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Emerging Electronics Technologies include: 1) Changes in the commercial semiconductor world; 2) Radiation Effects Sources (A sample test constraint); and 3) Challenges to Radiation Testing and Modeling: a) IC Attributes-Radiation Effects Implication b) Fault Isolation c) Scaled Geometry d) Speed e) Modeling Shortfall f) Knowledge Status
Constrained fitting for airfoil curvature smoothing (CFACS) is a splinebased method of interpolating airfoil surface coordinates (and, concomitantly, airfoil thicknesses) between specified discrete design points so as to obtain smoothing of surface-curvature profiles in addition to basic smoothing of surfaces. CFACS was developed in recognition of the fact that the performance of a transonic airfoil is directly related to both the curvature profile and the smoothness of the airfoil surface. Older methods of interpolation of airfoil surfaces involve various compromises between smoothing of surfaces and exact fitting of surfaces to specified discrete design points. While some of the older methods take curvature profiles into account, they nevertheless sometimes yield unfavorable results, including curvature oscillations near end points and substantial deviations from desired leading-edge shapes. In CFACS as in most of the older methods, one seeks a compromise between smoothing and exact fitting. Unlike in the older methods, the airfoil surface is modified as little as possible from its original specified form and, instead, is smoothed in such a way that the curvature profile becomes a smooth fit of the curvature profile of the original airfoil specification. CFACS involves a combination of rigorous mathematical modeling and knowledge-based heuristics. Rigorous mathematical formulation provides assurance of removal of undesirable curvature oscillations with minimum modification of the airfoil geometry. Knowledge-based heuristics bridge the gap between theory and designers best practices. In CFACS, one of the measures of the deviation of an airfoil surface from smoothness is the sum of squares of the jumps in the third derivatives of a cubicspline interpolation of the airfoil data. This measure is incorporated into a formulation for minimizing an overall deviation- from-smoothness measure of the airfoil data within a specified fitting error tolerance. CFACS has been extensively tested on a number of supercritical airfoil data sets generated by inverse design and optimization computer programs. All of the smoothing results show that CFACS is able to generate unbiased smooth fits of curvature profiles, trading small modifications of geometry for increasing curvature smoothness by eliminating curvature oscillations and bumps (see figure).
As a member of the Space Suit Assembly Development Engineering Team, I designed and built test equipment systems to support the development of the next generation of advanced space suits. During space suit testing it is critical to supply the subject with two functions: (1) cooling to remove metabolic heat, and (2) breathing air to pressurize the space suit. The objective of my first project was to design, build, and certify an improved Space Suit Cooling System for manned testing in a 1‐G environment. This design had to be portable and supply a minimum cooling rate of 2500 BTU/hr. The Space Suit Cooling System is a robust, portable system that supports very high metabolic rates. It has a highly adjustable cool rate and is equipped with digital instrumentation to monitor the flowrate and critical temperatures. It can supply a variable water temperature down to 34 deg., and it can generate a maximum water flowrate of 2.5 LPM. My next project was to design and build a Breathing Air System that was capable of supply facility air to subjects wearing the Z‐2 space suit. The system intakes 150 PSIG breathing air and regulates it to two operating pressures: 4.3 and 8.3 PSIG. It can also provide structural capabilities at 1.5x operating pressure: 6.6 and 13.2 PSIG, respectively. It has instrumentation to monitor flowrate, as well as inlet and outlet pressures. The system has a series of relief valves to fully protect itself in case of regulator failure. Both projects followed a similar design methodology. The first task was to perform research on existing concepts to develop a sufficient background knowledge. Then mathematical models were developed to size components and simulate system performance. Next, mechanical and electrical schematics were generated and presented at Design Reviews. After the systems were approved by the suit team, all the hardware components were specified and procured. The systems were then packaged, fabricated, and thoroughly tested. The next step was to certify the equipment for manned used, which included generating a Hazard Analysis and giving a presentation to the Test Readiness Review Board. Both of these test support systems will perform critical roles in the development of next‐generation space suits. They will used on a regular basis to test the NASA's new Z‐2 Space Suit. The Space Suit Cooling System is now the primary cooling system for all advanced suit tests.
