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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 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Speed Optimizations for Physics Ray Trace Algorithms

Ray tracing is a process used commonly in computer graphics and in physics to track light photons and particles, respectively. Much research was found on improving execution times for the computer graphics applications; however, in the short time frame of this literary review, almost no research was found on improving the execution times for the physics applications that were relevant to this problem. Two ray trace algorithms, a STL raytrace and a conebeam raytrace, were optimized using OpenMP and CUDA.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1981

Simulation of space plasma phenomena

The importance of computer modeling in investigating physical phenomena is discussed, noting that modern computers can follow the time evolution of systems containing many millions of degrees of freedom, all of which are simultaneously interacting with each other. The two types of models used in simulating plasma, that is, particle and fluid, are described. Attention is given to particle models and some recent results obtained concerning the diffuse aurora. The simulations are shown to give clear evidence that a type of nonlinear cyclotron resonance process for the cold particles is responsible for the saturation observed in these calculations. It is also responsible for the cold electron heating and it is found that at saturation the rate of heating of the cold electrons essentially balances the rate at which energy is fed into unstable waves.

Dawson, J. M.↗
NASA NTRS1993

A microbased shared virtual world prototype

Virtual reality (VR) allows sensory immersion and interaction with a computer-generated environment. The user adopts a physical interface with the computer, through Input/Output devices such as a head-mounted display, data glove, mouse, keyboard, or monitor, to experience an alternate universe. What this means is that the computer generates an environment which, in its ultimate extension, becomes indistinguishable from the real world. 'Imagine a wraparound television with three-dimensional programs, including three-dimensional sound, and solid objects that you can pick up and manipulate, even feel with your fingers and hands.... 'Imagine that you are the creator as well as the consumer of your artificial experience, with the power to use a gesture or word to remold the world you see and hear and feel. That part is not fiction... three-dimensional computer graphics, input/output devices, computer models that constitute a VR system make it possible, today, to immerse yourself in an artificial world and to reach in and reshape it.' Our research's goal was to propose a feasibility experiment in the construction of a networked virtual reality system, making use of current personal computer (PC) technology. The prototype was built using Borland C compiler, running on an IBM 486 33 MHz and a 386 33 MHz. Each game currently is represented as an IPX client on a non-dedicated Novell server. We initially posed the two questions: (1) Is there a need for networked virtual reality? (2) In what ways can the technology be made available to the most people possible?

Pitts, Gerald↗
DOE OSTI2023

Predicting wind-driven spatial deposition through simulated color images using deep autoencoders

Abstract For centuries, scientists have observed nature to understand the laws that govern the physical world. The traditional process of turning observations into physical understanding is slow. Imperfect models are constructed and tested to explain relationships in data. Powerful new algorithms can enable computers to learn physics by observing images and videos. Inspired by this idea, instead of training machine learning models using physical quantities, we used images, that is, pixel information. For this work, and as a proof of concept, the physics of interest are wind-driven spatial patterns. These phenomena include features in Aeolian dunes and volcanic ash deposition, wildfire smoke, and air pollution plumes. We use computer model simulations of spatial deposition patterns to approximate images from a hypothetical imaging device whose outputs are red, green, and blue (RGB) color images with channel values ranging from 0 to 255. In this paper, we explore deep convolutional neural network-based autoencoders to exploit relationships in wind-driven spatial patterns, which commonly occur in geosciences, and reduce their dimensionality. Reducing the data dimension size with an encoder enables training deep, fully connected neural network models linking geographic and meteorological scalar input quantities to the encoded space. Once this is achieved, full spatial patterns are reconstructed using the decoder. We demonstrate this approach on images of spatial deposition from a pollution source, where the encoder compresses the dimensionality to 0.02% of the original size, and the full predictive model performance on test data achieves a normalized root mean squared error of 8%, a figure of merit in space of 94% and a precision-recall area under the curve of 0.93.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1992

Newton in Space

In this 'Liftoff to Learning' series video, astronauts (Charles Veach, Gregory Harbaugh, Donald McMonagle, Michael Coats, L. Blaine Hammond, Guion Bluford, Richard Hieb) from the STS-39 Mission use physical experiments and computer animation to explain how weightlessness and gravity affects everything and everyone onboard the Space Shuttle. The physics behind the differences between weight and mass, and the concepts of 'free fall', are demonstrated along with explanations and experiments of Sir Issac Newton's three laws of motion.

