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DOE OSTI2015

Modern sensitivity analysis - 15651

Sensitivity analysis has been applied to complex models, such as performance assessment models, since at least the advent of computational tools to support such an approach to model evaluation. Initially, sensitivity analysis methods were applied to deterministic models and focused on changing one input variable at a time. Probabilistic modeling opened the door to evaluating changes in all input variables simultaneously. Standardized rank regression methods were primarily used in the last century to evaluate probabilistic models. These methods assume linearity in the rank response of the model to the inputs. At the very least this assumes monotonicity in the response, but for the most part the response needs to be near-linear. This is highly unlikely for performance assessment models that are based on differential equations, multiplication and division of variables that are usually not correlated, and conservatism that is not well understood. There are better statistical methods for evaluating highly non-linear and dynamic probabilistic models. Some of these methods were founded in the data mining industry. This century has seen a rapid development of the internet that led directly to the development of data mining techniques for massive databases. Although the goals were different, these techniques can be adapted to sensitivity analysis for complex probabilistic models, which, also, form large, or even massive, databases. These modern sensitivity analysis procedures assess the importance of every explanatory variable (input parameters) used in a probabilistic model. In practice, this means that for each endpoint of interest, every explanatory variable in the model is associated with a quantitative measure of importance. For a given explanatory variable, the importance value depends on the endpoint of interest. This global approach to sensitivity analysis allows all levels of interactions to be evaluated, so that the variation in the output can be measured at every level of possible interaction. The effects are collected together during sensitivity analysis processing, and then separated out to attribute the overall contribution of each input parameter to the model output. These global sensitivity analysis procedures have the ability to characterize non-linear and non-monotonic relationships between explanatory variables and endpoints of interest. This is critically important because of the need to characterize complex interactions among multiple explanatory variables in PA models that often have non-linear and non-monotonic effects. The options for sensitivity analysis are fully explored, including an evaluation of several methods when applying them to a well-known complex non-linear non-monotonic model. The results clearly demonstrate the relative benefit of using data mining methods over simple statistical approaches such as standardized rank regression. (authors)

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Deep data mining in a real space: Separation of intertwined electronic responses in a lightly doped BaFe 2 As 2

Electronic interactions present in material compositions close to the superconducting dome play a key role in the manifestation of high-T c superconductivity. In many correlated electron systems, however, the parent or underdoped states exhibit strongly inhomogeneous electronic landscape at the nanoscale that may be associated with competing, coexisting, or intertwined chemical disorder, strain, magnetic, and structural order parameters. Here we demonstrate an approach based on a combination of scanning tunneling microscopy/spectroscopy and advanced statistical learning for an automatic separation and extraction of statistically significant electronic behaviors in the spin density wave regime of a lightly (~1%) gold-doped BaFe 2 As 2 . Lastly, we show that the decomposed STS spectral features have a direct relevance to fundamental physical properties of the system, such as SDW-induced gap, pseudogap-like state, and impurity resonance states.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2018

Mining data in a dynamic PRA framework

Computational (also known as Dynamic) Probabilistic Risk Assessment (PRA) methods employs system simulation codes coupled with stochastic analysis tools in order to determine probabilities of certain outcomes such as system failure. In contrast to classical PRA methods (i.e., Event-Tree and Fault-Tree) in which timing and sequencing of events is set by the analyst, accident progression is dictated by the system control logic and its interaction with system temporal evolution. Due to the nature of the problem, Simulation-Based PRA methods require large amount of computationally expensive simulation runs. In addition, these methods usually generate a large number of simulation runs (database storage may be on the order of gigabytes or higher). In this paper we investigate and apply several methods and algorithms to analyze these large amounts of time-dependent data. Furthermore, the scope of this article is to present a broad overview of methods and algorithms that can be used to improve data quality and to analyze and extract information from large data sets containing time dependent data. In this context, “extracting information” means constructing input-output correlations, finding commonalities, and identifying outliers.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1974

