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All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

SRMS History, Evolution and Lessons Learned

Early in the development of the Space Shuttle, it became clear that NASA needed a method of deploying and retrieving payloads from the payload bay. The Shuttle Remote Manipulator System (SRMS) was developed to fill this need. The 50 foot long robotic arm is an anthropomorphic design consisting of three electromechanical joints, six degrees of freedom, and two boom segments. Its composite boom construction provided a light weight solution needed for space operations. Additionally, a method of capturing payloads with the arm was required and a unique End Effector was developed using an electromechanical snare mechanism. The SRMS is operated using a Displays and Controls Panel and hand controllers located within the aft crew compartment of the shuttle. Although the SRMS was originally conceived to deploy and retrieve payloads, its generic capabilities allowed it to perform many other functions not originally conceived of. Over the years it has been used for deploying and retrieving constrained and free flying payloads, maneuvering and supporting EVA astronauts, satellite repair, International Space Station construction, and as a viewing aid for on-orbit International Space Station operations. After the Columbia accident, a robotically compatible Orbiter Boom Sensor System (OBSS) was developed and used in conjunction with the SRMS to scan the Thermal Protection System (TPS) of the shuttle. These scans ensure there is not a breach of the TPS prior to shuttle re-entry. Ground operations and pre mission simulation, analysis and planning played a major role in the success of the SRMS program. A Systems Engineering Simulator (SES) was developed to provide a utility complimentary to open loop engineering simulations. This system provided a closed-loop real-time pilot-driven simulation giving visual feedback, display and control panel interaction, and integration with other vehicle systems, such as GN&C. It has been useful for many more applications than traditional training. Evolution of the simulations, guided by the Math Model Working Group, showed the utility of input from multiple modeling groups with a structured forum for discussion.There were many unique development challenges in the areas of hardware, software, certification, modeling and simulation. Over the years, upgrades and enhancements were implemented to increase the capability, performance and safety of the SRMS. The history and evolution of the SRMS program provided many lessons learned that can be used for future space robotic systems.

Jorgensen, Glenn↗
NASA NTRS2002

Multiscale Modeling, Simulation and Visualization and Their Potential for Future Aerospace Systems

This document contains the proceedings of the Training Workshop on Multiscale Modeling, Simulation and Visualization and Their Potential for Future Aerospace Systems held at NASA Langley Research Center, Hampton, Virginia, March 5 - 6, 2002. The workshop was jointly sponsored by Old Dominion University's Center for Advanced Engineering Environments and NASA. Workshop attendees were from NASA, other government agencies, industry, and universities. The objectives of the workshop were to give overviews of the diverse activities in hierarchical approach to material modeling from continuum to atomistics; applications of multiscale modeling to advanced and improved material synthesis; defects, dislocations, and material deformation; fracture and friction; thin-film growth; characterization at nano and micro scales; and, verification and validation of numerical simulations, and to identify their potential for future aerospace systems.

Noor, Ahmed K.↗
NASA NTRSDate not supplied

ISS Payload Operations Training During the COVID-19 Pandemic: Impacts and Solutions

