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All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

Investigation of the asymptotic state of rotating turbulence using large-eddy simulation

Study of turbulent flows in rotating reference frames has long been an area of considerable scientific and engineering interest. Because of its importance, the subject of turbulence in rotating reference frames has motivated over the years a large number of theoretical, experimental, and computational studies. The bulk of these previous works has served to demonstrate that the effect of system rotation on turbulence is subtle and remains exceedingly difficult to predict. A rotating flow of particular interest in many studies, including the present work, is examination of the effect of solid-body rotation on an initially isotropic turbulent flow. One of the principal reasons for the interest in this flow is that it represents the most basic turbulent flow whose structure is altered by system rotation but without the complicating effects introduced by mean strains or flow inhomogeneities. The assumption of statistical homogeneity considerably simplifies analysis and computation. The principal objective of the present study has been to examine the asymptotic state of solid-body rotation applied to an initially isotropic, high Reynolds number turbulent flow. Of particular interest has been to determine the degree of two-dimensionalization and the existence of asymptotic self-similar states in homogeneous rotating turbulence.

Squires, Kyle D.↗
NASA NTRS2008

Sensor Selection and Data Validation for Reliable Integrated System Health Management

For new access to space systems with challenging mission requirements, effective implementation of integrated system health management (ISHM) must be available early in the program to support the design of systems that are safe, reliable, highly autonomous. Early ISHM availability is also needed to promote design for affordable operations; increased knowledge of functional health provided by ISHM supports construction of more efficient operations infrastructure. Lack of early ISHM inclusion in the system design process could result in retrofitting health management systems to augment and expand operational and safety requirements; thereby increasing program cost and risk due to increased instrumentation and computational complexity. Having the right sensors generating the required data to perform condition assessment, such as fault detection and isolation, with a high degree of confidence is critical to reliable operation of ISHM. Also, the data being generated by the sensors needs to be qualified to ensure that the assessments made by the ISHM is not based on faulty data. NASA Glenn Research Center has been developing technologies for sensor selection and data validation as part of the FDDR (Fault Detection, Diagnosis, and Response) element of the Upper Stage project of the Ares 1 launch vehicle development. This presentation will provide an overview of the GRC approach to sensor selection and data quality validation and will present recent results from applications that are representative of the complexity of propulsion systems for access to space vehicles. A brief overview of the sensor selection and data quality validation approaches is provided below. The NASA GRC developed Systematic Sensor Selection Strategy (S4) is a model-based procedure for systematically and quantitatively selecting an optimal sensor suite to provide overall health assessment of a host system. S4 can be logically partitioned into three major subdivisions: the knowledge base, the down-select iteration, and the final selection analysis. The knowledge base required for productive use of S4 consists of system design information and heritage experience together with a focus on components with health implications. The sensor suite down-selection is an iterative process for identifying a group of sensors that provide good fault detection and isolation for targeted fault scenarios. In the final selection analysis, a statistical evaluation algorithm provides the final robustness test for each down-selected sensor suite. NASA GRC has developed an approach to sensor data qualification that applies empirical relationships, threshold detection techniques, and Bayesian belief theory to a network of sensors related by physics (i.e., analytical redundancy) in order to identify the failure of a given sensor within the network. This data quality validation approach extends the state-of-the-art, from red-lines and reasonableness checks that flag a sensor after it fails, to include analytical redundancy-based methods that can identify a sensor in the process of failing. The focus of this effort is on understanding the proper application of analytical redundancy-based data qualification methods for onboard use in monitoring Upper Stage sensors.

Garg, Sanjay↗
NASA NTRS2013

The Influence of Observation Errors on Analysis Error and Forecast Skill Investigated with an Observing System Simulation Experiment

The Global Modeling and Assimilation Office (GMAO) observing system simulation experiment (OSSE) framework is used to explore the response of analysis error and forecast skill to observation quality. In an OSSE, synthetic observations may be created that have much smaller error than real observations, and precisely quantified error may be applied to these synthetic observations. Three experiments are performed in which synthetic observations with magnitudes of applied observation error that vary from zero to twice the estimated realistic error are ingested into the Goddard Earth Observing System Model (GEOS-5) with Gridpoint Statistical Interpolation (GSI) data assimilation for a one-month period representing July. The analysis increment and observation innovation are strongly impacted by observation error, with much larger variances for increased observation error. The analysis quality is degraded by increased observation error, but the change in root-mean-square error of the analysis state is small relative to the total analysis error. Surprisingly, in the 120 hour forecast increased observation error only yields a slight decline in forecast skill in the extratropics, and no discernable degradation of forecast skill in the tropics.

