Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Accelerated Data Analytics and”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

Investigation of a geodesy coexperiment to the Gravity Probe B relativity gyroscope program

Geodesy is the science of measuring the gravitational field of and positions on the Earth. Estimation of the gravitational field via gravitation gradiometry, the measurement of variations in the direction and magnitude of gravitation with respect to position, is this dissertation's focus. Gravity Probe B (GP-B) is a Stanford satellite experiment in gravitational physics. GP-B will measure the precession the rotating Earth causes on the space time around it by observing the precessions of four gyroscopes in a circular, polar, drag-free orbit at 650 km altitude. The gyroscopes are nearly perfect niobium-coated spheres of quartz, operating at 1.8 K to permit observations with extremely low thermal noise. The permissible gyroscope drift rate is miniscule, so the torques on the gyros must be tiny. A drag-free control system, by canceling accelerations caused by nongravitational forces, minimizes the support forces and hence torques. The GP-B system offers two main possibilities for geodesy. One is as a drag-free satellite to be used in trajectory-based estimates of the Earth's gravity field. We described calculations involving that approach in our previous reports, including comparison of laser only, GPS only, and combined tracking and a preliminary estimate of the possibility of estimating relativistic effects on the orbit. The second possibility is gradiometry. This technique has received a more cursory examination in previous reports, so we concentrate on it here. We explore the feasibility of using the residual suspension forces centering the GP-B gyros as gradiometer signals for geodesy. The objective of this work is a statistical prediction of the formal uncertainty in an estimate of the Earth's gravitation field using data from GP-B. We perform an instrument analysis and apply two mathematical techniques to predict uncertainty. One is an analytical approach using a flat-Earth approximation to predict geopotential information quality as a function of spatial wavelength. The second estimates the covariance matrix arising in a least-squares estimate of a spherical harmonic representation of the geopotential using GP-B gradiometer data. The results show that the GP-B data set can be used to create a consistent estimate of the geopotential up to spherical harmonic degree and order 60. The formal uncertainty of all coefficients between degrees 5 and 50 is reduced by factors of up to 30 over current satellite-only estimates and up to 7 over estimates which include surface data. The primary conclusion resulting from this study is that the gravitation gradiometer geodesy coexperiment to GP-B is both feasible and attractive.

Everitt, C. W. F.↗
DOE OSTI2025

Precision beam diagnostics at the NuMI facility using muon monitor observations

The Neutrinos at the Main Injector (NuMI) facility at Fermilab delivers an intense neutrino beam for multiple experiments by producing pions that decay into neutrinos, muons, and other particles. Magnetic horns—the primary pion focusing elements in the NuMI beamline—exhibit predominantly linear optics, enabling a predictable relationship between the proton beam and the resulting pion and muon phase spaces. This study has two primary objectives: first, to evaluate and confirm the linearity of the horn focusing mechanism using analytical models and numerical simulations; and second, to demonstrate that key beam parameters—such as proton beam intensity, beam position on target, and horn current—can be extracted from muon monitor observations within this linear optics framework. Using a machine learning model trained on spill-by-spill muon monitor data, we infer the horn current with a precision of ±0.05%, the beam intensity with ±0.1%, and the beam position on target with ±0.018⁢ mm horizontally and ±0.013⁢ mm vertically. This approach provides a reliable cross-check of beam parameters, helping to reduce systematic uncertainties that are critical for future experiments such as the Deep Underground Neutrino Experiment, which will rely on the neutrino beam produced by the Long-Baseline Neutrino Facility.

