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NASA NTRS2021

TLSLEAF: Automatic Leaf Angle Estimates From Single-Scan Terrestrial Laser Scanning

Leaf angle distribution (LAD) in forest canopies affects estimates of leaf area, light interception, and global-scale photosynthesis, but is often simplified to a single theoretical value. Here, we present TLSLeAF (Terrestrial Laser Scanning Leaf Angle Function), an automated open-source method of deriving LADs from terrestrial laser scanning. TLSLeAF produces canopy-scale leaf angle and LADs by relying on gridded laser scanning data. The approach increases processing speed, improves angle estimates, and requires minimal user input. Key features are automation, leaf–wood classification, beta parameter output, and implementation in R to increase accessibility for the ecology community. TLSLeAF precisely estimates leaf angle with minimal distance effects on angular estimates while rapidly producing LADs on a consumer-grade machine. We challenge the popular spherical LAD assumption, showing sensitivity to ecosystem type in plant area index and foliage profile estimates that translate to c. 25% and c. 11% increases in canopy net photosynthesis (c. 25%) and solar-induced chlorophyll fluorescence (c. 11%). TLSLeAF can now be applied to the vast catalog of laser scanning data already available from ecosystems around the globe. The ease of use will enable widespread adoption of the method outside of remote-sensing experts, allowing greater accessibility for addressing ecological hypotheses and large-scale ecosystem modeling efforts.

3D↗

DOE OSTI2023

Deep Learning for Modeling Enhanced Geothermal Systems

Enhanced Geothermal Systems (EGS) offer a vast potential to expand the use of geothermal energy. Heat is extracted from this engineered system by injecting cold water into a subsurface fractures, which are in contact with the hot dry rock, and pulled through the production wells. Creating EGS requires improving the natural permeability of hot crystalline rocks. To develop economically viable EGS reservoirs, significant technical barriers (e.g., better stimulation technologies without adequate water and/or permeability) and non-technical barriers (e.g., land access and permitting) must be overcome. In this short conference paper, we present a workflow to address a part of this challenge – “How to develop economically viable EGS using existing technologies?”. Our workflow called the GeoThermalCloud (GTC) for EGS, leverages recent advances in machine learning, deep learning, and cloud computing. This GTC framework is open-source and available at https://github.com/SmartTensors/GeoThermalCloud.jl. The GTC framework provides trained deep learning (DL) models to estimate the net present value of a given EGS design scenario. The Geothermal Design Tool (https://github.com/GeoDesignTool/GeoDT.git), a fast and simplified multi-physics solver, is used to develop a database for training DL models. The database consists of EGS design parameters (inputs to DL model) and their net present value (output of DL model) in uncertain geologic systems. The EGS design parameters for constructing this training database are based on Utah FORGE but include the options of more wells and deeper depths. The DL models are trained by ingesting the EGS design parameters and estimating the corresponding net present value. Such an emulation allows us to screen various EGS designs quickly and identify good development strategies by coupling them with optimization techniques. Our preliminary results show promise in DL emulation of net present value. However, a lot more work is needed to improve the predictive capability of DL models (i.e., extensive hyperparameter tuning is necessary). This will be the primary focus of our future work.