Observation Error↗
NASA NTRS2017

Cancer Risk in Astronauts: A Constellation of Uncommon Consequences

Excess cancers resulting from external radiation exposures have been noted since the early 1950s, when a rise in leukemia rates was first reported in young atomic bomb survivors [1]. Further studies in atomic bomb survivors, cancer patients treated with radiotherapy, and nuclear power plant workers have confirmed that radiation exposure increases the risk of not only leukemia, but also a wide array of solid cancers [2,3]. NASA has long been aware of this risk and limits astronauts' risk of exposure-induced death (REID) from cancer by specifying permissible mission durations (PMD) for astronauts on an individual basis. While cancer is present among astronauts, current data does not suggest any excess of known radiation-induced cancers relative to a comparable population of U.S. adults; however, very uncommon cancers have been diagnosed in astronauts including nasopharyngeal cancer, lymphoma of the brain, and acral myxoinflammatory fibroblastic sarcoma. In order to study cancer risk in astronauts, a number of obstacles must be overcome. Firstly, several factors make the astronaut cohort considerably different from the cohorts that have previously been studied for effects resulting from radiation exposure. The high rate of accidents and the much healthier lifestyle of astronauts compared to the U.S. population make finding a suitable comparison population a problematic task. Space radiation differs substantially from terrestrial radiation exposures studied in the past; therefore, analyses of galactic cosmic radiation (GCR) in animal models must be conducted and correctly applied to the human experience. Secondly, a large enough population of exposed astronauts must exist in order to obtain the data necessary to see any potential statistically significant differences between the astronauts and the control population. Thirdly, confounders and effect modifiers, such as smoking, diet, and other space stressors, must be correctly identified and controlled for in those analyses. In order to begin work assessing the astronaut population, the earliest groups of astronauts (selection groups 1-7) provide a unique model. These astronauts were relatively homogenous, white males whose lifestyle characteristics were similar to an average U.S. citizen of the same birth cohort. This work reviews radiation exposure levels, age, and causes of mortality among these early NASA astronauts and discusses the benefits and limitations of assessing such a cohort for radiation-induced cancer risk.

Milder, Caitlin M.↗
NASA NTRS2021

Plumelets: Dynamic Filamentary Structures in Solar Coronal Plumes

Solar coronal plumes long seemed to possess a simple geometry supporting spatially coherent, stable outflow without significant fine structure. Recent high-resolution observations have challenged this picture by revealing numerous transient, small-scale, collimated out-flows (“jetlets”) at the base of plumes. The dynamic filamentary structure of solar plumes above these outflows, and its relationship with the overall plume structure, have remained largely unexplored. We analyzed the statistics of continuously observed fine structure in-side a single representative bright plume within a mid-latitude coronal hole during 2016 July2-3. By applying advanced edge-enhancement and spatiotemporal analysis techniques to ex-tended series of high-resolution images from the Solar Dynamics Observatory’s Atmospheric Imaging Assembly, we determined that the plume was composed of numerous time-evolving filamentary substructures, referred to as “plumelets” in this paper, that accounted for most of the plume emission. The number of simultaneously identifiable plumelets was positively correlated with plume brightness, peaked in the fully formed plume, and remained saturated thereafter. The plumelets had transverse widths of 10 Mm and intermittently supported upwardly propagating periodic disturbances with phase speeds of 190-260 km s−1and longitudinal wavelengths of 55-65 Mm. The characteristic frequency (3.5 mHz) is commensurate with that of solar p-modes. Oscillations in neighboring plumelets are uncorrelated, indicating that the waves could be driven by p-mode flows at spatial scales smaller than the plumelet separation. Multiple independent sources of outflow within a single coronal plume should impart significant fine structure to the solar wind that may be detectable by Parker Solar Probe and Solar Orbiter.

