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NASA NTRS2022

Molecular information theory meets protein folding

We propose an application of molecular information theory to analyze the folding of single domain proteins. We analyze results from various areas of protein science, such as sequence-based potentials, reduced amino acid alphabets, backbone configurational entropy, secondary structure content, residue burial layers, and mutational studies of protein stability changes. We found that the average information contained in the sequences of evolved proteins is very close to the average information needed to specify a fold ~2.2 ± 0.3 bits/(site operation). The effective alphabet size in evolved proteins equals the effective number of conformations of a residue in the compact unfolded state at around 5. We calculated an energy-to-information conversion efficiency upon folding of around 50%, lower than the theoretical limit of 70%, but much higher than human built macroscopic machines. We propose a simple mapping between molecular information theory and energy landscape theory and explore the connections between sequence evolution, configurational entropy and the energetics of protein folding.

Ignacio E. Sánchez↗

NASA NTRS2022

Mars Terrain Segmentation with Less Labels

Planetary rover systems need to perform terrain segmentation to identify drivable areas as well as identify specific types of soil for sample collection. The latest Martian terrain segmentation methods rely on supervised learning which is very data hungry and difficult to train where only a small number of labeled samples are available. Moreover, the semantic classes are defined differently for different applications (e.g., rover traversal vs. geological) and as a result the network has to be trained from scratch each time, which is an inefficient use of resources. This research proposes a semi-supervised learning framework for Mars terrain segmentation where a deep segmentation network trained in an unsupervised manner on unlabeled images is transferred to the task of terrain segmentation trained on few labeled images. The network incorporates a backbone module which is trained using a contrastive loss function and an output atrous convolution module which is trained using a pixel-wise cross-entropy loss function. Evaluation results using the metric of segmentation accuracy show that the proposed method with contrastive pre-training outperforms plain supervised learning by 2%-10%. Moreover, the proposed model is able to achieve a segmentation accuracy of 91.1% using only 161 training images (1% of the original dataset) compared to 81.9% with plain supervised learning.

Wilson, Brian D↗

NASA NTRS2022

The Psyche Planning Software Subsystem: Creating a Robust Toolset for a Discovery-class Mission

Psyche is a Discovery-class mission to the small metal-rich asteroid (16) Psyche, and is slated to launch in 2022. Psyche, like many missions, requires low-cost activity planning and sequence generation that serves as the backbone to overall uplink design. Such tools must be maintainable over long periods of operations, and powerful enough to solve complex issues that deep-space one-off missions encounter. In this paper we introduce cost-effective solutions that leverage inner- and open-source principles to meet a variety of common and novel use cases.The uplink process that was designed to meet these challenges is presented, as well as the data-flow through the high-level architecture of the planning software subsystem. The user-facing planning tools are described, particularly the Science Opportunity Analyzer, the Plan Editor, Psyche’s planning automation in the Blackbird framework, and Psyche Simulation Reports. All these applications are either new or have been substantially revamped to meet Psyche’s concept of operations. In particular, ensuring the entire toolchain can correctly process epoch-relative activities is discussed. Underlying the main applications are a common set of dependencies developed and maintained by a new cross-mission association of planning developers. In this way, Psyche can inherit well-tested functionality which saves effort and ensures its developers can focus on solving domain challenges. Quality control of the applications and libraries is ensured with a code-review and unit-test based novel ‘CM lite’ process. Collaboration with international industry and academia using the open-source modules is already occurring.The planning and scheduling software is designed to maximize operator awareness of the integrated plan at every step of the process and use common interfaces and file formats to easily transfer information. Design choices plus the team’s test-driven development process enables more expansive capabilities compared to the decentralized planning and sequence generation functions typical of Discovery-class orbiters without significant development cost increases. Benefits and drawbacks of Psyche’s approach are discussed, including comparison to other missions and tools where appropriate.

