital acquired infection.

Jordan McKaig↗

NASA NTRS2022

Deriving Severe Hail Likelihood from Satellite Observations and Model Reanalysis Parameters using a Deep Neural Network

Geostationary satellite imagers, such as those of the Geostationary Operational Environmental Satellite (GOES) series, have been observing severe convection at 15–60-minute intervals for over 40 years. When properly assessed, such a data record can be valuable in efforts of estimating severe storm risk throughout the diurnal cycle based on automated detection of patterns consistently found atop severe storms. Furthermore, environmental conditions favorable for severe weather are well-known and are thought to be represented well by modern reanalysis products. Promoting resilience against such hazards on local and global scales is a chief goal the NASA Disasters program, which seeks to encourage use of satellite observations to mitigate risk. For instance, hail is the costliest severe weather hazard across the globe in terms of insured loss, but reporting inconsistencies for hail events globally make it difficult to develop models that can quantify the risk. Satellite observation and model reanalysis taken together have the potential to, with reasonable skill and specificity, characterize environmental conditions that are favorable for hazardous weather, and thereby enable creation of hazard climatologie. Such climatologies are particularly useful over regions without extensive radar networks or storm reporting. By mapping the multivariate combination of observed cloud features and reanalysis environmental parameters/indices to United States Next Generation Weather Radar (NEXRAD) radar-estimated Maximum Expected Size of Hail (MESH) by way of a deep neural network (DNN), estimates of likelihood for potentially severe hail can be produced. Such estimates are of greater complexity and efficiency than could be performed with previous multivariate or logistic regression analyses for observed points within convective systems. Statistical distributions of convective parameters from satellite and reanalysis are shown to highlight non-severe/severe class separation for well-known hailstorm predictors, e.g., overshooting cloud top characteristics, deep-layer wind shear, mid-level stability, helicity, and convective inhibition. These complex, multivariate predictor relationships are exploited within a DNN, which can efficiently produce a quantitative hail risk metric with better than 70% detection rate and under 30% false alarms. These hail classifications can then be aggregated across the satellite record to yield a hazard climatology for hail frequency and severity – knowledge of which is of particular interest to those who manage risk (e.g., insurers) and are seeking opportunities to identify hail-prone regions, particularly in developing nations. This NASA study uses satellite observations and model parameters in a DNN to perform climatological hailstorm analysis in support of catastrophe model development, with the hope of promoting risk resilience particularly in regions without adequate weather radar coverage.

Passive Remote Sensing↗

NASA NTRS2015

Enhancing Interdisciplinary Human System Risk Research Through Modeling and Network Approaches

NASA's Human Research Program (HRP) supports research to reduce human health and performance risks inherent in future human space exploration missions. Understanding risk outcomes and contributing factors in an integrated manner allows HRP research to support development of efficient and effective mitigations from cross‐disciplinary perspectives, and to enable resilient human and engineered systems for spaceflight. The purpose of this work is to support scientific collaborations and research portfolio management by utilizing modeling for analysis and visualization of current and potential future interdisciplinary efforts.

Mindock, Jennifer↗

NASA NTRS2016

Testing and Resilience of the Impact Origin of the Moon

The leading hypothesis for the origin of the Moon is the giant impact model, which grew out of the post-Apollo science community. The hypothesis was able to explain the high E-M system angular momentum, the small lunar core, and consistent with the idea that the early Moon melted substantially. The standard hypothesis requires that the Moon be made entirely from the impactor, strangely at odds with the nearly identical oxygen isotopic composition of the Earth and Moon, compositions that might be expected to be different if Moon came from a distinct impactor. Subsequent geochemical research has highlighted the similarity of both geochemical and isotopic composition of the Earth and Moon, and measured small but significant amounts of volatiles in lunar glassy materials, both of which are seemingly at odds with the standard giant impact model. Here we focus on key geochemical measurements and spacecraft observations that have prompted a healthy re-evaluation of the giant impact model, provide an overview of physical models that are either newly proposed or slightly revised from previous ideas, to explain the new datasets.

