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NASA NTRS1990

Automated assembly of large space structures

Viewgraphs and discussion on automated assembly of large space structures are presented. The program research objective is to develop technology and demonstrate the potential for automated inspace assembly of large erectable structures. This is accomplished by merging experience in structural assembly and robotics at LaRC into an interdisciplinary program with focused effort on automated assembly of a generic structural configuration with a standard cell and by building into the system the capability to do expanded research with complex configurations.

Rhodes, Marvin D.↗

NASA NTRS1999

Unlocking the Keys to Vortex/Flame Interactions in Turbulent Gas-Jet Diffusion Flames--Dynamic Behavior Explored on the Space Shuttle

Most combustion processes in industrial applications (e.g., furnaces and engines) and in nature (e.g., forest fires) are turbulent. A better understanding of turbulent combustion could lead to improved combustor design, with enhanced efficiency and reduced emissions. Despite its importance, turbulent combustion is poorly understood because of its complexity. The rapidly changing and random behavior of such flames currently prevents detailed analysis, whether experimentally or computationally. However, it is possible to learn about the fundamental behavior of turbulent flames by exploring the controlled interaction of steady laminar flames and artificially induced flow vortices. These interactions are an inherent part of turbulent flames, and understanding them is essential to the characterization of turbulent combustion. Well-controlled and defined experiments of vortex interaction with laminar flames are not possible in normal gravity because of the interference of buoyancy- (i.e., gravity) induced vortices. Therefore, a joint microgravity study was established by researchers from the Science and Technology Development Corp. and the NASA Lewis Research Center. The experimental study culminated in the conduct of the Turbulent Gas-Jet Diffusion Flames (TGDF) Experiment on the STS-87 space shuttle mission in November 1997. The fully automated hardware, shown in photo, was designed and built at Lewis. During the mission, the experiment was housed in a Get Away Special (GAS) canister in the cargo bay.

Stocker, Dennis P.↗

NASA NTRS2020

Wind Tunnel Investigation of the Supersonic Stage Separation Aerodynamics of a Generic 0.0175-Scale Bimese Two-Stage-to-Orbit Reusable Launch Vehicle Configuration

A wind tunnel investigation was conducted of the supersonic stage separation aerodynamics of a generic two-stage-to-orbit bimese wingbody configuration in the NASA Langley Research Center Unitary Plan Wind Tunnel. Proximity and isolated model testing was conducted at Mach numbers of 2.3, 3.0, and 4.5 and a unit Reynolds number of 2.0 million per foot using 0.0175-scale models of the Langley Glide-Back Booster concept designated as the orbiter and booster in belly-to-belly and back-to-belly configurations. Longitudinal forces and moments were obtained on both models and surface static pressure measurements were obtained on the orbiter model at 328 relative proximity locations and at relative angles of attack of 0 degrees and 5 degrees. The test results supported a larger effort to develop and validate experimental and computational tools applicable to the design and simulation of stage separation and abort procedures for reusable launch vehicles composed of multiple bodies, including winged bodies. An initial proof-of-concept experiment featuring low-cost uninstrumented models was conducted to verify an emerging automated model control system and new support system hardware, and to identify potential model and support system blockage and unsteady aerodynamics/model dynamics prior to committing to higher-fidelity instrumented models. This investigation led to upgrades in the facility stage separation hardware, calibration and testing techniques and capabilities, and data analysis and documentation methodologies that have been extended to the more recent NASA Constellation and Space Launch System crew and cargo launch vehicle programs. A virtual diagnostics interface methodology was used to facilitate the design of the stage separation support hardware, to position the models in the test section, and to define the experimental test space. Advances in the facility automated model positioning system established a foundation for the development of a continuous-sweep data acquisition technique that is responsible for significant productivity improvements to the current NASA Space Launch System test program. The automated model positioning capability was leveraged to conduct a companion statistically-designed stage separation experiment requiring randomization of the relative proximity positions of the orbiter and booster models. The respective zones of influence and interference effects of the orbiter and booster were identified from three-dimensional scatter plots, contour and influence maps, and two-dimensional plotting methods. The highly-nonlinear, shock-dominated aerodynamic characteristics of the orbiter and booster in the Unitary Plan Wind Tunnel exhibited good agreement with independent test data obtained in a NASA Marshall Space Flight Center wind tunnel and with computational fluid dynamics predictions using a compressible, three-dimensional flow solver and an inviscid, unstructured Cartesian method.