Beam control↗
DOE OSTI2025

Feasibility of an Accelerometer-Based Structural Health Monitoring System for the LANL Blast Tube

A modeling- and simulation-based study was conducted on the feasibility of implementing an accelerometer-based SHM system on the Los Alamos National Laboratory blast tube. A blast tube experiment was modeled using the Abaqus explicit finite element solver. A custom user subroutine was written to apply test-like pressure loading to the inside surface of the blast tube. The subroutine applies analytically defined pressure loads derived from tracer output taken from a Compressible Flow Computational Fluid Dynamics Solver model of the blast tube. Five unique versions of the model were created: an undamaged reference model at 65°F was used as the baseline and compared to equivalent models at 10°F and 100°F. These three models were compared to models with small damage at the reference temperature. The two types of damage considered were a radial (circumferential) crack in the main tube body and a longitudinal crack in the supports. Acceleration outputs were extracted from accelerometer bodies included in the model and were post processed using a variety of standard SHM techniques. Different potential features signaling failure were extracted and compared using statistical methods in the time and frequency domains. A method was identified that clearly shows that differences in structural response resulting from the modeled damage can be differentiated from the structural response resulting from changing environmental conditions. However, the amount of damage applied to create observable differences in the accelerometer data was so large that simpler methods of damage detection would be more cost effective in locating damage.

42 ENGINEERING↗
NASA NTRS2013

Tuning and Robustness Analysis for the Orion Absolute Navigation System

The Orion Multi-Purpose Crew Vehicle (MPCV) is currently under development as NASA's next-generation spacecraft for exploration missions beyond Low Earth Orbit. The MPCV is set to perform an orbital test flight, termed Exploration Flight Test 1 (EFT-1), some time in late 2014. The navigation system for the Orion spacecraft is being designed in a Multi-Organizational Design Environment (MODE) team including contractor and NASA personnel. The system uses an Extended Kalman Filter to process measurements and determine the state. The design of the navigation system has undergone several iterations and modifications since its inception, and continues as a work-in-progress. This paper seeks to show the efforts made to-date in tuning the filter for the EFT-1 mission and instilling appropriate robustness into the system to meet the requirements of manned space ight. Filter performance is affected by many factors: data rates, sensor measurement errors, tuning, and others. This paper focuses mainly on the error characterization and tuning portion. Traditional efforts at tuning a navigation filter have centered around the observation/measurement noise and Gaussian process noise of the Extended Kalman Filter. While the Orion MODE team must certainly address those factors, the team is also looking at residual edit thresholds and measurement underweighting as tuning tools. Tuning analysis is presented with open loop Monte-Carlo simulation results showing statistical errors bounded by the 3-sigma filter uncertainty covariance. The Orion filter design uses 24 Exponentially Correlated Random Variable (ECRV) parameters to estimate the accel/gyro misalignment and nonorthogonality. By design, the time constant and noise terms of these ECRV parameters were set to manufacturer specifications and not used as tuning parameters. They are included in the filter as a more analytically correct method of modeling uncertainties than ad-hoc tuning of the process noise. Tuning is explored for the powered-flight ascent phase, where measurements are scarce and unmodelled vehicle accelerations dominate. On orbit, there are important trade-off cases between process and measurement noise. On entry, there are considerations about trading performance accuracy for robustness. Process Noise is divided into powered flight and coasting ight and can be adjusted for each phase and mode of the Orion EFT-1 mission. Measurement noise is used for the integrated velocity measurements during pad alignment. It is also used for Global Positioning System (GPS) pseudorange and delta- range measurements during the rest of the flight. The robustness effort has been focused on maintaining filter convergence and performance in the presence of unmodeled error sources. These include unmodeled forces on the vehicle and uncorrected errors on the sensor measurements. Orion uses a single-frequency, non-keyed GPS receiver, so the effects due to signal distortion in Earth's ionosphere and troposphere are present in the raw measurements. Results are presented showing the efforts to compensate for these errors as well as characterize the residual effect for measurement noise tuning. Another robustness tool in use is tuning the residual edit thresholds. The trade-off between noise tuning and edit thresholds is explored in the context of robustness to errors in dynamics models and sensor measurements. Measurement underweighting is also presented as a method of additional robustness when processing highly accurate measurements in the presence of large filter uncertainties.