artificial neural networks, geothermal↗

NASA NTRSDate not supplied

NASA’s Earth System Observatory Formulation Overview

NASA’s Earth System Observatory (ESO) is an array of Earth-focused, interconnected satellite missions focused on five core study areas: Surface Biology and Geology, Mass Change, Aerosols, Surface Deformation and Change, and Clouds, Convection, and Precipitation. Observatory development follows recommendations from the National Academies of Sciences, Engineering and Medicine’s 2017 Earth Science Decadal Survey. Data gathered by ESO missions will provide actionable science to inform decisions related to climate change, disaster mitigation, and wildfires, improve real-time agricultural processes, and many other applications. Targeting launch dates in the late 2020s and early 2030s, each ESO satellite will deliver valuable information, but by working together as a single observatory system, their combined data and imagery will provide the global community with a 4D, holistic view of Earth, from bedrock to atmosphere. The ESO is also building on the legacy of international collaboration in Earth science, with initial participation and collaboration on these missions across space agency partners, including the Japanese Aerospace Exploration Agency (JAXA), Centre National D'Etudes Spatiales (CNES), Canadian Space Agency (CSA), Deutsches Zentrum für Luft- und Raumfahrt (DLR), and Agenzia Spaziale Italiana (ASI). This paper provides an overview of ESO, as well as an update on ESO missions currently in formulation – the Atmosphere Observing System, Mass Change, and Surface Biology and Geology elements – including current mission architectures, international partner collaboration, science community engagement, and applications efforts, as well as how NASA and its partners will make ESO data accessible to users all over the world.

NASA Earth Science↗

DOE OSTI2020

Why Conventional Design Rules for C–H Activation Fail for Open-Shell Transition-Metal Catalysts

The design of selective and active C–H activation catalysts for direct methane-to-methanol conversion is challenging. Bioinspired complexes that form high-valent metal–oxo intermediates capable of hydrogen abstraction and rebound hydroxylation are promising candidates. This promise has made them a target for computational high-throughput screening, typically simplified through the use of linear free energy relationships (LFERs). However, their mid-row transition-metal centers have numerous accessible spin and oxidation states that increase the combinatorial scale of design efforts. Here, we carry out a computational design screen of over 2500 mid-row 3d transition-metal complexes with four metals in numerous spin and oxidation states. We demonstrate the importance of spin/oxidation state in dictating design principles, limiting the generalization of strategies derived for widely studied high-spin Fe(II) catalysts to other metals or spin/oxidation states. Combined assessment of the effect of ligand-field tuning on reaction step energetics and on the identity of the ground state allows us to propose refined design strategies for spin-allowed methane-to-methanol catalysis. We observe weak coupling of energetics and design principles between reaction steps (e.g., oxo formation vs methanol release), meaning that LFERs do not generalize across our larger catalyst set. To rationalize relative reactivity in known catalysts, we instead compute independent reaction energies and propose strategies for further improvements in catalyst design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1989

Quick actuating closure

A quick actuating closure for a pressure vessel 80 in which a wedge ring 30 with a conical outer surface 31 is moved forward to force shear blocks 40, with conical inner surfaces 41, radially outward to lock an end closure plug 70 within an opening 81 in the pressure vessel 80. A seal ring 60 and a preload ramp 50 sit between the shear blocks 40 and the end closure plug 70 to provide a backup sealing capability. Conical surfaces 44 and 55 of the preload ramp 50 and the shear blocks 40 interact to force the seal ring 60 into shoulders 73 and 85 in the end closure plug 70 and opening 81 to form a tight seal. The end closure plug 70 is unlocked by moving the wedge ring 30 rearward, which causes T-bars 32 of the wedge ring 30 riding within T -slots 42 of the shear blocks 40 to force them radially inward. The end closure plug 70 is then removed, allowing access to the interior of the pressure vessel 80.

White, III, Dorsey E.↗

NASA NTRS2010

Using PHP/MySQL to Manage Potential Mass Impacts

This paper presents a new application using commercially available software to manage mass properties for spaceflight vehicles. PHP/MySQL(PHP: Hypertext Preprocessor and My Structured Query Language) are a web scripting language and a database language commonly used in concert with each other. They open up new opportunities to develop cutting edge mass properties tools, and in particular, tools for the management of potential mass impacts (threats and opportunities). The paper begins by providing an overview of the functions and capabilities of PHP/MySQL. The focus of this paper is on how PHP/MySQL are being used to develop an advanced "web accessible" database system for identifying and managing mass impacts on NASA's Ares I Upper Stage program, managed by the Marshall Space Flight Center. To fully describe this application, examples of the data, search functions, and views are provided to promote, not only the function, but the security, ease of use, simplicity, and eye-appeal of this new application. This paper concludes with an overview of the other potential mass properties applications and tools that could be developed using PHP/MySQL. The premise behind this paper is that PHP/MySQL are software tools that are easy to use and readily available for the development of cutting edge mass properties applications. These tools are capable of providing "real-time" searching and status of an active database, automated report generation, and other capabilities to streamline and enhance mass properties management application. By using PHP/MySQL, proven existing methods for managing mass properties can be adapted to present-day information technology to accelerate mass properties data gathering, analysis, and reporting, allowing mass property management to keep pace with today's fast-pace design and development processes.

