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NASA NTRSDate not supplied

Ground Winds Experienced by the Space Launch System Rocket on the Pad before the Artemis I Launch

Over the course of the development of the Space Launch System (SLS) human-rated heavy-lift launch vehicle, the magnitude and nature of the ground winds experienced by the rocket and its Mobile Launcher (ML) on Pad 39B at the NASA Kennedy Space Center (KSC) became a more critical environment that required characterization as well as a more accurate prediction of the vehicle and ML response in the form of detailed wind loads. The numerous reasons for the sensitivity of the launch system to these ground winds and the growing importance of high fidelity rollout and launch pad wind loading estimates are discussed in this article. A description of the wind measurement instrumentation at and around the Launch Complex is provided. A methodology is developed to estimate from these raw anemometer measurements the winds experienced by the rocket and mobile launcher on the launch pad, either in real time or during post-processing. An analysis is then performed and documented on the 121 days worth of wind measurements recorded while the SLS vehicle was secured on the pad during various launch attempts and wet dress rehearsals. This included the occurrence of a tropical storm that evolved into a named hurricane by the time it arrived on Cape Canaveral and hit the vehicle while sitting on the launch pad. Plans for further developments to improve on this predictive capability in preparation for Artemis II are also presented.

Jeremy T Pinier↗

NASA NTRSDate not supplied

Ground Winds Experienced by the Space Launch System Rocket on the Pad before the Artemis I Launch

Jeremy T. Pinier↗

NASA NTRS2021

Simons Observatory HoloSim-ML: Machine Learning Applied to the Efficient Analysis of Radio Holography Measurements of Complex Optical Systems

Near-field radio holography is a common method for measuring and aligning mirror surfaces for millimeter andsub-millimeter telescopes. In instruments with more than a single mirror, degeneracies arise in the holographymeasurement, requiring multiple measurements and new fitting methods. We present HoloSim-ML, a Pythoncode for beam simulation and analysis of radio holography data from complex optical systems. This code usesmachine learning to efficiently determine the position of hundreds of mirror adjusters on multiple mirrors with fewmicrometer accuracy. We apply this approach to the example of the Simons Observatory 6 m telescope.

Grace E Chesmore↗

NASA NTRS2007

The DaveMLTranslator: An Interface for DAVE-ML Aerodynamic Models

It can take weeks or months to incorporate a new aerodynamic model into a vehicle simulation and validate the performance of the model. The Dynamic Aerospace Vehicle Exchange Markup Language (DAVE-ML) has been proposed as a means to reduce the time required to accomplish this task by defining a standard format for typical components of a flight dynamic model. The purpose of this paper is to describe an object-oriented C++ implementation of a class that interfaces a vehicle subsystem model specified in DAVE-ML and a vehicle simulation. Using the DaveMLTranslator class, aerodynamic or other subsystem models can be automatically imported and verified at run-time, significantly reducing the elapsed time between receipt of a DAVE-ML model and its integration into a simulation environment. The translator performs variable initializations, data table lookups, and mathematical calculations for the aerodynamic build-up, and executes any embedded static check-cases for verification. The implementation is efficient, enabling real-time execution. Simple interface code for the model inputs and outputs is the only requirement to integrate the DaveMLTranslator as a vehicle aerodynamic model. The translator makes use of existing table-lookup utilities from the Langley Standard Real-Time Simulation in C++ (LaSRS++). The design and operation of the translator class is described and comparisons with existing, conventional, C++ aerodynamic models of the same vehicle are given.

Hill, Melissa A.↗

NASA NTRSDate not supplied

Digital Assistance for System Requirement Discovery and Analysis using Machine Learning Natural Language Processing Algorithm

NASA’s Air Traffic Management-Exploration (ATM-X) Urban Air Mobility (UAM) Airspace Subproject is conducting research that evolves UAM airspace towards a highly automated and operationally flexible system of the future (see https://www.nasa.gov/uam-overview/ for more information). The complexity of UAM airspace, and its evolution through a series of transformative epochs, requires a planning tool to effectively organize, integrate, and communicate the research that will guide the evolution of UAM operations in the National Airspace System (NAS). The planning tool, called the UAM airspace research roadmap (or just roadmap), is being developed as a new system engineering methodology leveraging model based system engineering (MBSE) and machine learning natural language processing (ML NLP, or just NLP) capabilities. This presentation gives an overview of the NLP application within this system engineering methodology and will describe how it is being used to meet the ATM-X UAM Airspace Subproject’s overarching research goals.