Ramanathan, Keshav↗

NASA NTRS2022

Mars 2020 Perseverance Rover SHERLOC Instrument Isolation System

The NASA Jet Propulsion Laboratory (JPL) successfully landed the Mars 2020 Perseverance Rover at Jezero Crater on the Martian surface. Perseverance’s main mission objective is to cache Martian rock and regolith samples in hermetically sealed tubes to be brought back to Earth by future missions. To achieve this goal the rover is equipped with a 2-meter-long Robotic Arm (RA) which manipulates the Turret to interact with the surface. The Turret is comprised of science instruments and tools that enable surface sampling and science. The backbone of the Turret hardware is the Rotary Percussive Coring Drill. The science instruments, which are directly mounted to the structural housing of this drill, must be able to withstand not only the launch and entry, descent and landing (EDL) loads of the vehicle but also the dynamic percussive environment, induced by the drill throughout surface operations. This paper discusses the technical hardware design, development and testing of a vibration isolation system to protect the Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals (SHERLOC) Instrument from these dynamic environments. The hardware design aspect of this problem was limited by both size and mass constraints. The Turret is tightly configured and the physical space in which hardware can reside is restricted due to multiple axes of surface interaction points. Another driving requirement is the extreme range of thermal non-operational environment from -135C to +90C. This, in addition to SHERLOC being a heavier instrument than previously integrated on a turret, made the use of isolation systems employed on previous rover missions problematic. The final design incorporates custom wire mesh springs preloaded within titanium hexapod struts equipped with flexure ends. Custom versions of commercially available wire mesh springs were designed and tested. These custom springs differed in dimension, density and stiffness from the off the shelf options. The newly designed springs went through various stages of flight acceptance testing. Detailed physics-based modeling to understand what loads the isolation system would experience was used to define the test conditions for the springs and entire isolation subsystem. The testing program developed around this was extensive to fully characterize the isolation performance over cleanliness levels, temperature and life. Another major aspect of this work was developing a method of replicating the drill’s percussive environment for performance and life testing of Turret mounted hardware. Attaching the instrument to an operating drill for testing was not a viable option. A method of using a Highly Accelerated Life Test (HALT) table was developed and applied across the project as the most representative and tunable option to physically simulate this new environment. This method, along with launch environment random vibration testing, was used to build an entire dynamic qualification and flight hardware test program. The results of the design, modeling, testing and analysis of the SHERLOC Isolation System is described throughout the following paper.

Krafchak, Todd↗

NASA NTRSDate not supplied

A Holistic Approach to Procedures

Aviation is a dynamic industry which is constantly changing. These changes require the continuous update of people’s knowledge and skills. A fundamental part of that knowledge and skill is procedures, because procedures form the backbone of aviation operations. On the flightdeck, on the ramp, and in the maintenance shop, procedures and checklists help support pilots, line crews, and mechanics in performing their work effectively, efficiently, and thus -- safely. To design effective and efficient procedures and checklists, one must take into account the full operational context within which these procedures are embedded. This context is defined by the requirements of the technology, the capabilities and limitations of the human operators, and the constraints and affordances of the operational environment. The complexity of this context arises from the interactions of the human, machine, and environment. Procedures are in place to govern those interactions. We present a model of that operational context, namely THE Model (Technology, Human, Environment in the context of a Mission), that lays a foundation for the analysis of each of these elements and their interactions. But procedures have their limitations, and there are risks involved in over-proceduralizing. To understand these limitations and risks, and to determine when to proceduralize and when not, we present the 4P Framework (Practice, Procedures, Policies, Philosophy) as a holistic approach to procedures. This approach brings together the understanding of human behavior, of organizational and operational factors, and of the technologies involved.

procedures↗

NASA NTRS2023

xPLSS Structural Backplate Design, Manufacture, and Test Overview

NASA Johnson Space Center (JSC) has been developing and building a new detailed design of the Exploration Extravehicular Mobility Unit (xEMU) space suit to support future International Space Station (ISS) and Lunar Artemis missions. The Exploration Portable Life Support System (xPLSS) of the xEMU contains some clever new technologies to meet the requirements to provide Extra Vehicular Activity (EVA) capability on the ISS and the Moon. The suit must interface with and fit through ports and airlocks of at least three vehicles: ISS, Human Landing System (HLS), and Gateway. The first partial xPLSS experimental flight unit called SWME EXPRESS Rack Flight Experiment (SERFE) that was a rack-deployed thermal control system payload on the ISS for two years (2020-2022). The first complete assembly of the xEMU was built as a Design Verification Test (DVT) unit (2022); similar to but more extensive than an Engineering Development Unit (EDU). The xPLSS Backplate serves not only as the structural backbone of the xPLSS and SERFE systems but also contains innovative design features to reduce the: mass, complexity, parts count, number ofseals (and therefore leak potential), and outer dimensions of the entire assembly. This paper provides an overview of these new design features, manufacturing processes, system interfaces, and SERFE/DVT test results of the Backplate as part of the xPLSS/xEMU.

