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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

A Hybrid Parachute Simulation Environment for the Orion Parachute Development Project

A parachute simulation environment (PSE) has been developed that aims to take advantage of legacy parachute simulation codes and modern object-oriented programming techniques. This hybrid simulation environment provides the parachute analyst with a natural and intuitive way to construct simulation tasks while preserving the pedigree and authority of established parachute simulations. NASA currently employs four simulation tools for developing and analyzing air-drop tests performed by the CEV Parachute Assembly System (CPAS) Project. These tools were developed at different times, in different languages, and with different capabilities in mind. As a result, each tool has a distinct interface and set of inputs and outputs. However, regardless of the simulation code that is most appropriate for the type of test, engineers typically perform similar tasks for each drop test such as prediction of loads, assessment of altitude, and sequencing of disreefs or cut-aways. An object-oriented approach to simulation configuration allows the analyst to choose models of real physical test articles (parachutes, vehicles, etc.) and sequence them to achieve the desired test conditions. Once configured, these objects are translated into traditional input lists and processed by the legacy simulation codes. This approach minimizes the number of sim inputs that the engineer must track while configuring an input file. An object oriented approach to simulation output allows a common set of post-processing functions to perform routine tasks such as plotting and timeline generation with minimal sensitivity to the simulation that generated the data. Flight test data may also be translated into the common output class to simplify test reconstruction and analysis.

Moore, James W.↗
NASA NTRSDate not supplied

OSIRIS-REx Earth Return Orbit Determination Analysis and Performance

Origins, Spectral Interpretation, Resource Identification, and Security–Regolith Explorer (OSIRIS-REx) was a NASA New Frontiers–class asteroid sample return mission to (101955) Bennu. OSIRIS-REx performed a series of Trajectory Cor-rection Maneuver (TCM)s to target an Entry Interface (EI) point for Earth Return and entry, descent, and landing (EDL). On September 24, 2023, the Sample Re-turn Capsule (SRC) separated from the spacecraft, entered the atmosphere, and ultimately landed at the Utah Test and Training Range for recovery. This paper gives an overview of the updated orbit determination (OD) covariance analysis that went into the Earth return and reentry analysis after asteroid departure. Tracking data assumptions and multiple filter cases are analyzed to show performance. Operational OD results from TCM-9 and subsequent targeting maneuvers up to entry interface are provided. We show parameter error analysis contributions from the OD setup and their breakdown in the B-Plane error ellipses. Finally, we discuss the terminal OD solutions into EI and reconstruction results using onboard image data.

Jason M. Leonard↗
DOE OSTI2024

Component pose reconstruction using a single robotic total station for panelized building envelopes

The construction industry can benefit greatly from increased automation in construction processes in terms of precision, time, and labor costs. This is especially true for prefabricated construction for either new buildings or envelope retrofits. Here, prefabricated components are manufactured offsite according to design specifications and installed onsite. Accurate information on the component’s position and orientation (pose) is needed to achieve this. A tool named “Real-Time Evaluator” (RTE), designed to autonomously track prefabricated components as they are being installed and provide real-time installation instructions, is currently under development using a single robotic total station. This is challenging since at least three points are needed to determine the pose of an object in space. To achieve this goal with a single robotic total station, two key algorithms were developed: the “resection” algorithm aligns the digital twin with the physical twin regardless of the location of the total station, and the “transformation” algorithm gives instructions of translations and rotations to installers to achieve the desired installation pose. The algorithms were evaluated experimentally on a lab-scale demonstration. Results show that the resection algorithm achieved an average error of < 3.1 mm, while the transformation algorithm predicted the rotation angle along a single axis with an error of < 0.5◦.

