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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Protein abundances can distinguish between naturally-occurring and laboratory strains of Yersinia pestis , the causative agent of plague

Adaptive processes in bacterial species can occur rapidly in laboratory culture, leading to genetic divergence between naturally occurring and laboratory-adapted strains. Differentiating wild and closely-related laboratory strains is clearly important for biodefense and bioforensics; however, DNA sequence data alone has thus far not provided a clear signature, perhaps due to lack of understanding of how diverse genome changes lead to adapted phenotypes. Protein abundance profiles from mass spectrometry-based proteomics analyses are a molecular measure of phenotype. Proteomics data contains sufficient information that powerful statistical methods can uncover signatures that distinguish wild strains of Yersinia pestis from laboratory-adapted strains.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Fully Convolutional Spatio-Temporal Models for Representation Learning in Plasma Science

We have trained a fully convolutional spatio-temporal model for fast and accurate representation learning in the challenging exemplar application area of fusion energy plasma science. The onset of major disruptions is a critically important fusion energy science issue that must be resolved for advanced tokamak plasmas such as the $25B burning plasma international thermonuclear experimental reactor (ITER) experiment. While a variety of statistical methods have been used to address the problem of tokamak disruption prediction and control, recent approaches based on deep learning have proven particularly compelling. In the present paper, we introduce further improvements to the fusion recurrent neural network (FRNN) software suite, which delivered cross-machine disruption predictions with unprecedented accuracy using a large database of experimental signals from two major tokamaks. Up to now, FRNN was based on the long short-term memory (LSTM) variant of recurrent neural networks to leverage the temporal information in the data. Here, we implement and apply the "temporal convolutional neural network (TCN)" architecture to the time-dependent input signals. Furthermore, this allows highly optimized convolution operations to carry the majority of the computational load of training, thus enabling a reduction in training time, and the effective use of high-performance computing resources for hyperparameter tuning. At the same time, the TCN-based architecture achieves better predictive performance when compared with the LSTM architecture for various tasks for a representative fusion database.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2011

Real-time characterization of partially observed epidemics using surrogate models.

We present a statistical method, predicated on the use of surrogate models, for the 'real-time' characterization of partially observed epidemics. Observations consist of counts of symptomatic patients, diagnosed with the disease, that may be available in the early epoch of an ongoing outbreak. Characterization, in this context, refers to estimation of epidemiological parameters that can be used to provide short-term forecasts of the ongoing epidemic, as well as to provide gross information on the dynamics of the etiologic agent in the affected population e.g., the time-dependent infection rate. The characterization problem is formulated as a Bayesian inverse problem, and epidemiological parameters are estimated as distributions using a Markov chain Monte Carlo (MCMC) method, thus quantifying the uncertainty in the estimates. In some cases, the inverse problem can be computationally expensive, primarily due to the epidemic simulator used inside the inversion algorithm. We present a method, based on replacing the epidemiological model with computationally inexpensive surrogates, that can reduce the computational time to minutes, without a significant loss of accuracy. The surrogates are created by projecting the output of an epidemiological model on a set of polynomial chaos bases; thereafter, computations involving the surrogate model reduce to evaluations of a polynomial. We find that the epidemic characterizations obtained with the surrogate models is very close to that obtained with the original model. We also find that the number of projections required to construct a surrogate model is O(10)-O(10{sup 2}) less than the number of samples required by the MCMC to construct a stationary posterior distribution; thus, depending upon the epidemiological models in question, it may be possible to omit the offline creation and caching of surrogate models, prior to their use in an inverse problem. The technique is demonstrated on synthetic data as well as observations from the 1918 influenza pandemic collected at Camp Custer, Michigan.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2012

