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All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

Magnetic reconnection driven by current repulsion

The evolution of an equilibrium consisting of two magnetic islands with oppositely directed currents embedded in a strong magnetic field is investigated, using numerical simulation methods. The rapid development of an ideal magnetohydrodynamic instability is observed, which first rotates and then expels the islands. The growth rate is on the order of the inverse of the Alfven transit time and is much higher than that for magnetic island coalescence. In the nonlinear stage, resistivity becomes important as the reconnection process ensues and dissipates the magnetic energy. The growth rate of the instability is a weak function of the plasma beta and other plasma parameters such as S, the magnetic Reynolds number. An energy principle analysis, based on eigenfunctions obtained from the simulation, confirms the existence of the instability.

Richard, R. L.↗
DOE OSTI2018

Automatic Imagery Data Analysis for Proactive Computer-Based Workflow Management during Nuclear Power Plant Outages

This report is being submitted for the task “Final Report” of DOE NEUP Project 15-8121 “Automatic Imagery Data Analysis for Proactive Computer-Based Workflow Management during Nuclear Power Plant Outages.” Typical nuclear power plant (NPP) outages always involve thousands of maintenance and refueling activities and a large number of workers in limited workspaces, while having tight schedules and zero-tolerance for accidents. During an outage, thousands of workers will be working in various workspaces across the NPP. High outage costs and expensive delays (approximately 1.5 million dollars of loss per day of delay) in NPP maintenance demand tight outage schedules. In packed workspaces, an automatic system that monitors human behaviors in real-time and provides insights about current and pending schedule deviations from the plan is critical for ensuring: 1) effective collaboration among workers and worker teams from different trades; 2) less waste of time and resources due to the lack of situational awareness; and 3) proactive outage project control. The overall goal of this project is to test the hypothesis that real-time imagery-based object tracking and spatial analysis, as well as human behavior modeling of outage participants, will significantly improve the efficiency of outage control while lowering the rates of accidents and incidents. Three objectives of this project are: 1) Establish real-time object tracking and spatiotemporal analysis methods that automatically assess the productivity of field activities and detect anomalous spatiotemporal relationships among activities that cause inefficiencies and risks; 2) Establish realtime human tracking and human factor modeling methods for automatically diagnosing unexpected actions of and interactions between outage participants, those which cause inefficient collaborations between Advanced Outage Control Center (AOCC), satellite outage centers, NPP workers, and maintenance service providers; and 3) Test the proposed automated object tracking, human behavior modeling, and spatiotemporal analysis methods in outage control case studies in order to characterize the effectiveness of automated imagery-data-driven methods in proactively improving the efficiency and safety of workflows in outage coordination and risk management. Recent studies of detailed human behavior monitoring on construction sites have examined the potential of applying advanced computer vision algorithms in detecting and tracking anomalous workers (i.e., workers who do not wear hard hats or safety vests) for ensuring job site safety. Some studies of human factor studies revealed the importance of modeling detailed interactions between individuals within and across teams in better understanding the impact of human in proactive project control. Other studies in the construction domain have developed computational simulation frameworks that formalize detailed spatiotemporal interactions between tasks in order to simulate the impacts of individual tasks on the performance of workflows. While integrated analyses that combine human factor assessment, as well as image processing and simulation, are in demand for effective decision-making, limited studies have examined the potential of such integrated analyses in NPP outage control. This research project has examined an automatic outage monitoring and control system that integrates human factor analysis, computer vision techniques, and simulation methods in order to enable engineers to better understand the interactions between humans, resources, and workflows during outage processes. The project aims at providing NPP maintenance agencies insights into more efficient use of limited resources in extending the life of a nuclear plant, as well as reducing waste while ensuring sufficient generation of electricity. This study is significant for the safety of nuclear plants, sustainable electricity generation for livable communities, and cost savings for maintaining electricity infrastructures in the United States.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2013