Tang, Mengjia↗
DOE OSTI2026

NuGraph2 with context-aware inputs: physics-inspired improvements in semantic segmentation

Graph neural networks have recently shown strong promise for event reconstruction tasks in Liquid Argon Time Projection Chambers, yet their performance remains limited for underrepresented classes of particles, such as Michel electrons. In this work, we investigate physics-informed strategies to improve semantic segmentation within the NuGraph2 architecture. We explore three complementary approaches: (i) enriching the input representation with context-aware features derived from detector geometry and track continuity, (ii) introducing auxiliary decoders to capture class-level correlations, and (iii) incorporating energy-based regularization terms motivated by Michel electron energy distributions. Experiments on MicroBooNE public datasets show that physics-inspired feature augmentation yields the largest gains, particularly boosting Michel electron precision and recall by disentangling overlapping latent space regions. In contrast, auxiliary decoders and energy-regularization terms provided limited improvements, partly due to the hit-level nature of NuGraph2, which lacks explicit particle- or event-level representations. Our findings highlight that embedding physics context directly into node-level inputs is more effective than imposing task-specific auxiliary losses, and suggest that future hierarchical architectures such as NuGraph3, with explicit particle- and event-level reasoning, will provide a more natural setting for advanced decoders and physics-based regularization. The code for this work is publicly available on Github at https://github.com/vitorgrizzi/nugraph_phys/tree/main_phys.

Other Experiments↗
DOE OSTI2025

Advancing the Frontiers of Deep Learning for Low-Dose 3D Cone-Beam CT Reconstruction

X-ray computed tomography (CT) is an important noninvasive medical imaging modality for studying the structural details of internal organs. Image reconstruction in CT is an inverse problem of recovering an object's internal structure from the absorption profile of X-ray beams (sinogram) measured using a detector. The classical variational approach for CT reconstruction minimizes an energy functional using an appropriate iterative algorithm. Motivated by the success of deep learning (DL), researchers have begun to leverage training data and enhanced computing capabilities in recent years to produce high-fidelity reconstructed images. Nonetheless, much of the academic research in DL algorithms for CT has focused primarily on the two-dimensional setting (with simplified forward operators and noise model) for proofs-of-concept, and a comprehensive benchmarking of various classical and data-driven CT reconstruction approaches has not beenundertaken. The key objective of our CT reconstruction grand challenge was to promote methodological advancements for both classical and DL-based approaches for clinical CT with a reasonably accurately simulated 3D CT forward operator and noise model. We have utilized the publicly available LIDC-IDRI dataset and simulated sinograms and FDK images corresponding to two dose levels (clinical- and low-dose, constituting two tracks of the challenge) starting from the normal-dose images as the ground truth. In this paper, we summarize the motivation, context, and results of our challenge, and highlight the future research directions in DL for clinical CT.

X-ray tomography↗
DOE OSTI2022

Autoencoder Neural Network for chemically reacting systems

Incorporating detailed chemical kinetic models is critical for accurate simulations of reacting flows. However, detailed models involve a large number of thermochemical (TC) state variables. Solving the governing equations to evolve these TC variables becomes impractical for real-world applications. In this work, we propose an autoencoder (AE) neural network (NN)-based reduced model to accelerate such simulations. The AE NN is first trained to find a low-dimensional latent representation of the TC states. Then, the evolving state of a chemical system can be tracked by solving the equations of the latent variables instead of the original TC equations. We demonstrate the reduced model in a syngas CO/H 2 combustion system, using training data collected from canonical perfectly stirred reactors (PSRs). It is found that the AE model can reduce the dimension of the combustion system from 12 to 2 while maintaining low reconstruction error and excellent elemental mass conservation for the test dataset. In the a posteriori test, the combustion states obtained from solving the two latent equations are compared to those from solving the 12 equations of the full model. The AE reduced method is found to be able to capture the diverse combustion states on the top two branches of the S-curve well including the extinction turning point, but with higher prediction errors for states near the ignition turning point.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1998

Navigation of the space VLBI mission-HALCA

In February 1997, the Japanese Space Agency ISAS launched the first space VLBI satellite, HALCA, with an 8 meter diameter wire mesh antenna and radio astronomy receivers capable of observing at 1.6, 4.8, and 22 Ghz. In a 560 by 21000 km orbit with a 6 hour period and 31 degree inclination, it observes celestial radio sources in conjunction with a world wide network of ground radio telescopes as part of an international collaborative effort which includes facilities in Japan, the U.S., Canada, Australia, and Europe. JPL is providing tracking and navigation support using a dedicated subnet of 11 meter antennas as well as co-observations using the DSN 70 meter antennas. This paper describes the spacecraft dynamics model and orbit determination strategies developed to meet the stringent trajectory accuracy requirements for generating predictions for the transfer of a stable uplink frequency to the spacecraft and for determining reconstructed orbits for delivery to the NRAO VLBI correlator and the international VLBI science community.