Holt, Greg N.↗
DOE OSTI2025

Understanding the Transport Properties of Methane–Hydrogen Mixtures under the Interior Conditions of Ice Giants

Inaccuracy in our knowledge of the transport properties of relevant mixtures under planetary interior conditions is a roadblock in predicting the observable properties of planets in our solar system and beyond. In this work, we investigate methane–hydrogen mixtures using data sets obtained from density functional theory calculations for the electronic structure, combined with molecular dynamics simulations for the ions for a wide range of pressure and temperature. Hydrogen concentration significantly affects the equation of state (EOS) but has little influence on transport properties. We provide an analytical expression to model thermal EOS and transport properties as a function of hydrogen content, with the maximum deviation observed at low P–T conditions. These insights are particularly relevant to improve the planetary models and enhance our ability to predict the properties of “ice” giants and beyond.

Methane↗
NASA NTRS2018

Finite Element Simulations of Two Vertical Drop Tests of F-28 Fuselage Sections

In March 2017, a vertical drop test of a forward fuselage section of a Fokker F-28 MK4000 aircraft was conducted as part of a joint NASA/FAA project to investigate the performance of transport aircraft under realistic crash conditions. In June 2017, a vertical drop test was conducted of a wing-box fuselage section of the same aircraft. Both sections were configured with two rows of aircraft seats, in a triple-double configuration. A total of ten Anthropomorphic Test Devices (ATDs) were secured in seats using standard lap belt restraints. The forward fuselage section was also configured with luggage in the cargo hold. Both sections were outfitted with two hat racks, each with added ballast mass. The drop tests were performed at the Landing and Impact Research facility located at NASA Langley Research Center in Hampton, Virginia. The measured impact velocity for the forward fuselage section was 346.8-in/s onto soil. The wing-box section was dropped with a downward facing pitch angle onto a sloping soil surface in order to create an induced forward acceleration in the airframe. The vertical impact velocity of the wing-box section was 349.2-in/s. A second objective of this project was to assess the capabilities of finite element simulations to predict the test responses. Finite element models of both fuselage sections were developed for execution in LS-DYNA(Registered Trademark), a commercial explicit nonlinear transient dynamic code. The models contained accurate representations of the airframe structure, the hat racks and hat rack masses, the floor and seat tracks, the luggage in the cargo hold for the forward section, and the detailed under-floor structure in the wing-box section. Initially, concentrated masses were used to represent the inertial properties of the seats, restraints, and ATD occupants. However, later simulations were performed that included finite element representations of the seats, restraints, and ATD occupants. These models were developed to more accurately replicate the seat loading of the floor and to enable prediction of occupant impact responses. Models were executed to generate analytical predictions of airframe responses, which were compared with test data to validate the model. Comparisons of predicted and experimental structural deformation and failures were made. Finally, predicted and experimental soil deformation and crater depths were also compared for both drop test configurations.

Jackson, Karen E.↗
DOE OSTI2024

Graph Analytics for CEBAF Operations

We report on the progress achieved during a 2-year Laboratory Directed Research and Development (LDRD) project titled “Graph Analytics for CEBAF Operations”. The objective of this project is to leverage deep learning on graph representations of CEBAF’s injector beamline in order to create a tool for improving the efficiency of beam tuning tasks. Specifically, we use graphs to represent the injector beamline at any arbitrary date and time and invoke a graph neural network (GNN) to extract a low-dimensional, informative representation that can be visualized in two-dimensions. By analyzing years of operational data from the CEBAF archiver, good and bad regions of parameter space can be identified. The goal is to exercise this framework as a real-time tool to aid beam tuning, which represents the dominant source of machine downtime.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Accelerating Multivariate Functional Approximation Computation with Domain Decomposition Techniques⋆

