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DOE OSTI2023

Investigations of correlation and coherence in turbulence from a large-eddy simulation

Abstract. Microscale flow descriptions are often given in terms of mean quantities, turbulent kinetic energy, and/or stresses. Those metrics, while valuable, give limited information about turbulent eddies and coherent turbulent structures. This work investigates the structure of an atmospheric boundary layer using coherence and correlation in space and time with a range of separation distances. We calculate spatial correlations over entire planes of velocity fluctuations, from which we can evaluate the correlation along different directions at different spacings. Similarly, coherence of the three velocity components over separations in the three directions is also investigated. We apply these analyses to a mesoscale–microscale coupled scenario with time-varying conditions and examine nuances in spatial correlations that are often overlooked. Through these analyses and results, this work highlights important differences observed in terms of coherence when comparing large-eddy simulation data to simpler models and suggests ways to improve these simpler models. We note that such differences are important for disciplines like wind energy structural dynamic analysis, in which blade loading and fatigue depend strongly on the structure of the turbulence. We emphasize the additional wealth of data that can be provided by typical atmospheric boundary layer large-eddy simulation when correlation and coherence analysis is included, and we also state the limitations of large-eddy simulation data, which inherently truncate the smaller scales of turbulence.

17 WIND ENERGY↗

DOE OSTI2024

Elimination LArTPC Simulation Uncertainty

Liquid Argon Time Projection Chambers (LArTPC) are essential for detecting muons and neutrinos by capturing electrons released during particle collisions, which drift toward wire planes under an electric field and induce currents measured to reconstruct particle paths. However, LArTPCs face challenges from effects such as electron-ion recombination, electron diffusion, and electron attenuation, complicating data simulation. The Short Baseline Neutrino (SNB) detector aims to measure neutrinos before oscillation occurs. To bridge the gap between simulation and actual data, we propose modifying the amplitude and width of signals on the TPC wires, addressing uncertainties by adjusting signal characteristics to better match observed data. A Gaussian fit to current waveforms produces hits with associated charge and width, and by comparing data and simulated values, discrepancies highlight areas where the model fails. Initial results indicate the current modification algorithm may increase divergence between simulation and data, necessitating further refinement. A discovered bug in the WireModMakeHist_plug.cpp file, which incorrectly computed simulation and data ratios, underscores the need for precise algorithm adjustments. Future work involves correcting code errors, fine-tuning the model, and conducting multiple simulation runs to enhance statistical confidence and reduce uncertainties, ultimately aiming for accurate LArTPC operation and reliable neutrino detection.

Mkrtchyan, Ka'ren↗

DOE OSTI2015

Constraints on cosmological parameters in power-law cosmology

In this paper, we examine observational constraints on the power law cosmology; essentially dependent on two parameters H{sub 0} (Hubble constant) and q (deceleration parameter). We investigate the constraints on these parameters using the latest 28 points of H(z) data and 580 points of Union2.1 compilation data and, compare the results with the results of ΛCDM . We also forecast constraints using a simulated data set for the future JDEM, supernovae survey. Our studies give better insight into power law cosmology than the earlier done analysis by Kumar [arXiv:1109.6924] indicating it tuning well with Union2.1 compilation data but not with H(z) data. However, the constraints obtained on and i.e. H{sub 0} average and q average using the simulated data set for the future JDEM, supernovae survey are found to be inconsistent with the values obtained from the H(z) and Union2.1 compilation data. We also perform the statefinder analysis and find that the power-law cosmological models approach the standard ΛCDM model as q → −1. Finally, we observe that although the power law cosmology explains several prominent features of evolution of the Universe, it fails in details.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Integrated, Interoperable Software Environment for Fusion Simulation and Data Analysis Tools SBIR Phase II