You, Tung Han↗
DOE OSTI2021

Evaluating the ProtoDUNE-SP Detector Performance to Measure a 6 GeV/c Positive Kaon Inelastic Cross Section on Argon

The ProtoDUNE Single-Phase Liquid Argon Time Projection Chamber \\ (ProtoDUNE-SP LArTPC) is a prototype for the Deep Underground Neutrino Experiment (DUNE), a future long-baseline neutrino oscillation experiment. Based at the CERN Neutrino Platform, ProtoDUNE-SP LArTPC collected data from a charged test beam in the fall of 2018. It then took data of cosmic-ray muons from November 2018 to the summer of 2020. The main goals of the prototype were to measure parameters related to charged particle passage in argon and evaluate the performance of the detector to inform future DUNE Far Detector development. The test beam provided kaons, pions, muons, protons, and electrons to the detector. These particles represent common final state particles in neutrino interactions, therefore providing information to DUNE on modeling charged particles in argon for its neutrino physics program. In addition to neutrino physics, DUNE has proposed an analysis using the DUNE Far Detector module to set limits for proton decay through the decay channel $p\rightarrow K^++\bar{\nu}$. This measurement would require information on kaons in argon, providing ProtoDUNE-SP LArTPC another opportunity to aid DUNE. This thesis describes the calibration of ProtoDUNE-SP and its detector performance, which serves as a benchmark for the performance of the DUNE Far Detector modules. A specific calibration highlighted is the evaluation of the liquid argon purity in the detector. These measurements use cosmic-ray muons reconstructed in the detector that are calibrated and matched to data from scintillator strips external to the TPC, known as the Cosmic Ray Tagger (CRT). The thesis will discuss the algorithms to match the data between the ProtoDUNE-SP LArTPC and the CRT and discuss the liquid argon purity measurements using one of the algorithms. Data sets of calibrated tracks measured the liquid argon contamination as consistently below 100 ppt oxygen equivalent. After these discussions on the ProtoDUNE-SP LArTPC detector, the thesis will present an evaluation of the inclusive inelastic, sometimes referred to as a reactive, cross section on argon of kaons from the ProtoDUNE-SP test beam with an average momentum of 6 GeV/c.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

A Python Tool for Reconstructing MCNP6 Particle Histories from an HDF5 PTRAC File [Slides]

A Python tool for converting the MCNP6 HDF5 PTRAC file to a list of Python trees is presented. The particle trees store MCNP6 simulated events for each history using parent-child relationships, which ensures that branching processes are accurately reproduced. A variety of post-processing scripts are presented and used in conjunction with the Python particle trees to make special tallies that are currently not available in the MCNP6 software and visualize the particle tracks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A pion-argon cross section measurement in the ProtoDUNE-SP experiment with cosmogenic muon