The onset of the COVID-19 pandemic brought a dramatic and rapid transformation to almost every aspect of humanity in 2020. The world’s space agencies and their missions were not immune to the wide-sweeping changes. One discipline principally affected was mission operations and the various groups supporting that function. Mission support teams, especially for complex and crewed missions like the International Space Station were forced to rapidly rethink how and where control center staff performed their vital work. Operations training – an essential element to mission ops, had unique hurdles to overcome. Operations training is responsible for preparing astronaut crews for their missions, providing training to flight controllers, as well as ensuring that new team members are ready to join their colleagues. Every element of training was impacted by COVID restrictions. From orientation and introductory classes for new controllers, simulations and advanced lessons, On the Job Training and final evaluations; all aspects faced new challenges. Trainers at NASA’s Marshall Space Flight Center in Huntsville, Alabama, were forced to become more efficient with trainees and resources to continue supporting payload operations. The pandemic started in the USA in March 2020. Immediately, NASA mandated that the support for ISS real-time operations was critical. As a result, physical access to key facilities was restricted. Trainers and trainees had to quickly shift to 100 percent remote learning. In the short term, this was not a problem. However, instructors discovered lessons that they were accustomed to delivering in a classroom environment did not translate to remote teaching. Another hurdle to operations training was the mandate that all simulations could only be held remotely. The logistics of even small simulations proved to be challenging due to IT restrictions and public internet limitations. With simulations essentially stopped, as well as the restrictions on most OJT, trainees were essentially stopped in their advancement towards certification. Once limitations were identified, trainers prioritized new options. Transitioning to all electronic learning materials was a relatively easy fix. Teaching to large groups took additional shifts in the training paradigm. Methods for preparing astronauts for their missions were revised. Simulation supervisors found more efficient techniques to provide realistic training experience. Communication and approvals from management was essential. In every case, the payload operations instructors found novel solutions to all functions listed. This paper will discuss the factors and solutions payloads operations trainers found to keep scientific research on the ISS flying forward to mission success.

ISS↗
DOE OSTI2022

Fast Gaussian Process Estimation for Large-Scale In Situ Inference using Convolutional Neural Networks

Exascale computing will bring with it significant I/O limitations. One foreseeable consequence of such restrictions is that the user can save only a small fraction of complex simulation data to disk for subsequent analysis. An alternative is to fit statistical models to data in situ, that is, inside the simulation as it runs. This option requires extremely fast statistical estimation to avoid slowing down the simulation. Gaussian processes (GPs) have state-of-the-art predictive performance for modeling spatial data. However, standard estimation methods for GPs scale quite poorly to large data sets as parameter estimation requires inverting a covariance matrix to the size of the data set. In the presented work, we use a convolutional neural network (CNN) to predict the GP parameters for a spatial data set, from a simulation or otherwise, rather than optimize the parameters directly. Here, our presented case study models spatial data from E3SM, the Department of Energy’s Exascale climate model. The CNN is trained on synthetic data simulated from GP models with known parameters and then applied to data from the climate simulation. In the presented examples, the neural network scheme produces parameter estimates that compare well with standard methods such as maximum likelihood estimation in predictive performance but is obtained four orders of magnitude faster.

big data↗
DOE OSTI2025

Multi-Stage FNO

This is the code developed for paper - Reducing Frequency Bias of Fourier Neural Operators in 3D Seismic Wavefield Simulations Through Multi-Stage Training (https://arxiv.org/abs/2503.02023), it contains training the multi-stage FNO model to generate the seismic forward simulations in 3D medium.

Kong, Qingkai [Lawrence Livermore National Laborat↗
NASA NTRS1990

FACE computer simulation

A computer simulation of the FACE (Flexible Arm Controls Experiment) was conducted to assess its design for use in the Space Shuttle. The FACE is supposed to be a 14-ft long articulate structure with 4 degrees of freedom, consisting of shoulder pitch and yaw, elbow pitch, and wrist pitch. Kinematics of the FACE was simulated to obtain data on arm operation, function, workspace and interaction. Payload capture ability was modeled. The simulation indicates the capability for detailed kinematic simulation and payload capture ability analysis, and the feasibility of real-time simulation was determined. In addition, the potential for interactive real-time training through integration of the simulation with various interface controllers was revealed. At this stage, the flexibility of the arm was not yet considered.