Righter, K.↗

NASA NTRS1999

Ocean Turbulence I: One-Point Closure Model Momentum and Heat Vertical Diffusivities

Since the early forties, one-point turbulence closure models have been the canonical tools used to describe turbulent flows in many fields. In geophysics, Mellor and Yamada applied such models using the 1980 state-of-the art. Since then, no improvements were introduced to alleviate two major difficulties: 1) closure of the pressure correlations, which affects the correct determination of the critical Richardson number Ri(sub cr) above which turbulent mixing is no longer possible and 2) the need to express the non-local third-order moments (TOM) in terms of lower order moments rather than via the down-gradient approximation as done thus far, since the latter seriously underestimates the TOMs. Since 1) and 2) are still being dealt with adjustable parameters which weaken the credibility of the models, alternative models, not based on turbulence modeling, have been suggested. The aim of this paper is to show that new information, partly derived from the newest 2-point closure model discussed, can be used to solve these shortcomings. The new one-point closure model, which in its simplest form is algebraic and thus simple to implement, is first shown to reproduce a variety of data. Then, it is used in a Ocean-General Circulation Model (O-GCM) where it reproduces well a large variety of ocean data. While phenomenological models are specifically tuned to ocean turbulence, the present model is not. It is first tested against laboratory data on stably stratified flows and then used in an O-GCM. It is more general, more predictive and more resilient, e.g., it can incorporate phenomena like wave-breaking at the surface, salinity diffusivity, non-locality, etc. One important feature that naturally comes out of the new model is that the predicted Richardson critical value Ri(sub cr) is Ri (sub cr approx. = 1) in agreement with both Large Eddy Simulations (LES) and empirical evidence while all previous models predicted Ri (sub cr approx. = 0.2) which led to a considerable underestimate of the extent of turbulent mixing and thus to an incorrect mixed layer depth. The predicted temperature and salinity profiles (vs. depth) are presented and compared with those of the Kolmogorov-Petruvsky-Piskunuv (KPP) model and Levitus data.

Canuto, V. M.↗

NASA NTRS2018

A Prognostics Framework Development for Swarm Satellite Formations

Prognostics is the science of predicting the failure(s) of a component or a system and understanding how the performance will change in the event of a failure or degradation mechanism. With accurate predictions of possible failures, autonomous mitigative actions can be taken to correct/repair any issues or alert human operators of a failure threshold exceedance requiring condition-based maintenance. Although there is extensive research on failure predictions for a component or a system, there are significantly more opportunities to foray into failure predictions and prognostics for a system of systems such as an airspace consisting of multiple aircraft, a fleet of unmanned aerial vehicles, and a swarm of intelligent satellite systems. Failure prediction and mitigation are particularly important in autonomous systems such as satellite swarm systems that need effective resource management and minimal human interactions. Based on NASA's decadal survey, there is a clear need to prioritize the development of satellite swarm technology for studies of space physics and Earth science. The science community will propose future missions that return in-situ measurements from a 3-D (three-dimensional) volume of space, with relative spacecraft motion and inter-satellite baselines controlled according to the mission objectives. For such multi-spacecraft missions, it is required that ground operations resources do not scale with the number of satellites, thus compromising the swarm or leading to inefficiencies in resource allocation. Swarms of tens or hundreds of small satellites will require autonomy in attitude control, navigation and failure. Although significant research has been conducted in the areas of autonomous formation flying algorithms, less attention has been given to the development of resilient systems robust to failures.The focus of this research paper is the integration of model-based prognostics into the swarm dynamics control and decision-making algorithms. We simulate swarm management strategies for a subsystem failure to demonstrate the importance of failure predictions by comparing two cases: (i) no health information is provided to the system and utilized in the decision-making process and (2) system health information is obtained using prognostics and employed by the control system. One example scenario presented is for the GPS (Global Positioning System) system of an individual satellite to perform off-nominally due to increasing estimated error. In this scenario, the keep-out zone for that satellite would become more conservative, thereby decreasing the risk of collision. This is achieved via tuning the individual artificial repulsive functions assigned to each satellite.This paper is structured as follows. First we provide an overview of current swarm technology development, where we specifically use the term swarm to define multiple satellites flying in formation in similar orbits, with cross-link communication and station-keeping capabilities. Second, we give an introduction to the Swarm Orbital Dynamics Advisor (SODA), a tool that accepts high-level configuration commands and provides the orbital maneuvers required to achieve the prescribed formation configuration. Third, we provide the details of the model-based prognostics algorithm implementation in SODA. Finally, we present different case studies for potential component/subsystem failures and the swarm responses based with and without failure prediction information.