On the accuracy of van der Waals inclusive density-functional theory exchange-correlation functionals for ice at ambient and high pressures

Density-functional theory (DFT) has been widely used to study water and ice for at least 20 years. However, the reliability of different DFT exchange-correlation (xc) functionals for water remains a matter of considerable debate. This is particularly true in light of the recent development of DFT based methods that account for van der Waals (vdW) dispersion forces. Here, we report a detailed study with several xc functionals (semi-local, hybrid, and vdW inclusive approaches) on ice Ih and six proton ordered phases of ice. Consistent with our previous study [B. Santra, J. Klimeš, D. Alfè, A. Tkatchenko, B. Slater, A. Michaelides, R. Car, and M. Scheffler, Phys. Rev. Lett. 107, 185701 (2011)] which showed that vdW forces become increasingly important at high pressures, we find here that all vdW inclusive methods considered improve the relative energies and transition pressures of the high-pressure ice phases compared to those obtained with semi-local or hybrid xc functionals. However, we also find that significant discrepancies between experiment and the vdW inclusive approaches remain in the cohesive properties of the various phases, causing certain phases to be absent from the phase diagram. Therefore, room for improvement in the description of water at ambient and high pressures remains and we suggest that because of the stern test the high pressure ice phases pose they should be used in future benchmark studies of simulation methods for water.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Multinucleon structure and dynamics via quantum computing

We propose a framework for computing the structure and dynamics for second-quantized many-nucleon Hamiltonians on quantum computers. We develop an oracle-based Hamiltonian input model that computes the many-nucleon states and nonzero Hamiltonian matrix elements of the many-nucleon system. With our Fock-state based input model, we show how to implement the sparse matrix simulation algorithms to calculate the dynamics of the second-quantized many-nucleon Hamiltonian. Based on the dynamics simulation methods, we also present the methodology for structure calculations of the many-nucleon system. In this work, we provide an explicit circuit design of our input model of the second-quantized Hamiltonian within a direct encoding scheme that maps the occupation of each available single-particle state in the many-nucleon state to the state of specific qubit in a quantum register. Here, we analyze our method and provide the asymptotic cost in computing resources for structure and dynamics calculations of many-nucleon systems. For pedagogical purposes, we demonstrate our input model with two model problems in restricted model spaces.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Simulation of Plasma Emission in Magnetized Plasmas

The recent Parker Solar Probe observations of type III radio bursts show that the effects of the finite background magnetic field can be an important factor in the interpretation of data. In the present paper, the effects of the background magnetic field on the plasma-emission process, which is believed to be the main emission mechanism for solar coronal and interplanetary type III radio bursts, are investigated by means of the particle-in-cell simulation method. The effects of the ambient magnetic field are systematically surveyed by varying the ratio of plasma frequency to electron gyrofrequency. The present study shows that for a sufficiently strong ambient magnetic field, the wave–particle interaction processes lead to a highly field-aligned longitudinal mode excitation and anisotropic electron velocity distribution function, accompanied by a significantly enhanced plasma emission at the second-harmonic plasma frequency. For such a case, the polarization of the harmonic emission is almost entirely in the sense of extraordinary mode. On the other hand, for moderate strengths of the ambient magnetic field, the interpretation of the simulation result is less clear. The underlying nonlinear-mode coupling processes indicate that to properly understand and interpret the simulation results requires sophisticated analyses involving interactions among magnetized plasma normal modes, including the two transverse modes of the magneto-active plasma, namely, the extraordinary and ordinary modes, as well as electron-cyclotron-whistler, plasma oscillation, and upper-hybrid modes. At present, a nonlinear theory suitable for quantitatively analyzing such complex-mode coupling processes in magnetized plasmas is incomplete, which calls for further theoretical research, but the present simulation results could provide a guide for future theoretical efforts.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Chapter 8: A 'Global' View on Bifacial Gain: Dependence on Geographic Location and Environmental Conditions