This SBIR effort was focused on developing a production ready system to address the integration and interoperability challenges with analysis and visualization in fusion simulations. Our overarching technical objective was to minimize the code development simulation scientists incur when coupling their simulation codes with different analysis frameworks. To this end, we developed an open-source software library to make data exchange between application easier and a Web application to manage and display analysis extracts from simulations. We have also augmented existing libraries funded by DOE such as ADIOS and VTK-m. When used together, these make it significantly easier to integrate simulation and analysis capability. We demonstrated the flexibility of our approach using two common simulation codes in the fusion community, XGC1 and GTC.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2015

MCNP6. Simulating Correlated Data in Fission Events

This report is a series of slides discussing the MCNP6 code and its status in simulating fission. Applications of interest include global security and nuclear nonproliferation, detection of special nuclear material (SNM), passive and active interrogation techniques, and coincident neutron and photon leakage.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2026

DUNE: Michel Electron Selection with SPINE

This poster details the evaluation of a Michel electron selection algorithm centered on the neural network-based software SPINE (Scalable Particle Imageing with Neural Embeddings). The algorithm was developed and calibrated using simulated data from the SBND (Short-Baseline Neutrino Detector) experiment before being applied to simulated data from DUNE (Deep Underground Neutrino Experiment).

Wilson, Dante [U. Colorado, Boulder]↗

DOE OSTI2019

Robust Predictive Design of Field Measurements for Evapotranspiration Barriers Using Universal Multiple linear Regression

Surface barriers are normally installed to reduce downward water movement into contaminated zones. Specifically, evapotranspiration (ET) barriers are used to store water and release it, via ET, before it can percolate into an underlying waste zone. To assess the effectiveness of a surface barrier, we used an existing data set, model-simulated data, and a dimensionality reduction approach called universal multiple linear regression (uMLR) to optimize the required number of sensors in a 2-m thick surface barrier. To understand the usefulness of implementing predictive uMLR to accommodate multiple monitoring objectives, we compare several network designs, selected based on down-sampling of existing data, with a recommended sensor design based on model simulations performed without consideration of existing data. We further added consideration of “fuzzy” design, which allows more practical guidelines for field implementation of uMLR. We discovered that uMLR, combined with robust decision-making, provides a simple, flexible, and high-quality network design for monitoring the total water stored in a surface barrier across multiple uncertain conditions.

58 GEOSCIENCES↗

DOE OSTI2018

Parametric Reconstruction of Glass Fiber-reinforced Polymer Composites from X-ray Projection Data—A Simulation Study

We present a new approach to estimate geometry parameters of glass fibers in glass fiber-reinforced polymers from simulated X-ray micro-computed tomography scans. Traditionally, these parameters are estimated using a multi-step procedure including image reconstruction, pre-processing, segmentation and analysis of features of interest. Each step in this chain introduces errors that propagate through the pipeline and impair the accuracy of the estimated parameters. In the approach presented in this paper, we reconstruct volumes from a low number of projection angles using an iterative reconstruction technique and then estimate position, direction and length of the contained fibers incorporating a priori knowledge about their shape, modeled as a geometric representation, which is then optimized. Using simulation experiments, we show that our method can estimate those representations even in presence of noisy data and only very few projection angles available.

42 ENGINEERING↗

DOE OSTI2016

A unified relation for the solid-liquid interface free energy of pure FCC, BCC, and HCP metals

We study correlations between the solid-liquid interface (SLI) free energy and bulk material properties (melting temperature, latent heat, and liquid structure) through the determination of SLI free energies for bcc and hcp metals from molecular dynamics (MD) simulation. Values obtained for the bcc metals in this study were compared to values predicted by the Turnbull, Laird, and Ewing relations on the basis of previously published MD simulation data. We found that of these three empirical relations, the Ewing relation better describes the MD simulation data. Moreover, whereas the original Ewing relation contains two constants for a particular crystal structure, we found that the first coefficient in the Ewing relation does not depend on crystal structure, taking a common value for all three phases, at least for the class of the systems described by embedded-atom method potentials (which are considered to provide a reasonable approximation for metals).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Solovay-Kitaev Algorithm and Randomized Compilation Data Availability