Hager, Benjamin I.↗

NASA NTRS2017

Development and Testing of Harpoon-Based Approaches for Collecting Comet Samples (Video Supplement)

This video supplement contains a set of videos created during the approximately 10-year-long course of developing and testing the Goddard Space Flight Center (GSFC) harpoon-based approach for collecting comet samples. The purpose of the videos is to illustrate various design concepts used in this method of acquiring samples of comet material, the testing used to verify the concepts, and the evolution of designs and testing. To play the videos this PDF needs to be opened in the freeware Adobe Reader. They do not seem to play while within a browser. While this supplement can be used as a stand-alone document, it is intended to augment its parent document of the same title, Development and Testing of Harpoon-Based Approaches for Collecting Comet Samples (NASA/CR-2017-219018; this document is accessible from the website: https://ssed.gsfc.nasa.gov/harpoon/SAS_Paper-V1.pdf). The parent document, which only contains text and figures, describes the overall development and testing effort and contains references to each of the videos in this supplement. Thus, the videos are primarily intended to augment the information provided by the text and figures in the parent document. This approach was followed to allow the file size of the parent document to remain small enough to facilitate downloading and storage. Some of the videos were created by other organizations, Johns Hopkins University Applied Physics Laboratory (JHU APL) and the German Aerospace Center called, the Deutsches Zentrum für Luft- und Raumfahrt (DLR), who are partnering with GSFC on developing this technology. Each video is accompanied by text that provides a summary description of its nature and purpose, as well as the identity of the authors. All videos have been edited to only show key parts of the testing. Although not all videos have sound, the sound has been retained in those that have it. Also, each video has been given one or more title screens to clarify what is going in different phases of the video.

Purves, Lloyd↗

DOE OSTI2023

Oxygen consumption of sexually mature adult, first-feeding larval, and yearling Pacific Lampreys

Abstract Objective The Pacific Lamprey Entosphenus tridentatus is considered a first food of Native American tribes, such as the Confederated Tribes of the Umatilla Indian Reservation. Populations of this imperiled species have declined such that harvest for traditional use is limited. As a result, propagation and culture of Pacific Lampreys have been initiated to supply animals for restoration and recovery efforts. These efforts require transport and periodic holding of both adults and larvae under static (no-flow) conditions. Hence, guidelines for ensuring an adequate oxygen supply are needed for all life stages of this species. Methods We measured oxygen consumption rates of mature adults, first-feeding larvae, and yearlings under static conditions at 12–15°C. In addition, we recorded indicators of stress during hypoxia for adults and estimated routine respiration rates during and after larval feeding. Result Adults exhibited surprisingly high metabolic rates when at rest in static chambers. At 12°C, a single adult typically reduced dissolved oxygen levels in 10 L to <2 mg/L in just 90 min (oxygen consumption rates of 100–200 mg/kg/h). Adults often started to climb the walls of open chambers when dissolved oxygen levels approached 2 mg/L, allowing them to raise their branchiopores into air. Larvae remained quiescent, even when oxygen levels dropped below 1 mg/L, and costs of feeding increased routine respiration by 22–56%. Conclusion Based on these observations, we recommend that adult Pacific Lampreys always be transported in aerated containers and with access to air at the top of the tank. Although larvae exhibited hypoxia tolerance, care should be taken to ensure adequate oxygen availability, particularly during and immediately after feeding.