Mineral resources, geological structure, and landform surveys

Diagnostic ERTS imagery has been used to pinpoint surface conditions associated with known mining districts. These include enhancements which depict hitherto unrecognized surface alteration and allow analysis of ore-controlling fractures distribution in a regional context. ERTS has likewise provided observational data containing previously unrecognized surface anomalies in large oil-producing basins which correlate closely with known oil fields. These observational data offer promise of providing new and powerful techniques for oil exploration, especially if further work using more sophisticated enhancement-processing proves capable of emphasizing the anomalies. ERTS is showing a better-than-anticipated potential for producing accurate small-scale (large-area) geologic maps, often containing details that were previously not recorded on similar regional maps. The maps produced from ERTS imagery can be prepared more effectively than previously possible, mainly because of the synoptic, multispectral, and repetitive character of ERTS data. ERTS has also provided extensive information on possible geologic hazards. Many new fractures have been identified in several regions of the Pacific Coast seismic belt that have histories of recent earthquakes. This has obvious implications for engineering projects such as dams, aqueducts, and transportation routes. In the mid-continent area, ERTS data have been used to predict zones of rooffall danger in a working coal mine from newly discovered lineations (probably fractures) used as indicators of hazards.

Short, N. M.↗

DOE OSTI2016

Chemical and isotope compositions of shallow groundwater in areas impacted by hydraulic fracturing and surface mining in the Central Appalachian Basin, Eastern United States

Here, hydraulic fracturing of shale deposits has greatly increased the productivity of the natural gas industry by allowing it to exploit previously inaccessible reservoirs. Previous research has demonstrated that this practice has the potential to contaminate shallow aquifers with methane (CH 4 ) from deeper formations. This study compares concentrations and isotopic compositions of CH 4 sampled from domestic groundwater wells in Letcher County, Eastern Kentucky in order to characterize its occurrence and origins in relation to both neighboring hydraulically fractured natural gas wells and surface coal mines. The studied groundwater showed concentrations of CH 4 ranging from 0.05 mg/L to 10 mg/L, thus, no immediate remediation is required. The δ 13 C values of CH 4 ranged from 66‰ to 16‰, and δ 2 H values ranged from –286‰ to –86‰, suggesting an immature thermogenic and mixed biogenic/thermogenic origin. The occurrence of CH 4 was not correlated with proximity to hydraulically fractured natural gas wells. Generally, CH 4 occurrence corresponded with groundwater abundant in Na + , Cl – , and HCO 3 – , and with low concentrations of SO 4 –2 . The CH 4 and SO 4 –2 concentrations were best predicted by the oxidation/reduction potential of the studied groundwater. CH 4 was abundant in more reducing waters, and SO 4 –2 was abundant in more oxidizing waters. Additionally, groundwater in greater proximity to surface mining was more likely to be oxidized. This, in turn, might have increased the likelihood of CH 4 oxidation in shallow groundwater.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

Multi-configuration Membrane Distillation Model (MCMD)

Many membrane distillation models have been created to simulate the heat and mass exchange process involved but most of the literature only validates models to a couple of cases with minor configuration changes. Tools are needed that allow tradeoffs between many configurations. The multiconfiguration membrane distillation model handles many configurations. This report introduces membrane distillation, provides theory, and presents the work to verify and validate the model against experimental data from Colorado School of Mines and a lower resolution model created at the National Renewable Energy Laboratory. Though more data analysis and testing are needed, an initial look at the model to experimental comparisons indicates that the model correlates to the data well but that design comparisons are likely to be incorrect across a broad range of configurations. More accurate quantification of heat and mass transfer through computational fluid mechanics is suggested.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Data Mining of Polymer Phase Transitions upon Temperature Changes by Small and Wide-Angle X-ray Scattering Combined with Raman Spectroscopy

The complex physical transformations of polymers upon external thermodynamic changes are related to the molecular length of the polymer and its associated multifaceted energetic balance. The understanding of subtle transitions or multistep phase transformation requires real-time phenomenological studies using a multi-technique approach that covers several length-scales and chemical states. A combination of X-ray scattering techniques with Raman spectroscopy and Differential Scanning Calorimetry was conducted to correlate the structural changes from the conformational chain to the polymer crystal and mesoscale organization. Current research applications and the experimental combination of Raman spectroscopy with simultaneous SAXS/WAXS measurements coupled to a DSC is discussed. In particular, we show that in order to obtain the maximum benefit from simultaneously obtained high-quality data sets from different techniques, one should look beyond traditional analysis techniques and instead apply multivariate analysis. Data mining strategies can be applied to develop methods to control polymer processing in an industrial context. Crystallization studies of a PVDF blend with a fluoroelastomer, known to feature complex phase transitions, were used to validate the combined approach and further analyzed by MVA.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Pahute Mesa-Oasis Valley Hydrostratigraphic Framework Model for Corrective Action Units 101 and 102: Central and Western Pahute Mesa, Nye County, Nevada (Rev. 0)

