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A Simulation system has been developed which is capable of bit level simulation of spacecraft data systems. Object oriented techniques and an embedded interpreted language hae been employed to produce a highly configurable tool for control and viewing of spacecraft states.
The Mars Entry, Descent, and Landing Instrumentation (MEDLI2) sensor suite collected data during entry of the Mars 2020 Perseverance rover into Mars’ atmosphere. This suite included a network of MEDLI2 Instrumented Sensor Plugs (MISPs). Each MISP was comprised of a cylinder made of Thermal Protection System (TPS) material with 1-3 embedded thermocouples (TCs), and it was flush mounted into the heatshield or backshell. Data from these in-depth TCs were used to reconstruct the aeroheating environment of the vehicle throughout entry. Surface heating was posed as an inverse problem, with the goal of estimating the surface heating by minimizing an objective function of the difference between MISP temperature measurements during flight and the temperature predictions derived from the Fully Implicit Ablation and Thermal response (FIAT) program. Given an aerothermal environment, FIAT calculates the material response and provides in-depth temperatures throughout the TPS material. To achieve the reverse, an internal tool called FIAT_Opt runs through multiple different environments until the output temperature at the TC depth closely matches the flight data. 95% confidence intervals on the reconstructed surface heating were obtained using Monte Carlo analysis, in which uncertainties in the thermocouple depth and the TPS material properties (e.g., density, thermal conductivity, heat capacity, emissivity) based on flight-lot material testing were included. A variance decomposition method using Sobol indices was employed to assess the sensitivity of the reconstructed peak heating to the TC placement and material property uncertainties. Variance decomposition was found to require tens of thousands of FIAT_Opt runs in order for the Sobol indices to converge. With a single FIAT_Opt run taking on the order of 40 minutes, the required number of computations would take months to complete, even if using multiple CPUs. To mitigate this problem, three machine learning models (ridge regression with cross-validation, random forest regression, and a deep neural network) were trained and tested using the 2000 Monte Carlo runs that were already completed. A subset of 1600 runs were used to train the model (i.e., training set), while the remaining 400 runs were used as the test set. The predictions from the deep neural network (DNN) on the test set showed nearly perfect agreement to the actual values computed with FIAT_Opt (R2 > 0.99). Using the DNN as a surrogate model, the variance decomposition using 50,000 runs was completed within minutes. The resulting Sobol indices showed that the reconstructed peak surface heating was most sensitive to the uncertainties in the thermal conductivity (ST = 0.37) and heat capacity (ST = 0.26). This method can be leveraged to provide requirements for material property measurements needed to improve the accuracy of surface heating prediction and ultimately lead to the reduction of design margins in the future. This presentation will include background on the MEDLI2 suite; the method used for inverse heating estimation; the way that material property uncertainties were accounted for using Monte Carlo analysis; a brief background on variance decomposition; the motivation for using machine learning in this context; how a neural network was trained on the data to enable variance decomposition in a fraction of the time; and the variance decomposition results for one of the MISPs.
Cyber-physical systems (CPS) are engineered systems in which the computational components and physical components of the system are interdependent and deeply linked. Discovery and development of CPS technologies are changing the way people interact with engineered systems. To demonstrate this point, consider the Global Positioning System (GPS) which revolutionized personal mobility. Its research origins stem from the US Department of Defense in 1973 for military use. By 2000 limitations on civilian consumption of GPS service were lifted. Immediately, personal GPS products were shipped for in-car navigation systems. Soon after, GPS receivers were embedded into mobile phones with the introduction of the Nokia N95 and Apple iPhone for maps and turn-by-turn navigation. With the benefit of hindsight we can see that GPS has become a cornerstone technology of our daily life. Satellite-based navigation has gone from a radical space technology, to a matter of uninteresting availability; it is the typical way that people navigate through roadways now. The combination of the satellite infrastructure, the integration of receiver modules, and powerful maps applications has significantly changed mobility in the 21st century.
