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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

SU-E-T-659: Quantitative Evaluation of Patient Setup Accuracy of Stereotactic Radiotherapy with the Frameless 6D-ExacTrac System Using Statistical Modeling

Purpose: To evaluate patient setup accuracy and quantify individual and cumulative positioning uncertainties associated with different hardware and software components of the stereotactic radiotherapy (SRS/SRT) with the frameless-6D-ExacTrac system. Methods: A statistical model was used to evaluate positioning uncertainties of the different components of SRS/SRT treatment with the BrainLAB 6D-ExacTrac system using the positioning shifts of 35 patients having cranial lesions (49 total lesions treated in 1, 3, 5 fractions). All these patients were immobilized with rigid head-and-neck masks, simulated with BrainLAB-localizer and planned with iPlan treatment planning system. Infrared imaging (IR) was used initially to setup patients. Then, stereoscopic x-ray images (XC) were acquired and registered to corresponding digitally-reconstructed-radiographs using bony-anatomy matching to calculate 6D-translational and rotational shifts. When the shifts were within tolerance (0.7mm and 1°), treatment was initiated. Otherwise corrections were applied and additional x-rays were acquired (XV) to verify that patient position was within tolerance. Results: The uncertainties from the mask, localizer, IR-frame, x-ray imaging, MV and kV isocentricity were quantified individually. Mask uncertainty (Translational: Lateral, Longitudinal, Vertical; Rotational: Pitch, Roll, Yaw) was the largest and varied with patients in the range (−1.05−1.50mm, −5.06–3.57mm, −5.51−3.49mm; −1.40−2.40°, −1.24−1.74°, and −2.43−1.90°) obtained from mean of XC shifts for each patient. Setup uncertainty in IR positioning (0.88,2.12,1.40mm, and 0.64,0.83,0.96°) was extracted from standard-deviation of XC. Systematic uncertainties of the localizer (−0.03,−0.01,0.03mm, and −0.03,0.00,−0.01°) and frame (0.18,0.25,−1.27mm,−0.32,0.18, and 0.47°) were extracted from means of all XV setups and mean of all XC distributions, respectively. Uncertainties in isocentricity of the MV radiotherapy machine were (0.27,0.24,0.34mm) and kV-imager (0.15,−0.4,0.21mm). Conclusion: A statistical model was developed to evaluate the individual and cumulative systematic and random uncertainties induced by the different hardware and software components of the 6D-ExacTrac-system. The immobilization mask was associated with the largest positioning uncertainty.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

An efficient computational approach to model statistical correlations in photon counting x-ray detectors

Purpose: To introduce and evaluate an increment matrix approach (IMA) describing the signal statistics of energy-selective photon counting detectors including spatial–spectral correlations between energy bins of neighboring detector pixels. The importance of the occurring correlations for image-based material decomposition is studied. Methods: An IMA describing the counter increase patterns in a photon counting detector is proposed. This IMA has the potential to decrease the number of required random numbers compared to Monte Carlo simulations by pursuing an approach based on convolutions. To validate and demonstrate the IMA, an approximate semirealistic detector model is provided, simulating a photon counting detector in a simplified manner, e.g., by neglecting count rate-dependent effects. In this way, the spatial–spectral correlations on the detector level are obtained and fed into the IMA. The importance of these correlations in reconstructed energy bin images and the corresponding detector performance in image-based material decomposition is evaluated using a statistically optimal decomposition algorithm. Results: The results of IMA together with the semirealistic detector model were compared to other models and measurements using the spectral response and the energy bin sensitivity, finding a good agreement. Correlations between the different reconstructed energy bin images could be observed, and turned out to be of weak nature. These correlations were found to be not relevant in image-based material decomposition. An even simpler simulation procedure based on the energy bin sensitivity was tested instead and yielded similar results for the image-based material decomposition task, as long as the fact that one incident photon can increase multiple counters across neighboring detector pixels is taken into account. Conclusions: The IMA is computationally efficient as it required about 10{sup 2} random numbers per ray incident on a detector pixel instead of an estimated 10{sup 8} random numbers per ray as Monte Carlo approaches would need. The spatial–spectral correlations as described by IMA are not important for the studied image-based material decomposition task. Respecting the absolute photon counts and thus the multiple counter increases by a single x-ray photon, the same material decomposition performance could be obtained with a simpler detector description using the energy bin sensitivity.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

