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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 1,351 records · Page 75
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Preliminary Report on the Study of Beam-Induced Background Effects at a Muon Collider

Physics at a multi-TeV muon collider needs a change of perspective for the detector design due to the large amount of background induced by muon beam decays. Preliminary studies, based on simulated data, on the composition and the characteristics of the particles originated from the muon decays and reaching the detectors are presented here. The reconstruction performance of the physics processes $H\to b\bar b$ and $Z\to b\bar b$ has been investigated for the time being without the effect of the machine induced background. A preliminary study of the environment hazard due to the radiation induced by neutrino interactions with the matter is presented using the FLUKA simulation program.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Critical analysis of velocimetry methods for particulate flows from synthetic data

Particle tracking methods that extract high-fidelity particle velocity data from high speed video of particle laden flows is a common experimental technique applied to chemical processes. These measurements are used to better understand the motion of particles and fluids in complex systems and create data against which computational models are validated. However, the methods, codes, and experimental setups all have limitations. It is imperative that practitioners verify the methods and their implementation as well as understand the limitations of experimental setups. This work focuses on quantifying the visible depth of field in a high particle concentration fluidized bed. Following a precedent set by the particle imaging velocimetry community, a particle velocity field is manufactured using a computational fluid dynamics and discrete element method simulation. Photo realistic high-speed videos are rendered based on the simulated data using the three-dimensional creation software Blender. Particle velocities are extracted from the synthetic high-speed videos using three variants of Particle Tracking Velocimetry and Optical Flow Velocimetry methodologies. Here, the tracked results are then compared to the known solution, quantifying the error associated with the assumed visible depth. The results indicate that at depth of one particle diameter, all three particle tracking codes give accurate measurements, largely within 5%. However, the error increases when the full bed video measurements are compared to the known solution at one particle diameter, i.e., mimicking a validation study. Finally, for some statistics the constant depth assumption only increases the error slightly, for others significantly.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRSDate not supplied

Fast Radiative Transfer Model and Retrieval Algorithm Development for Satellite Remote Sensing Applications

The radiative transfer model (RTM) has a wide range of applications in satellite remote sensing and atmospheric radiation studies. For example, it can be used as a forward model for an inversion algorithm and a satellite data assimilation system, or as a L1 data simulator for pre-launch end-to-end satellite sensor performance studies. However, millions of line-by-line (LBL) radiative transfer calculations at fine monochromatic frequencies are needed in order to properly calculate spectral contributions of water vapor and trace gases in the atmosphere in infrared and solar spectral regions. Therefore, fast, and accurate radiative transfer models are needed. A Principal Component-based radiative transfer model (PCRTM) was developed at NASA Langley to fulfil this need. The PCRTM can simulate the top-of-atmosphere (TOA) radiance or reflectance spectra from 250 nm to 2000 micrometers with several orders of magnitude faster speed as compared to a LBL RTM. It is also extremely accurate compared to LBL RTM benchmarks. The PCRTM model has been developed for hyperspectral sensors such as AIRS, CrIS, IASI, NAST-I, SHIS, CPF, TEMPO, EMIT, OMI, and SCIAMACHY. By using the PCRTM as forward model for an inversion algorithm, one can reduce the data dimension significantly while maintaining original information content by compressing the TOA radiance spectrum into PC-scores. The PCRTM can directly compute the PC-scores and their derivatives with respect to retrieved parameters. Examples of using various PCRTM inversion algorithms to retrieve atmospheric temperature, water vapor, and trace gas profiles, as well as cloud and surface properties from satellite hyperspectral remote sensors will be given. Some of the algorithms have been transitioned to NASA's Goddard Earth Sciences Data and Information Services Center (GES DISC) for public access of high-quality L2 and L3 data.