V M Uritsky↗
NASA NTRSDate not supplied

Small-Sample Estimator Decisions – Certainly Uncertain in Human Spaceflight at NASA

Within statistics when estimating means, we rely heavily on the Central Limit Theorem (CLT) to aid in our inference, however in cases where the CLT does not apply we begin to be strongly limited in our choices. If a violation to the CLT comes in the form of small samples and unknown population variance, yet we maintain the assumption of normality, the use of t-distributions is perfectly valid. We explore cases where population normality is not assumed, where sample sizes are small, and where some actionable estimate of the central tendency of a distribution is needed such as for spaceflight operational decisions. Here we identify different scenarios based on measures taken from spaceflight, characterizing performance of estimators of central tendency within our set of simulations. We examine bias and variability of standard estimators of central tendency as they apply to varying small sample sizes and varying population distributions. Further work in this area is needed to develop a framework or set of guidelines for individuals set in these situations. Even with limited information, decisions need to be made.

Central Tendency↗
DOE OSTI2026

Precise Modeling of a Complex Solenoidal Magnetic Field Using a Combination of Analytic Functions and a PINN

We demonstrate an iterative approach to modeling a sparsely measured magnetic field in a large-bore solenoid. This approach uses a hybrid of traditional and machine learning techniques. The traditional technique is a linear least-squares fit using a series solution to Laplace's equation, while the machine learning technique involves the training of a physics-informed neural network (PINN) on the least-squares fit residuals. We use a newly defined activation function "DELTAsnake," a modification to the snake activation function proposed by Ziyin et al. that allows for stronger curvature and non-monotonicity. The combined model approximately obeys Maxwell's equations to a level sufficient for producing high quality physics simulations and analysis. Our approach is applied to a highly realistic calculation of the expected magnetic field in the Mu2e experiment's Detector Solenoid which includes a simple model for the expected statistical measurement uncertainties. Using ten toy measurement simulations, we demonstrate the capabilities of our model in comparison to the least-squares method alone; the least-squares method alone results in a reduced chi-squared statistic of ${2.15 \pm 0.01}$, while our approach improves the reduced chi-square to ${1.034 \pm 0.005}$. Furthermore, for an average toy simulation, we show that the range of the RMS of the three field component residuals reduces from ${0.07-0.37}$ Gauss to ${0.05-0.07}$ Gauss. We find that this novel method is robust against a realistic systematic uncertainty deriving from Hall probe calibration bias and can be used to significantly reduce the number of measurements required to achieve an accurate model.

Kampa, Cole [Caltech] (ORCID:0000000192972920)↗
NASA NTRS1990

Line identification studies using traditional techniques and wavelength coincidence statistics

Traditional line identification techniques result in the assignment of individual lines to an atomic or ionic species. These methods may be supplemented by wavelength coincidence statistics (WCS). The strength and weakness of these methods are discussed using spectra of a number of normal and peculiar B and A stars that have been studied independently by both methods. The present results support the overall findings of some earlier studies. WCS would be most useful in a first survey, before traditional methods have been applied. WCS can quickly make a global search for all species and in this way may enable identifications of an unexpected spectrum that could easily be omitted entirely from a traditional study. This is illustrated by O I. WCS is a subject to well known weakness of any statistical technique, for example, a predictable number of spurious results are to be expected. The danger of small number statistics are illustrated. WCS is at its best relative to traditional methods in finding a line-rich atomic species that is only weakly present in a complicated stellar spectrum.

Cowley, Charles R.↗
NASA NTRS1990

Line identification studies using traditional techniques and wavelength coincidence statistics

Traditional line identification techniques result in the assignment of individual lines to an atomic or ionic species. These methods may be supplemented by wavelength coincidence statistics (WCS). The strength and weakness of these methods are discussed using spectra of a number of normal and peculiar B and A stars that have been studied independently by both methods. The present results support the overall findings of some earlier studies. WCS would be most useful in a first survey, before traditional methods have been applied. WCS can quickly make a global search for all species and in this way may enable identifications of an unexpected spectrum that could easily be omitted entirely from a traditional study. This is illustrated by O I. WCS is a subject to well known weakness of any statistical technique, for example, a predictable number of spurious results are to be expected. The danger of small number statistics are illustrated. WCS is at its best relative to traditional methods in finding a line-rich atomic species that is only weakly present in a complicated stellar spectrum.