Moser, Mary↗

NASA NTRS2023

NLSP: NASA Life Sciences Portal

NASA’s Life Sciences Ports (NLSP) serves the scientific community by providing curated data from space life science experiment. The Human Research Program (HRP) with the help of NLSP is currently transforming their life sciences data archive systems and processes to improve compliance with the FAIR principles. Some of these improvements will at the same time support the twin pillars of Open Science: transparency of methods and reproducibility of results. This video is a high level overview of the NLSP for existing and new users.

Life Sciences data↗

DOE OSTI2024

Strain Field Around Individual Dislocations Controls Failure

Understanding material failure on a fundamental level is a key aspect in the design of robust structural materials, especially for metals and alloys capable to undergo plastic deformation. In the last decade, significant progress is made in quantifying the stresses associated with failure in both experiments and simulations. Nonetheless, the processes occurring on the most essential level of individual dislocations that govern semi-brittle and ductile fracture are still experimentally not accessible, limiting the failure prediction capabilities. Therefore, in the present work, a one-of-a-kind nanoscale fracture experiment is conducted on a single crystalline Cr bending beam in situ in the transmission electron microscope and for the first time quantify the transient strains around individual dislocations, as well as of the whole dislocation network during crack opening. The results reveal the importance of both pre-existing and newly emitted dislocations for crack-tip shielding via their intrinsic strain field and provide guidelines to design more damage tolerant materials.

36 MATERIALS SCIENCE↗

DOE OSTI2023

DGaaS: GPU as a Service on Distributed Computing System

In the rapidly evolving landscape of scientific computing, Graphics Processing Units (GPUs) have become indispensable for their unparalleled ability to handle parallel tasks in complex calculations, simulations, and data analysis. Their utility is further magnified in machine learning and AI applications, where they significantly accelerate model training and predictive analytics. Within this context, the Triton Inference Server emerges as a pivotal open-source tool, specializing in AI inferencing and optimizing GPU utilization across various platforms and frameworks. This paper presents an in-depth study on distributed High Throughput Computing (HTC), specifically focusing on the HTCondor framework and its resource provisioning tools, GlideinWMS and HEPCloud. These systems enable large-scale scientific experiments like CMS and DUNE to efficiently access and utilize vast computational resources. The paper explores the core architectural components of GlideinWMS, including jobs, user pools, and worker nodes, and discusses their integration with GPUs and the Triton server. The primary aim of this research is to develop a solution that optimizes GPU utilization by leveraging Glideins and containers. This approach allows computational jobs, particularly those involving AI models, to use GPUs only when essential, thereby facilitating efficient sharing of limited GPU resources. To validate this architecture, the study conducted three key tests involving custom scripts, container-based servers, and Triton server deployments. However, the study faces challenges, notably in locating the Triton server and ensuring secure remote access. To address these issues, future work will focus on developing a proxy mechanism and enhancing security protocols. In conclusion, this study offers a comprehensive roadmap for effective and efficient GPU utilization in distributed High Throughput Computing. It aims to contribute significantly to the scientific community by solving pressing problems and implementing robust solutions in collaboration with the GlideinWMS and HEPCloud teams. The research sets the stage for a more efficient, scalable, and cost-effective paradigm in scientific computing.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1994

Reproducible direct exposure environmental testing of metal-based magnetic media

A flow geometry and flow rate for mixed flowing gas testing is proposed. Use of an impinging jet of humid polluted air can provide a uniform and reproducible exposure of coupons of metal-based magnetic media. Numerical analysis of the fluid flow and mass transfer in such as system has shown that samples confined within a distance equal to the nozzle radius on the surface of impingement are uniformly accessible to pollutants in the impinging gas phase. The critical factor is the nozzle height above the surface of impingement. In particular, the uniformity of exposure is less than plus/minus 2% for a volumetric flow rate of 1600 cm(exp 3)/minute total flow with the following specifications: For a one inch nozzle, the height of the nozzle opening above the stage should be 0.177 inches; for a 2 inch nozzle - 0.390 inches. Not only is the distribution uniform, but one can calculate the maximum delivery rate of pollutants to the samples for comparison with the observed deterioration.