xPLSS↗

NASA NTRS2023

Quantitative Gas Property Measurements By Filtered Rayleigh Scattering: A Review

Filtered Rayleigh scattering (FRS) is a laser-based diagnostic technique used to nonintrusively quantify various thermodynamic properties of a light-scattering gas. The backbone of FRS is the molecular filtering of Rayleigh scattered light. This concept was initially introduced by the atmospheric LIDAR community before being adopted within the aerospace research field in the early 1990s. Since then, FRS has matured into a versatile quantitative diagnostic tool and has found use in a variety of flow regimes ranging from sub- to supersonic speeds in both reacting and nonreacting environments. This adoption can be attributed to the wealth of information that can be obtained via FRS, including the gas density, pressure, temperature, velocity, species composition, or, in some cases, several of these properties at once. This article reviews the current state of FRS methodology in recovering such gas properties. As knowledge of the fundamentals of Rayleigh scattering and spectral light filtering is crucial to the design of an FRS experiment, we begin by briefly reviewing these areas. Subsequently, we conduct a survey of experimental design strategies, assumptions, and data reduction methods used to measure different gas properties using FRS. We conclude the review with a short discussion on quantification of experimental uncertainty and future trends in FRS.

filtered Rayleigh scattering↗

NASA NTRS2023

Elastic Modulus Testing for TriTruss Struts

The TriTruss is a novel structural module developed by researchers at NASA Langley Research Center (LaRC) that can be used in space to assemble large backing structures for a variety of applications. One such application is the metering truss and primary mirror backbone support structure of an in-space assembled telescope(iSAT). For the iSAT application, the TriTruss will be supporting mirror segments, payloads, and instruments, all of which require the TriTruss to have robust structural integrity. An accurate elastic modulus value is crucial to the finite element analysis(FEA) of the structure. Initial testing indicated that the equivalent elastic modulus provided by the manufacturer and used in the analyses did not accurately predict the behavior of the structure. In this paper, the in-house testing method developed at LaRC to accurately measure the equivalent elastic modulus of the composite struts for use in the analyses is described. A tensile and compressive equivalent elastic modulus was computed from the data obtained in this test. The testing resulted in a tensile equivalent elastic modulus of 24.6 Msi (standard deviation of 0.888 Msi) or 169 GPa (standard deviation of 6.20 GPa) and a compressive equivalent elastic modulus of 22.8 Msi (standard deviation of 0.780 Msi) or 157 GPa(standard deviation of 5.38 GPa) for the single wall thickness struts. For double wall thickness struts, the testing resulted in a tensile equivalent elastic modulus of32.1 Msi (standard deviation of 1.003 Msi) or 221 GPa (standard deviation of 6.91GPa) and a compressive equivalent elastic modulus of 31.9 Msi (standard deviation of 1.123) or 220 GPa (standard deviation of 7.74 GPa).1

TriTruss↗

NASA NTRSDate not supplied

Interactive Framework to Support Open Model-Data Validation Efforts at the CCMC

Validation of models using observation data has been a central activity at the Community Coordinated Modeling Center (CCMC) over more than a decade. The Comprehensive Assessment of Models and Events using Library Tools (CAMEL) framework is a database-driven implementation of the interactive analysis of model-data comparisons that simultaneously provides a view across a multitude of locations and time periods. CAMEL thus extends the single-location or single-trajectory timeseries data comparison capabilities provided by the CCMC online visualization that formed the backbone of initial validation efforts. We demonstrate CAMEL capabilities for an example application to study Neutral Density in the upper atmosphere among comparisons in the heliosphere, the Earth’s radiation belt, magnetosphere, and ionosphere-thermosphere-mesosphere domains.