Sadeh, Willy Z.↗
NASA NTRS2002

STS-112 Crew Training Clip

Footage shows the crew of STS-112 (Jeffrey Ashby, Commander; Pamela Melroy, Pilot; David Wolf, Piers Sellers, Sandra Magnus, and Fyodor Yurchikhin, Mission Specialists) during several parts of their training. The video is arranged into short segments. In 'Topside Activities at the NBL', Wolf and Sellers are fitted with EVA suits for pool training. 'Pre-Launch Bailout Training in CCT II' shows all six crew members exiting from the hatch on a model of a shuttle orbiter cockpit. 'EVA Training in the VR Lab' shows a crew member training with a virtual reality simulator, interspersed with footage of Magnus, and Wolf with Melroy, at monitors. There is a 'Crew Photo Session', and 'Pam Melroy and Sandy Magnus at the SES Dome' also features a virtual reality simulator. The final two segments of the video involve hands-on training. 'Post Landing Egress at the FFT' shows the crew suiting up into their flight suits, and being raised on a harness, to practice rapelling from the cockpit hatch. 'EVA Prep and Post at the ISS Airlock' shows the crew assembling an empty EVA suit onboard a model of a module. The crew tests oxygen masks, and Sellers is shown on an exercise bicycle with an oxygen mask, with his heart rate monitored (not shown).

Source record↗
DOE OSTI2024

Machine Learning and IAST-Aided High-Throughput Screening of Cationic and Silica Zeolites for Alkane Capture, Storage, and Separations

We present an approach for quantitatively predicting the temperature-dependent single-component adsorption behavior of linear alkanes in silica and Na-exchanged cationic zeolites using machine learning (ML) models trained from extensive molecular simulations based on force fields with coupled cluster accuracy. A high-performing classification model was developed to distinguish between instances with negligible and non-negligible adsorption. Subsequently, two ML models were trained to predict the single-component adsorption loading and the heat of adsorption at any pressure at 300 K for any zeolite topology and silicon-to-aluminum ratio. The ML models were trained on International Zeolite Association (IZA) zeolites, and their transferability to hypothetical zeolites was successfully validated. We then expand the power of these predictions to adsorbed mixtures at arbitrary temperatures by integrating them with the Clausius–Clapeyron equation and ideal adsorbed solution theory (IAST). This approach was validated and then applied to a temperature swing adsorption separation process to demonstrate its practical utility. We demonstrate how predictions from this ML-enabled approach can allow the selection of high-performing materials that are then validated using detailed molecular simulations based on quantitatively accurate force fields.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1981

Eastern Airlines LOFT program

Beginning with scenario design and development issues, Eastern Airlines committed itself to the full four-hour LOFT training format without the additional time for specific maneuvers. Abnormals and emergency conditions, pacing, and quiet periods are included in the scenarios which are written for the instructor to follow verbatim. Simulator capabilities, performance assessment; training vs. checking; crew composition and scheduling; satisfactory completion; the use of video performance printouts; the number of instructors; instructor training and standardization; and initial, transition, and upgrade training are discussed.

Beach, B. E.↗
NASA NTRSDate not supplied

Towards Autonomous Lunar Resource Excavation via Reinforcement Learning

To continue on a sustainable and flexible path, NASA needs to address the challenge of collecting and moving large amounts of regolith at the destination. NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) is principally designed to mine and deliver regolith for In-Situ Resource Utilization (ISRU) processing. RASSOR’s design enables it to efficiently collect and deposit regolith, return collected material for processing, and myriad related ISRU activities. To reliably perform these operations on the lunar surface, RASSOR software and sensory systems need to be robust and maximize the information extracted from a reduced sensor payload. Herein, we present preliminary findings from the Intelligent Capabilities Enhanced RASSOR project. We created reduced-order simulation environments to develop autonomous trenching controllers via reinforcement learning and prototype state estimation architectures. The goal of reinforcement learning is for an agent to learn a policy (task strategy) through interactions with an environment. When the agent performs an action, a change occurs in environment state and a numerical reward is received which informs the agent whether the action performed was good or not. Since reinforcement learning algorithms learn through trial-and-error, a simulation is a desirable first environment for development and learning. We developed two simulations, the first is a 2D excavation simulation developed to facilitate parameter selection, and a 3D simulation developed using a game physics engine, to simulate simplified soil interactions and increase the fidelity of the dynamic models of the robotic agents. The development of this 3D simulation has enabled the training of additional sensing capabilities and research both at the granular mechanics and operations levels. We experimented with various virtual sensor payloads to identify a combination that enabled efficient excavation operation and learning. Our reward function is based on how much material is excavated per step. A penalty is also received for leaving the dig site and to smooth the acceleration of the drum arms. We implemented pseudo time-of-flight sensors to report distance from each drum to ground and the height above ground which was found to be more efficient than existing solutions. Our findings suggest that reinforcement learning for autonomous operations has learned viable trenching strategies within 3000 training episodes in our simplified 2D environment and helped identify desirable sensing capabilities, arrangements, and considerations such as the positioning of time-of-flight sensors. Future work includes expanding our simulation to more complex environments and scenarios, and transfer learning from simulation to RASSOR 2.0 hardware for deployment in the Regolith Test Bin at NASA's Kennedy Space Center.