Thermal Fluids and Analysis Workshop, Silver Spring MD NCTS 21070-15. NASA, the Defense Department and commercial interests are actively engaged in developing miniaturized spacecraft systems and scientific instruments to leverage smaller cheaper spacecraft form factors such as CubeSats. This paper outlines research and development efforts among Goddard Space Flight Center personnel and its several partners to develop innovative embedded thermal control subsystems. Embedded thermal control subsystems is a cross cutting enabling technology integrating advanced manufacturing techniques to develop multifunctional intelligent structures to reduce Size, Weight and Power (SWaP) consumption of both the thermal control subsystem and overall spacecraft. Embedded thermal control subsystems permit heat acquisition and rejection at higher temperatures than state of the art systems by employing both advanced heat transfer equipment (integrated heat exchangers) and high heat transfer phenomena. The Goddard Space Flight Center Thermal Engineering Branch has active investigations seeking to characterize advanced thermal control systems for near term spacecraft missions. The embedded thermal control subsystem development effort consists of fundamental research as well as development of breadboard and prototype hardware and spaceflight validation efforts. This paper will outline relevant fundamental investigations of micro-scale heat transfer and electrically driven liquid film boiling. The hardware development efforts focus upon silicon based high heat flux applications (electronic chips, power electronics etc.) and multifunctional structures. Flight validation efforts include variable gravity campaigns and a proposed CubeSat based flight demonstration of a breadboard embedded thermal control system. The CubeSat investigation is technology demonstration will characterize in long-term low earth orbit a breadboard embedded thermal subsystem and its individual components to develop optimized operational schema.
We search the American Association of Variable Star Observers (AAVSO) archives of the two best studied dwarf novae in an attempt to find light curves for long out bursts that are extremely well-characterized. The systems are U Gem and S8 Cyg. Our goal is to search for embedded precursors such as those that have been found recently in the high fidelity Kepler data for superoutbursts of some members of the 8U UMa subclass of dwarf novae. For the vast majority of AAV80 data, the combination of low data cadence and large errors associated with individual measurements precludes one from making any strong statement about the shape of the long outbursts. However, for a small number of outbursts, extensive long term monitoring with digital photometry yields high fidelity light curves. We report the finding of embedded precursors in two of three candidate long outbursts. This reinforces van Paradijs' finding that long outbursts in dwarf novae above the period gap and superoutbursts in systems below the period gap constitute a unified class. The thermal-tidal instability to account for superoutbursts in the SU UMa stars predicts embedded precursors only for short orbital period dwarf novae, therefore the presence of embedded precursors in long orbital period systems - U Gem and SS Cyg - argues for a more general mechanism to explain long outbursts.
NASA has identified structural health monitoring and damage detection and verification as critical needs in multiple technology roadmaps. The sensor systems can be customized for detecting location, damage size, and depth, with velocity options and can be designed for particular environments for monitoring of impact or physical damage to a structure. The damage detection system has been successfully demonstrated in a harsh environment and remote integration tested over 1000 miles apart. Multiple applications includes: Spacecraft and Aircraft; Inflatable, Deployable and Expandable Structures; Space Debris Monitoring; Space Habitats; Military Shelters; Solar Arrays, Smart Garments and Wearables, Extravehicular activity (EVA) suits; Critical Hardware Enclosures; Embedded Composite Structures; and Flexible Hybrid Printed Electronics and Systems. For better implementation and infusion into more flexible architectures, important and improved designs in advancing embedded software and GUI interface, and increasing flexibility, modularity, and configurable capabilities of the system are currently being carried out.
The management of redundancy in computer systems was studied and guidelines were provided for the development of NASA's fault-tolerant distributed systems. Fault recovery and reconfiguration mechanisms were examined. A theoretical foundation was laid for redundancy management by efficient reconfiguration methods and algorithmic diversity. Algorithms were developed to optimize the resources for embedding of computational graphs of tasks in the system architecture and reconfiguration of these tasks after a failure has occurred. The computational structure represented by a path and the complete binary tree was considered and the mesh and hypercube architectures were targeted for their embeddings. The innovative concept of Hybrid Algorithm Technique was introduced. This new technique provides a mechanism for obtaining fault tolerance while exhibiting improved performance.
A comprehensive review of methods of numerically generating curvilinear coordinate systems with coordinate lines coincident with all boundary segments is given. Some general mathematical framework and error analysis common to such coordinate systems is also included. The general categories of generating systems are those based on conformal mapping, orthogonal systems, nearly orthogonal systems, systems produced as the solution of elliptic and hyperbolic partial differential equations, and systems generated algebraically by interpolation among the boundaries. Also covered are the control of coordinate line spacing by functions embedded in the partial differential operators of the generating system and by subsequent stretching transformation. Dynamically adaptive coordinate systems, coupled with the physical solution, and time-dependent systems that follow moving boundaries are treated. References reporting experience using such coordinate systems are reviewed as well as those covering the system development.