This zipped folder contains simulation notebooks, simulated data, and experimental data from the QSCOUT trapped-ion device that were used in the publication "Solovay-Kitaev Algorithm and Randomized Compilation" (https://doi.org/10.1103/ll6m-dbl7). The raw data is in the form of measurement outcomes of simple tomographic quantum circuits that were executed on the QSCOUT device and simulated using JAQALPAQ. These data are used to create plots within the jupyter notebooks that were included in the publication.

Quantum benchmarking↗

DOE OSTI2024

Sampling Size Optimization for Bioburden Density Estimation in Planetary Protection

Planetary protection (PP) is a discipline that focuses on minimizing the biological contamination of spacecraft to ensure compliance with international policy. Precise estimation of bioburden - the total number of microbes in or on spacecraft hardware – and the bioburden density are of utmost importance for PP. Such estimation is the way concordance with requirements is demonstrated, and it is critical for quantifying the potential risk of inadvertently contaminating other planetary bodies. Although a suite of molecular techniques have been used to thoroughly characterize and profile the microbiome of various cleanroom environments and spacecraft, the gold standard remains the physical enumeration of microbes via culturing of samples directly taken from spacecraft and associated surfaces. However, due to technical, budgetary, and programmatic constraints, only a manageable portion (around 10%) of the entire spacecraft surface is directly sampled with cotton swabs or wipes. To generate the bioburden current best estimate (CBE) for components not directly verifiable, the accepted approach is to apply a NASA-defined bioburden estimate based on the components’ manufacturing or assembly environment. This approach utilizes a prespecified bioburden density estimation that applies a maximum value across the total surface area of the specified component. For hardware components that underwent similar assembly processes, an implied bioburden is adopted for all components, based on a direct verification of a representative component within the same lot. Once all components have a CBE, the bioburden estimates are generated. In previous publication [ 1], we have shown that statistical risks quantifying the accuracy of the estimates for sampled, prespecified, and implied components can be derived and ranked. For mean squared error (MSE) function, the risks are available analytically and hence a cost function can be obtained to optimize the risks with respect to the sampling area and sampling cost. Since the sampling area and sampling cost are two complimentary variables, their sum will have a well-defined minimum. This paper presents the multivariate optimization of the integrated risk of an empirical Bayes estimator to determine the optimal sampling schedule for a given number of components. It is assumed that given a number of components, N, the bioburden density for each component can either be sampled, implied, or prespecified. The multivariate optimization searches through different options to sample, imply or prespecify the bioburden density for a component, and account for the component’s surface area and cost of sampling. The idea of the optimization is based on the observation that the statistical risk of using an estimator is a monotonically decreasing function of the sampled area. The larger the sampled area, the lower the risk of using the estimator as the estimator becomes more and more accurate as the sampling area increases. On the other hand, the cost of sampling is monotonically increasing as the sampled surface grows. This makes the risk and total cost of sampling complimentary variables which can be counterbalanced to achieve an optimal overall value with respect to the sampled surface. In this paper, the integrated risk has been used to quantify the accuracy of the estimator. This risk has been selected because it depends on neither the true value of the parameter nor on the collected data. The cost of each sample was also available to obtain the total cost of sampling of N components. The paper will present the results based on computer-simulated data as well as the data collected during the InSight mission. The computer-simulated data have N components with randomly generated total areas and each component assigned to one of the three categories according to the method of estimating of bioburden density: sampled, implied, or prespecified. The cost of sampling is also available. The cost of sampling is estimated based on a cost model provided by the planetary protection group at JPL. For this paper, the overall cost was assumed to be a linear function of exposure. The optimization process finds the allocation of the components to the three categories that minimizes the tradeoff between integrated risk and total cost. For the InSight data, a set of components is selected representing all three categories, and optimization is performed to determine if the performed allocation was optimal or if a better allocation could have been obtained. To the best of our knowledge, this work is the first attempt not only perform an accurate estimation of bioburden density but also do it in an optimal way.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2014