Sides, Paul J.↗

NASA NTRS1989

Suitport extra-vehicular access facility

In a system for entering and leaving a space station, a bulkhead divides the module into an antechamber and an airlock. A space suit has a portable life support system (PLSS) interface on its back. The suit is removably attached to the bulkhead by the interface at a hatch in the bulkhead. A PLSS is detachably mounted in the hatch cover, which is pivotally mounted to move away from the hatch to allow an astronaut to enter the suit through the open hatch and the PLSS interface. After entering the suit, the astronaut closes the hatch and attaches the PLSS to the suit by the operating control to which the glove portion of the suit is attached. The astronaut initiates pumpdown of the airlock with the control. When the pumpdown is complete, the astronaut opens the hatch, disconnects the PLSS from the hatch cover, pivots the pressure vessels of the control to one side on their supports, disconnects the glove portions from the pressure vessels and goes EVA.

Cohen, Marc M.↗

DOE OSTI2019

Linking human-building interactions in shared offices with personality traits

Occupant behavior influences office building energy performance. The level of human-building interactions (HBIs) in shared offices strongly influences building energy use and occupant well-being. This study explored the link between occupant personality types and their behaviors of sharing energy and environment control systems and interactions with their colleagues. Inspired by the Five-Factor Model (FFM), we classified HBI behaviors into four dimensions: willingness to share control, knowledge of control, group decision behavior, and adaptive strategies. These four variables can be mapped to the four personality traits proposed by the FFM: agreeableness, openness, extraversion, and conscientiousness. Our cluster analysis identified six behavioral patterns: average (17.7%), reserved (15.3%), environmentally friendly (16.6%), role model (24.2%), self-centered (17.2%), and mechanist (9.0%). We further applied association rules, a widely utilized machine learning technique, to discover how demographics, building-related contextual factors, and perception-attitudinal factors influence HBI behaviors. Country, control feature accessibility, and group dynamics were found to be the three most influential factors that determine occupants’ HBI behaviors. Finally, the study provides insights about building design and operation, as well as policy to promote socially and environmentally desirable HBI behaviors in a shared office environment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRSDate not supplied

Developing Open-Source Training Materials for AI/ML and Space Biological Sciences Using NASA Cloud-Based Data

Artificial Intelligence (AI) and Machine Learning (ML) has gained significant traction in the biological and biomedical research fields in the last two decades, in part thanks to an increasing culture of open data sharing and reuse. Due to its capability for identifying complex relationships and patterns, AI/ML methodology is particularly well suited to recognize and predict biological patterns from high-dimensional next-generation sequencing data (e.g. whole genome sequencing, transcriptomic sequencing), as well as from biological or medical imaging data (e.g. microscopy, computed tomography, ultrasound, magnetic resonance imaging, radiography). These methodologies hold particular promise for space biosciences research and automated space health monitoring systems. However, there are many key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Even with the positive culture of Open Science and data sharing, inexperienced researchers working quickly without proper checks can produce models that perform poorly outside of the immediate training dataset. Lessons learned from biological AI/ML research indicate that Open Science principles such as data sharing and open-source code must go hand-in-hand with publicly available, high-quality training curricula in best practices, with modules centered on real-life scientific use cases and data so future AI/ML practitioners gain experience on real problems. Here we present the development of open-source training materials for AI/ML and space biosciences, as part of the NASA Transform to Open Science Training (TOPST) initiative. We develop 4 independent training programs, focused on the following topics: 1) Fundamentals of Machine Learning and Space Biosciences Domain, 2) Open Science, Artificial Intelligence, and Ethical Best Practices for Data Sharing and Analysis, 3) Using AI/ML Classification to Identify Gene Networks Affected By Space Exposure in Mouse Liver, and 4) Using Neural Networks to Find DNA Damage Patterns in Immune Cells after Radiation. All programs leverage cloud-based NASA biological datasets. The curriculum we present will enable worldwide access to training in AI/ML and scientific analysis.