Herbert, Dexter↗
NASA NTRS2015

Utilizing Direct Numerical Simulations of Transition and Turbulence in Design Optimization

Design optimization methods that use the Reynolds-averaged Navier-Stokes equations with the associated turbulence and transition models, or other model-based forms of the governing equations, may result in aerodynamic designs with actual performance levels that are noticeably different from the expected values because of the complexity of modeling turbulence/transition accurately in certain flows. Flow phenomena such as wake-blade interaction and trailing edge vortex shedding in turbines and compressors (examples of such flows) may require a computational approach that is free of transition/turbulence models, such as direct numerical simulations (DNS), for the underlying physics to be computed accurately. Here we explore the possibility of utilizing DNS data in designing a turbine blade section. The ultimate objective is to substantially reduce differences between predicted performance metrics and those obtained in reality. The redesign of a typical low-pressure turbine blade section with the goal of reducing total pressure loss in the row is provided as an example. The basic ideas presented here are of course just as applicable elsewhere in aerodynamic shape optimization as long as the computational costs are not excessive.

Direct numerical simulations of transition and tur↗
DOE OSTI2025

Simulation of Physics-Based 0-10Hz Strong Motion Using High Performance Computing Supporting Refinements to Regional Ground Motion Models for the Central Eastern US

In collaboration with the U.S. Nuclear Regulatory Commission (NRC) the LLNL has developed a computationally efficient simulation platform designed to perform physics-based ground motion simulations for crustal earthquakes in the Stable Continental Regions of Central and Eastern US (CEUS), using high-performance computing. The main objective of the earthquake simulations was to use synthetic ground motion to provide constrains to refinements of existing ergodic Ground Motion Models (GMMs), for large magnitude earthquakes and near-fault distances, for which these models are less reliable. Physics-based broadband (0-10Hz) ground motion simulations were used to estimate the near-fault ground motion amplitudes and within event and between-event variabilities associated with fault rupture characteristics. In our simulations we used a 3D regional velocity model that was based on Saikia’s 1D velocity model (1994). In simulations performed during the first stage of this project the Saikia’s velocity model demonstrated better performance in modelling high frequency regional wave propagation for the CEUS region recorded during the Mw5.0 November 7, 2016, Cushing Oklahoma (Taylor et al., 2017), and Mw5.8 September 3, 2016, Pawnee Oklahoma earthquakes. The proposed regional 3D model includes random perturbations to the 1D background model using the stochastic scheme of Pitarka and Mellors (2021). In addition, validation analysis of the rupture generator and regional wave propagation models, using comparisons with different GMMs for Mw6.5 and Mw7.0 scenario earthquakes in the CEUS region resulted in a very good match between the simulated and empirical ground motion models. For the purposes of seismic hazard assessment at the existing and planned nuclear power plants, NRC is interested in studies aimed at improving the current ground motion models (GMM) for both Stable Continental Regions (SCR) in the Central and Eastern US and Active Crustal Regions (ACR) in the Western US. Due to lack of recorded data, these improvements require synthetic data for short fault distances and large magnitude earthquakes for which the existing recorded data is not enough to uniquely constrain the GMMs. The need for simulations and strong motion data is especially critical for the CEUS region where we do not have recorded data from potentially large damaging earthquakes with moment magnitudes 6.0 and higher. In this the project, we focused on 10Hz simulations of Mw7.0 scenario earthquakes with strike slip and thrust faulting mechanisms. We used more than 50 Mw7.0 earthquake rupture scenarios to investigate the ground motion uncertainty due to unknown earthquake rupture parameters, in particular, the slip distribution, rupture velocity, and faulting mechanism, and their implication on ground motion amplification due to forward rupture directivity effects.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Aerosol Deposition Inside a Spent Fuel Transportation and Storage Canister (Interim Report)

This report documents the progress in preparing the ANSYS/Fluent ® and MELCOR models to perform characterization studies of aerosols dispersal and deposition within a select spent fuel cask system. Steady state thermal-hydraulic cask response is modeled with both codes, at present, while the MELCOR source code is being modified to allow imposed thermal-hydraulic conditions with aerosol physics calculations. This will allow the MELCOR model to assume the thermal-hydraulic calculation from the ANSYS/Fluent ® , while only computing the aerosol physics. Detailed results are presented on the thermal-hydraulic analysis of the MAGNASTOR ® cask for the current ANSYS/Fluent ® model, with convergent conditions observed over two fidelities. While the MELCOR computation computes the steady state conditions, they differ sufficiently from that computed with ANSYS/Fluent ® . The MELCOR analysis include a set of UO 2 sources to investigate system response for interim reporting. The airborne concentrations and evolving distributions are presented. Model development is anticipated to continue as additional components impacting flow conditions or aerosol deposition within the MAGNASTOR ® cask and Westinghouse fuel assemblies are identified.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2019