WE-AB-202-04: Statistical Evaluation of Lung Function Using 4DCT Ventilation Imaging: Proton Therapy VS IMRT

Purpose: Variation in function of different lung regions has been ignored so far for conventional lung cancer treatment planning, which may lead to higher risk of radiation induced lung disease. 4DCT based lung ventilation imaging provides a novel yet convenient approach for lung functional imaging as 4DCT is taken as routine for lung cancer treatment. Our work aims to evaluate the impact of accounting for spatial heterogeneity in lung function using 4DCT based lung ventilation imaging for proton and IMRT plans. Methods: Six patients with advanced stage lung cancer of various tumor locations were retrospectively evaluated for the study. Proton and IMRT plans were designed following identical planning objective and constrains for each patient. Ventilation images were calculated from patients’ 4DCT using deformable image registration implemented by Velocity AI software based on Jacobian-metrics. Lung was delineated into two function level regions based on ventilation (low and high functional area). High functional region was defined as lung ventilation greater than 30%. Dose distribution and statistics in different lung function area was calculated for patients. Results: Variation in dosimetric statistics of different function lung region was observed between proton and IMRT plans. In all proton plans, high function lung regions receive lower maximum dose (100.2%–108.9%), compared with IMRT plans (106.4%–119.7%). Interestingly, three out of six proton plans gave higher mean dose by up to 2.2% than IMRT to high function lung region. Lower mean dose (lower by up to 14.1%) and maximum dose (lower by up to 9%) were observed in low function lung for proton plans. Conclusion: A systematic approach was developed to generate function lung ventilation imaging and use it to evaluate plans. This method hold great promise in function analysis of lung during planning. We are currently studying more subjects to evaluate this tool.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Statistical theory of diffusion in concentrated bcc and fcc alloys and concentration dependencies of diffusion coefficients in bcc alloys FeCu, FeMn, FeNi, and FeCr

The statistical theory of diffusion in concentrated bcc and fcc alloys with arbitrary pairwise interatomic interactions based on the master equation approach is developed. Vacancy–atom correlations are described using both the second-shell-jump and the nearest-neighbor-jump approximations which are shown to be usually sufficiently accurate. General expressions for Onsager coefficients in terms of microscopic interatomic interactions and some statistical averages are given. Both the analytical kinetic mean-field and the Monte Carlo methods for finding these averages are described. The theory developed is used to describe sharp concentration dependencies of diffusion coefficients in several iron-based alloy systems. For the bcc alloys FeCu, FeMn, and FeNi, we predict the notable increase of the iron self-diffusion coefficient with solute concentration c, up to several times, even though values of c possible for these alloys do not exceed some percent. For the bcc alloys FeCr at high temperatures T ≳ 1400 K, we show that the very strong and peculiar concentration dependencies of both tracer and chemical diffusion coefficients observed in these alloys can be naturally explained by the theory, without invoking exotic models discussed earlier.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Challenging Practices of Algebraic Battery Life Models through Statistical Validation and Model Identification via Machine-Learning

Various modeling techniques are used to predict the capacity fade of Li-ion batteries. Algebraic reduced-order models, which are inherently interpretable and computationally fast, are ideal for use in battery controllers, technoeconomic models, and multi-objective optimizations. For Li-ion batteries with graphite anodes, solid-electrolyte-interphase (SEI) growth on the graphite surface dominates fade. This fade is often modeled using physically informed equations, such as square-root of time for predicting solvent-diffusion limited SEI growth, and Arrhenius and Tafel-like equations predicting the temperature and state-of-charge rate dependencies. In some cases, completely empirical relationships are proposed. However, statistical validation is rarely conducted to evaluate model optimality, and only a handful of possible models are usually investigated. This article demonstrates a novel procedure for automatically identifying reduced-order degradation models from millions of algorithmically generated equations via bi-level optimization and symbolic regression. Identified models are statistically validated using cross-validation, sensitivity analysis, and uncertainty quantification via bootstrapping. On a LiFePO 4 /Graphite cell calendar aging data set, automatically identified models utilizing square-root, power law, stretched exponential, and sigmoidal functions result in greater accuracy and lower uncertainty than models identified by human experts, and demonstrate that previously known physical relationships can be empirically "rediscovered" using machine learning.