The R-factor gap in macromolecular crystallography: an untapped potential for insights on accurate structures

In macromolecular crystallography, the agreement between observed and predicted structure factors (Rcryst and Rfree) is seldom better than 20%. This is much larger than the estimate of experimental error (Rmerge). The difference between Rcryst and Rmerge is the R-factor gap. There is no such gap in small-molecule crystallography, for which calculated structure factors are generally considered more accurate than the experimental measurements. Perhaps the true noise level of macromolecular data is higher than expected? Or is the gap caused by inaccurate phases that trap refined models in local minima? By generating simulated diffraction patterns using the program MLFSOM, and including every conceivable source of experimental error, we show that neither is the case. Processing our simulated data yielded values that were indistinguishable from those of real data for all crystallographic statistics except the final Rcryst and Rfree. These values decreased to 3.8% and 5.5% for simulated data, suggesting that the reason for high R-factors in macromolecular crystallography is neither experimental error nor phase bias, but rather an underlying inadequacy in the models used to explain our observations. The present inability to accurately represent the entire macromolecule with both its flexibility and its proteinsolvent interface may be improved by synergies between small-angle X-ray scattering, computational chemistry and crystallography. The exciting implication of our finding is that macromolecular data contain substantial hidden and untapped potential to resolve ambiguities in the true nature of the nanoscale, a task that the second century of crystallography promises to fulfill.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2016

Online Monitoring and Prognostics for Passive Components in Nuclear Power Plants

This paper describes research towards developing prognostics technologies for light-water nuclear power reactors components. The focus of this paper is on passive components (those that do not need to change state or move to perform their function), though the technologies are applicable to other classes of components as well. A prototypic failure mechanism (high-cycle fatigue) is used to focus the efforts and provide context for the development effort. A Bayesian framework is proposed for the prognostics of remaining useful life, and applied to simulated data sets representing nondestructive measurements of high-cycle fatigue damage. Initial results of prognostics, based on simulated data sets are presented.

