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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Real-Time Anomaly Detection for Searches Beyond the Standard Model in the ProtoDUNE Horizontal Drift Detector

This paper summarizes work conducted throughout a SULI internship at Fermi National Accelerator Laboratory focused on building an unsupervised machine learning model for real-time anomaly detection in ProtoDUNE Horizontal Drift. Using simulated data, we trained an autoencoder model on a pure cosmic dataset, and evaluated it on both cosmic and neutrino events—making the model an anomaly detector. The goal was to make a model which matches or exceeds the current ADC Simple Window trigger algorithm so that our model can perform at the same rate but provide sensitivity to potential beyond-the-Standard-Model (BSM) signatures. In the end, we were able to construct a model which slightly exceeds the capabilities of the ADC Simple Window while remaining completely unsupervised, achieving 31.9 ± 0.2% (26.6 ± 0.2%) ν efficiency at 5 Hz (2 Hz), a 3.6 (3.2) percentage point increase. Additionally, 17.5 ± 0.3% (18.3 ± 0.3%) of the events that passed the autoencoder at 5 Hz (2 Hz) were missed by the current trigger algorithm. Future work will investigate alternative normalization methods, including quantile transformation, and evaluate the model on ProtoDUNE-HD detector-glitch data if that data becomes available.

Wilson, C. [Cincinnati U., RWC]↗
NASA NTRS1992

Automating simulator operations

A set of requirements are defined for an operations support system for automating operations in the Space Station Training Facility (SSTF). An operations concept is described based on utilization studies and facility-operations plans with specific attention given to the SSTF components. The Operations Support System (OSS) is designed to have an integrated set of tools for system configuration and realtime data monitoring, analysis, and control. The concept relies on a system configuration tool for connecting and verifying hardware and software components, and a status-and-control monitor is envisaged to collect data. The SSTF OSS is expected to enhance the effectiveness of simulator training by configuring the components for training, monitoring and controlling components, and conducting all operations in real time.

Jacobs, Rebecca J.↗
DOE OSTI2023

Efficient learning of power grid voltage control strategies via model-based deep reinforcement learning

Here this article proposes a model-based deep reinforcement learning (DRL) method to design emergency control strategies for short-term voltage stability problems in power systems. Recent advances show promising results for model-free DRL-based methods in power systems control problems. But in power systems applications, these model-free methods have certain issues related to training time (clock time) and sample efficiency; both are critical for making state-of-the-art DRL algorithms practically applicable. DRL-agent learns an optimal policy via a trial-and-error method while interacting with the real-world environment. It is also desirable to minimize the direct interaction of the DRL agent with the real-world power grid due to its safety-critical nature. Additionally, the state-of-the-art DRL-based policies are mostly trained using a physics-based grid simulator where dynamic simulation is computationally intensive, lowering the training efficiency. We propose a novel model-based DRL framework where a deep neural network (DNN)-based dynamic surrogate model (SM), instead of a real-world power grid or physics-based simulation, is utilized within the policy learning framework, making the process faster and more sample efficient. However, having stable training in model-based DRL is challenging because of the complex system dynamics of large-scale power systems. We addressed these issues by incorporating imitation learning to have a warm start in policy learning, reward-shaping, and multi-step loss in surrogate model training. Finally, we achieved 97.5% reduction in samples and 87.7% reduction in training time for an application to the IEEE 300-bus test system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Radiation Source Localization Using Surrogate Models Constructed from 3-D Monte Carlo Transport Physics Simulations

Recent research has focused on the development of surrogate models for radiation source localization in a simulated urban domain. We employ the Monte Carlo N-Particle (MCNP) code to provide high- delity simulations of radiation transport within an urban domain. The model is constructed to employ a source location (x, y, z) as input and return the estimated count rate for a set of speci ed detector locations. Because MCNP simulations are computationally expensive, we develop e cient and accurate surrogate models of the detector responses. We construct surrogate models using Gaussian processes (GP) and neural networks (NN) that we train and verify using the MCNP simulations. The trained surrogate models provide an e cient framework for Bayesian inference and experimental design. We employ Delayed Rejection Adaptive Metropolis (DRAM), a Markov Chain Monte Carlo (MCMC) algorithm, to infer the location and intensity of an unknown source. The DRAM results yield a posterior probability distribution for the source's location conditioned on the observed detector count rates. The posterior distribution exhibits regions of high and low probability within the simulated environment identifying potential source locations. In this manner, we can quantify the source location to within at least one of these regions of high probability in the considered cases. Employing these methods, we are able to reduce the space of potential source locations by at least 60%.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Probabilistic Inference of Low-Surface-Brightness Galaxy Morphological Parameters Using Simulation-Based Inference