25 ENERGY STORAGE↗
DOE OSTI2026

Object-Based Evaluation of Dynamical and Statistical Downscaled Precipitation Products over CONUS

High-resolution precipitation data, generated through dynamical downscaling (DD) or statistical downscaling (SD) of global climate model output, provide critical information for regional climate assessment and adaptation planning. Most downscaling development and validation have focused on accurate gridscale precipitation construction and ignored the spatial structure of precipitation across model grids and at the event scale. However, many applications, e.g., hydrologic modeling and the analysis using the downscaled precipitation, require a reasonable representation of the spatial structure of precipitation within watersheds. Therefore, a set of standard metrics to evaluate the representation of the spatial structure of individual storms across diverse downscaled precipitation products is desired. To address this need, we conducted an object-based evaluation of precipitation in decades-long DD and SD products over the contiguous United States (CONUS). Specifically, we evaluate their ability to reproduce various features of precipitation objects in the observations: total volume, precipitation area, peak intensity, and spatial structure. Multiple metrics (bias, Perkins score, and nonparametric statistical tests) are used to quantify model performance. Our evaluation reveals notable variations in performance among individual products across different climate zones and seasons, as well as between extreme and nonextreme events. In general, most DD products exhibit balanced performance across the four precipitation object features, while SD products vary more significantly in their performance across products. Based on this comprehensive evaluation, we provide guidance on choosing downscaled products for specific regions, seasons, and precipitation object features. These findings and recommendations can inform precipitation-relevant modeling and analysis over CONUS, guide future downscaling technique developments, and provide actionable information for climate impact assessment and adaptation.

Downscaling↗
DOE OSTI2020

Differential credibility assessment for statistical downscaling

Climate science is increasingly using (i) ensembles of climate projections from multiple models derived using different assumptions and/or scenarios and (ii) process-oriented diagnostics of model fidelity. Efforts to assign differential credibility to projections and/or models are also rapidly advancing. A framework to quantify and depict the credibility of statistically downscaled model output is presented and demonstrated. Here, the approach employs transfer functions in the form of robust and resilient generalized linear models applied to downscale daily minimum and maximum temperature anomalies at 10 locations using predictors drawn from ERA-Interim reanalysis and two global climate models (GCM; GFDL-ESM2M and MPI-ESM-LR). The downscaled time series are used to derive several impact relevant CLIMDEX temperature indices that are assigned credibility based on (1) the reproduction of relevant large-scale predictors by the GCMs (i.e. fraction of regression beta-weights derived from predictors that are well-reproduced) and (2) the degree of variance in the observations reproduced in the downscaled series following application of a new variance inflation technique. Credibility of the downscaled predictands varies across locations, between the two GCM and is generally higher for minimum temperature than maximum temperature. The differential credibility assessment framework demonstrated here is easy to use and flexible. It can be applied as is to inform decision makers regarding projection confidence, and/or extended to include other components of the transfer functions, and/or used to weight members of a statistically downscaled ensemble.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Need for Caution in Interpreting Extreme Weather Statistics

Given the reality of anthropogenic global warming, it is tempting to seek an anthropogenic component in any recent change in the statistics of extreme weather. This paper cautions that such efforts may, however, lead to wrong conclusions if the distinctively skewed and heavy-tailed aspects of the probability distributions of daily weather anomalies are ignored or misrepresented. Departures of several standard deviations from the mean, although rare, are far more common in such a distinctively non-Gaussian world than they are in a Gaussian world. This further complicates the problem of detecting changes in tail probabilities from historical records of limited length and accuracy. A possible solution is to exploit the fact that the salient non-Gaussian features of the observed distributions are captured by so-called stochastically generated skewed (SGS) distributions that include Gaussian distributions as special cases. SGS distributions are associated with damped linear Markov processes perturbed by asymmetric stochastic noise and as such represent the simplest physically based prototypes of the observed distributions. The tails of SGS distributions can also be directly linked to generalized extreme value (GEV) and generalized Pareto (GP) distributions. The Markov process model can be used to provide rigorous confidence intervals and to investigate temporal persistence statistics. The procedure is illustrated for assessing changes in the observed distributions of daily wintertime indices of large-scale atmospheric variability in the North Atlantic and North Pacific sectors over the period 1872–2011. No significant changes in these indices are found from the first to the second half of the period.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Modeling Seasonal Sudden Stratospheric Warming Climatology Based on Polar Vortex Statistics