ATM↗

NASA NTRS1977

Lightning Protection System for Space Shuttle

The suitability and cost effectiveness of using a lightning mast for the shuttle service and access tower (SSAT) similar to the type used for the Apollo Soyuz Test Project (ASTP) mobile launcher (ML) was evaluated. Topics covered include: (1) ASTP launch damage to mast, mast supports, grounded overhead wires, and the instrumentation system; (2) modifications required to permit reusing the ASTP mast on the SSAT; (3) comparative costing factors per launch over a 10 year period in repetitive maintenance and refurbishment of the existing and modified masts, mast supports, grounded overhead wires, and ground instrumentation required to sustain mechanical and electrical integrity of the masts; (4) effects of blast testing samples of the ASTP ML type mast (corrosion and electrical flashover); (5) comparison of damages from ASTP launch and from blast testing.

Source record↗

NASA NTRS2003

Quantitating Iron in Serum Ferritin by Use of ICP-MS

A laboratory method has been devised to enable measurement of the concentration of iron bound in ferritin from small samples of blood (serum). Derived partly from a prior method that depends on large samples of blood, this method involves the use of an inductively-coupled-plasma mass spectrometer (ICP-MS). Ferritin is a complex of iron with the protein apoferritin. Heretofore, measurements of the concentration of serum ferritin (as distinguished from direct measurements of the concentration of iron in serum ferritin) have been used to assess iron stores in humans. Low levels of serum ferritin could indicate the first stage of iron depletion. High levels of serum ferritin could indicate high levels of iron (for example, in connection with hereditary hemochromatosis an iron-overload illness that is characterized by progressive organ damage and can be fatal). However, the picture is complicated: A high level of serum ferritin could also indicate stress and/or inflammation instead of (or in addition to) iron overload, and low serum iron concentration could indicate inflammation rather than iron deficiency. Only when concentrations of both serum iron and serum ferritin increase and decrease together can the patient s iron status be assessed accurately. Hence, in enabling accurate measurement of the iron content of serum ferritin, the present method can improve the diagnosis of the patient s iron status. The prior method of measuring the concentration of iron involves the use of an atomic-absorption spectrophotometer with a graphite furnace. The present method incorporates a modified version of the sample- preparation process of the prior method. First, ferritin is isolated; more specifically, it is immobilized by immunoprecipitation with rabbit antihuman polyclonal antibody bound to agarose beads. The ferritin is then separated from other iron-containing proteins and free iron by a series of centrifugation and wash steps. Next, the ferritin is digested with nitric acid to extract its iron content. Finally, a micronebulizer is used to inject the sample of the product of the digestion into the ICPMS for analysis of its iron content. The sensitivity of the ICP-MS is high enough to enable it to characterize samples smaller than those required in the prior method (samples can be 0.15 to 0.60 mL).

Smith, Scott M.↗

NASA NTRS2016

Brain Gene Expression Signatures From Cerebrospinal Fluid Exosome RNA Profiling

While the Visual Impairment and Intracranial Pressure (VIIP) syndrome observations have focused on ocular symptoms, spaceflight has been also associated with a number of other performance and neurologic signs, such as headaches, cognitive changes, vertigo, nausea, sleep/circadian disruption and mood alterations, which, albeit likely multifactorial, can also result from elevation of intracranial pressure (ICP). We therefore hypothesize that these various symptoms are caused by disturbances in the neurophysiology of the brain structures and are correlated with molecular markers in the cerebrospinal fluid (CSF) as indicators of neurophysiological changes. Exosomes are 30-200 nm microvesicles shed into all biofluids, including blood, urine, and CSF, carrying a highly rich source of intact protein and RNA cargo. Exosomes have been identified in human CSF, and their proteome and RNA pool is a potential new reservoir for biomarker discovery in neurological disorders. The purpose of this study is to investigate changes in brain gene expression via exosome analysis in patients suffering from ICP elevation of varied severity (idiopathic intracranial hypertension -IIH), a condition which shares some of the neuroophthalmological features of VIIP, as a first step toward obtaining evidence suggesting that cognitive function and ICP levels can be correlated with biomarkers in the CSF. Our preliminary work, reported last year, validated the exosomal technology applicable to CSF analysis and demonstrated that it was possible to obtain gene expression evidence of inflammation processes in traumatic brain injury patients. We are now recruiting patients with suspected IIH requiring lumbar puncture at Baylor College of Medicine. Both CSF (5 ml) and human plasma (10 ml) are being collected in order to compare the pattern of differentially expressed genes observed in CSF and in blood. Since blood is much more accessible than CSF, we would like to determine whether plasma biomarkers for elevated ICP can be identified. This may eventually lead to a blood test to diagnose intracranial hypertension.