Parameterization and analysis of 3-D radiative transfer in clouds

This report provides a summary of major accomplishments from the project. The project examines the impact of radiative interactions between neighboring atmospheric columns, for example clouds scattering extra sunlight toward nearby clear areas. While most current cloud models don't consider these interactions and instead treat sunlight in each atmospheric column separately, the resulting uncertainties have remained unknown. This project has provided the first estimates on the way average solar heating is affected by interactions between nearby columns. These estimates have been obtained by combining several years of cloud observations at three DOE Atmospheric Radiation Measurement (ARM) Climate Research Facility sites (in Alaska, Oklahoma, and Papua New Guinea) with simulations of solar radiation around the observed clouds. The importance of radiative interactions between atmospheric columns was evaluated by contrasting simulations that included the interactions with those that did not. This study provides lower-bound estimates for radiative interactions: It cannot consider interactions in cross-wind direction, because it uses two-dimensional vertical cross-sections through clouds that were observed by instruments looking straight up as clouds drifted aloft. Data from new DOE scanning radars will allow future radiative studies to consider the full three-dimensional nature of radiative processes. The results reveal that two-dimensional radiative interactions increase overall day-and-night average solar heating by about 0.3, 1.2, and 4.1 Watts per meter square at the three sites, respectively. This increase grows further if one considers that most large-domain cloud simulations have resolutions that cannot specify small-scale cloud variability. For example, the increases in solar heating mentioned above roughly double for a fairly typical model resolution of 1 km. The study also examined the factors that shape radiative interactions between atmospheric columns and found that local effects were often much larger than the overall values mentioned above, and were especially large for high sun and near convective clouds such as cumulus. The study also found that statistical methods such as neural networks appear promising for enabling cloud models to consider radiative interactions between nearby atmospheric columns. Finally, through collaboration with German scientists, the project found that new methods (especially one called stepwise kriging) show great promise in filling gaps between cloud radar scans. If applied to data from the new DOE scanning cloud radars, these methods can yield large, continuous three-dimensional cloud structures for future radiative simulations.

58 GEOSCIENCES↗
DOE OSTI2012

An Assessment of Geological Carbon Storage Options in the Illinois Basin: Validation Phase

The Midwest Geological Sequestration Consortium (MGSC) assessed the options for geological carbon dioxide (CO 2 ) storage in the 155,400 km 2 (60,000 mi 2 ) Illinois Basin, which underlies most of Illinois, western Indiana, and western Kentucky. The region has annual CO 2 emissions of about 265 million metric tonnes (292 million tons), primarily from 122 coal-fired electric generation facilities, some of which burn almost 4.5 million tonnes (5 million tons) of coal per year (U.S. Department of Energy, 2010). Validation Phase (Phase II) field tests gathered pilot data to update the Characterization Phase (Phase I) assessment of options for capture, transportation, and storage of CO 2 emissions in three geological sink types: coal seams, oil fields, and saline reservoirs. Four small-scale field tests were conducted to determine the properties of rock units that control injectivity of CO 2 , assess the total storage resources, examine the security of the overlying rock units that act as seals for the reservoirs, and develop ways to control and measure the safety of injection and storage processes. The MGSC designed field test operational plans for pilot sites based on the site screening process, MVA program needs, the selection of equipment related to CO 2 injection, and design of a data acquisition system. Reservoir modeling, computational simulations, and statistical methods assessed and interpreted data gathered from the field tests. Monitoring, Verification, and Accounting (MVA) programs were established to detect leakage of injected CO 2 and ensure public safety. Public outreach and education remained an important part of the project; meetings and presentations informed public and private regional stakeholders of the results and findings. A miscible (liquid) CO 2 flood pilot project was conducted in the Clore Formation sandstone (Mississippian System, Chesterian Series) at Mumford Hills Field in Posey County, southwestern Indiana, and an immiscible CO 2 flood pilot was conducted in the Jackson sandstone (Mississippian System Big Clifty Sandstone Member) at the Sugar Creek Field in Hopkins County, western Kentucky. Up to 12% incremental oil recovery was estimated based on these pilots. A CO 2 huff puff (HNP) pilot project was conducted in the Cypress Sandstone in the Loudon Field. This pilot was designed to measure and record data that could be used to calibrate a reservoir simulation model. A pilot project at the Tanquary Farms site in Wabash County, southeastern Illinois, tested the potential storage of CO 2 in the Springfield Coal Member of the Carbondale Formation (Pennsylvanian System), in order to gauge the potential for large-scale CO 2 storage and/or enhanced coal bed methane recovery from Illinois Basin coal beds. The pilot results from all four sites showed that CO 2 could be injected into the subsurface without adversely affecting groundwater. Additionally, hydrocarbon production was enhanced, giving further evidence that CO 2 storage in oil reservoirs and coal beds offers an economic advantage. Results from the MVA program at each site indicated that injected CO 2 did not leave the injection zone. Topical reports were completed on the Middle and Late Devonian New Albany Shale and Basin CO 2 emissions. The efficacy of the New Albany Shale as a storage sink could be substantial if low injectivity concerns can be alleviated. CO 2 emissions in the Illinois Basin were projected to be dominated by coal-fired power plants.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2013

A comparison of methods for representing sparsely sampled random quantities.