This report explores the climatological frequency distribution of sudden stratospheric warmings (SSWs). General circulation models (GCMs) tend to produce SSW maxima later in winter than observations, which has been considered as a model deficiency. Yet, the observed record is short, calling into question the representativeness of the observational record. To study the seasonality of SSWs and the factors behind it, the authors use observations, a long control simulation with a stratosphere resolving GCM, and also a simple statistical model that is based on the climatological seasonal cycle of the polar vortex winds. From the combined analysis, the authors conclude that the late-winter SSW maximum seen in most climate models is realistic and that observations would also have a late-winter SSW maximum if more data were available. The authors identify the seasonally varying strengths of the polar vortex and stratospheric wave driving as the two main factors behind the seasonal SSW distribution. The statistical model also indicates that there exists a continuum of weak polar vortex states and that SSWs simply form the tail of normally distributed stratospheric winds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Forced Component Estimation Statistical Method Intercomparison Project (ForceSMIP)

Anthropogenic climate change is unfolding rapidly, yet its regional manifestation can be obscured by internal variability. A primary goal of climate science is to identify the externally forced climate response from among the noise of internal variability. Separating the forced response from internal variability can be addressed in climate models by using a large ensemble to average over different possible realizations of internal variability. However, with only one realization of the real world, it is a major challenge to isolate the forced response directly in observations. In the Forced Component Estimation Statistical Method Intercomparison Project (ForceSMIP), contributors used existing and newly developed statistical and machine learning methods to estimate the forced response over 1950–2022 within individual realizations of the climate system. Participants used neural networks, linear inverse models, fingerprinting methods, and low-frequency component analysis, among other approaches. These methods were trained using large ensembles from multiple climate models and then applied to observations. Here, we evaluate method performance within large ensembles and investigate the estimates of the forced response in observations. Our results show that many different types of methods are skillful for estimating the forced response in climate models, though the relative skill of individual methods varies depending on the variable and evaluation metric. Methods with comparable skill in models can give a wide range of estimates of the forced response pattern in observations, illustrating the epistemic uncertainty in forced response estimates. ForceSMIP gives new insights into the forced response in observations, its uncertainty, and methods for its estimation.

Climate attribution↗
DOE OSTI2019

Accurately estimating and minimizing costs for the cellulosic biomass supply chain with statistical process control and the Taguchi Loss Function

This research focuses on the statistical evaluation of the feedstock attributes of the biomass supply chain and the estimation of attribute costs as a function of the feedstock variability. Challenges of using cellulosic feedstocks include the variability of feedstock quality (e.g., ash content and moisture content), which impacts the final cost of the manufactured product. Statistical Process Control (SPC), Taguchi Loss Function, and components of variance techniques were illustrated for quantifying cumulative variance in the biomass supply chain. Costs in the presence of cumulative variance were estimated for switchgrass (Panicum virgatum L.) and loblolly pine residues (Pinus taeda L.). Findings of the study indicated that additional costs from ash content variability in switchgrass increased the net cost by $\$19.15$ per dry tonne. Additional costs from densification due to particle size variation increased net cost by $\$11.59$ per dry tonne. Moisture content variation increased costs by $\$14.86$ per dry tonne. This would represent a 50 to 100% increase in costs due to variation based on a $\$60$ to $\$70$ per dry tonne manufactured product cost. This study illustrates that total costs may be considerably underestimated if the influence of variance for key factors in the supply chain and associated costs are not estimated.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Statistical Projections for Multi-resolution, Multi-dimensional Visual Data Exploration and Analysis