Zanello, S. B.↗

NASA NTRSDate not supplied

Interpretable Machine Learning Models for Autonomous Characterization of Analogue Ocean World Seawater Chemistry and Biosignature Potential Using Isotope Ratio Data

Background: Future missions to ocean worlds, such as Enceladus and Europa, will attempt to characterize the subsurface seawater chemistry and assess the potential for life. Such missions will be equipped with capabilities to precisely measure volatile isotopes in plumes, atmospheres, and exospheres. Motivation: While large isotopic fractionations can indicate a biological source, there are signatures resulting from abiotic geochemical processes that mimic isotopic biosignatures. While machine learning (ML) has the potential to disentangle competing effects and biotic mimicry, high-dimensional isotope ratio mass spectrometry (IRMS) data is likely to contain noise/irrelevant features and involve complex statistical interactions that make human inference and interpretation difficult. Further, ML predictions with as far-reaching implications as an extraterrestrial biosignature on an ocean world requires the use of interpretable models (i.e., not “black box” models) with physically and mathematically meaningful feature spaces along with false positive diagnostics. Methods: We use volatile CO2 IRMS data of analogue ocean world seawaters to validate an ML approach to provide biogeochemical context for biosignature detection. We employ a feature selection method called nearest-neighbor projected distance regression (NPDR) that detects statistical interactions and helps elucidate the mechanisms of the Random Forest classification models. Results: We train and validate predictive ML models on volatile CO2 IRMS data of analogue ocean world seawaters to predict major salt components (e.g., MgSO4, NaHCO3), pH, ionic strength, and the presence of biosignatures. Features derived from IRMS measurements are augmented with extracted time-series features. Our results show high test accuracy and interpretability, which is increased by interaction network visualization, sample-wise variable importance scores, and single-sample class probability estimates. We demonstrate an ML mission software solution that triggers autonomous data transmission and biogeochemical sample prediction.

geochemistry↗

NASA NTRSDate not supplied

Architecting Safer Autonomous Aviation Systems

The aviation literature gives relatively little guidance to practitioners about the specifics of architecting systems for safety, particularly the impact of architecture on allocating safety requirements, or the relative ease of system assurance resulting from system or subsystem level architectural choices. As an exemplar, this paper considers common architectural patterns used within traditional aviation systems and explores their safety and safety assurance implications when applied in the context of integrating artificial intelligence (AI) and machine learning (ML) based functionality. Considering safety as an architectural property, we discuss both the allocation of safety requirements and the architectural trade-offs involved early in the design lifecycle. This approach could be extended to other assured properties, similar to safety, such as security. We conclude with a discussion of the safety considerations that emerge in the context of candidate architectural patterns that have been proposed in the recent literature for enabling autonomy capabilities by integrating AI and ML. A recommendation is made for the generation of a property-driven architectural pattern catalogue.

Architecture patterns↗

NASA NTRSDate not supplied

Architecting Safer Autonomous Aviation Systems

Architecture patterns↗

NASA NTRS2022

A Robust Machine Learning Schema for Developing, Maintaining, and Disseminating Machine Learning Models