Erickson, Gary E.↗

DOE OSTI2022

Bayesian Active Learning for Scanning Probe Microscopy: From Gaussian Processes to Hypothesis Learning

Recent progress in machine learning methods and the emerging availability of programmable interfaces for scanning probe microscopes (SPMs) have propelled automated and autonomous microscopies to the forefront of attention of the scientific community. However, enabling automated microscopy requires the development of task-specific machine learning methods, understanding the interplay between physics discovery and machine learning, and fully defined discovery workflows. This, in turn, requires balancing the physical intuition and prior knowledge of the domain scientist with rewards that define experimental goals and machine learning algorithms that can translate these to specific experimental protocols. Here, we discuss the basic principles of Bayesian active learning and illustrate its applications for SPM. We progress from the Gaussian process as a simple data-driven method and Bayesian inference for physical models as an extension of physics-based functional fits to more complex deep kernel learning methods, structured Gaussian processes, and hypothesis learning. These frameworks allow for the use of prior data, the discovery of specific functionalities as encoded in spectral data, and exploration of physical laws manifesting during the experiment. Here, the discussed framework can be universally applied to all techniques combining imaging and spectroscopy, SPM methods, nanoindentation, electron microscopy and spectroscopy, and chemical imaging methods and can be particularly impactful for destructive or irreversible measurements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Machine Intelligence-Centered System for Automated Characterization of Functional Materials and Interfaces

Classic design of experiment relies on a time-intensive workflow that requires planning, data interpretation, and hypothesis building by experienced researchers. Here, in this paper, we describe an integrated, machine-intelligent experimental system which enables simultaneous dynamic tests of electrical, optical, gravimetric, and viscoelastic properties of materials under a programmable dynamic environment. Specially designed software controls the experiment and performs on-the-fly extensive data analysis and dynamic modeling, real-time iterative feedback for dynamic control of experimental conditions, and rapid visualization of experimental results. The system operates with minimal human intervention and enables time-efficient characterization of complex dynamic multifunctional environmental responses of materials with simultaneous data processing and analytics. The system provides a viable platform for artificial intelligence (AI)-centered material characterization, which, when coupled with an AI-controlled synthesis system, could lead to accelerated discovery of multifunctional materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Digital twins and deep learning segmentation of defects in monolayer MX 2 phases

Developing methods to understand and control defect formation in nanomaterials offers a promising route for materials discovery. Monolayer MX 2 phases represent a particularly compelling case for defect engineering of nanomaterials due to the large variability in their physical properties as different defects are introduced into their structure. However, effective identification and quantification of defects remain a challenge even as high-throughput scanning transmission electron microscopy methods improve. This study highlights the benefits of employing first principles calculations to produce digital twins for training deep learning segmentation models for defect identification in monolayer MX 2 phases. Around 600 defect structures were obtained using density functional theory calculations, with each monolayer MX 2 structure being subjected to multislice simulations for the purpose of generating the digital twins. Several deep learning segmentation architectures were trained on this dataset, and their performances evaluated under a variety of conditions such as recognizing defects in the presence of unidentified impurities, beam damage, grain boundaries, and with reduced image quality from low electron doses. Further, this digital twin approach allows benchmarking different deep learning architectures on a theory dataset, which enables the study of defect classification under a broad array of finely controlled conditions. It thus opens the door to resolving the underpinning physical reasons for model shortcomings and potentially chart paths forward for automated discovery of materials defect phases in experiments.

36 MATERIALS SCIENCE↗

DOE OSTI2024

GlideinBenchmark: collecting resource information to optimize provisioning

Choosing the right resource can speedup jobs completion, better utilize the available hardware and visibly reduce costs, especially when renting computers on the cloud. This was demonstrated in earlier studies on HEPCloud. But the benchmarking of the resources proved to be a laborious and time-consuming process. This paper presents GlideinBenchmark, a new Web application leveraging the pilot infrastructure of GlideinWMS to benchmark resources, and shows how to use the data collected and published by GlideinBenchmark to automate the optimal selection of resources. An experiment can select the benchmark or the set of benchmarks that most closely evaluate the performance of its workflows. With GlideinBenchmark and the help of the GldieinWMS Factory it controls the benchmark execution. Finally, a scheduler like HEPCloud’s Decision Engine can use the results to optimize resource provisioning.