A new, revised three-dimensional (3-D) hydrostratigraphic framework model (HFM) for the Central and Western Pahute Mesa Corrective Action Units 101 and 102 was completed in 2019. The initial (Phase I) model was completed in 2002 and was documented in A Hydrostratigraphic Model and Alternatives for the Groundwater Flow and Contaminant Transport Model of Corrective Action Units 101 and 102: Central and Western Pahute Mesa, Nye County, Nevada (BN, 2002). Subsequent flow and transport modeling revealed the need to collect additional data to reduce uncertainties. The Phase II data collection and characterization effort included extensive well drilling and testing activities, and rebuilding of the Phase I 3-D HFM, resulting in the Phase II HFM (NSTec, 2014). Further drilling (Well ER-20-12), data collection, and interpretive work (e.g., Thirsty Canyon Lineament [Wurtz and Day, 2018]) were incorporated into the Pahute Mesa-Oasis Valley (PM-OV) HFM. The PM-OV HFM incorporates the area of interest from the Phase II HFM and adds additional areas, primarily to the north and west with minor additions to the south and east. The new, expanded area matches the PM-OV groundwater basin as defined in Fenelon et al. (2016) and includes Pahute Mesa, a former nuclear testing area at the Nevada National Security Site; and Oasis Valley, a groundwater discharge area downgradient from contaminant source areas on Pahute Mesa. The data and information necessary to build the PM-OV HFM are provided in Navarro (2019). The PM-OV HFM will be used by hydrologic modelers who are tasked with developing a model to determine how contaminants are transported by groundwater flow in an area of complex geology. The model area is large (more than 6,250 square kilometers) and geologically complex, including Paleozoic- to Mesozoic-age structures (e.g., thrust faults, Thirsty Canyon Lineament), at least seven Tertiary-age calderas, several intrusive bodies, and many relatively recent basin-and-range normal faults. Investigators from the U.S. Geological Survey; National Security Technologies, LLC; National Laboratories; and U.S. Department of Energy contractors have organized the more than 300 volcanic units and other rocks in the model area into 77 hydrostratigraphic units (HSUs). The volcanic rocks are subdivided into 42 aquifers, 11 confining units, and 9 composite units (containing both aquifer and confining unit rocks). The underlying pre-Tertiary rocks were divided into 5 HSUs, comprising 2 aquifers and 3 confining units. The model also includes 1 alluvial aquifer unit, 8 intrusive confining units, and 1 granite confining unit. The model depicts the thickness, extent, and geometric relationships of these HSUs (“layers” in the model) along with all the major structural features that control them, including calderas and faults. The most substantive differences between the Phase II HFM and the PM-OVHFM include (1) addition of the new data and resulting revised interpretations from the additional area encompassed by the PM-OV groundwater basin; (2) incorporation of 16 new faults; (3) 12 boreholes from the northern extension; (4) 2 additional caldera-collapse collars; and (5) correlation of the simulated faulting and hydrostratigraphy in the eastern part of the model domain with the delineations represented in the neighboring Rainier Mesa-Shoshone Mountain HFM-Prime and Yucca Flat-Climax Mine HFM. All PM-OV HFM delineations were based on published and unpublished data from geophysical investigations, surface geologic maps, and drill-hole records. Phase II approached model uncertainty using the model-building software to test model configurations and hydrologic consequences using a range of values for parameters such as fault offset amounts and HSU elevations. The PM-OV HFM expands on this approach to encompass the entire PM-OV groundwater basin.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2012

Hydrogeochemical Modelling for Groundwater in Neyveli Aquifer, Tamil Nadu, India, Using PHREEQC: A Case Study