Lutz Rastaetter↗

NASA NTRSDate not supplied

Integrated In-Situ Resource Utilization Modeling of a Lunar Water Processing System

A key element of achieving a sustained surface presence, such as defined in NASA’s Artemis plan, is In-Situ Resource Utilization (ISRU). ISRU is the practice of using local resources to provide mission consumables that reduce system launch mass requirements, and regenerate resources (chiefly, water and oxygen) for propulsion and life support supporting both Lunar and Martian missions. ISRU systems require multiple complex processes, such as excavation, chemical reactors, and electrolysis subsystems that must operate in harmony to optimize the overall system process from beginning to end. The Mission Analysis and Integration Tool (MAIT) connects individual subsystem models into a customized, flexible framework for the purpose of technology downselect, optimization, and end-to-end process planning. MATLAB was favorable to use as the main software integration tool due to its ability to communicate with a vast number of other programming languages and makes up the backbone of data flow between inputs and outputs to the subsystem models. MAIT initially evaluated a suite of technologies, including but not limited to, the water processing Lunar Auger Dryer for ISRU (LADI) system and an oxygen extraction carbothermal reduction process. With individual models consolidated, the MAIT tool generated over 60,000 cases during its parametric sweeps; these system iterations produced valuable insight into the optimal LADI geometry for minimizing heater energy demands, estimating carbothermal reactor and radiator mass relationships, and calculated the power dynamics of the electrolysis unit. Efforts to update the MAIT tool are ongoing to accommodate and scale ISRU technologies supporting the Space Technology Mission Directorate’s (STMD) commercialization strategy, and increase the MAIT software capability to handle a wide array of ISRU system models beyond the Lunar environment, e.g. production of propellant for a Martian lander.

Avery Carlson↗

NASA NTRSDate not supplied

Operational Guidance

Procedures and checklists form the backbone of all operations. But a given procedure does not exist in a vacuum. It is part of a large system, and it must fit well within it. For all procedures to fit well, we must take a comprehensive approach to their design. We present such an approach and the results we got when we applied it.

procedures↗

NASA NTRSDate not supplied

In-Situ Resource Utilization Modeling of a Lunar Water Processing System

A key element of achieving a sustained surface presence, such as defined in NASA’s Artemis plan, is In-Situ Resource Utilization (ISRU). ISRU is the practice of using local resources to provide mission consumables that reduce system launch mass requirements, and regenerate resources (chiefly, water and oxygen) for propulsion and life support supporting both Lunar and Martian missions. ISRU systems require multiple complex processes, such as excavation, chemical reactors, and electrolysis subsystems that must operate in harmony to optimize the overall system process from beginning to end. The Mission Analysis and Integration Tool (MAIT) was previously developed with MATLAB in FY22 to connect individual subsystem models into a customized, flexible framework for the purpose of technology downselect, optimization, and end-to-end process planning. Beginning in FY24, MAIT was leveraged and evolved using MATLAB/Simulink due to its ability to communicate with a vast number of other programming languages and makes up the backbone of data flow between inputs and outputs to the subsystem models. MAIT initially evaluated a suite of ISRU-related technologies, including the water processing Lunar Auger Dryer for ISRU (LADI) system with integrated upstream excavation and downstream electrolysis subsystems. With individual models consolidated, the MAIT tool generated over 60,000 cases during its parametric sweeps; these system iterations produced valuable insight into the optimal LADI geometry for minimizing energy demands, estimating carbothermal reactor and radiator mass relationships, and calculated the power dynamics of the electrolysis unit. Advanced efforts with advanced models will include examining multiple production targets to demonstrate the ability to scale ISRU technologies supporting the Space Technology Mission Directorate’s (STMD) commercialization strategy, and increase the MAIT software capability to handle a wide array of ISRU system models beyond the Lunar environment, e.g. production of propellant for a Martian lander.