Neutrinos are tiny mysterious fundamental particles with small cross sections. Through neutrino physics, scientists across the world are trying to answer many intriguing questions about nature such as the dominance of matter over antimatter, CP violation in the lepton sector, number of supernovas in the early universe, etc. Detection of neutrinos requires massive particle detectors and intense neutrino beam owing to their small cross section. Deep Underground Neutrino Experiment (DUNE) is a next-generation neutrino experiment that is planned to start taking data beginning in 2026. DUNE will consist of 4 massive detectors, the first of which will be using single-phase liquid argon time projection chamber (LArTPC) technology. The ProtoDUNE-SP experiment is a prototype of the DUNE built at the CERN neutrino platform and uses the same detector technology that will be used in DUNE first module. The ProtoDUNE-SP experiment collected months of test beam and cosmic ray data beginning in September 2018. It was built to provide a testbed for the installation of detector parts for DUNE, showing long-term stability of the detector, understanding detector response for different test beam particles (including protons, pions, electrons, kaons, muons), and measurement of hadron-argon cross sections. When a particle passes through LArTPC electron-ion pairs are produced. To reconstruct the position and energy of a particle passing through the medium knowledge of ionization electron drift velocity is essential. The electron drift velocity is distorted by an excess positive charge built up in the detector, known as space charge. This study discusses a novel technique for measuring the ionization electron drift velocity using cosmic-ray muons. The technique uses tracks that travel the entire drift distance of the TPC for drift velocity determination. Secondly, the study discusses a method for converting the charge deposited into energy. The method is carried out in two step s. In th e first step detector response for energetic cosmic ray muons crossing the entire the TPC is used to make the charge deposition uniform throughout the TPC, and in the second step stopping cosmic-ray muons are used for determining the energy scale. Finally, the study discusses a pion-argon cross section measurement based on reweighting of Monte Carlo simulations using J. Calcutt's Geant4Reweight framework. Neutrinos cannot be directly detected; they are identified based on the interaction products. Pions are a common interaction product in a neutrino interaction. For precise modeling of neutrino event generators, it is essential to understand the pion-argon interaction. Pion-argon cross section measurement serves as an important input for neutrino interaction models. The results of the pion-argon total reaction cross section using the Geant4 reweighting technique are found to be in good agreement with Geant4 predictions. The many studies carried out in the ProtoDUNE-SP experi ment wil l be useful for current and future neutrino experiments using LArTPC technology including ICARUS, MicroBooNE, DUNE

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1991

A nonlinear estimator for reconstructing the angular velocity of a spacecraft without rate gyros

A scheme for estimating the angular velocity of a spacecraft without rate gyros is presented. It is based upon a nonlinear estimator whose inputs are measured inertial vectors and their calculated time derivatives relative to vehicle axes. It works for all spacecraft attitudes and requires no knowledge of attitude. It can use measurements from a variety of onboard sensors like Sun sensors, star trackers, or magnetometers, and in concert. It can also use look angle measurements from onboard tracking antennas for tracking and data relay satellites or global positioning system satellites. In this paper, it is applied to a Sun point scheme on the Hubble Space Telescope assuming all or most of its onboard rate gyros have failed. Simulation results are presented for verification.

Polites, M. E.↗
DOE OSTI2020

The CLAS12 drift chamber system

The CEBAF Large Acceptance Spectrometer at 12~GeV (CLAS12) is located in Hall~B, one of the experimental halls at Jefferson Lab. The forward part of CLAS12 is built around a superconducting toroidal magnet. The six coils of the toroid divide the detector azimuthally into six sectors. Each sector contains three multi-layer drift chambers for reconstructing the trajectories of charged particles originating from a fixed target. Each of the 18 planar chambers has two ``superlayers'' of six layers each, with the wires in the two adjacent superlayers oriented at ± 6° stereo angles. Each layer has 112 hexagonal cells spanning a range from about 5° to 40° in polar angle. The six-layer structure provides redundancy in track segment finding and good tracking efficiency even in the presence of some individual wire inefficiency. The design, construction, operation, and calibration methods are described, and estimates of the efficiency and resolution are presented from in-beam measurements.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1991

Final STS-35 Columbia descent BET products and results for LaRC OEX investigations