Sophisticated geochemical models have been used to describe and predict the chemical behaviour of complex natural waters and also to protect the groundwater resources from future contamination. One such model is used to study the hydrogeochemical complexity in a mine area. Extraction of groundwater from the coastal aquifer has been in progress for decades to mine lignite in Neyveli. This extraction has developed a cone of depression around the mine site. This cone of depression is well established by the geochemical nature of groundwater in the region. 42 groundwater samples were collected in a definite pattern and they were analysed for major cations, anions and trace elements. The saturation index (SI) of the groundwater for carbonate, sulphate and silica minerals was studied and it has been correlated with the recharge and the discharge regions. The SI of alumino silicates has been used to decipher the stage of weathering. The SI{sub Gibbsite} - SI{sub K-feldspar} has been spatially distributed and the regions of discharge and recharge were identified. Then two flow paths A1 and A2 were identified and inverse modelling using PHREEQC were carried out to delineate the geochemical process that has taken place from recharge to discharge. The initial and final solutions in both the flow paths were correlated with the thermodynamic silicate stability diagrams of groundwater and it was found that the state of thermodynamic stability of the end solutions along the flow path were approaching similar states of equilibrium at the discharge.

58 GEOSCIENCES↗

DOE OSTI2017

Full-flow-regime storage-streamflow correlation patterns provide insights into hydrologic functioning over the continental US

Interannual changes in low, median, and high regimes of streamflow have important implications for flood control, irrigation, and ecologic and human health. The Gravity Recovery and Climate Experiment (GRACE) satellites record global terrestrial water storage anomalies (TWSA), providing an opportunity to observe, interpret, and potentially utilize the complex relationships between storage and full-flow-regime streamflow. Here we show that utilizable storage-streamflow correlations exist throughout vastly different climates in the continental US (CONUS) across low- to high-flow regimes. A panoramic framework, the storage-streamflow correlation spectrum (SSCS), is proposed to examine macroscopic gradients in these relationships. SSCS helps form, corroborate or reject hypotheses about basin hydrologic behaviors. SSCS patterns vary greatly over CONUS with climate, land surface, and geologic conditions. Data mining analysis suggests that for catchments with hydrologic settings that favor storage over runoff, e.g., a large fraction of precipitation as snow, thick and highly-permeable permeable soil, SSCS values tend to be high. Based on our results, we form the hypotheses that groundwater flow dominates streamflows in South-eastern CONUS and Great Plains, while thin soils in a belt along the Appalachian Plateau impose a limit on water storage. SSCS also suggests shallow water table caused by high-bulk density soil and flat terrain induces rapid runoff in several regions. Our results highlight the importance of subsurface properties and groundwater flow in capturing flood and drought. Finally, we propose that SSCS can be used as a fundamental hydrologic signature to constrain models and to provide insights that lead us to better understand hydrologic functioning.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1974

Multidisciplinary applications of ERTS and Skylab data in Ohio

Experimental studies of ERTS-1 and Skylab earth resources data, in combination with correlative aircraft and on-site data, for environmental quality, land use, and resource management applications in Ohio show several areas of operational promise. Prime data use candidates demonstrated to date include definition and enforcement of surface mining (all minerals) legislation; Lake Erie modeling/management; land use classification and mapping studies at state, regional, and localized levels; and resources' inventories particularly of forested areas on both regional (multicounty) and localized scales.

Sweet, D. C.↗

DOE OSTI2025

Review of High‐Speed Digital Image Correlation: Advancements and Good Practices

This paper reviews the current state of the art in high‐speed (HS) and ultrahigh‐speed (UHS) digital image correlation (DIC) techniques, emphasizing their critical role in experimental research across various scientific domains. HS and UHS DIC have evolved significantly, driven by advancements in camera systems, image processing algorithms and experimental methodologies. These developments have opened new avenues for capturing and analysing dynamic events with unprecedented temporal and spatial detail, but not without introducing challenges such as optical distortions, motion blur and lighting issues that can affect measurement quality. This review advocates for standardized reporting practices in HS/UHS DIC methodologies to improve reproducibility and reliability across studies, drawing on guidelines from the International Digital Image Correlation Society (iDICs). Through a comprehensive analysis of over 150 articles, this review identifies key advancements in imaging technology and their application in six research domains: material characterization, test development, fracture mechanics, model validation, ballistic and explosive phenomena assessment and measurement uncertainties. Distinctions between two‐dimensional (2D) and stereo‐DIC applications are explored, offering insights into their practical implementation and trade‐offs. Good practices for HS/UHS DIC applications are proposed along with suggestions for future directions for this evolving field, highlighting the indispensable role of technological innovation in expanding the capabilities of optical metrology.