The simulation results presented in this chapter demonstrate how bifacial PV systems can bring a performance gain all around the globe, resolving a certain misbelief that they may only form a niche market restricted to specific locations or installations. Global predictions, based on irradiance, indicate bifacial gains in the order of 50% of the natural albedo for equator-facing systems with optimized tilt angle. In locations with low clearness index, the bifacial gain can even be higher. At system level the bifacial gain will be reduced by 20% to 50% relative to a free-standing panel, due to increased self-shading and to limitation of the diffuse irradiance on the rear by adjacent sheds. Even though the rear side irradiance of a bifacial module is inherently inhomogeneous, due to self-shading and external mounting structures, our simulations have shown that the RSD of the total irradiance does not exceed 5%, even at very high albedo, and will usually be much smaller. The simulation methods applied in this chapter and their ongoing integration into commercially available software tools, like PVsyst, will give system designers and installers more visibility on the configuration layout of a bifacial PV plant at their specific location, the associated bifacial gain and the effect on their return on investment.

14 SOLAR ENERGY↗
DOE OSTI2022

Designing, synthesizing, and modeling active fluids

We review recent advances in the design, synthesis, and modeling of active fluids. Active fluids have been at the center of many technological innovations and theoretical advances over the past two decades. Research on this new class of fluids has been inspired by the fascinating and remarkably efficient strategies that biological systems employ, leading to the development of biomimetic nano- and micro-machines and swimmers. The review encompasses active fluids on both the nano- and micro-scale. We start with examples of biological active systems before we discuss how experimentalists leverage novel propulsion mechanisms to power nano- and micro-machines. Furthermore, we then examine how the study of these far-from-equilibrium systems has prompted the development of new simulation methods and theoretical models in nonequilibrium physics to account for their mechanical, thermodynamic, and emergent properties. Recent advances in the field have paved the way for the design, synthesis, and modeling of autonomous systems at the nano- and micro-scale and opened the door to the development of soft matter robotics.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Update on the Benchmark Analysis of the Jupiter Experiments: Plutonium Moderated by Lead

The Jupiter experiment was a series of measurements in the summer of 2017 to analyze the effects of voids in lead moderated plutonium assemblies. This experiment was part of a larger campaign that took place at the National Criticality Experiments Research Center (NCERC) in collaboration with the Japan Atomic Energy Agency (JAEA) that also investigated assemblies with differing uranium enrichments. The purpose for this campaign was to provide data useful for a potential lead-bismuth cooled accelerator driven system. This data is needed due to the low number of benchmarks involving lead. Given the wide use of lead for shielding in addition to new reactor designs, there is a need for further benchmarking that this experiment could help fulfill. In addition, adding more benchmarks to the International Criticality Safety Benchmark Evaluation Project (ICSBEP) is helpful for further validation of simulation methods used for criticality safety. This summary is an update on the current progress of this analysis, and all results shown are preliminary.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2014

Sequential lateral solidification of silicon thin films on low-k dielectrics for low temperature integration

We present the excimer laser crystallization of amorphous silicon on a low dielectric constant (low-k) insulator for very large scale integration monolithic 3D integration and demonstrate that low dielectric constant materials are suitable substrates for 3D integration through laser crystallization of silicon thin films. We crystallized 100 nm amorphous silicon on top of SiO{sub 2} and SiCOH (low-k) dielectrics, at different material thicknesses (1 μm, 0.75 μm, and 0.5 μm). The amorphous silicon crystallization on low-k dielectric requires 35% less laser energy than on an SiO{sub 2} dielectric. This difference is related to the thermal conductivity of the two materials, in agreement with one dimensional simulations of the crystallization process. We analyzed the morphology of the material through defect-enhanced microscopy, Raman spectroscopy, and X-ray diffraction analysis. SEM micrographs show that polycrystalline silicon is characterized by micron-long grains with an average width of 543 nm for the SiO{sub 2} sample and 570 nm for the low-k samples. Comparison of the Raman spectra does not show any major difference in film quality for the two different dielectrics, and polycrystalline silicon peaks are closely placed around 517 cm{sup −1}. From X-ray diffraction analysis, the material crystallized on SiO{sub 2} shows a preferential (111) crystal orientation. In the SiCOH case, the 111 peak strength decreases dramatically and samples do not show preferential crystal orientation. A 1D finite element method simulation of the crystallization process on a back end of line structure shows that copper (Cu) damascene interconnects reach a temperature of 70 °C or lower with a 0.5 μm dielectric layer between the Cu and the molten Si layer, a favorable condition for monolithic 3D integration.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2013