prognostics↗

NASA NTRSDate not supplied

Technology Assessment and Modelling of Three Terminal Tandem Solar Cells for In-Space Utilization

NASA seeks a sustained human presence on the lunar surface. High-efficiency and resilient energy systems are critical to supporting habitats, scientific outposts, and lunar surface operations. Photovoltaics have long been the backbone of space power systems, transitioning from silicon solar cells to multi-junction III-V solar cells, which now dominate state-of-the-art (SOA) technology. Multi-junction cells achieve high efficiency by layering semiconductors, each absorbing a specific portion of the solar spectrum. However, efficiency gains are increasingly constrained by device physics and manufacturing complexity. Additionally, higher-order junctions pose challenges for accurate modelling and performance characterization due to difficulties in replicating the AM0 spectrum.

solar cells↗

NASA NTRS2015

Assessing and Promoting Functional Resilience in Flight Crews During Exploration Missions

NASA plans to send humans to Mars in about 20 years. The NASA Human Research Program supports research to mitigate the major risks to human health and performance on extended missions. However, there will undoubtedly be unforeseen events on any mission of this nature - thus mitigation of known risks alone is not sufficient to ensure optimal crew health and performance. Research should be directed not only to mitigating known risks, but also to providing crews with the tools to assess and enhance resilience, as a group and individually. We can draw on ideas from complexity theory and network theory to assess crew and individual resilience. The entire crew or the individual crewmember can be viewed as a complex system that is composed of subsystems (individual crewmembers or physiological subsystems), and the interactions between subsystems are of crucial importance for overall health and performance. An understanding of the structure of the interactions can provide important information even in the absence of complete information on the component subsystems. This is critical in human spaceflight, since insufficient flight opportunities exist to elucidate the details of each subsystem. Enabled by recent advances in noninvasive measurement of physiological and behavioral parameters, subsystem monitoring can be implemented within a mission and also during preflight training to establish baseline values and ranges. Coupled with appropriate mathematical modeling, this can provide real-time assessment of health and function, and detect early indications of imminent breakdown. Since the interconnected web of physiological systems (and crewmembers) can be interpreted as a network in mathematical terms, we can draw on recent work that relates the structure of such networks to their resilience (ability to self-organize in the face of perturbation). There are many parameters and interactions to choose from. Normal variability is an established characteristic of a healthy physiological response. Healthy coupling has been investigated less extensively, but there are cases in which too tight or too loose coupling can be problematic. This might be in inter-individual behaviors, such as sleep cycles, coordination of work and meal times, and coupled motions during communication. Less apparent are couplings of physiological systems, nevertheless examples abound of coupled systems which might be monitored: cardio-respiratory rhythms; circadian rhythms, body temperature, and sleep; stress markers and cognition, sleep, and performance; profiles of biochemical markers related to immune function and nutritional status; sensorimotor aspects such as motion sickness, ataxia, reaction time, and manual control. Tools for resilience are then the means to measure and analyze these parameters, incorporate them into appropriate models of normal variability and interconnectedness, and recognize when parameters or their couplings are outside of normal limits. What to do when a problem is identified depends on its nature. Changes can be made to crew procedures, work pacing, interpersonal interactions, sleep cycles, meal timing and content, as guided by the model. Use and continued development of these methods could not only provide tools for resilience, but also meaningful autonomous work for the crew on an extended flight.