experimental mechanics↗

DOE OSTI2013

MINING NUCLEAR TRANSIENT DATA THROUGH SYMBOLIC CONVERSION

Dynamic Probabilistic Risk Assessment (DPRA) methodologies generate enormous amounts of data for a very large number of simulations. The data contain temporal information of both the state variables of the simulator and the temporal status of specific systems/components. In order to measure system performances, limitations and resilience, such data need to be carefully analyzed with the objective of discovering the correlations between sequence/timing of events and system dynamics. A first approach toward discovering these correlations from data generated by DPRA methodologies has been performed by organizing scenarios into groups using classification or clustering based algorithms. The identification of the correlations between system dynamics and timing/sequencing of events is performed by observing the temporal distribution of these events in each group of scenarios. Instead of performing “a posteriori” analysis of these correlations, this paper shows how it is possible to identify the correlations implicitly by performing a symbolic conversion of both continuous (temporal profiles of simulator state variables) and discrete (status of systems and components) data. Symbolic conversion is performed for each simulation by properly quantizing both continuous and discrete data and then converting them as a series of symbols. After merging both series together, a temporal phrase is obtained. This phrase preserves duration, coincidence and sequence of both continuous and discrete data in a uniform and consistent manner. In this paper it is also shown that by using specific distance measures, it is still possible to post-process such symbolic data using clustering and classification techniques but in considerably less time since the memory needed to store the data is greatly reduced by the symbolic conversion.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information

Reservoirs are fundamental human–built infrastructures that collect, store, and deliver fresh surface water in a timely manner for many purposes. Efficient reservoir operation requires policy makers and operators to understand how reservoir inflows are changing under different hydrological and climatic conditions to enable forecast–informed operations. Over the last decade, the uses of Artificial Intelligence and Data Mining [AI & DM] techniques in assisting reservoir streamflow subseasonal to seasonal forecasts have been increasing. In this study, Random Forest [RF), Artificial Neural Network (ANN), and Support Vector Regression (SVR) are employed and compared with respect to their capabilities for predicting 1 month–ahead reservoir inflows for two headwater reservoirs in USA and China. Both current and lagged hydrological information and 17 known climate phenomenon indices, i.e., PDO and ENSO, etc., are selected as predictors for simulating reservoir inflows. Here, the results show three methods are capable of providing monthly reservoir inflows with satisfactory statistics; the results obtained by Random Forest have the best statistical performances compared with the other two methods; another advantage of Random Forest algorithm is its capability of interpreting raw model inputs; climate phenomenon indices are useful in assisting monthly or seasonal forecasts of reservoir inflow; and different climate conditions are autocorrelated with up to several months, and the climatic information and their lags are cross correlated with local hydrological conditions in our case studies.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Comparative Assessment of Data-driven Process Models in Health Information Technology

Process mining for conformance analysis consists of comparing a reference process model against a data-driven process model generated via log files from information technology systems. However, in the absence of a complete reference process model, we found no suggested approaches in the literature to address the need for evaluating process conformance among different healthcare facilities to assess standardization of care. Our goal is to find similarities and dissimilarities in data-driven process models among US Veterans Health Administration (VHA) facilities that can be indicative of patient safety issues. Our hypothesis was that the analysis would not produce statistically significant differences in outcome. We present a unique implementation of conformance analysis in process mining that consists of combining process mining, process mapping and statistical metrics. We illustrate our approach by applying it to the analysis of two clinical radiology order process models generated from healthcare data provided by two similar facilities in the VHA. The comparative assessment showed that about 70% of the orders completed successfully and 30% were not completed due to policy and duplications. Our analysis found a good statistical correlation between both facilities, as the Spearman’s correlation coefficient between facilities for the frequency of cases per total hours was 0.87879, for the frequency of cases by state transition was 0.79702 and for the throughput time per state transition was 0.63582. Additional statistical analyses using the Mann-Whitney U test and the root mean square error both produced values that were not significant. The foregoing approach validated our hypothesis by demonstrating a good statistical correlation of data describing the flow of clinical radiology orders absent a credible reference model. Finding good agreement between both facilities was important in confirming that the clinical orders flow in a similar manner, suggesting standardization of care.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1974