rassor↗
DOE OSTI2022

Neural Phase Simulation

The Neural Phase Simulation (NPS) is a package of codes for simulating microstructure evolution and accelerated molecular dynamics with deep neural-networks based surrogate models. NPS is designed to offer quantitatively accurate and computationally efficient simulation capabilities by leveraging modern machine-learning techniques. The primary intended use cases of NPS are training neural network surrogate models, though performing simulations on a single node is also supported. The NPS surrogate models can be trained from ground truth simulation methods, which are supposed to be accurate but expensive, such as molecular dynamics, phase field methods, kinetic Monte Carlo and discrete dislocation dynamics.

Zhou, Fei↗
DOE OSTI2020

Development of interatomic potential for Al–Tb alloys using a deep neural network learning method

An interatomic potential for the Al–Tb alloy around the composition of Al 90 Tb 10 is developed using the deep neural network (DNN) learning method. The atomic configurations and the corresponding total potential energies and forces on each atom obtained from ab initio molecular dynamics (AIMD) simulations are collected to train a DNN model to construct the interatomic potential for the Al–Tb alloy. Here we show that the obtained DNN model can well reproduce the energies and forces calculated by AIMD simulations. Molecular dynamics (MD) simulations using the DNN interatomic potential also accurately describe the structural properties of the Al 90 Tb 10 liquid, such as partial pair correlation functions (PPCFs) and bond angle distributions, in comparison with the results from AIMD simulations. Furthermore, the developed DNN interatomic potential predicts the formation energies of the crystalline phases of the Al–Tb system with an accuracy comparable to ab initio calculations. The structure factors of the Al 90 Tb 10 metallic liquid and glass obtained by MD simulations using the developed DNN interatomic potential are also in good agreement with the experimental X-ray diffraction data. The development of short-range order (SRO) in the Al 90 Tb 10 liquid and the undercooled liquid is also analyzed and three dominant SROs, i.e., Al-centered distorted icosahedron (DISICO) and Tb-centered ‘3661’ and ‘15551’ clusters, respectively, are identified.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Deep learning to estimate permeability using geophysical data