Final STS-35 'Columbia' descent Best Estimate Trajectory (BET) products have been developed for Langley Research Center (LaRC) Orbiter Experiments (OEX) investigations. Included are the reconstructed inertial trajectory profile; the Extended BET, which combines the inertial data and, in this instance, the National Weather Service atmospheric information obtained via Johnson Space Center; and the Aerodynamic BET. The inertial BET utilized Inertial Measurement Unit 1 (IMU1) dynamic measurements for deterministic propagation during the ENTREE estimation process. The final estimate was based on the considerable ground based C-band tracking coverage available as well as Tracking Data and Relay Satellite System (TDRSS) Doppler data, a unique use of the latter for endo-atmospheric flight determinations. The actual estimate required simultaneous solutions for the spacecraft position and velocity, spacecraft attitude, and six IMU parameters - three gyro biases and three accelerometer scale factor correction terms. The anchor epoch for this analysis was 19,200 Greenwich Mean Time (GMT) seconds which corresponds to an initial Shuttle altitude of approximately 513 kft. The atmospheric data incorporated were evaluated based on Shuttle derived considerations as well as comparisons with other models. The AEROBET was developed based on the Extended BET, the measured spacecraft configuration information, final mass properties, and the final Orbiter preoperation databook. The latter was updated based on aerodynamic consensus incrementals derived by the latest published FAD. The rectified predictions were compared versus the flight computed values and the resultant differences were correlated versus ensemble results for twenty-two previous STS entry flights.

Oakes, Kevin F.↗
NASA NTRS2013

2011 Mars Science Laboratory Trajectory Reconstruction and Performance from Launch Through Landing

The Mars Science Laboratory (MSL) mission successfully launched on an Atlas V 541 Expendable Evolved Launch Vehicle (EELV) from the Eastern Test Range (ETR) at Cape Canaveral Air Force Station (CCAFS) in Florida at 15:02:00 UTC on November 26th, 2011. At 15:52:06 UTC, six minutes after the MSL spacecraft separated from the Centaur upper stage, the spacecraft transmitter was turned on and in less than 20 s spacecraft carrier lock was achieved at the Universal Space Network (USN) Dongara tracking station located in Western Australia. MSL, carrying the most sophisticated rover ever sent to Mars, entered the Martian atmosphere at 05:10:46 SpaceCraft Event Time (SCET) UTC, and landed inside Gale Crater at 05:17:57 SCET UTC on August 6th, 2012. Confirmation of nominal landing was received at the Deep Space Network (DSN) Canberra tracking station via the Mars Odyssey relay spacecraft at 05:31:45 Earth Received Time (ERT) UTC. This paper summarizes in detail the actual vs. predicted trajectory performance in terms of launch vehicle events, launch vehicle injection performance, actual DSN/USN spacecraft lockup, trajectory correction maneuver performance, Entry, Descent, and Landing events, and overall trajectory and geometry characteristics.

trajectory↗
DOE OSTI2020

Three-dimensional super line-localization in low signal-to-noise microscope images via prior-apprised unsupervised learning (PAUL)

Biological processes such as processive enzyme turnover and intracellular cargo tracking involve the dynamic motion of a small "article" along a curvilinear biopolymer track. To understand these processes that occur across multiple length and time scales, one must acquire both the trajectory of the particle and the position of the track along which it moves, possibly by combining high-resolution single-particle tracking with conventional microscopy. Yet, usually there is a significant resolution mismatch between these modalities: while the tracked particle is localized with a precision of 10 nm, the image of the surroundings is limited by optical difraction, with 200 nm lateral and 500 nm axial resolutions. Compared to the particle's trajectory, the surrounding curvilinear structure appears as a blurred and noisy image. This disparity in the spatial resolutions of the particle trajectory and the surrounding curvilinear structure image makes data reconstruction, as well as interpretation, particularly challenging. Analysis is further complicated when the curvilinear structures are oriented arbitrarily in 3D space. Here, we present a prior-apprised unsupervised learning (PAUL) approach to extract information from 3D images where the underlying features resemble a curved line such as a filament or microtubule. This three-stage framework starts with a Hessian-based feature enhancement, which is followed by feature registration, where local line segments are detected on repetitively sampled subimage tiles. In the final stage, statistical learning, segments are clustered based on their geometric relationships. Principal curves are then approximated from each segment group via statistical tools including principal component analysis, bootstrap and kernel transformation. This procedure is characterized on simulated images, where sub-voxel medium deviations from true curves have been achieved. The 3D PAUL approach has also been implemented for successful line localization in experimental 3D images of gold nanowires obtained using a multifocal microscope. Lastly, this work not only bridges the resolution gap between two microscopy modalities, but also allows us to conduct 3D super line-localization imaging experiments, without using super-resolution techniques.