Identification and interpretation of tectonic features from ERTS-A imagery

The author has identified the following significant results. A set of criteria characterizing known mineralized areas are: (1) occurrence at structural bends, discontinuities, complex deformations along fault zones and intersections; (2) complex terrain textures produced by fracture intersections; (3) color and tone anomalies produced by igneous intrusives, alteration effects, and oxidation. Significant fracture systems which appear to be most commonly related to mineralization in central and northeastern Nevada trend northeast-southwest, north-south, and north-northwest. In the area from Goldfield and Beatty, Nevada, to south of Las Vegas, ERTS-1 and Skylab imagery have been examined together to study an apparent correlation of mineralized areas with suspected hydrothermal alteration effects. It was observed that areas of mineralization are of complex structure, usually cut by many fractures, and sometimes have a similar mottled or variegated appearance. Although the Beatty-Rhyolite mining district is now largely inactive, several analogous areas not known to be mineralized have been identified. Identification of specific mineral prospects within general targets requires extensive field work and detailed geophysical exploration.

Abdel-Gawad, M.↗

NASA NTRS2018

High‐Resolution Mapping of Nitrogen Dioxide With TROPOMI: First Results and Validation Over the Canadian Oil Sands

TROPOspheric Monitoring Instrument (TROPOMI), on-board the Sentinel-5 Precursersatellite, is a nadir-viewing spectrometer measuring reflected sunlight in the ultraviolet, visible,near-infrared, and shortwave infrared. From these spectra several important air quality and climate-relatedatmospheric constituents are retrieved, including nitrogen dioxide (NO2) at unprecedented spatialresolution from a satellite platform. We present the first retrievals of TROPOMI NO2over the Canadian OilSands, contrasting them with observations from the Ozone Monitoring Instrument satellite instrument,and demonstrate TROPOMI's ability to resolve individual plumes and highlight its potential for derivingemissions from individual mining facilities. Further, the first TROPOMI NO2validation is presented,consisting of aircraft and surface in situ NO2observations, and ground-based remote-sensingmeasurements between March and May 2018. Our comparisons show that the TROPOMI NO2verticalcolumn densities are highly correlated with the aircraft and surface in situ NO2observations, and theground-based remote-sensing measurements with a low bias (15–30 %); this bias can be reduced byimproved air mass factors. Plain Language Summary: Nitrogen dioxide (NO2) is a pollutant that is linked to respiratoryhealth issues and has negative environmental impacts such as soil and water acidification. Near thesurface the most significant sources of NO2are fossil fuel combustion and biomass burning. With a recentlylaunched satellite instrument (TROPOspheric Monitoring Instrument [TROPOMI]), NO2can be measuredwith an unprecedented combination of accuracy, spatial coverage, and resolution. This work presents thefirst TROPOMI NO2measurements near the Canadian Oil Sands and shows that these measurements havean outstanding ability to detect NO2on a very high horizontal resolution that is unprecedented for satelliteNO2observations. Further, these satellite measurements are in excellent agreement with aircraft andground-based measurements.

TROPOspheric Monitoring Instrument (TROPOMI)↗

DOE OSTI2016

Effective Long-term Monitoring Strategies by Integrating Reactive Transport Models with In situ Geochemical Measurements - 16212

Long-term monitoring of contaminant transport in groundwater is expected to account for a large fraction of future life-cycle cleanup costs at the DOE sites. The Attenuation-Based Remedies in the Subsurface Applied Field Research Initiative (ABRS AFRI) is developing an innovative approach for cost-effective in situ long-term monitoring. The approach is based on strategically adding the measurements of controlling variables (such as pH, redox potential, electrical conductivity, and groundwater level), which control the plume mobility and its spatial and temporal distribution. In situ measurements of these variables - supplemented with a reduced number of standard periodic groundwater samples - are expected to lead to more cost-effective monitoring, and can also serve as an early warning system for detecting unexpected plume migration. The objective of this study is to support the development of such a long-term monitoring approach through the application of advanced computational methods, including (1) statistical data mining and analysis, and (2) three-dimensional flow and reactive chemical transport modeling. We have performed the statistical data analysis of historical and current monitoring data at the Savannah River Site (SRS) F-Area to quantify the correlations between the controlling variables and radioactive contaminant concentrations. In parallel, we have used 3D modeling of flow and contaminant transport to provide the prediction of the contaminant plume evolution. The results of both data analysis and modeling confirmed that the correlations between controlling variables and contaminant concentrations are significant. Modeling results also suggest that the correlation parameters will change in the future, which is important to assess the long-term efficacy of the proposed monitoring approach. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