Time-lapse electrical resistivity tomography (ERT) is a popular geophysical method to estimate three-dimensional (3D) permeability fields from electrical potential difference measurements. Traditional inversion and data assimilation methods are used to ingest this ERT data into hydrogeophysical models to estimate permeability. Due to ill-posedness and the curse of dimensionality, existing inversion strategies provide poor estimates and low resolution of the 3D permeability field. Recent advances in deep learning provide us with powerful algorithms to overcome this challenge. This paper presents a deep learning (DL) framework to estimate the 3D subsurface permeability from time-lapse ERT data. To test the feasibility of the proposed framework, we train DL-enabled inverse models on simulation data. Each measurement in both synthetic and field data is standardized by removing the mean and scaling the time-series to unit variance. This pre-processing step is necessary to bring simulation data closer to field observations. Subsurface process models based on hydrogeophysics are used to generate this synthetic data. Training performed on limited simulation data resulted in the DL model over-fitting. An advanced data augmentation based on mixup is implemented to generate additional training samples to overcome this issue. This mixup technique creates weakly labeled (low-fidelity) samples from strongly labeled (high-fidelity) data. The weakly labeled training data is then used to develop DL-enabled inverse models and reduce over-fitting. As both time-lapse ERT (1133048 features/realization) and 3D permeability (585453 features/realization) data samples are from a high-dimensional space, principal component analysis (PCA) is employed to reduce dimensionality. Encoded ERT and encoded permeability are generated using the trained PCA estimators. A deep neural network is then trained to map the encoded ERT to encoded permeability. This mixup training and unsupervised learning allowed us to build a fast and reasonably accurate DL-based inverse model under limited simulation data. Results show that proposed weak supervised learning can capture salient spatial features in the 3D permeability field. Quantitatively, the average mean squared error (in terms of the natural log) on the strongly labeled training, validation, and test datasets is less than 0.5. The R 2 -score (global metric) is greater than 0.75, and the percent error in each cell (local metric) is less than 10%. Finally, an added benefit in terms of computational cost is that the proposed DL-based inverse model is at least O(10 4 ) times faster than running a forward model once it is trained. Data generation, DL model training, and hyperparameter tuning to identify optimal neural network architectures utilized high-performance computing resources while the DL inference is performed on a standard laptop. Approximately, O(10 5 ) processor hours are used for generating data and DL tuning and training. We acknowledge that the data generation and DL model development are expensive. But once a DL model is trained, it can be re-used for inversion rapidly for the given system, with set physics and domain. Note that traditional inversion may require multiple forward model simulations (e.g., in the order of 10 to 1000), which are very expensive. This computational savings ≈ O(10 5 ) – O(10 7 )) makes the proposed DL-based inverse model attractive for subsurface imaging and real-time ERT monitoring applications due to fast and yet reasonably accurate estimations of permeability field.

58 GEOSCIENCES↗
DOE OSTI2024

Towards a High Fidelity Training Environment for Autonomous Cyber Defense Agents

Cyber defenders are overwhelmed by the frequency and scale of attacks against their networks. This problem will only be exacerbated as attackers leverage AI to automate their workflows. Autonomous cyber defense capabilities could aid defenders by automating operations and adapting dynamically to novel threats. However, existing training environments fall short in areas such as generalization, explainability, scalability, and transferability, making it intractable to train agents that will be effective in real networks. In this paper we take an important step towards creating autonomous cyber defense agents — we present a high fidelity training environment called Cyberwheel that includes both simulation and emulation capabilities. Cyberwheel simplifies customization of the training network and easily allows redefining the agent’s reward function, observation space, and action space to support rapid experimentation of novel approaches to agent design. It also provides visibility into agent behaviors necessary for agent evaluation and sufficient documentation / examples to lower the barrier to entry. As an example use case of Cyberwheel, we present initial results training an autonomous agent to deploy cyber deception strategies in simulation.

Oesch, T↗
DOE OSTI2023

Enhancing Nanoparticle Detection in Interferometric Scattering (iSCAT) Microscopy Using a Mask R-CNN

Interferometric scattering microscopy (iSCAT) is a label-free optical microscopy technique that enables imaging of individual nano-objects such as nanoparticles, viruses, and proteins. Essential to this technique is the suppression of background scattering and identification of signals from nano-objects. In the presence of substrates with high roughness, scattering heterogeneities in the background, when coupled with tiny stage movements, cause features in the background to be manifested in background-suppressed iSCAT images. Traditional computer vision algorithms detect these background features as particles, limiting the accuracy of object detection in iSCAT experiments. Here, in this paper, we present a pathway to improve particle detection in such situations using supervised machine learning via a mask region-based convolutional neural network (mask R-CNN). Using a model iSCAT experiment of 19.2 nm gold nanoparticles adsorbing to a rough layer-by-layer polyelectrolyte film, we develop a method to generate labeled datasets using experimental background images and simulated particle signals and train the mask R-CNN using limited computational resources via transfer learning. We then compare the performance of the mask R-CNN trained with and without inclusion of experimental backgrounds in the dataset against that of a traditional computer vision object detection algorithm, Haar-like feature detection, by analyzing data from the model experiment. Results demonstrate that including representative backgrounds in training datasets improved the mask R-CNN in differentiating between background and particle signals and elevated performance by markedly reducing false positives. The methodology for creating a labeled dataset with representative experimental backgrounds and simulated signals facilitates the application of machine learning in iSCAT experiments with strong background scattering and thus provides a useful workflow for future researchers to improve their image processing capabilities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2011