Recent advances in the development of machine learning (ML) algorithms have enabled the creation of predictive models that can improve decision making, decrease computational cost, and improve efficiency in a variety of fields. As an organization begins to develop and implement such models, the data used in the training, validation, and testing of ML models, the model parameters, and the use cases or limitations of the models must be properly stored to ensure models are both fully traceable and used correctly. In the context of predicting material behavior, advances in computationally intense, physics-based modeling of material behavior at various length scales and the emergence of Integrated Computational Materials Engineering (ICME) have driven the need for developing data-driven surrogate models of the physics-based simulation tools using ML techniques. Surrogate model development allows for accurate material behavior prediction at a fraction of the cost of its physics-based counterpart, allowing for multiscale simulations of real-world applications, further enabling the ability to design fit-for-purpose materials for a reasonable computational investment. However, training such models requires extensive data, and thus, effective data management is necessary to reach the full potential that ML can offer to material design and ICME. This paper proposes a generalized, robust schema that allows organizations to store both real (experimental) and virtual (simulation) data used to train ML models and the defining model parameters and architectures within the Granta MI Platform. The developed schema allows for various types of data inputs and outputs, including single point values, time-series data, and images that can be used in the prediction of material behavior, while following outlined best practices for effective data management. An effective schema for ML data and models can help prevent the recreation of virtual/real training data and surrogate models, help reduce the time to create new models similar to existing ones by offering a starting point in the hyperparameter determination stages, minimize resources devoted to verification and validation (V&V) and certification of models, and ensure that data and surrogate models are not misused due to full traceability of both the data and ML model. It also allows organizations access to models that have already been developed, such that they can be used in the design of new materials, enabling the overall goals of ICME.

Brandon L. Hearley↗

NASA NTRSDate not supplied

Predicting Fiber Failure of Plain Weave Fabric with Recursive Multiscale Micromechanics

Recent advances in the development of machine learning (ML) algorithms have enabled the creation of predictive models that can improve decision making, decrease computational cost, and improve efficiency in a variety of fields. As an organization begins to develop and implement such models, the data used in the training, validation, and testing of machine learning models, the model parameters, and the use cases or limitations of the models must be properly stored to ensure models are both fully traceable and used correctly. In the context of predicting material behavior, advances in computationally intense, physics-based, modeling of material behavior at various length scales, and the emergence of Integrated Computational Materials Engineering (ICME) have driven the need for developing data-driven surrogate models of the physics-based simulation tools using machine learning (ML) techniques. Surrogate model development allows for accurate material behavior prediction at a fraction of the cost of its physics-based counterpart, allowing for multiscale simulations of real-world applications, further enabling the ability to design fit-for-purpose materials for a reasonable computational investment. However, training such models requires extensive data, and thus effective data management is necessary to reach the full potential that ML can offer to material design and ICME. This paper proposes a generalized, robust schema that allows organizations to store both real (experimental) and virtual (simulation) data used to train machine learning models and the defining model parameters and architectures. The developed schema allows for various types of data inputs and outputs, including single point values, time-series data, and images that can be used in for various types of machine learning models while following outlined best practices for effective data management. An effective schema for machine learning data and models can help prevent the recreation of virtual/real training data and surrogate models, can help reduce the time to create new models similar to existing ones by offering a starting point in the hyperparameter determination stages, minimize resources devoted to verification and validation (V&V) and certification of models, and ensure that data and surrogate models are not misused due to full traceability of both the data and ML model. It also allows organizations access to models that have already been developed, such that they can be used in the design of new materials, enabling the overall goals of ICME.

Failure↗

NASA NTRS2024

Recommendations on Evidence and Process for Certification of Learning-enabled Components in Aerospace Systems