Mambelli, Marco↗

DOE OSTI2025

GlideinBenchmark: collecting resource information to optimize provisioning

Choosing the right resource can speed up job completion, better utilize the available hardware, and visibly reduce costs, especially when renting computers in the cloud. This was demonstrated in earlier studies on HEPCloud. However, the benchmarking of the resources proved to be a laborious and time-consuming process. This paper presents GlideinBenchmark, a new Web application leveraging the pilot infrastructure of GlideinWMS to benchmark resources, and it shows how to use the data collected and published by GlideinBenchmark to automate the optimal selection of resources. An experiment can select the benchmark or the set of benchmarks that most closely evaluate the performance of its workflows. GlideinBenchmark, with the help of the GlideinWMS Factory, controls the benchmark execution. Finally, a scheduler like HEPCloud's Decision Engine can use the results to optimize resource provisioning.

Mambelli, Marco [Fermilab] (ORCID:0000000294892681↗

NASA NTRS1984

The US re-usable launch system - The Space Shuttle

The NASA Space Shuttle furnishes a range of readily available, and either already planned or yet-to-be-defined services, which already encompass satellite on-orbit servicing, refueling and repair, and satellite retrieval services. A gradual expansion will be made of services that capitalize on the presence of a large crew during commercial and scientific operations, while pursuing the development of automated, standardized procedures for the handling of experiments in order to reduce research costs.

Lee, C. M.↗

NASA NTRS1986

Artificial intelligence planning applications for space exploration and space robotics

Mission sequencing involves the plan for actuation of the experiments to be conducted aboard a spacecraft; automation is under study by NASA as a means to reduce time and manpower costs in mission planning and in robotic implementation. The development of a mission sequence is conditioned by the limited duration of advantageous spacecraft encounters with objects of study, more research requests than can be satisfied, and requested changes in objectives. Autonomous robot development is hampered by the absence of task-level programming languages, the existence of anomalies in real-world interactions, and a lack of required capabilities in current sensor technology.

Rokey, Mark↗

NASA NTRS1995

Solid surface combustion experiment flame spread in a quiescent, microgravity environment implications of spread rate and flame structure

A unique environment in which flame spreading, a phenomenon of fundamental, scientific interest, has importance to fire safety is that of spacecraft in which the gravitational acceleration is low compared with that of the Earth, i.e., microgravity. Experiments aboard eight Space Shuttle missions between October 1990 and February 1995 were conducted using the Solid Surface Combustion Experiment (SSCE) payload apparatus in an effort to determine the mechanisms of gas-phase flame spread over solid fuel surfaces in the absence of any buoyancy induced or externally imposed oxidizer flow. The overall SSCE effort began in December of 1984. The SSCE apparatus consists of a sealed container, approximately 0.039 cu m, that is filled with a specified O2/N2 mixture at a prescribed pressure. Five of the experiments used a thin cellulosic fuel, ashless filter paper, 3 cm wide x 10 cm long, 0.00825 cm half-thickness, ignited in five different ambient conditions. Three of the experiments, the most recent, used thick polymethylmethacrylate (PMMA) samples 0.635 cm wide x 2 cm long, 0.32 cm half-thickness. Three experiments, STS 41, 40 and 43, were designed to evaluate the effect of ambient pressure on flame spread over the thin cellulosic fuel while flights STS 50 and 47 were at the same pressure as two of the earlier flights but at a lower oxygen concentration in order to evaluate the effect of ambient oxygen level on the flame spread process at microgravity. For the PMMA flights, two experiments, STS 54 and 63, were at the same pressure but different oxygen concentrations while STS 64 was at the same oxygen concentration as STS 63 but at a higher pressure. Two orthogonal views of the experiments were recorded on 16 mm cine-cameras operating at 24 frames/s. In addition to filmed images of the side view of the flames and surface view of the burning samples, solid- and gas-phase temperatures were recorded using thermocouples. The experiment is battery powered and follows an automated sequence upon activation by the Shuttle Crew. In this study we separate the SSCE data into two groups according to the fuel type: (1) thin cellulose; and (2) thick PMMA. The experimental spread rates are compared with prediction from a number of models in an effort to uncover the important physics that characterize microgravity flame spread. Both steady and unsteady solutions are employed to explore the flame evolution, especially for thick fuels. Finally, the flame structure in downward spread is compared with the microgravity flame structure and modeling results to delineate the difference between the two configurations and the influence of normal gravity.