Cowley, Charles R.↗
NASA NTRS1992

Experiments with recursive estimation in astronomical image processing

Recursive estimation concepts were applied to image enhancement problems since the 70's. However, very few applications in the particular area of astronomical image processing are known. These concepts were derived, for 2-dimensional images, from the well-known theory of Kalman filtering in one dimension. The historic reasons for application of these techniques to digital images are related to the images' scanned nature, in which the temporal output of a scanner device can be processed on-line by techniques borrowed directly from 1-dimensional recursive signal analysis. However, recursive estimation has particular properties that make it attractive even in modern days, when big computer memories make the full scanned image available to the processor at any given time. One particularly important aspect is the ability of recursive techniques to deal with non-stationary phenomena, that is, phenomena which have their statistical properties variable in time (or position in a 2-D image). Many image processing methods make underlying stationary assumptions either for the stochastic field being imaged, for the imaging system properties, or both. They will underperform, or even fail, when applied to images that deviate significantly from stationarity. Recursive methods, on the contrary, make it feasible to perform adaptive processing, that is, to process the image by a processor with properties tuned to the image's local statistical properties. Recursive estimation can be used to build estimates of images degraded by such phenomena as noise and blur. We show examples of recursive adaptive processing of astronomical images, using several local statistical properties to drive the adaptive processor, as average signal intensity, signal-to-noise and autocorrelation function. Software was developed under IRAF, and as such will be made available to interested users.

Busko, I.↗
DOE OSTI2024

Exploration of a Potential DOOR Endpoint for Hospital-acquired Bacterial Pneumonia and Ventilator-associated Bacterial Pneumonia Using Six Registrational Trials for Antibacterial Drugs

Abstract Background Desirability of outcome ranking (DOOR) is an innovative approach to clinical trial design and analysis that uses an ordinal ranking system to incorporate the overall risks and benefits of a therapeutic intervention into a single measurement. Here we derived and evaluated a disease-specific DOOR endpoint for registrational trials for hospital-acquired bacterial pneumonia and ventilator-associated bacterial pneumonia (HABP/VABP). Methods Through comprehensive examination of data from nearly 4000 participants enrolled in six registrational trials for HABP/VABP submitted to the Food and Drug Administration (FDA) between 2005 and 2022, we derived and applied a HABP/VABP specific endpoint. We estimated the probability that a participant assigned to the study treatment arm would have a more favorable overall DOOR or component outcome than a participant assigned to comparator. Results DOOR distributions between treatment arms were similar in all trials. DOOR probability estimates ranged from 48.3% to 52.9% and were not statistically different. There were no significant differences between treatment arms in the component analyses. Although infectious complications and serious adverse events occurred more frequently in ventilated participants compared to non-ventilated participants, the types of events were similar. Conclusions Through a data-driven approach, we constructed and applied a potential DOOR endpoint for HABP/VABP trials. The inclusion of syndrome-specific events may help to better delineate and evaluate participant experiences and outcomes in future HABP/VABP trials and could help inform data collection and trial design.

Immunology↗
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↗
NASA NTRS1999

On-Line Analysis of Physiologic and Neurobehavioral Variables During Long-Duration Space Missions