The timely detection of leaks within cryogenic fuel replenishment systems is of significant importance to operators on account of the safety and economic impacts associated with material loss and operational inefficiencies. Associated loss in control of pressure also effects the stability and ability to control the phase of cryogenic fluids during replenishment operations. Current research dedicated to providing Prognostics and Health Management (PHM) coverage of such cryogenic replenishment systems has focused on the detection of leaks to atmosphere involving relatively simple model-based diagnostic approaches that, while effective, are unable to isolate the fault to specific piping system components. The authors have extended this research to focus on the detection of leaks through closed valves that are intended to isolate sections of the piping system from the flow and pressurization of cryogenic fluids. The described approach employs model-based detection of leak-through conditions based on correlations of pressure changes across isolation valves and attempts to isolate the faults to specific valves. Implementation of this capability is enabled by knowledge and information embedded in the domain model of the system. The approach has been used effectively to detect such leak-through faults during cryogenic operational testing at the Cryogenic Testbed at NASA's Kennedy Space Center.
Patterned nanomagnet arrays (PNAs) have been shown to exhibit a strong geometrically frustrated dipole interaction. Some PNAs have also shown emergent domain wall dynamics. Previous works have demonstrated methods to physically probe these magnetization dynamics of PNAs to realize neuromorphic reservoir systems that exhibit chaotic dynamical behavior and high-dimensional nonlinearity. These PNA reservoir systems from prior works leverage echo state properties and linear/nonlinear short-term memory of component reservoir nodes to map and preserve the dynamical information of the input time-series data into nondelay spatial embeddings. Such mappings enable these PNA reservoir systems to imitate and predict/forecast the input time series data. However, these prior PNA reservoir systems are based solely on the nondelay spatial embeddings obtained at component reservoir nodes. As a result, they require a massive number of component reservoir nodes, or a very large spatial embedding (i.e., high-dimensional spatial embedding) per reservoir node, or both, to achieve acceptable imitation and prediction accuracy. These requirements reduce the practical feasibility of such PNA reservoir systems. To address this shortcoming, we present a mixed delay/nondelay embeddings-based PNA reservoir system. Our system uses a single PNA reservoir node with the ability to obtain a mixture of delay/nondelay embeddings of the dynamical information of the time-series data applied at the input of a single PNA reservoir node. Our analysis shows that when these mixed delay/nondelay embeddings are used to train a perceptron at the output layer, our reservoir system outperforms existing PNA-based reservoir systems for the imitation of NARMA 2, NARMA 5, NARMA 7, and NARMA 10 time series data, and for the short-term and long-term prediction of the Mackey Glass time series data.
A piloted simulation study was conducted comparing three different input methods for interfacing to a large-screen, multiwindow, whole-flight-deck display for management of transport aircraft systems. The thumball concept utilized a miniature trackball embedded in a conventional side-arm controller. The touch screen concept provided data entry through a capacitive touch screen. The voice concept utilized a speech recognition system with input through a head-worn microphone. No single input concept emerged as the most desirable method of interacting with the display. Subjective results, however, indicate that the voice concept was the most preferred method of data entry and had the most potential for future applications. The objective results indicate that, overall, the touch screen concept was the most effective input method. There was also significant differences between the time required to perform specific tasks and the input concept employed, with each concept providing better performance relative to a specific task. These results suggest that a system combining all three input concepts might provide the most effective method of interaction.
This paper describes a methodology for the extraction of a linear time invariant model from a nonlinear helicopter model, and followed by an examination of the interactions of the Higher Harmonic Control (HHC) and the Automatic Flight Control System (AFCS). This new method includes an embedded harmonic analyzer inside a linear time invariant model, which allows the periodicity of the helicopter response to be captured. The: coupled high-order model provides the needed level of dynamic fidelity to permit an analysis and optimization of the AFCS and HHC loops. Results of this study indicate that the closed-loop HHC system has little influence on the AFCS or on the vehicle handling qualities, which indicates that the AFCS does not need modification to work with the HHC system. The results also show that the vibration response to maneuvers must be considered during the HHC design process, which leads to much higher required HHC loop crossover frequencies. This research also demonstrates that the transient vibration response during maneuvers can be reduced by optimizing the closed-loop higher harmonic control laws using conventional control system analyses.
Powering missions to the outer solar system is a significant challenge. These missions are typically powered by a Radioisotope Thermoelectric Generator (RTG). Though these sources provide stable power regardless of location in space, they are expensive to produce, difficult to integrate, and have both safety concerns as well as negative sociopolitical connotations. Perhaps most importantly, the availability of their fuel, plutonium-238, is scarce. Solar power is often considered a more attractive option. However, photovoltaic generation falls off at the distance from the sun squared. This drives the size of traditional solar generators to infeasible levels for deep space and their utilization at locations deeper the Jupiter is currently non-existent. Herein, a hypothetical solution, the PowerSail, and its application to a non-nuclear Uranus mission is presented. The PowerSail is a marriage of solar sails and thin-film solar cell assemblies. Herein the application of PowerSail spacecraft to a high priority science mission, the Applied Physics Laboratory’s Uranus Probe and Explorer, is studied. The overall mission design along with key subsystems design changes are discussed, ultimately showing that a PowerSail could be utilized as a non-nuclear option to reach destinations very deep in our solar system. Key needed technology developments to make the PowerSail and such a mission a reality are given.