Collaboration with NSTX in Calculations of Radiofrequency and Neutral Beam Heating and Current Drive

The overall objective of this project was to advance the understanding of heating and current drive necessary for toroidal magnetic confinement fusion energy reactors, and to provide accurate models for these sources to aid in understanding magneto-hydrodynamic, transport and boundary physics issues. Increased understanding is provided by computer simulation using the underlying physics laws, and will lead to greater reliability and increased efficiency of a power producing fusion reactor. The proposed work entailed applications of existing CompX Fokker-Planck, ray tracing, full-wave microwave, and particle diffusion calculator codes to the National Spherical Torus Experiment at Princeton Plasma Physics Laboratory. Special features of NSTX include that large ion orbits are emphasized in its geometry and thus offer a major opportunity within fusion research for focused exploration of their ubiquitous effects, including comparison with simulation codes. The CompX CQL3D Fokker-Planck code was be further adapted to the finite-orbit-width physics. Comparison with experiment ensured that the computer codes have sufficient accuracy to accurately model the experiments. Nonthermal distributions of energetic ions are produced in experiments-- and modeled with the codes-- exhibiting the signatures of auxiliary heating, transport, and radiation processes within the plasma. Using physics-based modeling, in conjunction with experimental data, nonthermal distributions can provide keys to validation and quantification of auxiliary heating physics. Nonthermal distributions also hold particular information on radial transport processes, not obscured by the smoothing Maxwellization effects of Coulomb collisions. Generally, radial transport is not well understood but is a key to efficient fusion energy production. By comparison with Fokker-Planck modeling, the velocity dependence of radial transport coefficients becomes experimentally accessible, particularly in cases of well-modeled sources such as neutral beams, fast wave power, or the toroidal electric field. It is clear from modeling of fast-wave interactions with ions that finite-orbit-width effects play a significant role. For example, a different set of ions is computed to intersect a given cyclotron resonance depending on whether or not radial orbit drifts are taken into account. Also, radial transport, associated bootstrap current, and alpha channeling depend on radial drifts. Ion cyclotron heating above the first harmonic interacts preferentially with higher velocity ions, giving large radial drifts. The CQL3D Fokker-Planck code, maintained by the proposers, is a physics based model used extensively within the US fusion community to compare with and interpret experimental observations. At the beginning of this grant, bounce-averages of the rf quasilinear and other operators have been performed neglecting the radial drifts which give finite orbit widths to the particle orbits. The proposed work included a major upgrade to CQL3D to include finite-orbit-width effects be accounted for by performing the bounce-averages of rf and neutral beam fast ions along guiding center orbits. This CQL3D enhancement provided microwave and neutral beam contributions to neoclassical radial transport coefficients for nonthermal distributions, and substantially increased the comprehensiveness of the ion model. Additional work validated Fokker-Planck and ray tracing modeling of neutral beam and high harmonic fast wave absorption and current drive against the NSTX experiment, as had been very successful for electron cyclotron current drive in the DIII-D tokamak at General Atomics and lower hybrid waves on C-Mod at MIT. The validation work was in conjunction with staff and graduate students at Princeton Plasma Physics Laboratory. This DOE grant supported fusion energy research, a potential long-term solution to the world's energy needs. The research grant under consideration, DE-SC0006614, provided research support at the 0.3 Full-Time-Equivalent level for application of computer models to aid in understanding and projecting efficacy of heating and current drive sources in the National Spherical Torus Experiment, a tokamak variant, at the Princeton Plasma Physics Laboratory. The NSTX experiment explores the physics of very tight aspect ratio, almost spherical tokamaks, aiming at producing steady-state fusion plasmas. The current drive is an integral part of the steady-state concept, maintaining the magnetic geometry in the steady-state tokamak.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Towards grounding nuclear physics in QCD