This report primarily identifies a collection of relevant and necessary evidence for assurance of machine learnt components (MLCs)—also known as learning-enabled components—integrated into aircraft systems, and gives preliminary suggestions on the elements of a certification process that invoke the identified evidence. The main focus is on feedforward neural networks that are static and trained offline through supervised learning. A brief background on the generic elements of the lifecycle of an MLC is given to contextualize the assurance considerations and, consequently, the evidence that is relevant and necessary to support certification. At the level of an MLC, those considerations relate to: (i) the consistency and correctness of MLC contributions to system functions in the context of a validated functional intent; and (ii) the absence of MLC contributions to aircraft-level failure conditions. At an ML model level, confidence in model and data properties contribute to assurance of the containing MLC, in particular: (a) generalizability and robustness of models, in the presence of inputs not previously seen during training, disturbances to inputs, and unexpected inputs; and (b) valid data, i.e., data that are at least representative, relevant, complete, and accurate. Evidence for the above span the elements of the ML lifecycle, and includes, at a minimum, lifecycle artifacts that pertain to: (1) properties of requirements capturing functional intent, safety constraints, and aspects of the intended use and operating environment; (2) model performance, model complexity and design, and algorithm choice; (3) achievement of required performance at the levels of a trained model during model development, a trained model after model development is complete, and a trained model that is transformed into an executable equivalent; (4) model implementation aspects necessary for transforming a trained model into the executable equivalent; (5) integration of the executable trained model into the containing MLC, and eventually the larger system; and, (6) lastly, the verification and validation (V&V) of each of the above. Such V&V lifecycle artifacts themselves include: aspects of coverage, e.g., of various levels of requirements by the input space of the model and the data; traceability (where applicable); application of formal methods for property specification, analysis, and checking. Examples of evidence generation methods and tools further ground the discussion on what constitutes evidence, and the contribution to assurance during certification. The identified assurance considerations and supporting evidence is not a comprehensive set. Additionally, neither what should be considered as sufficient evidence relative to the assigned criticality of an MLC, nor how criticality ought to be determined and adjusted, have been considered in this report. However, suggestions are made for potential activities of the ML lifecycle that are aimed at providing confidence that an MLC can be relied upon when integrated into its containing (aircraft) system. Those activities are proposed as candidate elements of a certification process for MLCs. The main purpose of this report to inform regulatory guidance and consensus standards that may be used to meet the safety intent of the applicable regulations.

Aviation safety↗

NASA NTRS2002

Maximum Likelihood Estimation of the Broken Power Law Spectral Parameters with Detector Design Applications

The method of Maximum Likelihood (ML) is used to estimate the spectral parameters of an assumed broken power law energy spectrum from simulated detector responses. This methodology, which requires the complete specificity of all cosmic-ray detector design parameters, is shown to provide approximately unbiased, minimum variance, and normally distributed spectra information for events detected by an instrument having a wide range of commonly used detector response functions. The ML procedure, coupled with the simulated performance of a proposed space-based detector and its planned life cycle, has proved to be of significant value in the design phase of a new science instrument. The procedure helped make important trade studies in design parameters as a function of the science objectives, which is particularly important for space-based detectors where physical parameters, such as dimension and weight, impose rigorous practical limits to the design envelope. This ML methodology is then generalized to estimate broken power law spectral parameters from real cosmic-ray data sets.

Howell, Leonard W.↗

NASA NTRS2003

Syringe filtration methods for examining dissolved and colloidal trace element distributions in remote field locations

It is well-established that sampling and sample processing can easily introduce contamination into dissolved trace element samples if precautions are not taken. However, work in remote locations sometimes precludes bringing bulky clean lab equipment into the field and likewise may make timely transport of samples to the lab for processing impossible. Straightforward syringe filtration methods are described here for collecting small quantities (15 mL) of 0.45- and 0.02-microm filtered river water in an uncontaminated manner. These filtration methods take advantage of recent advances in analytical capabilities that require only small amounts of waterfor analysis of a suite of dissolved trace elements. Filter clogging and solute rejection artifacts appear to be minimal, although some adsorption of metals and organics does affect the first approximately 10 mL of water passing through the filters. Overall the methods are clean, easy to use, and provide reproducible representations of the dissolved and colloidal fractions of trace elements in river waters. Furthermore, sample processing materials can be prepared well in advance in a clean lab and transported cleanly and compactly to the field. Application of these methods is illustrated with data from remote locations in the Rocky Mountains and along the Yukon River. Evidence from field flow fractionation suggests that the 0.02-microm filters may provide a practical cutoff to distinguish metals associated with small inorganic and organic complexes from those associated with silicate and oxide colloids.

Colloids/analysis↗

NASA NTRSDate not supplied

Machine Learning Algorithms for Aerosol and Cloud Detection Using CATS on the ISS