42 ENGINEERING↗
DOE OSTI2023

Fast 2D Bicephalous Convolutional Autoencoder for Compressing 3D Time Projection Chamber Data

High-energy large-scale particle colliders produce data at high speed in the order of 1 terabytes per second in nuclear physics and petabytes per second in high energy physics. Developing real-time data compression algorithms to reduce such data at high throughput to fit permanent storage has drawn increasing attention. Specifically, at the newly constructed sPHENIX experiment at the Relativistic Heavy Ion Collider (RHIC), a time projection chamber is used as the main tracking detector, which records particle trajectories in a volume of three-dimensional (3D) cylinder. The resulting data are usually very sparse with occupancy around 10.8%. Such sparsity presents a challenge to conventional learning-free lossy compression algorithms, such as SZ, ZFP, and MGARD. The 3D convolutional neural network (CNN)-based approach, Bicephalous Convolutional Autoencoder (BCAE), outperforms traditional methods both in compression rate and reconstruction accuracy. BCAE can also utilize the computation power of graphical processing units suitable for deployment in a modern heterogeneous highperformance computing environment. This work introduces two BCAE variants: BCAE++ and BCAE-2D. BCAE++ achieves a 15% better compression ratio and a 77% better reconstruction accuracy measured in mean absolute error compared with BCAE. BCAE-2D treats the radial direction as the channel dimension of an image, resulting in a 3× speedup in compression throughput. In addition, we demonstrate an unbalanced autoencoder with a larger decoder can improve reconstruction accuracy without significantly sacrificing throughput. Lastly, we observe both the BCAE++ and BCAE-2D can benefit more from using half-precision mode in throughput (76 - 79% increase) without loss in reconstruction accuracy. The source code and links to data and pretrained models can be found at https://github.com/BNL-DAQ-LDRD/NeuralCompression_v2

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Dimensionality Reduction of SDSS Spectra with Variational Autoencoders

High-resolution galaxy spectra contain much information about galactic physics, but the high dimensionality of these spectra makes it difficult to fully utilize the information they contain. We apply variational autoencoders (VAEs), a nonlinear dimensionality reduction technique, to a sample of spectra from the Sloan Digital Sky Survey (SDSS). In contrast to principal component analysis (PCA), a widely used technique, VAEs can capture nonlinear relationships between latent parameters and the data. We find that a VAE can reconstruct the SDSS spectra well with only six latent parameters, outperforming PCA with the same number of components. Different galaxy classes are naturally separated in this latent space, without class labels having been given to the VAE. The VAE latent space is interpretable because the VAE can be used to make synthetic spectra at any point in latent space. For example, making synthetic spectra along tracks in latent space yields sequences of realistic spectra that interpolate between two different types of galaxies. Using the latent space to find outliers may yield interesting spectra: in our small sample, we immediately find unusual data artifacts and stars misclassified as galaxies. In this exploratory work, we show that VAEs create compact, interpretable latent spaces that capture nonlinear features of the data. While a VAE takes substantial time to train (≈1 day for 48,000 spectra), once trained, VAEs can enable the fast exploration of large astronomical data sets.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1993

Silhouettes - An automated three-dimensional plume visualization and tracking system for environmental monitoring

The method of silhouettes proceeds from the premise that the 3D location and shape of an arbitrary object can be approximated via the cross section of a series of conical volumes whose focal points are arranged at sites surrounding the object; the projection of each conical volume onto a picture plane at each focal point is identical to a projection of the outline of the object onto that picture plane. Attention is presently given to the use of the silhouettes method for gas plume dispersion monitoring through the 3D reconstruction of plumes from imagery acquired at strategically located stations in the plumes' path.

Cambridge, Vivien J.↗