Low-surface-brightness galaxies (LSBGs) are diffuse, often dark-matter-dominated systems whose faintness makes their structural parameters difficult to measure reliably in wide-field imaging surveys. Robust parameter inference, including uncertainty quantification, is important for population studies and for comparisons with models of galaxy formation, as future surveys are expected to produce increasingly large samples of diffuse galaxies. In practice, LSBG profile modeling is sensitive to sky- background errors, masking choices, contaminating background sources, and the computational cost of obtaining posterior-level uncertainties for large samples. Motivated by these questions, we develop a simulation-based inference (SBI) framework for estimating posterior distributions of LSBG morphological parameters from simulated galaxy images. Using PyImfit, we generate DES-like single-Sersic profile LSBG images with known position angle, ellipticity, Sersic index, effective surface brightness, and effective radius. We then train a normalizing-flow-based neural posterior estimator using the sbi package to infer these parameters from the simulated images. For isolated simulated galaxies, the SBI posterior recovers the true input parameters, produces posterior predictive residuals consistent with the assumed noise model, and shows good empirical calibration in a DES-motivated test regime. We also compare SBI with PyImfit-based MCMC inference and find broadly comparable posterior constraints, while SBI enables substantially faster posterior sampling after training. Finally, we test robustness to compact background contaminants. A model trained only on isolated galaxies produces undercovered posteriors on contaminated images, whereas training on simulations with variable contaminant positions and fluxes improves calibration across contaminated test sets. These results demonstrate the promise of SBI for scalable, uncertainty-aware LSBG morphology inference, while emphasizing that posterior reliability strongly depends on whether training simulations include relevant observational complications.

Batbayar, Bilguun [U. Chicago (main)]↗
NASA NTRS2006

Demonstration of an Ice Contamination Effects Flight Training Device

The development of a piloted flight simulator called the Ice Contamination Effects Flight Training Device (ICEFTD) was recently completed. This device demonstrates the ability to accurately represent an iced airplane s flight characteristics and is utilized to train pilots in recognizing and recovering from aircraft handling anomalies that result from airframe ice formations. The ICEFTD was demonstrated at three recent short courses hosted by the University of Tennessee Space Institute. It was also demonstrated to a group of pilots at the National Test Pilot School. In total, eighty-four pilots and flight test engineers from industry and the regulatory community spent approximately one hour each in the ICEFTD to get a "hands on" lesson of an iced airplane s reduced performance and handling qualities. Additionally, pilot cues of impending upsets and recovery techniques were demonstrated. The purpose of this training was to help pilots understand how ice contamination affects aircraft handling so they may apply that knowledge to the operations of other aircraft undergoing testing and development. Participant feedback on the ICEFTD was very positive. Pilots stated that the simulation was very valuable, applicable to their occupations, and provided a safe way to explore the flight envelope. Feedback collected at each demonstration was also helpful to define additional improvements to the ICEFTD; many of which were then implemented in subsequent demonstrations

Ratvasky, Thomas P.↗
NASA NTRS2002

Airplane Upset Training Evaluation Report

Airplane upset accidents are a leading factor in hull losses and fatalities. This study compared five types of airplane-upset training. Each group was composed of eight, non-military pilots flying in their probationary year for airlines operating in the United States. The first group, 'No aero / no upset,' was made up of pilots without any airplane upset training or aerobatic flight experience; the second group, 'Aero/no upset,' of pilots without any airplane-upset training but with aerobatic experience; the third group, 'No aero/upset,' of pilots who had received airplane-upset training in both ground school and in the simulator; the fourth group, 'Aero/upset,' received the same training as Group Three but in addition had aerobatic flight experience; and the fifth group, 'In-flight' received in-flight airplane upset training using an instrumented in-flight simulator. Recovery performance indicated that clearly training works - specifically, all 40 pilots recovered from the windshear upset. However few pilots were trained or understood the use of bank to change the direction of the lift vector to recover from nose high upsets. Further, very few thought of, or used differential thrust to recover from rudder or aileron induced roll upsets. In addition, recovery from icing-induced stalls was inadequate.

Gawron, Valerie J.↗
DOE OSTI2021

Davis Computational Spectroscopy Workflow—From Structure to Spectra

Here, we describe an automated workflow that connects a series of atomic simulation tools to investigate the relationship between atomic structure, lattice dynamics, materials properties, and inelastic neutron scattering (INS) spectra. Starting from the atomic simulation environment (ASE) as an interface, we demonstrate the use of a selection of calculators, including density functional theory (DFT) and density functional tight binding (DFTB), to optimize the structures and calculate interatomic force constants. We present the use of our workflow to compute the phonon frequencies and eigenvectors, which are required to accurately simulate the INS spectra in crystalline solids like diamond and graphite as well as molecular solids like rubrene. We have also implemented a machine-learning force field based on Chebyshev polynomials called the Chebyshev interaction model for efficient simulation (ChIMES) to improve the accuracy of the DFTB simulations. We then explore the transferability of our DFTB/ChIMES models by comparing simulations derived from different training sets. We show that DFTB/ChIMES demonstrates ~100× reduction in computational expense while retaining most of the accuracy of DFT as well as yielding high accuracy for different materials outside of our training sets. The DFTB/ChIMES method within the workflow expands the possibilities to use simulations to accurately predict materials properties of increasingly complex structures that would be unfeasible with ab initio methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2018