Modeling large datasets through Multivariate Functional Approximations (MFA) provide an elegant way to handle many visualization and scientific analysis workflows. The process necessitates scalable data partitioning methods to compute MFA representations efficiently without compromising the accuracy or continuity of the reconstructed solution. We propose a domain -decomposed method for computing the MFA with B -spline bases, which reduces the total work per task and uses a restricted Additive Schwarz (RAS) method to converge the control point data degrees -of -freedom along subdomain boundaries. We provide an in-depth analysis of the parallel approach with domain decomposition solvers, aiming to minimize local subdomain error residuals and recover high -order continuity at subdomain interfaces with appropriate choices of knot overlaps. The communication cost, determined by the overlap regions in the RAS implementation, is optimized to recover the numerical error profile of the single subdomain case. Our proposed method stands in contrast to previous methods, which typically only recover either C 0 or at best C 1 continuity for arbitrary B -spline degree expansions, or those that require post -processing to blend discontinuities in the reconstructed data. We demonstrate the effectiveness of our approach using analytical and real -world datasets in 1D, 2D, and 3D through both strong and weak scaling studies. The performance results indicate that the overall cost of computing the approximation is directly proportional to the underlying nearest -neighbor communication implementation, and is only weakly dependent on the overlap region size that determines the size of the messages. This finding underscores the efficiency and scalability of our proposed method, making it a promising solution for handling large datasets in scientific workflows.

additive Schwarz solvers↗
NASA NTRS2023

Modeling and Simulation for Exercise Vibration Isolation and Stabilization System Design

The microgravity environment that crew members experience on orbit presents a well-known health challenge, particularly when it comes to loss of muscle and bone mass. To counteract these negative effects, exercise countermeasures play a critical role in the daily routine of the crew on the International Space Station (ISS). To help inform requirements for upcoming exploration missions such as the Gateway Program, a new device, called the European Enhanced Exploration Exercise Device (E4D), is being built by the European Space Agency through their contractor, the Danish Aerospace Company. The E4D is being demonstrated on the ISS and is unique from other current exercise devices in that it provides four separate modalities in a single device: resistive, cycle ergometry, seated aerobic rowing, and rope pulling. To support the integration of E4D on ISS, a passive Vibration Isolation and Stabilization (VIS) system was required by NASA, and this responsibility was given to the Johnson Space Center. This paper describes the end-to-end process of modeling, simulation, and analysis used to inform the mechanical design of the VIS system. The process begins with the collection of representative exerciser motion capture (MoCap) through ground-based testing with the developmental E4D in both the Prototype Immersive Technology (PIT) Laboratory and the Active Response Gravity Offload System (ARGOS) facility at the Johnson Space Center. These collected MoCap data are processed through human biomechanics modeling to create forcing functions as input to a multibody dynamics simulation of the combined E4D/VIS system, with numerous resulting outputs. These outputs include microgravity accelerations, overall system displacements, internal and transmitted loads, as well as collisions. Microgravity accelerations are compared for compliance against ISS requirements while displacements are used for sway space determinations on the design and volumetric constraints within the targeted module. Internal loads are supplied to the supporting stress analysis teams and external loads for structural loads and dynamics teams, both at NASA and ESA. Finally, contact and clearance analysis is performed using the simulation to eliminate potential design issues. To ensure that the elements of the multibody simulation were verified and validated, correlation against multiple VIS related ground hardware testbeds was performed and characterized. In addition to the isolation part of the VIS problem, stabilization is also key to the integrated performance and evaluation. Due to the difficulty in defining ISS requirements in this area, the stability of the exerciser was inspected via analysis. Loss of balance was defined analytically as occurring when the resultant force vector acting on the exerciser lies outside the base of support of the feet. Both the VIS and E4D teams have recently gone through their Critical Design Reviews (CDRs) and the iterative model-based approach has been integral to inform mechanical design, particularly in the case of the VIS. This same end-to-end approach is now being applied for the Gateway Program, where an Exploration Exercise Device (EED) derived from the E4D and notional VIS for the device are under concept development.