Triboluminescence exploited in fiber-optic sensor system for measuring changes in pressures, strains, vibrations, and acoustic emissions, in structural members. Sensors embedded in members for in situ monitoring of condition of structure. System passive in sense no source of radiation required to interrogate optical fiber. Technique has potential for wide range of applications in which detection and measurement of structural stress required.
Modern space systems are increasing in complexity and scale at an unprecedented pace. Consequently, innovative methods, processes, and tools are needed to cope with the increasing complexity of architecting these systems. A key systems challenge in practice is the ability to scale processes, methods, and tools used to architect complex space systems. Traditionally, the process for specifying space system architectures has largely relied on capturing the system architecture in informal descriptions that are often embedded within loosely coupled design documents and domain expertise. Such informal descriptions often lead to misunderstandings between design teams, ambiguous specifications, difficulty in maintaining consistency as the architecture evolves throughout the system development life cycle, and costly design iterations. Therefore, traditional methods are becoming increasingly inefficient to cope with ever-increasing system complexity. We apply the principles of component-based design and platform-based design to the development of the system architecture for a practical space system to demonstrate feasibility of our approach using SysML. Our results show that we are able to apply a systematic design method to manage system complexity, thus enabling effective data management, semantic coherence and traceability across different levels of abstraction in the design chain. Just as important, our approach enables interoperability among heterogeneous tools in a concurrent engineering model based design environment.
Autonomy is the ability of a system to make decisions and take actions in the presence of uncertainty. Validating autonomy, therefore, is a matter of validating that the system makes intelligent decisions even when there might be discrepancies between the observed and the expected state of the world as well as when the possible outcome of each of the possible courses of action is uncertain. During the requirements engineering phase, the functions, including those that are required to be autonomous and their expected behavior are specified. However, in the case of autonomous software system, it is challenging to specify all possible scenarios that the software may encounter. Additionally, since autonomous behaviors cannot always be fully predetermined, it is difficult to reason about their completeness and correctness. While these challenges are also faced with non-autonomous system, they are more prevalent for autonomous system due to their complexity and emergent behavior. Our efforts have been three-fold. On one hand, we have developed a goal-based method for requirements decomposition. We have embedded this approach into a higher-level systems engineering framework and then developed methods for decision and reliability analysis within this framework. And finally, we use simulation analysis to validate the more theoretical methods and provide a feedback mechanism. The decision analysis approach (DA) helps in identifying and mitigating the run-time risks, by bringing to the forefront the uncertainties, decisions, interactions, and other factors that may cause autonomous software to make erroneous decisions. The DA framework is used to quantify, for each decision that the software can make, failure risk due to uncertainties. The information about the key decisions and circumstances that can cause autonomous system to make incorrect decisions are used to generate test scenarios to be run in a simulator to ensure that the system can handle error-prone circumstances. The results or outcomes from decisions, based on the simulated scenarios, can then be leveraged to further gain insight of the assured system. We combine a goal-based approach with simulation analysis to facilitate requirements development for autonomous systems and further provide a method for run-time systems level reliability considerations during the autonomous software development process. The DA and the reliability analysis methods provide a formalism for the consideration of uncertainty in the run time decision-making process for autonomous software. This formalism considers the uncertainty involved in the outcome of different courses of action, in terms of performance and cost, as well as a method to consider the system level reliability implications. System level reliability considerations for autonomous software decision making contribute to the satisfaction of the system level goals over the lifecycle of a given mission. We demonstrate our approach using a hypothetical rover path planning example. The requirements for our rover simulator are based on existing literature about Mars rovers and lessons learned from real world observations at JPL. The specifics of the autonomy design for the Mars rovers, that have not been previously cleared for external release, are not used for our demonstrations.
Low spectral resolution studies of DG Tau B, the faint system of knots south of the T Tauri star DG Tau, are described. The observations show this object to be bipolar, with the blueshifted lobe having extraordinarily low excitation. Infrared observations of the exciting star show it to be of very low luminosity, with a bolometric luminosity of 0.88 solar luminosity. The visual extinction indicates a highly nonspherical distribution of circumstellar dust around the exciting star. In spite of this lack of embedding within an obvious dark cloud, the system is identified as a young one.