Xu Liu↗
DOE OSTI2023

Studies of the muon momentum calibration and performance of the ATLAS detector with $pp$ collisions at $\sqrt{s}=13$ TeV

This paper presents the muon momentum calibration and performance studies for the ATLAS detector based on the $pp$ collisions data sample produced at $\sqrt{s}$ = 13 TeV at the LHC during Run 2 and corresponding to an integrated luminosity of 139 fb –1 . An innovative approach is used to correct for potential charge-dependent momentum biases related to the knowledge of the detector geometry, using the $Z$ → $μ^+$ $μ^–$ resonance. The muon momentum scale and resolution are measured using samples of $J/ψ$ → $μ^+$ $μ^–$ and $Z$ → $μ^+$ $μ^–$ events. A calibration procedure is defined and applied to simulated data to match the performance measured in real data. The calibration is validated using an independent sample of $Υ$ → $μ^+$ $μ^–$ events. At the Z ($J/ψ$) peak, the momentum scale is measured with an uncertainty at the 0.05% (0.1%) level, and the resolution is measured with an uncertainty at the 1.5% (2%) level. The charge-dependent bias is removed with a dedicated in situ correction for momenta up to 450 GeV with a precision better than 0.03 TeV –1 .

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Graph-EAM: An Interpretable and Efficient Graph Neural Network Potential Framework

The development of deep learning interatomic potentials has enabled efficient and accurate computations in quantum chemistry and materials science, circumventing computationally expensive ab initio calculations. However, the huge number of learnable parameters in deep learning models and their complex architectures hinder physical interpretability and affect the robustness of the derived potential. In this work, we propose graph-EAM, a lightweight graph neural network (GNN) inspired by the empirical embedded atom method to model the interatomic potential of single-element structures. Four material systems: platinum, niobium, silicon, and amorphous-carbon, for which quantum simulation data sets are publicly available, are examined to demonstrate that graph-EAM can achieve high energy and force prediction accuracy-comparable or better than existing state-of-the-art machine learning models-with much fewer parameters. It is also shown that the explicit inclusion of the angular information via three-body atomic density increases the prediction accuracy. In conclusion, the accuracy and efficiency of potentials obtained from graph-EAM can help accelerate the molecular dynamics simulation.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1993

SPNDL: A concept for a small satellite Doppler lidar wind sounder

Given current resources and the technical challenges in developing a full coverage space-based Doppler lidar wind measuring system such as LAWS (Laser Atmospheric Wind Sounder), it is not likely that the science community will have data streams with which to work before the end of this decade. Currently, a 'fast track' demonstration mission is being seriously considered by several U.S. agencies. Such a mission would have as its primary objectives the demonstration of coherent Doppler lidar technology in space and the delivery of wind observations for science algorithm evaluation and development. However, for such a mission to be achieved at modest costs and within a short time frame, deviations from the full system design are required. Simulation models have been developed over the last decade to aide in the design of Doppler lidar missions and to provide simulated data for use in wind computation algorithm development. SWA has used both models to examine some options that might be cost-effective for a demonstration mission. Over the past few months, Simpson Weather Associates has been studying SPNDL (Spinning Platform with a Non-rotating telescope Doppler Lidar), a new concept for Doppler lidar wind observations from space. Science and Technology Corporation has an interest in participating in an engineering and shuttle accommodation study for SPNDL.

Emmitt, G. D.↗
NASA NTRS1972

STADAN and data relay satellite simulation (emphasis on the scheduler)

A set of computer programs was developed which can be used to simulate the operation of NASA's Satellite Tracking and Data Acquisition Network (STADAN). The simulation consists of two major modules, a computerized algorithmic satellite scheduler (CASS) and a generalized evaluation model simulator (GEMS). CASS generates in-view time intervals of satellites over ground stations, and using these candidate passes, produces a schedule of service times for specific satellites to be serviced by specific ground stations in an attempt to fulfill certain inputted service requirements. GEMS than examines how well STADAN fulfills this schedule in terms of the availability and dependability of different (inputted) complements of equipment at the ground stations. The simulation was modified to allow for the introduction of one or more tracking and data relay satellites, which can serve as orbital receiving and command stations.