Ramuhalli, Pradeep↗

DOE OSTI2017

Partnership for Edge Physics Simulation

A major goal of our participation in the Edge Physics Simulation project has been to contribute to the understanding of the self-organization of tokamak turbulence fluctuations resulting in the formation of a staircase structure in the ion temperature. A second important goal is to demonstrate how small scale turbulence in plasmas self-organizes with dynamically driven quasi-stationary flow shear. These goals have been accomplished through the analyses of the statistical properties of XGC1 flux driven Gyrokinetic electrostatic ion temperature gradient (ITG) turbulence simulation data in which neutrals are included. The ITG turbulence data, and in particular fluctuation data, were obtained from a massively parallel flux-driven gyrokinetic full-f particle-in-cell simulation of a DIII-D like equilibrium. Below some the findings are summarized. It was observed that the emergence of staircase structure is related to the variations in the normalized temperature gradient length (R/LT) and the poloidal flow shear. Average turbulence intensity is found to be large in the vicinity of minima in R/LTi, where ITG growth is expected to be lower. The distributions of the occurrences of potential fluctuation are found to be Gaussian away from the staircase-step locations, but they are found to be non-Gaussian in the vicinity of staircase-step locations. The results of analytically derived expressions for the distribution of the occurrences of turbulence intensity and intensity flux were compared with the corresponding quantities computed using XGC1 simulation data and good agreement is found. The derived expressions predicts inward and outward propagation of turbulence intensity flux in an intermittent fashion. The outward propagation of turbulence intensity flux occurs at staircase-step locations and is related to the change in poloidal flow velocity shear and to the change in the ion temperature gradient. The standard deviation, skewness and kurtosis for turbulence quantities were computed and found to be large in the vicinity of the staircase-step structures. Large values of skewness and kurtosis can be explained by a temporary opening and closing of the structure which allows turbulence intensity events to propagate. The staircase patterns may reduce the ion heat transport and a manipulation of these patterns may be used to optimize heat transport in tokamaks. An additional objective of the research in support of the Edge Physics Simulation initiative has been to improve the understanding of scrape-off layer thermal transport. In planning experiments and designing future tokamaks, it is important to understand the physical effects that contribute to divertor heat-load fluxes. The research accomplished will contribute to developing new models for the scrape-off layer region. The XGC0 code was used to compute the heat fluxes and the heat-load width in the outer divertor plates of C-Mod and DIII-D tokamaks. It was observed that the width of the XGC0 neoclassical heat-load was approximately inversely proportional to the total plasma current. Anomalous transport in the H-mode pedestal region of five Alcator C-Mod discharges, representing a collisionality scan, was analyzed. The understanding of anomalous transport in the pedestal region is important for the development of a comprehensive model for the H-mode pedestal slope. It was found that the electron thermal anomalous diffusivities at the pedestal top increase with the electron collisionality. This dependence can point to the DRIBM as the modes that drive the anomalous transport in the plasma edge of highly collisional discharges. The effects of plasma shaping on the H-mode pedestal structure was also investigated. The differences in the predicted H-mode pedestal width and height for the DIII-D discharges with different elongation and triangularities were discussed. For the discharges with higher elongation, it was found that the gradients of the plasma profiles in the H-mode pedestal reach semi-steady states. In these simulations, the pedestal slowly continued to evolve to higher pedestal pressures and bootstrap currents until the peeling ballooning stability conditions were satisfied. The discharges with lower elongation do not reach the semi-steady state, and ELM crashes were triggered at earlier times. The plasma elongation was found to have a stronger stabilizing effect than the plasma triangularity. For the discharges with lower elongation and lower triangularity, the ELM frequency was large, and the H-mode pedestal evolves rapidly. It was found that the temperature of neutrals in the scrape-off-layer region can affect the dynamics of the H-mode pedestal buildup. However, the final pedestal profiles were nearly independent of the neutral temperature. The elongation and triangularity affected the pedestal widths of plasma density and electron temperature profiles differently. This study illustrated a new mechanism for controlling the pedestal bootstrap current and the pedestal stability.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Identification of Plasma Current Center by Neural Network Inference in EAST

For the efficient and safe operation of experimental advanced superconducting tokamak (EAST), it is necessary to accurately identify and control the plasma current and its central position. In this article, neural network is used to identify the position of the plasma current center. The model trained by the basic back-propagation neural network can well match the relationship between the electromagnetic diagnostic signals and plasma current center positions. Both noisy simulation data and experimental data are applied to train and test the neural network inference model. Adding 0.1% noise to the training data is proven to improve the noise immunity of the inference model. Basic neural networks trained with both noisy simulation data and actual experimental data show good results with sufficient inputs; however, in both cases, the performance degrades significantly when only the poloidal field coil currents are given as inputs. For this kind of time-series problem, the dynamic neural network containing delay and feedback architecture is introduced, and an improved model requiring much fewer inputs is trained and tested for current center inference. Furthermore, some parameters of this model are compared and analyzed in this article. With suitable neural network architecture, the mapping between the controlled variables (poloidal field coil currents) and response variables (plasma current center) can be well-established.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Human reliability analysis studies from simulator experiments using Bayesian inference