Global warming due to greenhouse gases and atmospheric aerosols alter precipitation rates, but the influence on extreme precipitation by aerosols relative to greenhouse gases is still not well known. Here we use the simulations from the Precipitation Driver and Response Model Intercomparison Project that enable us to compare changes in mean and extreme precipitation due to greenhouse gases with those due to black carbon and sulfate aerosols, using indicators for dry extremes as well as for moderate and very extreme precipitation. Generally, we find that the more extreme a precipitation event is, the more pronounced is its response relative to global mean surface temperature change, both for aerosol and greenhouse gas changes. Black carbon (BC) stands out with distinct behavior and large differences between individual models. Dry days become more frequent with BC-induced warming compared to greenhouse gases, but so does the intensity and frequency of extreme precipitation. An increase in sulfate aerosols cools the surface and thereby the atmosphere, and thus induces a reduction in precipitation with a stronger effect on extreme than on mean precipitation. A better understanding and representation of these processes in models will provide knowledge for developing strategies for both climate change and air pollution mitigation.
To date, NASA’s spaceflight operations in the past 5 decades have been limited to a narrow range of conditions from a fire safety perspective. The currently anticipated missions outside of low earth orbit will substantially expand this parameter space to include, extended durations, dormancy intervals, increased oxygen concentrations, partial gravity conditions and the presence of surface dust. All of these changes can have significant impacts on fire safety system design and operations. The overall state of understanding is discussed in this paper along with the identification of the needs for spacecraft fire safety technology development. These needs have been assembled into a roadmap maintained by the Environmental Control and Life Support System Capability Leadership Team that has evolved as the exploration mission concepts have changed. This roadmap continues to communicate the spacecraft fire safety needs for exploration and guide technology development efforts. This paper summarizes the major recent developments in our understanding of spacecraft fire behavior and mitigation. A review of the major technology development needs and discussion of their objectives, status, and future plans is presented. The plan for transitioning knowledge, hardware, and modeling capability resulting from these development efforts to specific exploration vehicle programs and missions is also discussed.
At the intersection of mission, technology, and place is NASA’s need to modernize for a digital-forward future. Digitalization, the process of moving toward digital business, is occurring everywhere and remains an ongoing process across the federal government.”[1] Whereas, Digital Transformation is “employing digitization/digital technologies (e.g., Artificial Intelligence (AI), mobile, cloud, data) to change a process, product, or capability so dramatically (e.g., real-time, intelligent, personalized, anywhere, anytime) that it is unrecognizable compared to its traditional form.” [2] In order to facilitate a digital transformation it is essential for NASA to understand and identify where data exists today and which data are value-needed in the future, understand where there are unfulfilled data needs that limit the advancement of NASA work, and ensure NASA efficiency through Findable, Accessible, Interoperable, and Reusable (FAIR) digital assets in the future. Therefore, NASA’s Reliability & Maintainability (R&M) Enterprise Data Sharing team is working to leverage both Digitization and Digital Transformation to achieve their vision of developing an R&M data discovery framework that enables our community, our partners, and our stakeholders with the ability to efficiently, robustly, and seamlessly access information that enables real-time knowledge and model-based, analytics driven, decision-making impacting R&M. As a result the R&M Enterprise Data Sharing team has conducted a survey of its Reliability, Maintainability, and Availability (RMA) community members to identify data existence (created or used) and where there are corresponding barriers to data acquisition and/or R&M or other issues as shown within this presentation.
Cube satellite (CubeSat) launches have increased exponentially over the last 20 years. This class of miniature spacecraft is well-suited for a set of nonconventional satellite architectures collectively known as formation flying. With the exponential pace of launches expected to continue, the prospect of spectrum management for these complex formations arises. In previous work, investigators focus on terrestrial applications of spectrum sensing, which have the luxury to utilize hardware with high size, weight, and power (SWaP) resources. In this work, we develop and test a spectrum sensor for CubeSat radio applications. Given that CubeSat radios are inherently designed for low SWaP, they cannot implement the computationally expensive spectral correlation analyzer (SCA) algorithms for signal detection. To that end, our investigation focuses on the application of the SCA to square-root-raised-cosine (SRRC) pulse-shaped quadrature amplitude modulation (QAM) waveforms using a field-programmable gate array (FPGA). This model requires no prior knowledge of the radio-frequency (RF) channel. We show that this model can consistently and accurately detect the symbol rate and center frequencies of waveforms located in a spectrum.