Kerne, B.↗
DOE OSTI2025

Machine Learning for Predicting Multipactor Susceptibility in Planar RF Structures

Multipactor discharge is a persistent challenge in high-power microwave (HPM) and accelerator systems, where secondary electron avalanches can cause heating, vacuum degradation, and failure. This work presents the first supervised machine learning (ML) framework for multipactor prediction, trained on high-fidelity 3D Particle-in-Cell (PIC) simulation data in planar geometries. The model maps operational, geometric, and material-dependent secondary electron yield (SEY) parameters to the time-averaged electron growth rate, enabling rapid reconstruction of susceptibility charts. Among the models evaluated, tree-based ensemble methods such as Random Forest and Extra Trees demonstrate superior generalization to unseen materials compared to neural networks such as multilayer perceptron (MLP). Performance metrics, including Intersection over Union (IoU), Structural Similarity Index Measure (SSIM), and Pearson correlation, show close agreement with simulation benchmarks. Principal Component Analysis attributes generalization limits to material feature-space disjointedness.

43 PARTICLE ACCELERATORS↗
NASA NTRS2011

Data Serving Climate Simulation Science at the NASA Center for Climate Simulation

The NASA Center for Climate Simulation (NCCS) provides high performance computational resources, a multi-petabyte archive, and data services in support of climate simulation research and other NASA-sponsored science. This talk describes the NCCS's data-centric architecture and processing, which are evolving in anticipation of researchers' growing requirements for higher resolution simulations and increased data sharing among NCCS users and the external science community.

Salmon, Ellen M.↗
DOE OSTI2015

STAR FORMATION IN TURBULENT MOLECULAR CLOUDS WITH COLLIDING FLOW

Using self-gravitational hydrodynamical numerical simulations, we investigated the evolution of high-density turbulent molecular clouds swept by a colliding flow. The interaction of shock waves due to turbulence produces networks of thin filamentary clouds with a sub-parsec width. The colliding flow accumulates the filamentary clouds into a sheet cloud and promotes active star formation for initially high-density clouds. Clouds with a colliding flow exhibit a finer filamentary network than clouds without a colliding flow. The probability distribution functions (PDFs) for the density and column density can be fitted by lognormal functions for clouds without colliding flow. When the initial turbulence is weak, the column density PDF has a power-law wing at high column densities. The colliding flow considerably deforms the PDF, such that the PDF exhibits a double peak. The stellar mass distributions reproduced here are consistent with the classical initial mass function with a power-law index of –1.35 when the initial clouds have a high density. The distribution of stellar velocities agrees with the gas velocity distribution, which can be fitted by Gaussian functions for clouds without colliding flow. For clouds with colliding flow, the velocity dispersion of gas tends to be larger than the stellar velocity dispersion. The signatures of colliding flows and turbulence appear in channel maps reconstructed from the simulation data. Clouds without colliding flow exhibit a cloud-scale velocity shear due to the turbulence. In contrast, clouds with colliding flow show a prominent anti-correlated distribution of thin filaments between the different velocity channels, suggesting collisions between the filamentary clouds.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

An Initial Study of the Convergence Rate of Griffin’s Pebble Bed Reactors Algorithm

This paper presents an initial study of the convergence properties of an iterative algorithm for computing the burnup distribution in a pebble bed reactor (PBR) in its equilibrium core condition. The algorithm is implemented in the Griffin code. Griffin is a reactor multiphysics analysis application jointly developed by Idaho National Laboratory (INL) and Argonne National Laboratory (ANL). Griffin’s PBR algorithm is discussed and simulation data are presented. An alternative matrix formulation of the algorithm is presented that facilitates analysis of the iterative algorithm. The dependence of the spectral radius of the iterative algorithm on operational and discretization parameters is investigated.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Dark Energy Survey year 6 results: Magnification modeling and its impact on galaxy clustering and galaxy-galaxy lensing cosmology