This report discusses the treatment of uncertainties stemming from relatively few samples of random quantities. The importance of this topic extends beyond experimental data uncertainty to situations involving uncertainty in model calibration, validation, and prediction. With very sparse data samples it is not practical to have a goal of accurately estimating the underlying probability density function (PDF). Rather, a pragmatic goal is that the uncertainty representation should be conservative so as to bound a specified percentile range of the actual PDF, say the range between 0.025 and .975 percentiles, with reasonable reliability. A second, opposing objective is that the representation not be overly conservative; that it minimally over-estimate the desired percentile range of the actual PDF. The presence of the two opposing objectives makes the sparse-data uncertainty representation problem interesting and difficult. In this report, five uncertainty representation techniques are characterized for their performance on twenty-one test problems (over thousands of trials for each problem) according to these two opposing objectives and other performance measures. Two of the methods, statistical Tolerance Intervals and a kernel density approach specifically developed for handling sparse data, exhibit significantly better overall performance than the others.

Romero, Vicente Jose↗
DOE OSTI2012

Epigenomics of Development in Populus

We conducted research to determine the role of epigenetic modifications during tree development using poplar (Populus trichocarpa), a model woody feedstock species. Using methylated DNA immunoprecipitation (MeDIP) or chromatin immunoprecipitation (ChIP), followed by high-throughput sequencing, we are analyzed DNA and histone methylation patterns in the P. trichocarpa genome in relation to four biological processes: bud dormancy and release, mature organ maintenance, in vitro organogenesis, and methylation suppression. Our project is now completed. We have 1) produced 22 transgenic events for a gene involved in DNA methylation suppression and studied its phenotypic consequences; 2) completed sequencing of methylated DNA from eleven target tissues in wildtype P. trichocarpa; 3) updated our customized poplar genome browser using the open-source software tools (2.13) and (V2.2) of the P. trichocarpa genome; 4) produced summary data for genome methylation in P. trichocarpa, including distribution of methylation across chromosomes and in and around genes; 5) employed bioinformatic and statistical methods to analyze differences in methylation patterns among tissue types; and 6) used bisulfite sequencing of selected target genes to confirm bioinformatics and sequencing results, and gain a higher-resolution view of methylation at selected genes 7) compared methylation patterns to expression using available microarray data. Our main findings of biological significance are the identification of extensive regions of the genome that display developmental variation in DNA methylation; highly distinctive gene-associated methylation profiles in reproductive tissues, particularly male catkins; a strong whole genome/all tissue inverse association of methylation at gene bodies and promoters with gene expression; a lack of evidence that tissue specificity of gene expression is associated with gene methylation; and evidence that genome methylation is a significant impediment to tissue dedifferentiation and redifferentiation in vitro.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Effectiveness of State-Level Policies on Solar Market Development in Different State Contexts

In response to public interest in customer-sited distributed solar photovoltaics (PV), state and local policymakers have implemented policy initiatives with the goal of encouraging private investment and building a robust PV market. Policymakers face challenges, including limited budgets and incomplete information about the effectiveness of the various policy options in their specific situation, in crafting and executing policy that supports market development goals. Recent work investigated the effect of the order in which policies are implemented (referred to as 'policy stacking') and the presence of low-cost enabling policies, such as interconnection standards and net metering, can have on the success of states in promoting PV markets. Findings indicate that implementation of interconnection standards and policy related to the valuation of excess electricity (e.g., net metering), along with indicators of long term government support for a solar PV market (e.g., RPS) and a non-policy determinant (population), explain about 70% of the variation among states in new PV capacity. This paper builds on that research to determine the most effective policy strategies for different types of states, as determined by their physical, demographic and macroeconomic context. A number of researchers have investigated the effectiveness of state-level policy using various statistical methods to determine relationships between installed solar PV projects and policy initiatives. In this study, the grouping of states by non-policy factors adds dimension to these analyses by identifying how policies function in different non-policy environments.