Countermeasures↗
DOE OSTI2024

Analyzing inference workloads for spatiotemporal modeling

Ensuring power grid resiliency, forecasting climate conditions, and optimization of transportation infrastructure are some of the many application areas where data is collected in both space and time. Spatiotemporal modeling is about modeling those patterns for forecasting future trends and carrying out critical decision-making by leveraging machine learning/deep learning. Once trained offline, field deployment of trained models for near real-time inference could be challenging because performance can vary significantly depending on the environment, available compute resources and tolerance to ambiguity in results. Users deploying spatiotemporal models for solving complex problems can benefit from analytical studies considering a plethora of system adaptations to understand the associated performance-quality trade-offs. To facilitate the co-design of next-generation hardware architectures for field deployment of trained models, it is critical to characterize the workloads of these deep learning (DL) applications during inference and assess their computational patterns at different levels of the execution stack. In this paper, we develop several variants of deep learning applications that use spatiotemporal data from dynamical systems. We study the associated computational patterns for inference workloads at different levels, considering relevant models (Long short-term Memory, Convolutional Neural Network and Spatio-Temporal Graph Convolution Network), DL frameworks (Tensorflow and PyTorch), precision (FP16, FP32, AMP, INT16 and INT8), inference runtime (ONNX and AI Template), post-training quantization (TensorRT) and platforms (Nvidia DGX A100 and Sambanova SN10 RDU). Overall, our findings indicate that although there is potential in mixed-precision models and post-training quantization for spatiotemporal modeling, extracting efficiency from contemporary GPU systems might be challenging. Instead, co-designing custom accelerators by leveraging optimized High Level Synthesis frameworks (such as SODA High-Level Synthesizer for customized FPGA/ASIC targets) can make workload-specific adjustments to enhance the efficiency.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Magnesium isotopes of the bulk solar wind from Genesis diamond-like carbon films

–NASA’s Genesis Mission returned solar wind (SW) to the Earth for analysis to derive the composition of the solar photosphere from solar material. SW analyses control the precision of the derived solar compositions, but their ultimate accuracy is limited by the theoretical or empirical models of fractionation due to SW formation. Mg isotopes are “ground truth” for these models since, except for CAIs, planetary materials have a uniform Mg isotopic composition (within ≤1&) so any significant isotopic fractionation of SW Mg is primarily that of SW formation and subsequent acceleration through the corona. This study analyzed Mg isotopes in a bulk SW diamond-like carbon (DLC) film on silicon collector returned by the Genesis Mission. A novel data reduction technique was required to account for variable ion yield and instrumental mass fractionation (IMF) in the DLC. The resulting SW Mg fractionation relative to the DSM-3 laboratory standard was ( 14.4&, 30.2&) (4.1&, 5.5&), where the uncertainty is 2σ SE of the data combined with a 2.5& (total) error in the IMF determination. Two of the SW fractionation models considered generally agreed with our data. Their possible ramifications are discussed for O isotopes based on the CAI nebular composition of McKeegan et al. (2011).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Acceleration of Thermochimica Calculations in Bison

The performance of the open-source thermochemical software library Thermochimica has been enhanced by developing a re-initialization algorithm to make use of data from previous calculations to reduce the number of convergence steps in the Gibbs energy minimization procedure. This algorithm has been tested in the context of stand-alone Thermochimica calculations, and speedups in the range of 2x-3x achieved for cases with chemistries resembling those of irradiated nuclear fuels. Routines to make use of this re-initialization procedure have been implemented in Bison, in which each node uses the results of the previous calculation at that node as the initial conditions for the following Thermochimica call. Examples based on calculating the diffusion of oxygen in UO$_2$ LWR fuel in 1D and 3D have been tested, with speedups up to 7.38x demonstrated.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1988