The goal of this project is to develop reliable statistical algorithms for on-line analysis of physiologic and neurobehavioral variables monitored during long-duration space missions. Maintenance of physiologic and neurobehavioral homeostasis during long-duration space missions is crucial for ensuring optimal crew performance. If countermeasures are not applied, alterations in homeostasis will occur in nearly all-physiologic systems. During such missions data from most of these systems will be either continually and/or continuously monitored. Therefore, if these data can be analyzed as they are acquired and the status of these systems can be continually assessed, then once alterations are detected, appropriate countermeasures can be applied to correct them. One of the most important physiologic systems in which to maintain homeostasis during long-duration missions is the circadian system. To detect and treat alterations in circadian physiology during long duration space missions requires development of: 1) a ground-based protocol to assess the status of the circadian system under the light-dark environment in which crews in space will typically work; and 2) appropriate statistical methods to make this assessment. The protocol in Project 1, Circadian Entrainment, Sleep-Wake Regulation and Neurobehavioral will study human volunteers under the simulated light-dark environment of long-duration space missions. Therefore, we propose to develop statistical models to characterize in near real time circadian and neurobehavioral physiology under these conditions. The specific aims of this project are to test the hypotheses that: 1) Dynamic statistical methods based on the Kronauer model of the human circadian system can be developed to estimate circadian phase, period, amplitude from core-temperature data collected under simulated light- dark conditions of long-duration space missions. 2) Analytic formulae and numerical algorithms can be developed to compute the error in the estimates of circadian phase, period and amplitude determined from the data in Specific Aim 1. 3) Statistical models can detect reliably in near real- time (daily) significant alternations in the circadian physiology of individual subjects by analyzing the circadian and neurobehavioral data collected in Project 1. 4) Criteria can be developed using the Kronauer model and the recently developed Jewett model of cognitive -performance and subjective alertness to define altered circadian and neurobehavioral physiology and to set conditions for immediate administration of countermeasures.

Brown, Emery N.↗
NASA NTRS2002

A statistical model for interpreting computerized dynamic posturography data

Computerized dynamic posturography (CDP) is widely used for assessment of altered balance control. CDP trials are quantified using the equilibrium score (ES), which ranges from zero to 100, as a decreasing function of peak sway angle. The problem of how best to model and analyze ESs from a controlled study is considered. The ES often exhibits a skewed distribution in repeated trials, which can lead to incorrect inference when applying standard regression or analysis of variance models. Furthermore, CDP trials are terminated when a patient loses balance. In these situations, the ES is not observable, but is assigned the lowest possible score--zero. As a result, the response variable has a mixed discrete-continuous distribution, further compromising inference obtained by standard statistical methods. Here, we develop alternative methodology for analyzing ESs under a stochastic model extending the ES to a continuous latent random variable that always exists, but is unobserved in the event of a fall. Loss of balance occurs conditionally, with probability depending on the realized latent ES. After fitting the model by a form of quasi-maximum-likelihood, one may perform statistical inference to assess the effects of explanatory variables. An example is provided, using data from the NIH/NIA Baltimore Longitudinal Study on Aging.

NASA Discipline Neuroscience↗
DOE OSTI2020

Dark Energy Survey Year 3 results: cosmology with moments of weak lensing mass maps – validation on simulations

ABSTRACT We present a simulated cosmology analysis using the second and third moments of the weak lensing mass (convergence) maps. The second moment, or variances, of the convergence as a function of smoothing scale contains information similar to standard shear two-point statistics. The third moment, or the skewness, contains additional non-Gaussian information. The analysis is geared towards the third year (Y3) data from the Dark Energy Survey (DES), but the methodology can be applied to other weak lensing data sets. We present the formalism for obtaining the convergence maps from the measured shear and for obtaining the second and third moments of these maps given partial sky coverage. We estimate the covariance matrix from a large suite of numerical simulations. We test our pipeline through a simulated likelihood analyses varying 5 cosmological parameters and 10 nuisance parameters and identify the scales where systematic or modelling uncertainties are not expected to affect the cosmological analysis. Our simulated likelihood analysis shows that the combination of second and third moments provides a 1.5 per cent constraint on S8 ≡ σ8(Ωm/0.3)0.5 for DES Year 3 data. This is 20 per cent better than an analysis using a simulated DES Y3 shear two-point statistics, owing to the non-Gaussian information captured by the inclusion of higher order statistics. This paper validates our methodology for constraining cosmology with DES Year 3 data, which will be presented in a subsequent paper.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Flow and heat transfer in the wake of a triangular arrangement of spheres