14 SOLAR ENERGY↗
DOE OSTI2014

Collaborative Project: The problem of bias in defining uncertainty in computationally enabled strategies for data-driven climate model development

The objective of the project is to develop strategies for better representing scientific sensibilities within statistical measures of model skill that then can be used within a Bayesian statistical framework for data-driven climate model development and improved measures of model scientific uncertainty. One of the thorny issues in model evaluation is quantifying the effect of biases on climate projections. While any bias is not desirable, only those biases that affect feedbacks affect scatter in climate projections. The effort at the University of Texas is to analyze previously calculated ensembles of CAM3.1 with perturbed parameters to discover how biases affect projections of global warming. The hypothesis is that compensating errors in the control model can be identified by their effect on a combination of processes and that developing metrics that are sensitive to dependencies among state variables would provide a way to select version of climate models that may reduce scatter in climate projections. Gabriel Huerta was responsible for leading the effort to develop statistical methods for evaluating these field dependencies. The UT effort will incorporate these developments into MECS, which is a set of python scripts being developed at the University of Texas for managing the workflow associated with data-driven climate model development over HPC resources. We regularly consult with Peter Gleckler (LLNL), Mark Taylor (SNL), and Rich Neale (NCAR) to provide additional insight into issues concerning CAM5 model development and multi-model evaluation of the AR5 archive. This grant was originally assigned to Indiana University where both the PI (Huerta) and the Postdoc (Nosedal) worked on the project from March 1, 2012-March 31, 2013.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Integrating Solar PV in Utility System Operations

This study develops a systematic framework for estimating the increase in operating costs due to uncertainty and variability in renewable resources, uses the framework to quantify the integration costs associated with sub-hourly solar power variability and uncertainty, and shows how changes in system operations may affect these costs. Toward this end, we present a statistical method for estimating the required balancing reserves to maintain system reliability along with a model for commitment and dispatch of the portfolio of thermal and renewable resources at different stages of system operations. We estimate the costs of sub-hourly solar variability, short-term forecast errors, and day-ahead (DA) forecast errors as the difference in production costs between a case with “realistic” PV (i.e., subhourly solar variability and uncertainty are fully included in the modeling) and a case with “well behaved” PV (i.e., PV is assumed to have no sub-hourly variability and can be perfectly forecasted). In addition, we highlight current practices that allow utilities to compensate for the issues encountered at the sub-hourly time frame with increased levels of PV penetration. In this analysis we use the analytical framework to simulate utility operations with increasing deployment of PV in a case study of Arizona Public Service Company (APS), a utility in the southwestern United States. In our analysis, we focus on three processes that are important in understanding the management of PV variability and uncertainty in power system operations. First, we represent the decisions made the day before the operating day through a DA commitment model that relies on imperfect DA forecasts of load and wind as well as PV generation. Second, we represent the decisions made by schedulers in the operating day through hour-ahead (HA) scheduling. Peaking units can be committed or decommitted in the HA schedules and online units can be redispatched using forecasts that are improved relative to DA forecasts, but still imperfect. Finally, we represent decisions within the operating hour by schedulers and transmission system operators as real-time (RT) balancing. We simulate the DA and HA scheduling processes with a detailed unit-commitment (UC) and economic dispatch (ED) optimization model. This model creates a least-cost dispatch and commitment plan for the conventional generating units using forecasts and reserve requirements as inputs. We consider only the generation units and load of the utility in this analysis; we do not consider opportunities to trade power with neighboring utilities. We also do not consider provision of reserves from renewables or from demand-side options. We estimate dynamic reserve requirements in order to meet reliability requirements in the RT operations, considering the uncertainty and variability in load, solar PV, and wind resources. Balancing reserve requirements are based on the 2.5th and 97.5th percentile of 1-min deviations from the HA schedule in a previous year. We then simulate RT deployment of balancing reserves using a separate minute-by-minute simulation of deviations from the HA schedules in the operating year. In the simulations we assume that balancing reserves can be fully deployed in 10 min. The minute-by-minute deviations account for HA forecasting errors and the actual variability of the load, wind, and solar generation. Using these minute-by-minute deviations and deployment of balancing reserves, we evaluate the impact of PV on system reliability through the calculation of the standard reliability metric called Control Performance Standard 2 (CPS2). Broadly speaking, the CPS2 score measures the percentage of 10-min periods in which a balancing area is able to balance supply and demand within a specific threshold. Compliance with the North American Electric Reliability Corporation (NERC) reliability standards requires that the CPS2 score must exceed 90% (i.e., the balancing area must maintain adequate balance for 90% of the 10-min periods). The combination of representing DA forecast errors in the DA commitments, using 1-min PV data to simulate RT balancing, and estimates of reliability performance through the CPS2 metric, all factors that are important to operating systems with increasing amounts of PV, makes this study unique in its scope.