LADEE Simulation for Mission Operations

The Lunar Atmosphere Dust Environment Explorer (LADEE) model-based spacecraft simulator has been discussed previously at the Workshop on Spacecraft Flight Software including the verification and validation of the flight software, hardware integration of payloads, and multi-domain simulation. In addition to flight software development and testing the LADEE simulator was used by Mission Operations to develop and test spacecraft command scripts, train operators during mission simulations, verification of all tactical command sequence files uploaded to spacecraft during flight.This presentation will discuss the experience of the LADEE operations team using the spacecraft simulator including implementation, processes and lessons learned. We will also discuss a specific instance where the simulator was used in operations to debug and design a software fix for a spacecraft anomaly experienced with the star tracker.

Operations↗
DOE OSTI2023

Semi-supervised graph neural networks for pileup noise removal

Abstract The high instantaneous luminosity of the CERN Large Hadron Collider leads to multiple proton–proton interactions in the same or nearby bunch crossings (pileup). Advanced pileup mitigation algorithms are designed to remove this noise from pileup particles and improve the performance of crucial physics observables. This study implements a semi-supervised graph neural network for particle-level pileup noise removal, by identifying individual particles produced from pileup. The graph neural network is firstly trained on charged particles with known labels, which can be obtained from detector measurements on data or simulation, and then inferred on neutral particles for which such labels are missing. This semi-supervised approach does not depend on the neutral particle pileup label information from simulation, and thus allows us to perform training directly on experimental data. The performance of this approach is found to be consistently better than widely-used domain algorithms and comparable to the fully-supervised training using simulation truth information. The study serves as the first attempt at applying semi-supervised learning techniques to pileup mitigation, and opens up a new direction of fully data-driven machine learning pileup mitigation studies.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Improving the reliability of machine learned potentials for modeling inhomogeneous liquids

The atomic-scale response of inhomogeneous fluids at interfaces and surrounding solute particles plays a critical role in governing chemical, electrochemical, and biological processes. Classical molecular dynamics simulations have been applied extensively to simulate the response of fluids to inhomogeneities directly, but are limited by the accuracy of the underlying interatomic potentials. Here, we use neural network potentials (NNPs) trained to ab initio simulations to accurately predict the inhomogeneous responses of two distinct fluids: liquid water and molten NaCl. Although NNPs can be readily trained to model complex bulk systems across a range of state points, we show that to appropriately model a fluid's response at an interface, relevant inhomogeneous configurations must be included in the training data. In order to sufficiently sample appropriate configurations of such inhomogeneous fluids, we develop protocols based on molecular dynamics simulations in the presence of external potentials. We demonstrate that NNPs trained on inhomogeneous fluid configurations can more accurately predict several key properties of fluids—including the density response, surface tension and size-dependent cavitation free energies—for liquid water and molten NaCl, compared to both empirical interatomic potentials and NNPs that are not trained on such inhomogeneous configurations. This work therefore provides a first demonstration and framework to extract the response of inhomogeneous fluids from first principles for classical density-functional treatment of fluids free from empirical potentials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1994

Virtual environment application with partial gravity simulation

To support manned missions to the surface of Mars and missions requiring manipulation of payloads and locomotion in space, a training facility is required to simulate the conditions of both partial and microgravity. A partial gravity simulator (Pogo) which uses pneumatic suspension is being studied for use in virtual reality training. Pogo maintains a constant partial gravity simulation with a variation of simulated body force between 2.2 and 10 percent, depending on the type of locomotion inputs. this paper is based on the concept and application of a virtual environment system with Pogo including a head-mounted display and glove. The reality engine consists of a high end SGI workstation and PC's which drive Pogo's sensors and data acquisition hardware used for tracking and control. The tracking system is a hybrid of magnetic and optical trackers integrated for this application.