At the intersection of mission, technology, and place is NASA’s need to modernize for a digital-forward future. Digitalization, the process of moving toward digital business, is occurring everywhere and remains an ongoing process across the federal government.”[1] Whereas, Digital Transformation is “employing digitization/digital technologies (e.g., Artificial Intelligence (AI), mobile, cloud, data) to change a process, product, or capability so dramatically (e.g., real-time, intelligent, personalized, anywhere, anytime) that it is unrecognizable compared to its traditional form.” [2] In order to facilitate a digital transformation it is essential for NASA to understand and identify where data exists today and which data are value-needed in the future, understand where there are unfulfilled data needs that limit the advancement of NASA work, and ensure NASA efficiency through Findable, Accessible, Interoperable, and Reusable (FAIR) digital assets in the future. Therefore, NASA’s Reliability & Maintainability (R&M) Enterprise Data Sharing team is working to leverage both Digitization and Digital Transformation to achieve their vision of developing an R&M data discovery framework that enables our community, our partners, and our stakeholders with the ability to efficiently, robustly, and seamlessly access information that enables real-time knowledge and model-based, analytics driven, decision-making impacting R&M. As a result the R&M Enterprise Data Sharing team has conducted a survey of its Reliability, Maintainability, and Availability (RMA) community members to identify data existence (created or used) and where there are corresponding barriers to data acquisition and/or R&M or other issues as shown within this presentation.
Safety, failure, and incident reports are common artifacts across various domains, including aviation and wildfire response. These reports are often mandatory to submit, resulting in the culmination of large repositories of text-based documents. Simultaneously, these reports and corresponding repositories are often only manually analyzed and queried by users via out-of-date search engines. As a consequence, we have been developing the Manager for Intelligent Knowledge Access (MIKA) toolkit, which uses natural language processing to improve information access and reuse. In this presentation, we discuss natural language processing techniques for knowledge discovery and apply these methods to a repository of aerial wildfire mishap reports. Two methods are used for knowledge discovery: topic modeling and named-entity recognition. We use topic modeling to identify hazards and perform a trend analysis to produce a data-driven risk matrix. A custom named-entity recognition model, build from fine tuning a pre-trained language model, is used to identify failure modes, failure causes, failure effects, control processes, and recommendations to aid in failure modes and effects analysis (FMEA). Throughout the presentation, we discuss and apply natural language processing techniques to better leverage the vast amount of information contained in report repositories.
Future exploration missions will rely on designing and developing vehicles and complex systems from within NASA and through multiple external commercial partners to meet mission goals. Despite existing consistency-related agency requirements, NASA’s approach to commercial spaceflight development encourages providers’ flexibility and innovation. This strategy is resulting in significant design diversity across Artemis vehicles. Design best practices and guidelines champion interface consistency to promote mental model development and knowledge transfer. However, research investigating the benefits of consistency is mixed, and little is known about its role in complex systems. Determining the level of risk that system diversity presents is difficult, as there is no established method for quantifying the degree of consistency within and across interfaces, nor is there information about the differential impacts of different types of inconsistency. Phase I of this project (Characterization and Measurement) served as a starting point to better understand the construct of consistency, its application, and the range of studies and methods for measuring it. The project team created a taxonomy of consistency to apply to interfaces as a framework to guide the development of tools to assess intersystem consistency. Checklist and cognitive walkthrough methods were developed for use by human factors (HF) and human-computer interaction (HCI) experts. The Intersystem Consistency Scale (ICS) was developed for interface evaluations with crew. A pilot study evaluated the methods’ ability to distinguish differences between Artemis-like prototype pairs exhibiting either high or low design consistency. In addition, click errors and time on task were collected within the ICS (crew-like) group. Results from our exploratory analysis and lessons learned from the pilot study will be discussed. The project team will also present the status of Phase II (Risk Assessment, Standards and Guidelines). This includes incorporating feedback to redesign the assessment tools, and inputs from displays and training Subject Matter Experts to update tasks and prototype designs. The team will present the risk assessment study design to identify the types and levels of inconsistency that pose the greatest risk to performance. Plans to apply these results toward agency standards and guideline recommendations will also be discussed.