14 SOLAR ENERGY↗
DOE OSTI2015

Introducing Nuclear Data Evaluations of Prompt Fission Neutron Spectra [Slides]

Nuclear data evaluations provide recommended data sets for nuclear data applications such as reactor physics, stockpile stewardship or nuclear medicine. The evaluated data are often based on information from multiple experimental data sets and nuclear theory using statistical methods. Therefore, they are collaborative efforts of evaluators, theoreticians, experimentalists, benchmark experts, statisticians and application area scientists. In this talk, an introductions is given to the field of nuclear data evaluation at the specific example of a recent evaluation of the outgoing neutron energy spectrum emitted promptly after fission from 239 Pu and induced by neutrons from thermal to 30 MeV.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

Collaborative Project: The problem of bias in defining uncertainty in computationally enabled strategies for data-driven climate model development. Final Technical Report.

The objective of the project is to develop strategies for better representing scientific sensibilities within statistical measures of model skill that then can be used within a Bayesian statistical framework for data-driven climate model development and improved measures of model scientific uncertainty. One of the thorny issues in model evaluation is quantifying the effect of biases on climate projections. While any bias is not desirable, only those biases that affect feedbacks affect scatter in climate projections. The effort at the University of Texas is to analyze previously calculated ensembles of CAM3.1 with perturbed parameters to discover how biases affect projections of global warming. The hypothesis is that compensating errors in the control model can be identified by their effect on a combination of processes and that developing metrics that are sensitive to dependencies among state variables would provide a way to select version of climate models that may reduce scatter in climate projections. Gabriel Huerta at the University of New Mexico is responsible for developing statistical methods for evaluating these field dependencies. The UT effort will incorporate these developments into MECS, which is a set of python scripts being developed at the University of Texas for managing the workflow associated with data-driven climate model development over HPC resources. This report reflects the main activities at the University of New Mexico where the PI (Huerta) and the Postdocs (Nosedal, Hattab and Karki) worked on the project.

58 GEOSCIENCES↗
DOE OSTI2017

Climate Change Impacts at Department of Defense

This project is aimed at providing the U.S. Department of Defense (DoD) with a comprehensive analysis of the uncertainty associated with generating climate projections at the regional scale that can be used by stakeholders and decision makers to quantify and plan for the impacts of future climate change at specific locations. The merits and limitations of commonly used downscaling models, ranging from simple to complex, are compared, and their appropriateness for application at installation scales is evaluated. Downscaled climate projections are generated at selected DoD installations using dynamic and statistical methods with an emphasis on generating probability distributions of climate variables and their associated uncertainties. The sites selection and selection of variables and parameters for downscaling was based on a comprehensive understanding of the current and projected roles that weather and climate play in operating, maintaining, and planning DoD facilities and installations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Accident Source Terms for Pressurized Water Reactors with High-Burnup Cores Calculated using MELCOR 1.8.5.