Ray, David M.↗
NASA NTRS2014

Portable Simulator for On-Board International Space Station Emergency Training

The crew on-board the International Space Station (ISS) have to be prepared for any possible emergency. The emergencies of most concern are a fire, depressurization or a toxic atmosphere. The crew members train on the ground before launch but also need to practice their emergency response skills while they are on orbit for 6 months. On-Board Training (OBT) events for emergency response proficiency used to require the crew and ground teams to use paper "scripts" that showed the path through the emergency procedures. This was not very realistic since the participants could read ahead and never deviate from this scripted path. The new OBT emergency simulator allows the crew to view dynamic information on an iPad only when it would become available during an event. The simulator interface allows the crew member to indicate hatch closures, don and doff masks, read pressures, and sample smoke or atmosphere levels. As the crew executes their actions using the on-board simulator, the ground teams are able to monitor those actions via ground display data flowing through the ISS Ku Band communication system which syncs the on-board simulator software with a ground simulator which is accessible in all the control centers. The OBT Working Group (OBT WG), led by the Chief Training Office (CTO) at Johnson Space center is a Multilateral working group with partners in Russia, Japan, Germany and U.S.A. The OBTWG worked together to create a simulator based on these principles: (a) Create a dynamic simulation that gives real-time data feedback; (b) Maintain real-time interface between Mission Control Centers and crew during OBTs; (c) Provide flexibility for decision making during drill execution; (d) Materially reduce Instructor and Flight Control Team man-hour costs involved with developing, updating, and maintaining emergency OBT cases/scenarios; and (e) Introduce an element of surprise to emergency scenarios so the team can't tell the outcome of the case by reading ahead in a paper script. This paper details some of the background as the project evolved with the International Partners. It also provides more details on how the simulator information for the crew is also shared with the ground teams. Finally, the paper will discuss how this remote simulator concept can be useful for future, longer term space missions.

Bolt, Kathy↗
DOE OSTI2025

An experimentally informed design process for future inertial confinement fusion facilities

The achievement of ignition in the laboratory has renewed interest in defining the requirements for a future high-gain inertial confinement fusion (ICF) facility. Our best chance of predicting future ICF performance is with 3-D radiation hydrodynamic simulations that have been benchmarked against experimental data, but their high computational cost is prohibitive for use in practical design studies. We introduce a hierarchical approach where 3-D simulations are tuned to match experimental measurements and used to train 3-D degradation models in 1-D simulations allowing for accurate predictions over the entire OMEGA direct-drive database. A genetic algorithm was used in combination with the trained 1-D simulations to search for optimal direct-drive implosion designs at driver energies ranging from 20 kJ to 10 MJ. As the fidelity of 3-D codes improves, this approach will provide a viable experimentally informed tool for defining the next ICF facility.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Entropy-driven Optimal Sub-sampling of Fluid Dynamics for Developing Machine-learned Surrogates

Optimal sub-sampling of large datasets from fluid dynamics simulations is essential for training reduced-order machine learned models. A method using Shannon entropy was developed to weight flow features according to their level of information content, such that the most informative features can be extracted and used for training a surrogate model. The method is demonstrated in the canonical flow over a cylinder problem simulated with OpenFOAM. Both time-independent predictions and temporal forecasting were investigated as well as two types of prediction targets: local per-grid-point predictions and global per-time-step predictions. When tested on training a surrogate model, results indicate that our entropy-based sampling method typically outperforms random sampling and yields more reproducible results in less iterations. Finally, the method was used to train a surrogate model for modeling turbulence in magnetohydrodynamic flows, which revealed various challenges and opportunities for future research.

Brewer, Wes↗
NASA NTRS1992

Safer Weightlessness Simulator

System described in report simulates weightlessness of space for training of astronauts, without adverse effects of rapid decompression. As in previous simulators, trainee wearing space-suit performs training exercises while immersed in water. In new system, space-suit interior is not above atmospheric pressure. No need for trainee to undergo gradual decompression to prevent bends after lengthy exercise; nor is there need for medical team to be present.

Vykukal, Hubert C.↗
NASA NTRS2017

Autogenic-Feedback Training Exercise (AFTE) Mitigates the Effects of Spatial Disorientation to Simulated Orion Spacecraft Re-Entry: Individual Differences

NASA has identified a potential risk of spatial disorientation to future astronauts during re-entry of the proposed Orion spacecraft. The purpose of this study was to determine if a 6-hour physiological training procedure, Autogenic-Feedback Training Exercise (AFTE), can mitigate these effects. Twenty subjects were assigned to two groups (AFTE and Control) matched for motion sickness susceptibility and gender. All subjects received a standard rotating chair test to determine motion sickness susceptibility; three training sessions on a manual performance task; and four exposures to a simulated Orion re-entry test in the rotating chair. Treatment subjects were given two hours of AFTE training before each Orion test. A diagnostic scale was used to evaluate motion sickness symptom severity. Results showed that 2 hours of AFTE significantly reduced motion sickness symptoms during the second Orion test. AFTE subjects were able to maintain lower heart rates and skin conductance levels and other responses than the control group subjects during subsequent tests. Trends show that performance was less degraded for AFTE subjects. The results of this study indicate that astronauts could benefit from receiving at least 2 hours of preflight AFTE. In addition, flight crews could benefit further by practicing physiologic self-regulation using mobile devices.

human performance↗