Probabilistic Safety Assessment (PSA) of complex facilities is performed to arrive at the risk posed by them. PSA also accounts for the contribution of the human errors towards the overall risk through Human Reliability Analysis (HRA) in terms of Human Error Probability (HEP). Human operators are part of the system and do not work in isolation. Their performance is influenced by the context in which the actions are performed. As a result, quantification of HEP requires operator performance data under the given context. Some good sources of operator performance data are plant‘s operation data, simulator data and expert judgement. The plant operation data pertaining to HRA is generally sparse. In this situation, a full scope plant simulator provides a good alternative for operator performance data generation. Many of the currently practiced HRA methods have been developed by combining the empirical evidence with expert judgement and contain a lot of uncertainty in their estimates. Bayesian inference is suitable for updating the prior HRA estimates with the simulator evidence to obtain the posterior HEP. Here, posterior HEP has been calculated for postulated accident scenarios in advanced reactor (first of its kind) at design stage, using plant simulator.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Considerations for three-dimensional image reconstruction from experimental data in coherent diffractive imaging

Diffraction before destruction using X-ray free-electron lasers (XFELs) has the potential to determine radiation-damage-free structures without the need for crystallization. This article presents the three-dimensional reconstruction of the Melbournevirus from single-particle X-ray diffraction patterns collected at the LINAC Coherent Light Source (LCLS) as well as reconstructions from simulated data exploring the consequences of different kinds of experimental sources of noise. The reconstruction from experimental data suffers from a strong artifact in the center of the particle. This could be reproduced with simulated data by adding experimental background to the diffraction patterns. In those simulations, the relative density of the artifact increases linearly with background strength. This suggests that the artifact originates from the Fourier transform of the relatively flat background, concentrating all power in a central feature of limited extent. We support these findings by significantly reducing the artifact through background removal before the phase-retrieval step. Large amounts of blurring in the diffraction patterns were also found to introduce diffuse artifacts, which could easily be mistaken as biologically relevant features. Other sources of noise such as sample heterogeneity and variation of pulse energy did not significantly degrade the quality of the reconstructions. Larger data volumes, made possible by the recent inauguration of high repetition-rate XFELs, allow for increased signal-to-background ratio and provide a way to minimize these artifacts. The anticipated development of three-dimensional Fourier-volume-assembly algorithms which are background aware is an alternative and complementary solution, which maximizes the use of data.

47 OTHER INSTRUMENTATION↗

DOE OSTI2014

Adapting SAFT-γ perturbation theory to site-based molecular dynamics simulation. II. Confined fluids and vapor-liquid interfaces

In this work, a new classical density functional theory is developed for group-contribution equations of state (EOS). Details of implementation are demonstrated for the recently-developed SAFT-γ WCA EOS and selective applications are studied for confined fluids and vapor-liquid interfaces. The acronym WCA (Weeks-Chandler-Andersen) refers to the characterization of the reference part of the third-order thermodynamic perturbation theory applied in formulating the EOS. SAFT-γ refers to the particular form of “statistical associating fluid theory” that is applied to the fused-sphere, heteronuclear, united-atom molecular models of interest. For the monomer term, the modified fundamental measure theory is extended to WCA-spheres. A new chain functional is also introduced for fused and soft heteronuclear chains. The attractive interactions are taken into account by considering the structure of the fluid, thus elevating the theory beyond the mean field approximation. The fluctuations of energy are also included via a non-local third-order perturbation theory. The theory includes resolution of the density profiles of individual groups such as CH{sub 2} and CH{sub 3} and satisfies stoichiometric constraints for the density profiles. New molecular simulations are conducted to demonstrate the accuracy of each Helmholtz free energy contribution in reproducing the microstructure of inhomogeneous systems at the united-atom level of coarse graining. At each stage, comparisons are made to assess where the present theory stands relative to the current state of the art for studying inhomogeneous fluids. Overall, it is shown that the characteristic features of real molecular fluids are captured both qualitatively and quantitatively. For example, the average pore density deviates ∼2% from simulation data for attractive pentadecane in a 2-nm slit pore. Another example is the surface tension of ethane/heptane mixture, which deviates ∼1% from simulation data while the theory reproduces the excess accumulation of ethane at the interface.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