Shelhamer, Mark↗

NASA NTRS2011

Contact Graph Routing

Contact Graph Routing (CGR) is a dynamic routing system that computes routes through a time-varying topology of scheduled communication contacts in a network based on the DTN (Delay-Tolerant Networking) architecture. It is designed to enable dynamic selection of data transmission routes in a space network based on DTN. This dynamic responsiveness in route computation should be significantly more effective and less expensive than static routing, increasing total data return while at the same time reducing mission operations cost and risk. The basic strategy of CGR is to take advantage of the fact that, since flight mission communication operations are planned in detail, the communication routes between any pair of bundle agents in a population of nodes that have all been informed of one another's plans can be inferred from those plans rather than discovered via dialogue (which is impractical over long one-way-light-time space links). Messages that convey this planning information are used to construct contact graphs (time-varying models of network connectivity) from which CGR automatically computes efficient routes for bundles. Automatic route selection increases the flexibility and resilience of the space network, simplifying cross-support and reducing mission management costs. Note that there are no routing tables in Contact Graph Routing. The best route for a bundle destined for a given node may routinely be different from the best route for a different bundle destined for the same node, depending on bundle priority, bundle expiration time, and changes in the current lengths of transmission queues for neighboring nodes; routes must be computed individually for each bundle, from the Bundle Protocol agent's current network connectivity model for the bundle s destination node (the contact graph). Clearly this places a premium on optimizing the implementation of the route computation algorithm. The scalability of CGR to very large networks remains a research topic. The information carried by CGR contact plan messages is useful not only for dynamic route computation, but also for the implementation of rate control, congestion forecasting, transmission episode initiation and termination, timeout interval computation, and retransmission timer suspension and resumption.

Burleigh, Scott C.↗

NASA NTRS2020

Unprecedented Drought Challenges for Texas Water Resources in a Changing Climate: What Do Researchers and Stakeholders Need to Know?

Long‐range water planning is complicated by factors that are rapidly changing in the 21st century, including climate, population, and water use. Here, we analyze climate factors and drought projections for Texas as an example of a diverse society straddling an aridity gradient to examine how the projections can best serve water stakeholder needs. We find that climate models are robust in projecting drying of summer‐season soil moisture and decreasing reservoir supplies for both the eastern and western portions of Texas during the 21st century. Further, projections indicate drier conditions during the latter half of the 21st century than even the most arid centuries of the last 1,000 years that included megadroughts. To illustrate how accounting for drought non‐stationarity may increase water resiliency, we consider generalized case studies involving four key stakeholder groups: agricultural producers, large surface water suppliers, small groundwater management districts, and regional water planning districts. We also examine an example of customized climate information being used as input to long‐range water planning. We find that while stakeholders value the quantitative capability of climate model outputs, more specific climate‐related information better supports resilience planning across multiple stakeholder groups. New suites of tools could provide necessary capacity for both short and long‐term, stakeholder‐specific adaptive planning.

drought↗

NASA NTRS2012

Modeling Off-Nominal Behavior in SysML

Specification and development of fault management functionality in systems is performed in an ad hoc way - more of an art than a science. Improvements to system reliability, availability, safety and resilience will be limited without infusion of additional formality into the practice of fault management. Key to the formalization of fault management is a precise representation of off-nominal behavior. Using the upcoming Soil Moisture Active-Passive (SMAP) mission for source material, we have modeled the off-nominal behavior of the SMAP system during its initial spin-up activity, using the System Modeling Language (SysML). In the course of developing these models, we have developed generic patterns for capturing off-nominal behavior in SysML. We show how these patterns provide useful ways of reasoning about the system (e.g., checking for completeness and effectiveness) and allow the automatic generation of typical artifacts (e.g., success trees and FMECAs) used in system analyses.

fault protection↗

NASA NTRS2013

An Approach for the Assessment of System Upset Resilience

This report describes an approach for the assessment of upset resilience that is applicable to systems in general, including safety-critical, real-time systems. For this work, resilience is defined as the ability to preserve and restore service availability and integrity under stated conditions of configuration, functional inputs and environmental conditions. To enable a quantitative approach, we define novel system service degradation metrics and propose a new mathematical definition of resilience. These behavioral-level metrics are based on the fundamental service classification criteria of correctness, detectability, symmetry and persistence. This approach consists of a Monte-Carlo-based stimulus injection experiment, on a physical implementation or an error-propagation model of a system, to generate a system response set that can be characterized in terms of dimensional error metrics and integrated to form an overall measure of resilience. We expect this approach to be helpful in gaining insight into the error containment and repair capabilities of systems for a wide range of conditions.

Torres-Pomales, Wilfredo↗

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↗