Clouds and aerosols are one of the largest uncertainties in understanding and forecasting the Earth’s changing climate system. The type and height of aerosols are important factors in determining the top-of-atmosphere (TOA) radiation budget, either direct reflection of solar radiation back to space and/or absorption of solar radiation. In addition to their impact on the Earth’s climate system, aerosols near the surface from wildfires, man-made pollution events, and dust storms are hazardous to human health. The phase and height of clouds also play a critical role in determining the role of clouds in the Earth’s climate system. Cirrus clouds in the upper troposphere can induce a significant daytime TOA warming effect, while liquid water clouds near the surface cause a large corresponding cooling effect. Lidar measurements provide accurate vertically resolved information about clouds and aerosols, including complex multi-layer scenes where passive sensors are challenged and at night, when passive sensors are unable to measure cloud and aerosol properties. The Cloud-Aerosol Transport System (CATS) is a lidar instrument that operated for 33 months on the International Space Station (ISS) at the 1064 nm wavelength to measure attenuated total backscatter and depolarization ratio. These fundamental measurements are used to derive “vertical feature mask” cloud and aerosol products, including layer top/base heights, layer geometrical thickness, aerosol type, and cloud phase. While space-based lidar systems like CATS provide cloud and aerosol vertical distributions that improve our understanding of the climate system, averaging of the daytime data from these sensors is required, at the expense of spatial resolution, to improve the daytime signal-to noise (SNR) and thus atmospheric layer detection. This presentation shows results from machine learning (ML) techniques that, when applied to CATS data: 1. improve the 1064 nm SNR 2. enable detection of atmospheric features during daytime with a horizontal resolution of 350 m or 5 km (compared to the 60 km required for standard CATS data products) 3. increase the number of atmospheric layers detected in the CATS data. A Convolutional Neural Network (CNN) trained using CATS standard data products also demonstrated the potential for improved cloud-aerosol discrimination, cloud phase, and aerosol typing compared to the operational CATS algorithms for cloud edges and complex near-surface scenes during daytime. The ML tools described in this paper can facilitate the development of smaller, low-cost lidar systems in the future and enable real-time accessibility of lidar data products from future lidar systems for monitoring and forecasting of hazardous events.

John Yorks↗

NASA NTRSDate not supplied

Fusion of Test and Analysis: Artemis I Booster to Mobile Launcher Interface Validation

NASA is in the midst of bold and exciting next steps in human exploration and spaceflight. The designs of the new Space Launch System (SLS), the Orion spacecraft and the Exploration Ground Systems (EGS) for vehicle processing and launch are essentially complete and there has been significant progress in manufacturing and assembly of specific hardware for the Artemis I and Artemis II missions. Equally as important, the program level and integrated system level testing and analyses are also well underway to support integrated verification, validation, and Certificate of Flight Readiness (CoFR) for Artemis I. Testing and analysis are key to addressing technical challenges faced by the Artemis missions. Building block approaches are required that provide the right balance between component, element, and/or system level testing that satisfies verification and validation objectives where uncertainties are quantified and minimized. Artemis I is a system of systems that requires a fusion of test and analysis that adeptly characterizes critical interfaces between major program elements. An example of this fusion involves characterizing the interface between the SLS booster and the Mobile Launcher (ML) Vertical Support Post (VSP) interfaces. Proper characterization of this interface represents a number of challenges beginning with the fact that it is a mating of ground support structure in the form of a civil structure to flight hardware. Both sides of the interface are built to different construction standards, but are governed by interface requirements to ensure compatibility when mated. From past program experience, the flexibility at the booster to ML interface is critical in developing accurate prelaunch stacking and cryogenic preloads, squat loads, and pad separation release of preloads and squat loads. This same premise holds for Artemis I. To characterize the asymmetric characteristics at this interface, careful consideration of static forces due to gravity loading with the commensurate effects due to leveling during booster stacking (i.e., spacing and shimming) and nonlinear geometric forces are necessary for inclusion in pre-test assessments. This paper will look at these issues for the upcoming Booster Pull Test in which two boosters will be installed on the ML and one of these boosters will undergo static lateral loading followed afterwards with dynamic excitation into resonance and free-decay. This paper evaluates the booster to ML interface characteristics by characterizing the interface flexibility between the booster aft skirt and the ML VSP interfaces. Furthermore, this paper methodically evaluates the effect of the following on the test outcome: gravitational effects on the booster and ML, the effects of VSP leveling, spacing, and shimming under gravitational loading during booster stacking, the effect of geometric nonlinear follower force due to cg offset as booster is laterally displaced, and the system coupling between the booster under test, ML, and the second booster. Simulated results for a static load pull and dynamic excitation provide insight into the differences in measurement responses when boundary conditions and geometric conditions are included and not included.

Joel W Sills Jr.↗