Exascale computing could soon enable a predictive theory of nuclear structure and reactions rooted in the Standard Model, with quantifiable and systematically improvable uncertainties. Such a predictive theory will help exploit experiments that use nucleons and nuclei as laboratories for testing the Standard Model and its limitations. Examples include direct dark matter detection, neutrinoless double beta decay, and searches for permanent electric dipole moments of the neutron and atoms. It will also help connect QCD to the properties of cold neutron stars and hot supernova cores. We discuss how a quantitative bridge between QCD and the properties of nuclei and nuclear matter will require a synthesis of lattice QCD (especially as applied to two- and three- nucleon interactions), effective field theory, and ab initio methods for solving the nuclear many-body problem. While there are significant challenges that must be addressed in developing this triad of theoretical tools, the rapid advance of computing is accelerating progress. In particular, we focus this review on the anticipated advances from lattice QCD and how these advances will impact few-body effective theories of nuclear physics by providing critical input, such as constraints on unknown low-energy constants of the effective (field) theories. We also review particular challenges that must be overcome for the successful application of lattice QCD for low-energy nuclear physics. We describe progress in developing few-body effective (field) theories of nuclear physics, with an emphasis on HOBET, a non-relativistic effective theory of nuclear physics, which is less common in the literature. We use the examples of neutrinoless double beta decay and the nuclear-matter equation of state to illustrate how the coupling of lattice QCD to effective theory might impact our understanding of symmetries and exotic astrophysical environments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Digital Modeling on Large Kernel Metamaterial Neural Network

Deep neural networks (DNNs) utilized recently are physically deployed with computational units (e.g., CPUs and GPUs). Such a design might lead to a heavy computational burden, significant latency, and intensive power consumption, which are critical limitations in applications such as Internet of Things (IoT), edge computing, and usage of drones. Recent advances in optical computational units (e.g., metamaterial) have shed light on energy-free and light-speed neural networks. However, the digital design of the metamaterial neural network (MNN) is fundamentally limited by its physical limitations, such as precision, noise, and bandwidth during fabrication. Moreover, the unique advantages of MNN’s (e.g., light-speed computation) are not fully explored via standard 3×3 convolution kernels. In this paper, we propose a novel large kernel metamaterial neural network (LMNN) that maximizes the digital capacity of the state-of-the-art (SOTA) MNN with model re-parametrization and network compression, while also considering the optical limitation explicitly. The new digital learning scheme can maximize the learning capacity of MNN while modeling the physical restrictions of meta-optics. With the proposed LMNN, the computation cost of the convolutional front-end can be offloaded to fabricated optical hardware. The experimental results on two publicly available datasets demonstrate that the optimized hybrid design improved classification accuracy while reducing computational latency. In conclusion, the development of the proposed LMNN is a promising step towards the ultimate goal of energy-free and light-speed AI.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Extension of the quantum-kinetic model to lunar and Mars return physics

The ability to compute rarefied, ionized hypersonic flows is becoming more important as missions such as Earth reentry, landing high-mass payloads on Mars, and the exploration of the outer planets and their satellites are being considered. A recently introduced molecular-level chemistry model, the quantum-kinetic, or Q-K, model that predicts reaction rates for gases in thermal equilibrium and non-equilibrium using only kinetic theory and fundamental molecular properties, is extended in the current work to include electronic energy level transitions and reactions involving charged particles. Like the Q-K procedures for neutral species chemical reactions, these new models are phenomenological procedures that aim to reproduce the reaction/transition rates but do not necessarily capture the exact physics. These engineering models are necessarily efficient due to the requirement to compute billions of simulated collisions in direct simulation Monte Carlo (DSMC) simulations. The new models are shown to generally agree within the spread of reported transition and reaction rates from the literature for near equilibrium conditions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Exploratory calculation of 𝐾 L → 𝜇 + ⁢𝜇 − decay from lattice QCD at physical pion mass

We compute the complex, long-distance two-photon-exchange amplitude which contributes to the rare 𝐾 L → 𝜇 + ⁢𝜇 − decay from lattice QCD. We use a 24 3 × 64 physical-pion-mass gauge field ensemble at an inverse lattice spacing of 1.023 GeV and a QED ∞ -based formalism. Our implementation strategies for all five non-SU(3)-flavor-suppressed diagram topologies are given in detail. We achieve a 25% statistical precision on the dispersive part of this long-distance amplitude. This calculation is carried out with 2+1 quark flavors and therefore requires the addition of counterterms to compensate for the absence of the Glashow-Iliopoulos-Maiani mechanism. These counterterms are not included in the current calculation and will be the subject of a second paper. Although a direct comparison to experiment cannot yet be made because of those omitted counterterms, the present exploratory calculation allows one to identify principal sources of statistical uncertainty in this calculation. The precision of our results is limited by the reconstruction of the physical contribution of the 𝜂 intermediate state, for which various strategies are tested and compared.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1994

Non-Reflecting Regions for Finite Difference Methods in Modeling of Elastic Wave Propagation in Plates

Solution of the wave equation using techniques such as finite difference or finite element methods can model elastic wave propagation in solids. This requires mapping the physical geometry into a computational domain whose size is governed by the size of the physical domain of interest and by the required resolution. This computational domain, in turn, dictates the computer memory requirements as well as the calculation time. Quite often, the physical region of interest is only a part of the whole physical body, and does not necessarily include all the physical boundaries. Reduction of the calculation domain requires positioning an artificial boundary or region where a physical boundary does not exist. It is important however that such a boundary, or region, will not affect the internal domain, i.e., it should not cause reflections that propagate back into the material. This paper concentrates on the issue of constructing such a boundary region.