Shielding implications for secondary neutrons and photons produced within the patient during IMPT

Purpose: Intensity modulated proton therapy (IMPT) uses a combination of computer controlled spot scanning and spot-weight optimized planning to irradiate the tumor volume uniformly. In contrast to passive scattering systems, secondary neutrons and photons produced from inelastic proton interactions within the patient represent the major source of emitted radiation during IMPT delivery. Various published studies evaluated the shielding considerations for passive scattering systems but did not directly address secondary neutron production from IMPT and the ambient dose equivalent on surrounding occupational and nonoccupational work areas. Thus, the purpose of this study was to utilize Monte Carlo simulations to evaluate the energy and angular distributions of secondary neutrons and photons following inelastic proton interactions within a tissue-equivalent phantom for incident proton spot energies between 70 and 250 MeV.Methods: Monte Carlo simulation methods were used to calculate the ambient dose equivalent of secondary neutrons and photons produced from inelastic proton interactions in a tissue-equivalent phantom. The angular distribution of emitted neutrons and photons were scored as a function of incident proton energy throughout a spherical annulus at 1, 2, 3, 4, and 5 m from the phantom center. Appropriate dose equivalent conversion factors were applied to estimate the total ambient dose equivalent from secondary neutrons and photons.Results: A reference distance of 1 m from the center of the patient was used to evaluate the mean energy distribution of secondary neutrons and photons and the resulting ambient dose equivalent. For an incident proton spot energy of 250 MeV, the total ambient dose equivalent (3.6 Multiplication-Sign 10{sup -3} mSv per proton Gy) was greatest along the direction of the incident proton spot (0 Degree-Sign -10 Degree-Sign ) with a mean secondary neutron energy of 71.3 MeV. The dose equivalent decreased by a factor of 5 in the backward direction (170 Degree-Sign -180 Degree-Sign ) with a mean energy of 4.4 MeV. An 8 Multiplication-Sign 8 Multiplication-Sign 8 cm{sup 3} volumetric spot distribution (5 mm FWHM spot size, 4 mm spot spacing) optimized to produce a uniform dose distribution results in an ambient dose equivalent of 4.5 Multiplication-Sign 10{sup -2} mSv per proton Gy in the forward direction.Conclusions: This work evaluated the secondary neutron and photon emission due to monoenergetic proton spots between 70 and 250 MeV, incident on a tissue equivalent phantom. Example calculations were performed to estimate concrete shield thickness based upon appropriate workload and shielding design assumptions. Although lower than traditional passive scattered proton therapy systems, the ambient dose equivalent from secondary neutrons produced by the patient during IMPT can be significant relative to occupational and nonoccupational workers in the vicinity of the treatment vault. This work demonstrates that Monte Carlo simulations are useful as an initial planning tool for studying the impact of the treatment room and maze design on surrounding occupational and nonoccupational work areas.