Gravitational lensing magnification alters the observed spatial distribution of galaxies and must be accounted for to prevent biases in cosmological probes of the large-scale structure. We investigate its effects on the Dark Energy Survey Year 6 galaxy clustering and galaxy-galaxy lensing analyses using the fiducial lens (position tracer) sample M ag L im++. Magnification bias is parameterized by a coefficient that describes the response of the number of selected objects per unlensed area element to a change in the lensing convergence. We quantify this coefficient using the BALROG synthetic source injection catalog to account for the complexity of the selection function, and compare these results with simplified estimates. The resulting values of the magnification coefficients for each redshift bin are [3.16 ± 0.08, 2.76 ± 0.21, 4.09 ± 0.15, 4.42 ± 0.16, 4.90 ± 0.29, 4.83 ± 0.25]. Relative to Year 3, this analysis provides more precise and accurate magnification bias estimates through a larger BALROG area and reweighting to better match the data properties. Here, the cosmological results are robust when tested against various magnification parameter prior choices and also when adding cross-clustering between lens redshift bins. Neglecting magnification, however, introduces significant systematic shifts: relative to the fiducial analysis with Gaussian priors centered on the BALROG -derived estimates, we observe shifts of 1.37σ in S 8 and -0.84σ in Ω m (with cosmic shear included: -0.61σ in S 8 and -0.71σ in Ω m ), in agreement with findings from simulated data, demonstrating that magnification must be modeled to avoid biases. Freeing the magnification bias in lens bin 2 leads to unphysical negative values, further justifying its exclusion from the fiducial Year 6 analysis.

Cosmological parameters↗
DOE OSTI2017

Microfield dynamics in dense hydrogen plasmas with high- Z impurities

We use large-scale classical molecular dynamics to determine microfield properties for several dense plasma mixtures. By employing quantum statistical potentials (QSPs) to regularize the Coulomb interaction, our simulations follow motions of electrons as well as ions for times long enough to track relaxation phenomena involving both types of particles. Coulomb coupling, relative to temperature, of different pairs of species in the hot, dense matter being simulated ranges from weak to strong. We first study the effect of such coupling differences, along with composition and QSP differences, on the roles of electrons and various mixture components in determining probability distributions of instantaneous, total microfields experienced by the ions. Then, we address two important dynamical questions: (1) How is the quasistatic part of the total field to be extracted from the time-dependent simulation data? (2) Under what conditions does the commonly used approximation of ions with fixed Yukawa-like screening by free electrons accurately describe quasistatic fields? We identify a running, short-time average of the total field at each ion as its slowly evolving, quasistatic part. We consider several ways to specify the averaging interval, and note the influence of ion dynamics in this issue. When all species are weakly coupled, the quasistatic fields have probability distributions agreeing well with those we obtain from simulations of Yukawa-screened ions. However, agreement deteriorates as the coupling between high- Z ions increases well beyond unity, principally because the Yukawa model tends to underestimate the true screening of close high- Z pairs. Lastly, examples of this fact are given, and some consequences for the high-field portions of probability distributions are discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Real-Time Lifetime Prediction of Semiconductor Devices Using Hardware-in-the-Loop

This paper presents a unique approach to enable real-time lifespan prediction of semiconductor power modules using a Hardware-in-the-Loop (HIL) system. By integrating the module's overall loss characteristics-specifically switching and conduction losses-with a thermoelectric model of the thermal management system, this research demonstrates that the model can dynamically estimates the junction temperature profile of the semiconductor devices in response to a changing torque demand profile for the motor drive system. This capability enables continuous monitoring of the module's operational time and cumulative stress induced on the devices to compute accumulated remaining lifetime or time-to-failure (TTF). This study provides an architectural framework for the HIL system with high-fidelity component models of multiple physical domains, allowing simulation of dynamic behaviors of a closely-coupled motor drive system. The advanced real-time computation and measurement functionalities of the HIL system allow for both dynamic lifetime calculations based on simulated data and aggregate lifetime predictions utilizing historical data. Moreover, this paper details an algorithm that not only computes cumulative damage but also synthesizes these data into a comprehensive aggregated lifetime metric. This methodology can enhance the maintenance scheduling strategies and operational reliability of semiconductor devices in critical applications, ultimately extending their service life while optimizing performance.