An ongoing challenge in visual exploration and analysis of large, multi-dimensional datasets is how to present useful, concise information to a user for some specific visualization tasks. Typical approaches to this problem have proposed either reduced-resolution versions of data, or projections of data, or both. These approaches still have some limitations such as consuming high computation or suffering from errors. In this work, we explore the use of a statistical metric as the basis for both projections and reduced-resolution versions of data, with a particular focus on preserving one key trait in data, namely variation. We use two different case studies to explore this idea, one that uses a synthetic dataset, and another that uses a large ensemble collection produced by an atmospheric modeling code to study long-term changes in global precipitation. The primary findings of our work are that in terms of preserving the variation signal inherent in data, that using a statistical measure more faithfully preserves this key characteristic across both multi-dimensional projections and multi-resolution representations than a methodology based upon averaging.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Integrated Data Collection Analysis (IDCA) Program - Statistical Analysis of RDX Standard Data Sets

The Integrated Data Collection Analysis (IDCA) program is conducting a Proficiency Test for Small- Scale Safety and Thermal (SSST) testing of homemade explosives (HMEs). Described here are statistical analyses of the results for impact, friction, electrostatic discharge, and differential scanning calorimetry analysis of the RDX Type II Class 5 standard. The material was tested as a well-characterized standard several times during the proficiency study to assess differences among participants and the range of results that may arise for well-behaved explosive materials. The analyses show that there are detectable differences among the results from IDCA participants. While these differences are statistically significant, most of them can be disregarded for comparison purposes to assess potential variability when laboratories attempt to measure identical samples using methods assumed to be nominally the same. The results presented in this report include the average sensitivity results for the IDCA participants and the ranges of values obtained. The ranges represent variation about the mean values of the tests of between 26% and 42%. The magnitude of this variation is attributed to differences in operator, method, and environment as well as the use of different instruments that are also of varying age. The results appear to be a good representation of the broader safety testing community based on the range of methods, instruments, and environments included in the IDCA Proficiency Test.

round-robin test↗
DOE OSTI2016

A Statistical Testing Approach for Quantifying Software Reliability; Application to an Example System

The U.S. Nuclear Regulatory Commission (NRC) encourages the use of probabilistic risk assessment (PRA) technology in all regulatory matters, to the extent supported by the state-of-the-art in PRA methods and data. Although much has been accomplished in the area of risk-informed regulation, risk assessment for digital systems has not been fully developed. The NRC established a plan for research on digital systems to identify and develop methods, analytical tools, and regulatory guidance for (1) including models of digital systems in the PRAs of nuclear power plants (NPPs), and (2) incorporating digital systems in the NRC's risk-informed licensing and oversight activities. Under NRC's sponsorship, Brookhaven National Laboratory (BNL) explored approaches for addressing the failures of digital instrumentation and control (I and C) systems in the current NPP PRA framework. Specific areas investigated included PRA modeling digital hardware, development of a philosophical basis for defining software failure, and identification of desirable attributes of quantitative software reliability methods. Based on the earlier research, statistical testing is considered a promising method for quantifying software reliability. This paper describes a statistical software testing approach for quantifying software reliability and applies it to the loop-operating control system (LOCS) of an experimental loop of the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL).

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

MIDAS: Modeling Individual Differences using Advanced Statistics

This research explores novel methods for extracting relevant information from EEG data to characterize individual differences in cognitive processing. Our approach combines expertise in machine learning, statistics, and cognitive science, advancing the state-of-the art in all three domains. Specifically, by using cognitive science expertise to interpret results and inform algorithm development, we have developed a generalizable and interpretable machine learning method that can accurately predict individual differences in cognition. The output of the machine learning method revealed surprising features of the EEG data that, when interpreted by the cognitive science experts, provided novel insights to the underlying cognitive task. Additionally, the outputs of the statistical methods show promise as a principled approach to quickly find regions within the EEG data where individual differences lie, thereby supporting cognitive science analysis and informing machine learning models. This work lays methodological ground work for applying the large body of cognitive science literature on individual differences to high consequence mission applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Advances in statistical methods for cancer surveillance research: an age-period-cohort perspective