A real-time fault detection and diagnosis capability is absolutely crucial in the design of large-scale space systems. Some of the existing AI-based fault diagnostic techniques like expert systems and qualitative modelling are frequently ill-suited for this purpose. Expert systems are often inadequately structured, difficult to validate and suffer from knowledge acquisition bottlenecks. Qualitative modelling techniques sometimes generate a large number of failure source alternatives, thus hampering speedy diagnosis. In this paper we present a graph-based technique which is well suited for real-time fault diagnosis, structured knowledge representation and acquisition and testing and validation. A Hierarchical Fault Model of the system to be diagnosed is developed. At each level of hierarchy, there exist fault propagation digraphs denoting causal relations between failure modes of subsystems. The edges of such a digraph are weighted with fault propagation time intervals. Efficient and restartable graph algorithms are used for on-line speedy identification of failure source components.
An overview of aerodynamic models for the Low Density Supersonic Decelerator (LDSD) Supersonic Flight Dynamics Test (SFDT) campaign test vehicle is presented, with comparisons to reconstructed flight data and discussion of model updates. The SFDT campaign objective is to test Supersonic Inflatable Aerodynamic Decelerator (SIAD) and large supersonic parachute technologies at high altitude Earth conditions relevant to entry, descent, and landing (EDL) at Mars. Nominal SIAD test conditions are attained by lifting a test vehicle (TV) to 36 km altitude with a helium balloon, then accelerating the TV to Mach 4 and 53 km altitude with a solid rocket motor. Test flights conducted in June of 2014 (SFDT-1) and 2015 (SFDT-2) each successfully delivered a 6 meter diameter decelerator (SIAD-R) to test conditions and several seconds of flight, and were successful in demonstrating the SFDT flight system concept and SIAD-R technology. Aerodynamic models and uncertainties developed for the SFDT campaign are presented, including the methods used to generate them and their implementation within an aerodynamic database (ADB) routine for flight simulations. Pre- and post-flight aerodynamic models are compared against reconstructed flight data and model changes based upon knowledge gained from the flights are discussed. The pre-flight powered phase model is shown to have a significant contribution to off-nominal SFDT trajectory lofting, while coast and SIAD phase models behaved much as predicted.
Model-based prognostics captures system knowledge in the form of physics-based models of components, and how they fail, in order to obtain accurate predictions of end of life (EOL). EOL is predicted based on the estimated current state distribution of a component and expected profiles of future usage. In general, this requires simulations of the component using the underlying models. In this paper, we develop a simulation-based prediction methodology that achieves computational efficiency by performing only the minimal number of simulations needed in order to accurately approximate the mean and variance of the complete EOL distribution. This is performed through the use of the unscented transform, which predicts the means and covariances of a distribution passed through a nonlinear transformation. In this case, the EOL simulation acts as that nonlinear transformation. In this paper, we review the unscented transform, and describe how this concept is applied to efficient EOL prediction. As a case study, we develop a physics-based model of a solenoid valve, and perform simulation experiments to demonstrate improved computational efficiency without sacrificing prediction accuracy.
Electrolytic capacitors are used in several applications ranging from power supplies for safety critical avionics equipment to power drivers for electro-mechanical actuator. Past experiences show that capacitors tend to degrade and fail faster when subjected to high electrical or thermal stress conditions during operations. This makes them good candidates for prognostics and health management. Model-based prognostics captures system knowledge in the form of physics-based models of components in order to obtain accurate predictions of end of life based on their current state of heal th and their anticipated future use and operational conditions. The focus of this paper is on deriving first principles degradation models for thermal stress conditions and implementing Bayesian framework for making remaining useful life predictions. Data collected from simultaneous experiments are used to validate the models. Our overall goal is to derive accurate models of capacitor degradation, and use them to remaining useful life in DC-DC converters.