Avery Carlson↗

NASA NTRSDate not supplied

Enhancing Air Traffic Control Planning with Automatic Speech Recognition

The decisions made during the Federal Aviation Administration Air Traffic Control System Command Center's planning teleconferences hold significant sway over the National Airspace System. Held every two hours, these teleconferences convene air traffic managers and stakeholders from across the nation to discuss airspace conditions, weather, and constraints, leading to the formulation and adjustment of traffic management initiatives. Given the critical nature of these decisions, the need for accurate and efficient record-keeping is paramount. In recent years, the application of automatic speech recognition has gained popularity across diverse industries, including aviation. While traditional applications focus on transcribing air traffic control communication, this paper explores a unique application of automatic speech recognition by converting the audio from planning teleconferences into text transcriptions. This innovative approach addresses key challenges in the field, presenting potential benefits for quality assurance, real-time participation, and downstream natural language processing tasks. A notable breakthrough in the machine learning community, namely the transformer neural network architecture, forms the backbone of the proposed solution in this paper. The transformer architecture's role in this research represents a paradigm shift in the efficiency of automatic speech recognition models. By reducing the amount of in-domain training data required, this architecture allows for the fine-tuning of such models like Whisper, originally pretrained on vast English speech datasets. The adaptability of the transformer architecture proves invaluable in capturing the nuances of aviation terminology and specific language used in planning teleconferences. Leveraging the Whisper model as a baseline, our research details the fine-tuning and validation using a dataset comprising 20 hours of meticulously transcribed planning teleconferences. Notably, the baseline pretrained Whisper model exhibited a word error rate of 18.77%. Through the fine-tuning process, the model achieved a substantial improvement, demonstrating an impressive performance with a reduced word error rate of 6.82%. This substantial decrease in WER not only highlights the effectiveness of the transformer architecture but also emphasizes the practical advancements achieved through the application of automatic speech recognition in this specific domain. The utilization of automatic speech recognition in planning teleconferences in this work introduces several novelties. Firstly, the creation of text transcriptions offers a valuable tool for quality assurance and facilitates the efficient review of teleconferences. This is an important aspect of the proposed solution, given the time-sensitive and high-stakes nature of decisions made during these meetings. Furthermore, text-searchable transcriptions provide a streamlined approach for locating and validating critical information, potentially saving hours of manual effort in searching through audio recordings. Moreover, our research identifies a key use case for external facilities and stakeholders. In situations where attendance at the planning teleconference is not feasible, having access to text transcriptions in real-time or shortly after the teleconference ends, proves to be a time-saving and informative resource. This feature enhances collaboration and ensures that stakeholders can stay abreast of important discussions and decisions even in their absence. Despite the efficiency gains facilitated by the transformer architecture in automatic speech recognition technology, it is essential to acknowledge the human factors in data creation. Subject matter experts play a crucial role in accurately transcribing planning teleconferences due to the specificity and complexity of the information discussed. The research dataset, consisting of 20 hours of transcribed planning teleconferences, forms the foundation for fine-tuning and validating the Whisper model. The achieved word error rate of 6.82% demonstrates promising advancements, particularly in recognizing essential aviation terminology within the teleconferences. In conclusion, this paper presents a comprehensive exploration of the application of automatic speech recognition in Air Traffic Control System Command Center planning teleconferences, leveraging the transformer architecture for enhanced efficiency. The novel contributions lie in the improved accessibility of decision-making records, real-time participation opportunities for external stakeholders, and the potential for downstream natural language processing advancements. As the aviation industry continues to evolve, the integration of automatic speech recognition technologies holds the promise of revolutionizing decision-making processes and contributing to the overall safety and efficiency of air traffic management.

ATM↗

NASA NTRS2024

Structural Characterization of a TriTruss Module

The TriTruss is a novel structural module developed by researchers at NASA Langley Research Center (LaRC) that can be used in space to assemble large support structures for a variety of applications. One such application is the metering truss or primary mirror backbone support structure of an In-Space Assembled Telescope (iSAT). For the iSAT application, the TriTruss will be supporting mirror segments, payloads, and instruments, all of which require the TriTruss to have a high stiffness. Structural characterization from testing and analysis is needed to ensure the integrity of the struts that make up a TriTruss module is maintained when subjected to loads representative of the application. The test setup and loads applied to the TriTruss module as well as the analytical methods used to predict the response of the structure under conditions representative of those implemented during testing will be discussed. Also, the results obtained from testing and analysis will be summarized. The goal for the characterization study was to achieve a correlation within 10% between the test data and the analysis. Overall, the correlation varied for the struts with a few struts still having a larger error margin after further studies were conducted to improve the correlation through additional analysis.