Kishoni, Doron↗
DOE OSTI2021

XVC FTDI JTAG v1.0

the Xilinx tools relay on a component on the board (FTDI chip) to have a small memory from a particular manufacturer (Digilent) to serve as a bridge to the FPGA. What the software we're requesting to release simply does is to run between the Xilinx tools (one of them is called Vivado but there are others) and our boards so that we don't need that memory on our boards. The protocol the software uses to communicate with the Xilinx tools is called XVC (Xilinx Virtual Cable). This server code runs on the computer that is physically connected to the FPGA board, but the Xilinx tools that communicate with it can run either on that computer or some other computer connected to it over the network, this is where the network component comes into play.

Norum, William↗
DOE OSTI2021

An Algebra of Machine Learners with Applications

Machine learning (ML) methods are increasingly being applied to solve complex, data-driven problems in diverse areas, by exploiting the physical laws derived from first principles such as thermal hydraulics and the abstract laws developed recently for data and computing infrastructures. These physical and abstract laws encapsulate, typically in compact algebraic forms, the critical knowledge that complements data-driven ML models. We present a unified perspective of these laws and ML methods using an abstract algebra (A;⊕,Ⓧ), wherein the performance estimation and classification tasks are characterized by the additive ⊕ operations, and the diagnosis, reconstruction, and optimization tasks are characterized by the difference Ⓧ operations. This abstraction provides ML codes and their performance characterizations that are transferable across different areas. We describe practical applications of these abstract operations using examples of throughput profile estimation tasks in data transport infrastructures, and power-level and sensor error estimation tasks in nuclear reactor systems.

Rao, Nageswara↗
DOE OSTI2019

Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks

Advances in computational science offer a principled pipeline for predictive modeling of cardiovascular flows and aspire to provide a valuable tool for monitoring, diagnostics and surgical planning. Such models can be nowadays deployed on large patient-specific topologies of systemic arterial networks and return detailed predictions on flow patterns, wall shear stresses, and pulse wave propagation. However, their success heavily relies on tedious pre-processing and calibration procedures that typically induce a significant computational cost, thus hampering their clinical applicability. Here, we put forth a machine learning framework that enables the seamless synthesis of non-invasive in-vivo measurement techniques and computational flow dynamics models derived from first physical principles. We illustrate this new paradigm by showing how one-dimensional models of pulsatile flow can be used to constrain the output of deep neural networks such that their predictions satisfy the conservation of mass and momentum principles. Once trained on noisy and scattered clinical data of flow and wall displacement, these networks can return physically consistent predictions for velocity, pressure and wall displacement pulse wave propagation, all without the need to employ conventional simulators. A simple post-processing of these outputs can also provide a relatively cheap and effective way for estimating Windkessel model parameters that are required for the calibration of traditional computational models. Finally, the effectiveness of the proposed techniques is demonstrated through a series of prototype benchmarks, as well as a realistic clinical case involving in-vivo measurements near the aorta/carotid bifurcation of a healthy human subject.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2024

Open Call LDRD: Physically Informed Autoencoders for Galactic Redshift Regression

Physical constraints have been suggested to make neural network models more generalizable, act scientifically plausible, and be more data-efficient over unconstrained baselines. In this report, we present preliminary work on evaluating the effects of adding soft physical constraints to computer vision neural networks trained to estimate the conditional density of redshift on input galaxy images for the Sloan Digital Sky Survey. We introduce physically motivated soft constraint terms that are not implemented with differential or integral operators. We frame this work as a simple ablation study where the effect of including soft physical constraints is compared to an unconstrained baseline. We compare networks using standard point estimate metrics for photometric redshift estimation, as well as metrics to evaluate how faithful our conditional density estimate represents the probability over the ensemble of our test dataset. We find no evidence that the implemented soft physical constraints are more effective regularizers than augmentation.

97 MATHEMATICS AND COMPUTING↗