Hybrid deployable support truss designs for LDR

Concepts for a 20-meter diameter Large Deployable Reflector (LDR) deployable truss backup structure, and analytical predictions of its structural characteristics are discussed. The concept shown is referred to as the SIXPAC; It is a combination of the PACTRUSS concept and a single-fold beam, which would make up the desired backup structure. One advantage of retaining the PACTRUSS concept is its packaging density and its capability for synchronous deployment. Various 2-meter hexagonal panel arrangements are possible for this Hybrid PACTRUSS structure depending on the panel-to-structure attachment strategies used. Static analyses of the SIXPAC using various assumptions for truss designs and panel masses of 10 kg sq meters were performed to predict the tip displacement of the structure when supported at the center. The tip displacement ranged from 0.20 to 0.44 mm without the panel mass, and from 0.9 to 3.9 mm with the panel mass (in a 1-g field). The data indicate that the structure can be adequately ground tested to validate its required performance in space, assuming the required performance in space is approximately 100 microns. The static displacement at the tip of the structure when subjected to an angular acceleration of 0.001 rad/sec squared were estimated to range from 0.8 to 7.5 microns, depending on the type of truss elements.

Hedgepeth, J.↗
DOE OSTI2022

Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality?

Abstract The global decline of water quality in rivers and streams has resulted in a pressing need to design new watershed management strategies. Water quality can be affected by multiple stressors including population growth, land use change, global warming, and extreme events, with repercussions on human and ecosystem health. A scientific understanding of factors affecting riverine water quality and predictions at local to regional scales, and at sub‐daily to decadal timescales are needed for optimal management of watersheds and river basins. Here, we discuss how machine learning (ML) can enable development of more accurate, computationally tractable, and scalable models for analysis and predictions of river water quality. We review relevant state‐of‐the art applications of ML for water quality models and discuss opportunities to improve the use of ML with emerging computational and mathematical methods for model selection, hyperparameter optimization, incorporating process knowledge into ML models, improving explainablity, uncertainty quantification, and model‐data integration. We then present considerations for using ML to address water quality problems given their scale and complexity, available data and computational resources, and stakeholder needs. When combined with decades of process understanding, interdisciplinary advances in knowledge‐guided ML, information theory, data integration, and analytics can help address fundamental science questions and enable decision‐relevant predictions of riverine water quality.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Flow annealed importance sampling bootstrap meets differentiable particle physics

High-energy physics requires the generation of large numbers of simulated data samples from complex but analytically tractable distributions called matrix elements. Surrogate models, such as normalizing flows, are gaining popularity for this task due to their computational efficiency. We adopt an approach based on flow annealed importance sampling bootstrap (FAB) that evaluates the differentiable target density during training and helps avoid the costly generation of training data in advance. We show that FAB reaches higher sampling efficiency with fewer target evaluations in high dimensions in comparison to other methods.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Bottom-up design of actinide materials from molecular clusters: Demonstration of a general-purpose simulation capability leveraging machine-learned atomic potentials

Actinide thin-film coatings such as uranium dioxide (UO 2 ) play an important role in nuclear reactors and other mission-relevant applications, but realization of their potential requires a deep fundamental understanding of the chemical vapor deposition (CVD) processes used for their growth. The slow experimental progress can be attributed, in part, to the standard safety guidelines associated with handling uranium byproducts, which are often corrosive, toxic, and radioactive. Accurate simulation techniques, when used in concert with experiment, can improve laboratory safety, material durability, and deliverable timeframes. However, state-of-the-art computational methods are either insufficiently accurate or intractably expensive. To remedy this situation, in this project we suggested a machine-learning (ML) accelerated workflow for simulating molecular clustering toward deposition. As a benchmark test case, we considered molecular clustering in steam and assessed independent components of our workflow by comparing with measured thermodynamic properties of water. After analyzing each component individually and finding no fundamental barrier to realization of the workflow, we attempted to integrate the ML component, a Sandia-developed tool called FitSNAP. As this was the first application of FitSNAP to atoms and molecules in the gas phase at Sandia, the method required more fitting data than was originally anticipated. Systematic improvements were made by including in the fit data diatomic potentials, molecular single-bond-breaking curves, and symmetry-constrained intermolecular potentials. We concluded that our strategy provides a feasible pathway toward modeling CVD and related processes, but that extensive training data must be generated before it can be of practical use.

36 MATERIALS SCIENCE↗