Lockout/Tagout (LOTO) Simulator

The Lockout/Tagout (LOTO) Simulator is a portable training aid, or demonstration tool, designed to physically illustrate real-time critical-safety concepts of electrical lockout/tagout. The objective is to prevent misinterpretations of what is off and what is on during maintenance and repair of complex electrical systems. The simulator is designed in the form of a hinged box that opens up and stands on its own as an easel for demonstrations. On the outer face of the unit is a simulated circuit breaker box housing the switches. The breakers control the main power to the unit, a light bulb, and an electrical control cabinet. The light bulb is wired so that either of two breakers can provide power to it. When power is sent to the electrical control cabinet, a red indicator light illuminates. Inside the cabinet is the power supply from a personal computer. The power supply produces a 12-V dc output that is sent over to a small fan next to it, also from a computer, and an amber light on the front of the cabinet illuminates. A separate switch powers the fan on and off. The power supply is behind a plastic shield to protect against exposure to live conductors. Electrical banana jacks are mounted in the plastic shield to allow a voltmeter to be connected safely when opening the cabinet and taking a meter reading to verify de-energization as part of a simulation exercise. This LOTO simulator prototype is designed and fabricated as an all-in-one unit. All accessories can be stored inside the hinged case, and there is a handle on top for ease of transport. The circuit breaker labels attach with hook and loop fasteners so that they may be moved and changed to fit the training or demonstration scenario. The warning signs and labels on the electrical control box are magnetic, allowing for easy reconfiguration to emulate different equipment setups. A specially designed magnetic cover was made to disguise the indicator lights for demonstrations when these indicators are not used. The cover is disguised as an arc flash safety label that would typically be found on such a cabinet. One indicator light has a separate switch that can take it offline. This is to allow for demonstration to trainees on why it is important not to completely rely on indicator lights, but that they should always take a meter reading at the exposed conductors to absolutely verify de-energization before exposure. A clear plastic barrier and banana jacks inside the cabinet provide a safe way to plug in a voltmeter for demonstrations without exposure to the hazards of energized equipment. A small remote control unit is wired into the fan circuit. The remote allows the demonstrator to turn the fan on and off, provided that all of the breakers and switches leading to it are configured on as well. The remote feature was added in order to demonstrate the importance of starting the lockout/tagout task with energized equipment, then powering it down, isolating it, and locking it out to ensure that the correct breakers have been locked out.

Scheer, Jennifer↗
DOE OSTI2026

Track Matching in the DUNE Near Detectors

The Deep Underground Neutrino Experiment (DUNE) is an international particle physics experiment looking answer some of the largest unanswered questions in neutrino physics. DUNE uses a high power neutrino beam produced at Fermi National Accelerator Laboratory (Fermilab), and consists of a near detector (ND) also located at Fermilab and a far detector (FD) 1300 km away at the Sanford Underground Research Facility (SURF) in South Dakota. In the first phase of the experiment, the ND complex will contain a modular liquid argon TPC (ND-LAr) and a solid scintillator-based muon spectrometer (TMS), in addition to a beam monitoring detector (SAND) and systems for moving ND-LAr and TMS away from the neutrino beam axis (PRISM). A prototype of ND-LAr, the 2x2 demonstrator, alongside a solid scintillator muon tagger provided by repurposed MINERvA planes, has been built and taken data at Fermilab. For analyses with the ND, connecting particle tracks (such as muons) that exit the liquid argon active volume into the solid scintillator muon detector can improve particle identification and energy reconstruction, and alleviate pileup due to the intense beam. To match tracks between detectors during reconstruction, we have explored using Graph Neural Networks (GNNs) to connect tracks segments between the liquid argon detector region and the solid scintillator detector planes. We have trained a GNN on reconstructed simulated data from the 2×2 demonstrator and repurposed MINERvA planes. We will evaluate its performance and then train a similar network on reconstructed ND-LAr and TMS simulations.

Xing, Daniel [U. Colorado, Boulder]↗