hardware-in-the-loop (HIL)↗
DOE OSTI2020

Domain adaptation techniques for improved cross-domain study of galaxy mergers

In astronomy, neural networks are often trained on simulated data with the prospect of being applied to real observations. Unfortunately, simply training a deep neural network on images from one domain does not guarantee satisfactory performance on new images from a different domain. The ability to share cross-domain knowledge is the main advantage of modern deep domain adaptation techniques. Here we demonstrate the use of two techniques - Maximum Mean Discrepancy (MMD) and adversarial training with Domain Adversarial Neural Networks (DANN) - for the classification of distant galaxy mergers from the Illustris-1 simulation, where the two domains presented differ only due to inclusion of observational noise. We show how the addition of either MMD or adversarial training greatly improves the performance of the classifier on the target domain when compared to conventional machine learning algorithms, thereby demonstrating great promise for their use in astronomy.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1972

Procedure for estimating stability and control parameters from flight test data by using maximum likelihood methods employing a real-time digital system

A maximum likelihood parameter estimation procedure and program were developed for the extraction of the stability and control derivatives of aircraft from flight test data. Nonlinear six-degree-of-freedom equations describing aircraft dynamics were used to derive sensitivity equations for quasilinearization. The maximum likelihood function with quasilinearization was used to derive the parameter change equations, the covariance matrices for the parameters and measurement noise, and the performance index function. The maximum likelihood estimator was mechanized into an iterative estimation procedure utilizing a real time digital computer and graphic display system. This program was developed for 8 measured state variables and 40 parameters. Test cases were conducted with simulated data for validation of the estimation procedure and program. The program was applied to a V/STOL tilt wing aircraft, a military fighter airplane, and a light single engine airplane. The particular nonlinear equations of motion, derivation of the sensitivity equations, addition of accelerations into the algorithm, operational features of the real time digital system, and test cases are described.

Grove, R. D.↗
DOE OSTI2014

The radius distribution of planets around cool stars

We calculate an empirical, non-parametric estimate of the shape of the period-marginalized radius distribution of planets with periods less than 150 days using the small yet well-characterized sample of cool (T{sub eff} < 4000 K) dwarf stars in the Kepler catalog. In particular, we present and validate a new procedure, based on weighted kernel density estimation, to reconstruct the shape of the planet radius function down to radii smaller than the completeness limit of the survey at the longest periods. Under the assumption that the period distribution of planets does not change dramatically with planet radius, we show that the occurrence of planets around these stars continues to increase to below 1 R{sub ⊕}, and that there is no strong evidence for a turnover in the planet radius function. In fact, we demonstrate using many iterations of simulated data that a spurious turnover may be inferred from data even when the true distribution continues to rise toward smaller radii. Finally, the sharp rise in the radius distribution below ∼3 R{sub ⊕} implies that a large number of planets await discovery around cool dwarfs as the sensitivities of ground-based transit surveys increase.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2010

Orbit Determination Toolbox

The Orbit Determination Toolbox is an orbit determination (OD) analysis tool based on MATLAB and Java that provides a flexible way to do early mission analysis. The toolbox is primarily intended for advanced mission analysis such as might be performed in concept exploration, proposal, early design phase, or rapid design center environments. The emphasis is on flexibility, but it has enough fidelity to produce credible results. Insight into all flight dynamics source code is provided. MATLAB is the primary user interface and is used for piecing together measurement and dynamic models. The Java Astrodynamics Toolbox is used as an engine for things that might be slow or inefficient in MATLAB, such as high-fidelity trajectory propagation, lunar and planetary ephemeris look-ups, precession, nutation, polar motion calculations, ephemeris file parsing, and the like. The primary analysis functions are sequential filter/smoother and batch least-squares commands that incorporate Monte-Carlo data simulation, linear covariance analysis, measurement processing, and plotting capabilities at the generic level. These functions have a user interface that is based on that of the MATLAB ODE suite. To perform a specific analysis, users write MATLAB functions that implement truth and design system models. The user provides his or her models as inputs to the filter commands. The software provides a capability to publish and subscribe to a software bus that is compliant with the NASA Goddard Mission Services Evolution Center (GMSEC) standards, to exchange data with other flight dynamics tools to simplify the flight dynamics design cycle. Using the publish and subscribe approach allows for analysts in a rapid design center environment to seamlessly incorporate changes in spacecraft and mission design into navigation analysis and vice versa.

Carpenter, James R.↗