Bundy, Matthew↗

NASA NTRS2003

Protein Crystal Growth

In order to rapidly and efficiently grow crystals, tools were needed to automatically identify and analyze the growing process of protein crystals. To meet this need, Diversified Scientific, Inc. (DSI), with the support of a Small Business Innovation Research (SBIR) contract from NASA s Marshall Space Flight Center, developed CrystalScore(trademark), the first automated image acquisition, analysis, and archiving system designed specifically for the macromolecular crystal growing community. It offers automated hardware control, image and data archiving, image processing, a searchable database, and surface plotting of experimental data. CrystalScore is currently being used by numerous pharmaceutical companies and academic and nonprofit research centers. DSI, located in Birmingham, Alabama, was awarded the patent Method for acquiring, storing, and analyzing crystal images on March 4, 2003. Another DSI product made possible by Marshall SBIR funding is VaporPro(trademark), a unique, comprehensive system that allows for the automated control of vapor diffusion for crystallization experiments.

Source record↗

NASA NTRS2012

Rapid Analysis, Self-Calibrating Array for Air Monitoring

Human space missions have critical needs for monitoring and control for life support systems. These systems have monitoring needs that include feedback for closed loop processes and quality control for environmental factors. Sensors and monitoring technologies assure that the air environment and water supply for the astronaut crew habitat fall within acceptable limits, and that the life support system is functioning properly and efficiently. The longer the flight duration and the more distant the destination, the more critical it becomes to have carefully monitored and automated control systems for life support. Past experiments with the JPL ENose have demonstrated a lifetime of the sensor array, with the software, of around 18 months. The lifetime of the calibration, for some analytes, was as long as 24 months. We are working on a sensor array and new algorithms that will include sensor response time in the analysis. The preliminary array analysis for two analytes shows that the analysis time, of an event, can be dropped from 45 minutes to less than10 minutes and array training time can be cut substantially. We will describe the lifetime testing of an array and show lifetime data on individual sensors. This progress will lead to more rapid identification of analytes, and faster training time of the array.

Self Calibrating↗

NASA NTRS2017

Building Better Biosensors for Exploration into Deep-Space, Using Humanized Yeast

1.BioSentinel is 1 of 13 secondary payloads hitching a ride beyond Low Earth Orbit on Exploration Mission 1 (EM-1), set to launch from NASAs Space Launch System in 2019. EM-1 is our first opportunity to investigate the effects of the deep space environment on a eukaryotic biological system, the budding yeast S. cerevisiae. Though separated by a billion years of evolution we share hundreds of genes important for basic cell function, including responses to DNA damage. Thus, yeast is an ideal biosensor for detecting typesextent of damage induced by deep-space radiation.We will fly desiccated cells, then rehydrate to wake them up when the automated payload is ready to initiate the experiment. Rehydration solution contains SC (Synthetic Complete) media and alamarBlue, an indicator for changes in growth and metabolism. Telemetry of LED readings will then allow us to detect how cells respond throughout the mission. The desiccation-rehydration process can be extremely damaging to cells, and can severely diminish our ability to accurately measure and model cellular responses to deep-space radiation. The aim of this study is to develop a better biosensor: yeast strains that are more resistant to desiccation stress. We will over-express known cellular protectants, including hydrophilin Sip18, the protein disaggregase Hsp104, and thioredoxin Trx2, a responder to oxidative stress, then measure cell viability after desiccation to determine which factors improve stress tolerance. Over-expression of SIP18 in wine yeast starter cultures was previously reported to increase viability following desiccation stress by up to 70. Thus, we expect similar improvements in our space-yeast strains. By designing better yeast biosensors we can better prepare for and mitigate the potential dangers of deep-space radiation for future missions.This work is funded by NASAs AES program.