61 RADIATION PROTECTION AND DOSIMETRY↗
NASA NTRS2022

Radiative Losses in the Chromosphere During A C-Class Flare

Context. Solar flares release an enormous amount of energy (~10(exp 32) erg) into the corona. A substantial fraction of this energy is transported to the lower atmosphere, which results in chromospheric heating. The mechanisms that transport energy to the lower solar atmosphere during a flare are still not fully understood. Aims. We aim to estimate the temporal evolution of the radiative losses in the chromosphere at the footpoints of a C-class flare, in order to set observational constraints on the electron beam parameters of a RADYN flare simulation. Methods. We estimated the radiative losses from hydrogen, and singly ionized Ca and Mg using semiempirical model atmospheres, which were inferred from a multiline inversion of observed Stokes profiles obtained with the CRISP and CHROMIS instruments on the Swedish 1-m Solar Telescope. The radiative losses were computed taking into account the effect of partial redistribution and non-local thermodynamic equilibrium. To estimate the integrated radiative losses in the chromosphere, the net cooling rates were integrated between the temperature minimum and the height where the temperature reaches 10 kK. We also compared our time series of radiative losses with those from the RADYN flare simulations. Results. We obtained a high spatial-resolution map of integrated radiative losses around the flare peak time. The stratification of the net cooling rate suggests that the Ca IR triplet lines are responsible for most of the radiative losses in the flaring atmosphere. During the flare peak time, the contribution from Ca II H and K and Mgii h and k lines are strong and comparable to the Ca IR triplet (~32kW m(exp -2)). Since our flare is a relatively weak event, the chromosphere is not heated above 11 kK, which in turn yields a subdued Lyα contribution (~7kW m(exp -2)) in the selected limits of the chromosphere. The temporal evolution of total integrated radiative losses exhibits sharply rising losses (0.4kW m(exp -2) (s(exp -1)) and a relatively slow decay (0.23kW m(exp -2) s(exp -1)). The maximum value of total radiative losses is reached around the flare peak time and can go up to 175kWm􀀀2 for a single pixel located at footpoint. After a small parameter study, we find the best model-data consistency in terms of the amplitude of radiative losses and the overall atmospheric structure with a RADYN flare simulation in the injected energy flux of 5 × 10(exp 10) erg s(exp -1) cm(exp -2).

Chromosphere↗
DOE OSTI2015

Proton-induced x-ray fluorescence CT imaging

Purpose: To demonstrate the feasibility of proton-induced x-ray fluorescence CT (pXFCT) imaging of gold in a small animal sized object by means of experiments and Monte Carlo (MC) simulations. Methods: First, proton-induced gold x-ray fluorescence (pXRF) was measured as a function of gold concentration. Vials of 2.2 cm in diameter filled with 0%–5% Au solutions were irradiated with a 220 MeV proton beam and x-ray fluorescence induced by the interaction of protons, and Au was detected with a 3 × 3 mm{sup 2} CdTe detector placed at 90° with respect to the incident proton beam at a distance of 45 cm from the vials. Second, a 7-cm diameter water phantom containing three 2.2-diameter vials with 3%–5% Au solutions was imaged with a 7-mm FWHM 220 MeV proton beam in a first generation CT scanning geometry. X-rays scattered perpendicular to the incident proton beam were acquired with the CdTe detector placed at 45 cm from the phantom positioned on a translation/rotation stage. Twenty one translational steps spaced by 3 mm at each of 36 projection angles spaced by 10° were acquired, and pXFCT images of the phantom were reconstructed with filtered back projection. A simplified geometry of the experimental data acquisition setup was modeled with the MC TOPAS code, and simulation results were compared to the experimental data. Results: A linear relationship between gold pXRF and gold concentration was observed in both experimental and MC simulation data (R{sup 2} > 0.99). All Au vials were apparent in the experimental and simulated pXFCT images. Specifically, the 3% Au vial was detectable in the experimental [contrast-to-noise ratio (CNR) = 5.8] and simulated (CNR = 11.5) pXFCT image. Due to fluorescence x-ray attenuation in the higher concentration vials, the 4% and 5% Au contrast were underestimated by 10% and 15%, respectively, in both the experimental and simulated pXFCT images. Conclusions: Proton-induced x-ray fluorescence CT imaging of 3%–5% gold solutions in a small animal sized water phantom has been demonstrated for the first time by means of experiments and MC simulations.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Direct comparison between experiments and dislocation dynamics simulations of high rate deformation of single crystal copper