The typical approach to calculating dynamic launch loads in aerospace applications is coupled loads analysis (CLA). A component mode model of the launch vehicle is coupled with a component mode model of a payload, system modes are calculated, forcing functions are applied, and the dynamic responses are computed. This approach requires the explicit knowledge of the component models for the launch vehicle and payload, as well as the forcing functions. In many situations, the launch vehicle and forcing functions do not change from one analysis to the next only the payload is different. For this type of application, a method called reanalysis was developed to compute the response of a modified payload on the same launch vehicle. If the launch forcing functions are also the same, the approach eliminates the need for the launch vehicle model and the forcing functions and dramatically reduces the computation time. This work investigates the application and accuracy of three previously developed reanalysis methods using a typical launch vehicle and two different payloads. All three methods are based on knowledge of system modes from the original CLA, and Hurty/Craig-Bampton (HCB) models of the original and new payloads. The first method was developed at JPL (Jet Propulsion Lab) and is often referred to as substitution. It is a frequency-domain method, which requires transformation of time signals to and from the frequency domain. The other two methods are time-domain methods that more closely mimic the CLA process. The three methods were applied to two simple examples and a more complex one. The results indicate that the time-domain methods are considerably more robust with respect to modal truncation and other numerical errors than the frequency-domain JPL method.
A model of an air traffic controller performing a separation assurance task was produced. The model was designed to be simple to use and deploy in a simulator, but still provide realistic behavior. The model is based upon an evaluation of the safety function of the controller for separation assurance, and utilizes fast and frugal heuristics and belief networks to establish a knowledge set for the controller model. Based on this knowledge set, the controller acts to keep aircraft separated. Validation results are provided to demonstrate the model s performance.
This paper outlines three research directions related to domain-specific software development: (1) reuse of generic models for domain-specific software development; (2) empirical evidence to determine these generic models, namely elicitation of mental knowledge schema possessed by expert software developers; and (3) exploitation of generic domain models to assist modelling of specific applications. It focuses on knowledge acquisition for domain-specific software development, with emphasis on tool support for the most important phases of software development.
In this paper and its companion, Differential Equation Approximation Using Gradient-Boosted Quantile Regression, Robison et al., we examine an approach to quantifying model uncertainty with the aim of increasing the trustworthiness of computational models in human-machine interactions. In Differential Equation Approximation Using Gradient-Boosted Quantile Regression, we focus on gradient-boosted decision trees, while in this one, we give more details about deep ensembles. Uncertainty quantification is crucial for building trustworthy autonomous decision-making agents in human-machine teams. There are two types of uncertainties: aleatoric and epistemic. The former is related to the inherent stochasticity (noise) of the process, whereas the latter is associated with the lack of knowledge or representation capability of models, such as neural networks. By lack of knowledge, we mean the model’s inability to accurately predict outputs for all possible inputs. The aleatory uncertainty can be estimated fairly easily with, for example, filters, whereas epistemic uncertainty is challenging to compute. This paper uses deep ensembles to quantify both aleatory and epistemic uncertainty. It can act as an uncertainty-aware surrogate transition model for decision-making frameworks. "Uncertainty-aware" means that the surrogate transition model should make predictions along with confidence in those predictions. In the context of decision-making, the transition models are ordinary differential equations (ODEs). Since ODEs can be simulated to make one-step or multi-step predictions, a good surrogate model for them should perform reasonably well in both modes. In a multi-step approach, the trajectory sampling method TS∞ was used to propagate uncertainty over multiple steps. The cartpole dynamical system was selected to demonstrate the ability of deep ensembles as good surrogate transition models for decision-making frameworks. The deep ensembles modeled the dynamics of cartpole ODEs and made uncertainty-aware predictions in single-step and multi-step transition modes.