Background: Analysis of Lexis diagrams (population-based cancer incidence and mortality rates indexed by age group and calendar period) requires specialized statistical methods. However, existing methods have limitations that can now be overcome using new approaches. Methods: We assembled a “toolbox” of novel methods to identify trends and patterns by age group, calendar period, and birth cohort. We evaluated operating characteristics across 152 cancer incidence Lexis diagrams compiled from United States (US) Surveillance, Epidemiology and End Results Program data for 21 leading cancers in men and women in four race and ethnicity groups (the “cancer incidence panel”). Results: Nonparametric singular values adaptive kernel filtration (SIFT) decreased the estimated root mean squared error by 90% across the cancer incidence panel. A novel method for semi-parametric age-period-cohort analysis (SAGE) provided optimally smoothed estimates of age-period-cohort (APC) estimable functions and stabilized estimates of lack-of-fit (LOF). SAGE identified statistically significant birth cohort effects across the entire cancer panel; LOF had little impact. As illustrated for colon cancer, newly developed methods for comparative age-period-cohort analysis can elucidate cancer heterogeneity that would otherwise be difficult or impossible to discern using standard methods. Conclusions: Cancer surveillance researchers can now identify fine-scale temporal signals with unprecedented accuracy and elucidate cancer heterogeneity with unprecedented specificity. Birth cohort effects are ubiquitous modulators of cancer incidence in the US. The novel methods described here can advance cancer surveillance research.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

A STATISTICAL ANALYSIS OF 50 YEARS OF DAILY SOLAR WIND VELOCITY DATA

Daily mean value of solar wind velocity measured by various spacecraft near the Earth’s orbit from 1963 November 27 to 2013 December 31 given by OMNIWeb is utilized to investigate its characteristics through statistical analyses. The percent probability distribution of solar wind velocity can be described well by the Γ distribution function with the most probable velocity to be 373 km s{sup -1}. It is found that solar wind could be statistically classified into three groups: (1) the low-velocity wind, v<450 km s{sup -1}, which positively responds to, and in the cycle phase lags, the solar activity cycle; (2) the high-velocity wind, 450≤v<725 km s{sup -1}, which negatively responds to, and in the cycle phase leads, the solar activity cycle, but in a short timescale (one-rotation-period) lags the solar activity cycle; and (3) the extreme-high-velocity wind, v≥725 km s{sup -1}, which positively responds to, and in cycle phase leads, the solar activity cycle. A period of about 27 days is determined for solar wind in the first two groups, so that solar wind in the groups is modulated by the solar rotation and related with solar long-life magnetic structures. Solar wind of extreme high velocity appears mainly at the descending phases of the solar cycles.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

EVOLUTION OF THE MAGNETIC FIELD LINE DIFFUSION COEFFICIENT AND NON-GAUSSIAN STATISTICS

The magnetic field line random walk (FLRW) plays an important role in the transport of energy and particles in turbulent plasmas. For magnetic fluctuations that are transverse or almost transverse to a large-scale mean magnetic field, theories describing the FLRW usually predict asymptotic diffusion of magnetic field lines perpendicular to the mean field. Such theories often depend on the assumption that one can relate the Lagrangian and Eulerian statistics of the magnetic field via Corrsin’s hypothesis, and additionally take the distribution of magnetic field line displacements to be Gaussian. Here we take an ordinary differential equation (ODE) model with these underlying assumptions and test how well it describes the evolution of the magnetic field line diffusion coefficient in 2D+slab magnetic turbulence, by comparisons to computer simulations that do not involve such assumptions. In addition, we directly test the accuracy of the Corrsin approximation to the Lagrangian correlation. Over much of the studied parameter space we find that the ODE model is in fairly good agreement with computer simulations, in terms of both the evolution and asymptotic values of the diffusion coefficient. When there is poor agreement, we show that this can be largely attributed to the failure of Corrsin’s hypothesis rather than the assumption of Gaussian statistics of field line displacements. The degree of non-Gaussianity, which we measure in terms of the kurtosis, appears to be an indicator of how well Corrsin’s approximation works.

79 ASTRONOMY AND ASTROPHYSICS↗