In this study, risk-significant pressurized-water reactor severe accident sequences are examined using MELCOR 1.8.5 to explore the range of fission product releases to the reactor containment building. Advances in the understanding of fission product release and transport behavior and severe accident progression are used to render best estimate analyses of selected accident sequences. Particular emphasis is placed on estimating the effects of high fuel burnup in contrast with low burnup on fission product releases to the containment. Supporting this emphasis, recent data available on fission product release from high-burnup (HBU) fuel from the French VERCOR project are used in this study. The results of these analyses are treated as samples from a population of accident sequences in order to employ approximate order statistics characterization of the results. These trends and tendencies are then compared to the NUREG-1465 alternative source term prescription used today for regulatory applications. In general, greater differences are observed between the state-of-the-art calculations for either HBU or low-burnup (LBU) fuel and the NUREG-1465 containment release fractions than exist between HBU and LBU release fractions. Current analyses suggest that retention of fission products within the vessel and the reactor coolant system (RCS) are greater than contemplated in the NUREG-1465 prescription, and that, overall, release fractions to the containment are therefore lower across the board in the present analyses than suggested in NUREG-1465. The decreased volatility of Cs 2 MoO 4 compared to CsI or CsOH increases the predicted RCS retention of cesium, and as a result, cesium and iodine do not follow identical behaviors with respect to distribution among vessel, RCS, and containment. With respect to the regulatory alternative source term, greater differences are observed between the NUREG-1465 prescription and both HBU and LBU predictions than exist between HBU and LBU analyses. Additionally, current analyses suggest that the NUREG-1465 release fractions are conservative by about a factor of 2 in terms of release fractions and that release durations for in-vessel and late in-vessel release periods are in fact longer than the NUREG-1465 durations. It is currently planned that a subsequent report will further characterize these results using more refined statistical methods, permitting a more precise reformulation of the NUREG-1465 alternative source term for both LBU and HBU fuels, with the most important finding being that the NUREG-1465 formula appears to embody significant conservatism compared to current best-estimate analyses. ACKNOWLEDGEMENTS This work was supported by the United States Nuclear Regulatory Commission, Office of Nuclear Regulatory Research. The authors would like to thank Dr. Ian Gauld and Dr. Germina Ilas, of Oak Ridge National Laboratory, for their contributions to this work. In addition to development of core fission product inventory and decay heat information for use in MELCOR models, their insights related to fuel management practices and resulting effects on spatial distribution of fission products in the core was instrumental in completion of our work.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

Evaluating Equivalent Monte Carlo Calculations

We address several basic issues, primarily whether two sets of MCNP tallies are stochastically equivalent (i.e., that the sets of particle history scores follow the same statistical distribution) and, if di erent, quantify the signi cance level of the di erence. The underlying topics here are of longstanding interest to the statistical community, and no \new" theoretical work needs to be carried out to address them. Related comparisons are useful to code developers in assessing code modi cations and to code users in assessing other simulations and related physical measurements. However, these statistical methods must be applied with care to avoid obtaining misleading results, as we show in several examples. The text is written for MCNP practitioners.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Synthetic data generators for the evaluation of biosurveillance outbreak detection algorithms

The research and development of new algorithmic and statistical methods of outbreak detection is an ongoing research priority in the field of biosurveillance. The early detection of emergent disease outbreaks is crucial for effective treatment and mitigation. New detection methods must be compared to established approaches for proper evaluation. This comparison requires biosurveillance test data that accurately reflects the complexity of the real-world data it will be applied to. While the test and evaluation of new detection methods is best performed on real data, it is often impractical to obtain such data as it is either proprietary or limited in scope. Thus, scientists must turn to synthetic data generation to provide enough data to properly eval- uate new detection methodologies. This paper evaluates three such synthetic data sources: The WSARE dataset, the Noufilay equation-based approach, and the Project Mimic data generator.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Stochastic Approaches for Calculating and Aggregating Detection Probabilities for Nuclear Material Diversion

The goal of this project is to develop statistical methods and tools that can be used to rigorously calculate achieved DPs on individual material strata and then aggregate these to the facility and state levels. In particular, this project is evaluating the utility of stochastic ("Monte Carlo") methods as a potentially more powerful and flexible option to the analytical solutions traditionally employed by IAEA statisticians.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2019

Update to the Long-Term Monitoring System Design Report for USDOE/NNSA Pantex Plant Groundwater Remedial Action Progress

This report presents proposed modifications to the long-term groundwater monitoring well network that was originally developed in 2009 and updated in 2014. The original network was developed using statistical methods, fate and transport modeling, and site-specific knowledge for the evaluation of response actions (corrective/remedial actions) for Pantex Plant and monitoring uncertainties near source areas. The 2014 update was based on an evaluation of perched aquifer monitoring system and data collected during the first Five-Year Review, as well as updated expected conditions based on changing aquifer. Similarly, this update proposes modifications based on an evaluation of the perched aquifer monitoring system during the second Five-Year Review along with updated expected conditions based on changing aquifer conditions.

54 ENVIRONMENTAL SCIENCES↗