A long standing challenge in computational materials science is to establish a quantitative connection between the macroscopic properties of plastic deformation with the microscopic mechanisms of dislocations in crystalline materials. Although the discrete dislocation dynamics (DDD) simulation method has been developed for several decades with the goal of addressing this challenge, a one-to-one comparison between the DDD predictions on single crystal stress–strain curves and experimental measurements under identical conditions has not been possible to date. Such a comparison is an essential step towards establishing a dislocation-physics based theory of plasticity and a multiscale framework of the plastic behaviors of crystalline materials. Here we provide direct comparisons between the stress–strain curves of Cu single crystals under high strain rate loading in the [0 0 1] and [0 1 1] directions obtained from miniaturized desktop Kolsky bar experiments and those from DDD simulations under identical loading conditions. With an appropriate set of parameters, DDD simulations can produce stress–strain curves that are in reasonable agreement with the experimental results. However, the dislocation mobility values needed to achieve this agreement are an order of magnitude lower than expected based on previous measurements and atomistic simulations. We hypothesize that this discrepancy could be caused by drag forces from jogs and point defects produced during the plastic deformation. Cross-slip of screw dislocations is also found to be necessary to capture the experimental stress–strain behavior, especially for the [0 1 1] loading direction. Finally, this work provides an example of how direct comparisons between DDD simulations and experimental measurements can provide new insight into the fundamental mechanisms of plastic deformation.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Modeling the 4D discharge of lithium-ion batteries with a multiscale time-dependent deep learning framework

The lithium-ion battery (LIB) field is moving towards the direction of investigating spatially resolved physical phenomena in the 3D porous microstructure of electrodes. These pore-scale simulations give new insights into the local dynamics of lithiation/de-lithiation and charge transport, Nevertheless, the computational time of these simulations limits the integration of these models in optimization workflows of cycling conditions or electrode manufacturing processes. Machine learning models present a way of assessing in real-time the performance of materials. While several successful techniques for replicating simulations with machine learning have been proposed, this case study presents a more demanding problem, due to the necessity of understanding the behavior of heterogeneous 3D local data, as it evolves in time: this poses both a scientific and a technical challenge. To this end, we propose an autoregressive multiscale convolutional neural network model to predict relevant quantities at the pore-scale in the solid phase: the lithium concentration (in the active material) and potential (in the active material and carbon binder). Here, these are ultimately used to reconstruct the battery discharge curve. 3D images of the electrode microstructures are the input to the network, trained with a dataset of finite element method simulations to predict the discharge behavior of the cathode side in lithium ion batteries. We propose this machine learning model as a proof-of-concept of the applicability of multiscale networks for time-dependent physics problems. The trained model exhibits very high accuracy (with errors lower than 2 %) in forecasting the discharge behavior of new unseen cathodes.

25 ENERGY STORAGE↗
DOE OSTI2024

Development of a machine learning model for polyethylene pyrolysis using a detailed reaction mechanism

Waste plastics have recently received significant attention as the issue of waste generation continues to increase. Thermal conversion processes, such as pyrolysis and gasification, are attractive potential technologies for utilizing waste plastics and reducing overall waste generation. Efficient utilization of plastics requires a detailed understanding of the conversion process such as pyrolysis and gasification. However, a mechanistic understanding of these processes lead to large and complex kinetic schemes that are not suited for large-scale and long-time simulation methods. Currently, most modeling approaches for pyrolysis and gasification rely on globally lumped, simplified kinetic schemes that provide results that are classified by their product type and not individual species, which limit the level of fidelity achieved via modeling. A machine learning (ML) model has been developed for the primary reactions of high-density polyethylene (HDPE) in an attempt to increase computational efficiency while still maintaining a high level of detail and accuracy. The ML model is trained on a detailed reaction mechanism containing 42 total species and 737 chemical reactions. A DeepONet branch and trunk architecture was adopted to train the model using time-steps relevant to computational fluid dynamics simulations. The ML used physics-informed loss functions to ensure mass conservation. The surrogate model has been deployed in simple MFiX CFD simulations, single particle and an experimental drop tube reactor, and has shown promising performance compared to the original scheme.