This research work seeks to investigate the influence of spacing and heat transfer on the wake behavior of a triangular arrangement of spheres. Four experimental configurations have been investigated at three Reynolds numbers, Re 1 = 350, Re 2 = 700, and Re 3 = 1050. Two isothermal cases were investigated with spacing between the spheres of zero and one sphere diameter, and two cases were investigated with an applied heat flux at the same spacing conditions. The time resolved particle image velocimetry results revealed various flow phenomena including flow separations, von Karman vortex shedding, and Kelvin–Helmholtz instabilities. The turbulent statistics reveal the effect of proximity and heat transfer on the time averaged values of the wake size, turbulent strengths, and Reynolds shear stress in the wake of each sphere, namely, the laminarization effects from the addition of heat and the suppression of the lead sphere wake from the proximity of the trailing spheres. These results are complemented by the application of proper orthogonal decomposition (POD) to the flow fields, which extracts the coherent structures from the flow. Here the modes that describe the coherent structures are extracted and described in detail, which provide further insight into effects of the experimental conditions on the temporal behavior of the flow. Many of the low order modes are found to be associated in pairs, corresponding to asymmetric structures or advection of a given structure downstream. The capability of POD to produce reduced order models of the flow is then utilized to facilitate vortex identification analysis. A turbulent kinetic energy based mode truncation criteria, which has been found to enhance vortex identification capability, is applied to select the POD modes and temporal coefficients to be used in the reduced order modeling. The reconstructed velocity fields are then analyzed with vortex identification algorithms to extract the vortex cores and boundaries. The combination of these approaches allows the study of the effect of proximity and heat transfer on the vortex characteristics, such as size, strength, and distribution.

42 ENGINEERING↗
DOE OSTI2024

Measurement of energy resolution with the NEXT-White silicon photomultipliers

The NEXT-White detector, a high-pressure gaseous xenon time projection chamber, demonstrated the excellence of this technology for future neutrinoless double beta decay searches using photomultiplier tubes (PMTs) to measure energy and silicon photomultipliers (SiPMs) to extract topology information. This analysis uses $^{83m}$Kr data from the NEXT-White detector to measure and understand the energy resolution that can be obtained with the SiPMs, rather than with PMTs. The energy resolution obtained of (10.9 ± 0.6)%, full-width half-maximum, is slightly larger than predicted based on the photon statistics resulting from very low light detection coverage of the SiPM plane in the NEXT-White detector. The difference in the predicted and measured resolution is attributed to poor corrections, which are expected to be improved with larger statistics. Furthermore, the noise of the SiPMs is shown to not be a dominant factor in the energy resolution and may be negligible when noise subtraction is applied appropriately, for high-energy events or larger SiPM coverage detectors. These results, which are extrapolated to estimate the response of large coverage SiPM planes, are promising for the development of future, SiPM-only, readout planes that can offer imaging and achieve similar energy resolution to that previously demonstrated with PMTs.[graphic not available: see fulltext]

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Experimental investigation of multi-scale strain-field heterogeneity in rocks

Rocks have inherently heterogeneous micro-structures. The mechanical response of these heterogeneous micro-structures primarily governs the overall macroscopic behavior of intact rocks, as these features cause heterogeneity in the distribution of stress and strain within a rock volume. These attributes have generally been studied through numerical tools, but in this study, the stress-induced multi-scale spatial heterogeneity in the strain-field of three types of rocks was analyzed experimentally. The full-field non-contact strain measurement approach of 2D Digital Image Correlation (DIC) was employed for real-time evaluation of the progression of strain-field heterogeneity across the surface of the rock specimens when subjected to uniaxial compression. The results show that the strain-field heterogeneity is a characteristic of the inherent heterogeneity of the rock specimens, and that it amplifies with the magnitude of stress applied on the rock specimens. The changes in the strain-field heterogeneity with loading were consistent with established microcrack evolution processes in the rock specimens. The statistical fluctuation in the strain-field heterogeneity of the three rocks at varying gauge lengths (GL) was then analyzed for the determination of the Representative Volume Element (RVE) length-scale at increasing load levels. The evolution of the RVE length-scale with loading facilitates an objective way of determination of the stress at which the strain-field heterogeneity increases and begins to influence the overall global response of the material. The RVE length-scale variation with loading showed that at the RVE length-scale reaches the specimen-scale at load levels well below the peak strength.

2D-digital image correlation↗