James Andrew Casaletto↗

NASA NTRSDate not supplied

Developing Open-Source Training Materials for AI/ML and Space Biological Sciences Using NASA Cloud-Based Data

Artificial Intelligence (AI) and Machine Learning (ML) has gained significant traction in the biological and biomedical research fields, in part due to a culture of open data sharing and reuse. AI/ML methodology is well-suited to recognize and predict biological patterns from high-dimensional next-generation sequencing data (e.g. whole genome sequencing, transcriptomic sequencing), as well as from biological or medical imaging data (e.g. microscopy, computed tomography, ultrasound, magnetic resonance imaging, radiography). These methodologies hold particular promise for space biosciences research and automated space health monitoring systems. However, there are key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Inexperienced researchers can produce models that perform poorly outside of the training dataset. Open Science principles such as data sharing and open-source code must go hand-in-hand with publicly available, high-quality training curricula in best practices, with modules centered on real-life scientific use cases and data so future AI/ML practitioners gain experience on real problems. Here we present the development of open-source training materials for AI/ML and space biosciences, as part of the NASA Transform to Open Science Training (TOPST) initiative. We develop 4 independent training programs, focused on the following topics: 1) Fundamentals of Machine Learning and Space Biosciences Domain, 2) Open Science, Artificial Intelligence, and Ethical Best Practices for Data Sharing and Analysis, 3) Using AI/ML Classification to Identify Gene Networks Affected By Space Exposure in Mouse Liver, and 4) Using Neural Networks to Find DNA Damage Patterns in Immune Cells after Radiation. All programs leverage cloud-based NASA biological datasets. The curriculum we present will enable worldwide access to training in AI/ML and scientific analysis.

James Casaletto↗

NASA NTRSDate not supplied

BEAST: Expanding Sustainable Data Infrastructure for High-Enthalpy Facilities

Reproducible, data-driven thermal protection system (TPS) research requires that experimental records from high-enthalpy testing be consistently structured, traceable, and accessible across campaigns and institutions. In practice, however, arcjet and plasma facilities data remain largely fragmented: raw diagnostics are stored in ad hoc formats, material sample histories are disconnected from test conditions, and metadata standards are absent, precluding systematic cross-campaign analysis and long-term reuse. BEAST (Backend for Experiment Analysis, Storage, and Traceability) is an open-source, web-based platform that addresses these limitations by providing a unified, queryable infrastructure for high-enthalpy ground-test data [1]. First presented at the 15th Ablation Workshop [2], BEAST has since undergone significant development. The platform ingests and structures multi-channel time-series diagnostics, facility configurations, and material property records within a common provenance model, ensuring end-to-end traceability from raw sensor acquisition to reduced experimental quantities. A versioned material library links specimen identity and processing history to the specific runs in which each sample was tested. An integrated modeling workbench enables training and evaluation of regression models directly on archived experimental data, supporting condition interpolation and the construction of empirical material response databases. Beyond its original deployment at NASA Ames Research Center, BEAST has been designed to be facility-agnostic, with ongoing efforts to extend its adoption to other facilities. Its modular architecture accommodates heterogeneous diagnostic setups and facility types, and its future open-source distribution allows institutions to build on a common data standard rather than maintaining isolated, bespoke solutions. BEAST is further integrated within a broader ecosystem of companion tools: arcjetCV [3] extracts recession rates and shock standoff distances from high-speed video using computer vision, and miniSTARscan [4] provides sub-minute, portable photogrammetric surface reconstruction of test articles before and after exposure. All tools share a common data schema, enabling seamless ingestion of surface geometry, imagery, and time-series data into a single, coherent experimental record.

Database↗

NASA NTRSDate not supplied

BEAST: Expanding Sustainable Data Infrastructure for High-Enthalpy Facilities

Database↗