biosensor↗

NASA NTRS2018

History and Status of ALSEP and the Apollo Lunar Data Project

A suite of automated scientific instruments (the Apollo Lunar Surface Experiment Package, or ALSEP) was installed at each of the landing sites of Apollo 12, 14, 15, 16, and 17 from 1969 to 1972. They operated from deployment until decommissioning on 30 September 1977. These data were continuously transmitted to Earth and saved on the Range Tapes, which were recorded at the Manned Space Flight Network stations. These data were also broken out by experiment and sent to the experiment Principal Investigators on what were called the P.I. Tapes. Starting in April 1973 the Range Tape data were stored in digital format on 7-track magnetic tapes, the ARCSAV Tapes. In February 1976, the handling of the Range Tapes was transferred to UT Galveston. They produced 9-track tapes referred to as the Work Tapes. Following the Apollo program the Range and ARCSAV tapes, which were never archived, were lost. The Work Tapes were archived at the National Space Science Data Center (NSSDC). Some investigators archived their individual experiment data with NSSDC as well, but much of the data had minimal documentation, were not in digital form, or were stored in difficult to translate formats. Data from many experiments were never delivered to the NSSDC. The Lunar Data Project was started to address the problem of both missing and not readily usable data. Our effort has resulted in recovery of some of the ARCSAV tapes, recovery and digitization of a large volume of Apollo scientific and technical documentation, and restoration of many ALSEP and other Apollo data collections. Restoration involves deciphering formats, assembling necessary ancillary data (metadata), and packaging data in digital format to be archived with the Planetary Data System (PDS). Recovery of the data from the ARCSAV tapes involved having the tapes read on special equipment and extracting the individual experiment data out of the integrated data stream. We will report on the history and status of the various recovery efforts.

Work Tapes↗

NASA NTRSDate not supplied

Automating MISSE Specimens Image Analysis

The Materials International Space Station Experiment (MISSE) Project consists of experiments focused on the investigation of the effects that occur through the exposure of materials specimens in the space environment. To determine if there is a change in a specimen, the data obtained from the MISSE high-resolution photographic images can be monitored. For this, a compilation of folders was created to provide an organizational system. Each mission was divided into the specific direction in space, materials specimen, and date the image was taken. This organization helps to identify how a specimen behaves as a function of time in the space environment. Once the images were classified, a qualitative process was first used to detect an evolution in the materials of MISSE-9 - MISSE-14.

MISSE ISS Materials↗

DOE OSTI2022

Development of the MCNP-ORIGEN activation automation tool

This paper introduces the MCNP-ORIGEN activation automation tool for streamlining the calculation of experiment source terms. This tool couples the Monte-Carlo radiation-transport solver, MCNP to the depletion tool, ORIGEN-S. To showcase its current capabilities, this paper presents an activation analysis exercise, which is conducted with Serpent, and the results are com- pared. The experiment consists in a 90%-enriched uranium sphere surrounded by light water. A non-fissile cylinder is placed in the water, representing an irradiation experiment. We conducted simulations for 2 different cylinder materials: iron and cobalt at two different temperatures: 300 K and 900 K. The exercise consists of an irradiation of 50 days at a constant power of 5 MW, followed by a decay of 50 days. These studies highlight the discrepancies between how the different tools handle nuclear data. Finally, the results underscore multiple areas of possible improvement.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Automated RF Phase Adjustment for Beam Stabilization in the Fermilab Linac

The Fermilab Linac experiences longitudinal beam phase drift, leading to increased particle loss, conventionally corrected through labor-intensive manual RF adjustments. This project explores machine learning-based automation for drift correction, employing a prototype-based classification approach. Our model utilizes a 34-dimensional feature set (RF settings and BPM readings) and leverages a 7x27 response matrix for system modeling. To overcome limited real-world data, we generate synthetic data, enhancing model training and generalizability. Custom loss functions, including a surrogate energy-consistent loss and a temporal smoothness constraint, ensure physically plausible drift predictions. The goal is a robust system for autonomous phase adjustments, ensuring stable beam acceleration and reduced manual intervention.

Chichili, R. R. [Illinois U., Chicago]↗