Houston, Ross↗
DOE OSTI2022

Improving deep learning performance for predicting large-scale geological ${{CO}_{2}}$ sequestration modeling through feature coarsening

Physics-based reservoir simulation for fluid flow in porous media is a numerical simulation method to predict the temporal-spatial patterns of state variables (e.g. pressure p) in porous media, and usually requires prohibitively high computational expense due to its non-linearity and the large number of degrees of freedom (DoF). This work describes a deep learning (DL) workflow to predict the pressure evolution as fluid flows in large-scale 3-dimensional(3D) heterogeneous porous media. In particular, we develop an efficient feature coarsening technique to extract the most representative information and perform the training and prediction of DL at the coarse scale, and further recover the resolution at the fine scale by spatial interpolation. We validate the DL approach to predict pressure field against physics-based simulation data for a field-scale 3D geologic CO 2 sequestration reservoir model. We evaluate the impact of feature coarsening on DL performance, and observe that the feature coarsening not only decreases the training time by >74% and reduces the memory consumption by >75%, but also maintains temporal error 0.63% on average. Besides, the DL workflow provides predictive efficiency with 1406 times speedup compared to physics-based numerical simulation. The key findings from this research significantly improve the training and prediction efficiency of deep learning model to deal with large-scale heterogeneous reservoir models, and thus it can also be further applied to accelerate workflows of history matching and reservoir optimization for close-loop reservoir management.

58 GEOSCIENCES↗
NASA NTRS1988

Random Process Simulation for stochastic fatigue analysis

A simulation technique is described which directly synthesizes the extrema of a random process and is more efficient than the Gaussian simulation method. Such a technique is particularly useful in stochastic fatigue analysis because the required stress range moment E(R sup m), is a function only of the extrema of the random stress process. The family of autoregressive moving average (ARMA) models is reviewed and an autoregressive model is presented for modeling the extrema of any random process which has a unimodal power spectral density (psd). The proposed autoregressive technique is found to produce rainflow stress range moments which compare favorably with those computed by the Gaussian technique and to average 11.7 times faster than the Gaussian technique. The autoregressive technique is also adapted for processes having bimodal psd's. The adaptation involves using two autoregressive processes to simulate the extrema due to each mode and the superposition of these two extrema sequences. The proposed autoregressive superposition technique is 9 to 13 times faster than the Gaussian technique and produces comparable values for E(R sup m) for bimodal psd's having the frequency of one mode at least 2.5 times that of the other mode.

Larsen, Curtis E.↗
NASA NTRS1993

Multigrid methods for numerical simulation of laminar diffusion flames

This paper documents the result of a computational study of multigrid methods for numerical simulation of 2D diffusion flames. The focus is on a simplified combustion model, which is assumed to be a single step, infinitely fast and irreversible chemical reaction with five species (C3H8, O2, N2, CO2 and H2O). A fully-implicit second-order hybrid scheme is developed on a staggered grid, which is stretched in the streamwise coordinate direction. A full approximation multigrid scheme (FAS) based on line distributive relaxation is developed as a fast solver for the algebraic equations arising at each time step. Convergence of the process for the simplified model problem is more than two-orders of magnitude faster than other iterative methods, and the computational results show good grid convergence, with second-order accuracy, as well as qualitatively agreement with the results of other researchers.

Liu, C.↗