Characterization↗

NASA NTRSDate not supplied

Lessons Learned from NASA Goddard Space Flight Center’s Product Development Lead Training Schedule and Cost Development Workshop: Continuous Improvement

This presentation provides a status of the Goddard Space Flight Center (GSFC) effort to increase foundational knowledge of Product Development Leads (PDLs) in schedule and cost management including earned value management (EVM). In 2012, GSFC’s Engineering and Technology Directorate (ETD) implemented an in-house training program to prepare PDLs for managing the technical, cost, schedule, and risk aspects of spaceflight systems to meet their subsystem commitments. Developed in-house, the PDL training program provides an integrated approach to requirements development, risk, schedule and cost management, EVM, performance tracking, and other areas. The program has been held twice yearly since its inception with 531 participating and 451 completing the curriculum. In 2017, the program won the Robert H. Goddard award for Quality and Process Improvement. Program development and evolution were presented in the 2018 NASA Schedule and Cost Symposium. The presentation was so well received that this year we focus on one workshop within the program: Schedule and Cost Development, including EVM. We examine the on-going logic modeling process and how participant and stakeholder data influence workshop content and design, and how the disciplines of schedule and cost contribute to mission success. In this presentation we refresh you on how the approach integrates lecture, small group discussion, estimating, case study exercises, and problem solving. We update you on the data collected from participants and stakeholders, and we discuss how we use these data to measure training effectiveness. Specific topics include: • How the logic model is used as the backbone for continuous program improvement, • How feedback influences implementation and curriculum updates, • How data collection and analysis inform workshop content and development, including participant discoveries of EVM data, • How including the resource analyst and planner in the product development team supports project success.

Lessons Learned↗

NASA NTRS2024

Experimentally Modeling the Emergence of Prebiotically Plausible Phospholipid Vesicles

The prebiotic emergence of protocells is an important part of any origins of life scenario. Although fatty-acid-based vesicles are well studied, how they transitioned to phospholipid vesicles is uncertain. Herein, we show that cyclic-phospholipids formed from fatty acids and glycerol could have played a role by generating a heterogeneous library of vesicles with diverse morphologies and tolerance to a range of metal ions, temperature, and pH. The cyclic phosphate moiety facilitates the natural emergence of vesicles composed of diacyl-phospholipids to become part of the chemical evolutionary process. Competing emergent properties of the various systems (facilitated by additives) could have led to an early preference of the sn-1,2-acyl-substitution on the glycerol backbone coincidental with extant biology. Thus, cyclic-phospholipids could have played a significant role not only in early prebiotic protocellular chemistry but also in facilitating the chemical evolution of protocells from the structurally simple to the functionally more complex.

Sunil Pulletikurti↗

NASA NTRSDate not supplied

Thinking About Procedures

procedures↗

NASA NTRSDate not supplied

Clifford Neural Operators on Atmospheric Data Influenced Partial Differential Equations

Mathematical representations of the atmosphere are key to forecasting and research tasks across Earth science. Numerically solving the underlying partial differential equations(PDEs) of the atmosphere, however, can be difficult and computationally expensive with numerous trade-offs between computing efficiency and accuracy. Utilizing neural net-works to learn approximations of the PDE solutions from the data can help us model complex phenomena more efficiently than traditional numerical schemes. Here, we have applied Clifford algebra-based neural operators for predicting atmospheric variables. Clifford Fourier neural operators are used with two different backbone architectures, ResNet and UNet, on custom data of U10, V10 and surface pressure as well as U500, V500 and Z500. Clifford Fourier neural operators, coupled with ResNet and UNet architectures, are applied to a key reanalysis dataset. Model performance is initially strong, but we observe increasing errors, resulting in the model becoming highly unstable.

Sujit Roy↗