Engineering topics

Wainwright, Haruko

Publications and source records attributed to Wainwright, Haruko.

58 records · Page 4

DOE OSTI2017

Coupling Big Data Analytics and Reactive Transport Modeling for Cost-effective Groundwater Monitoring - 17163

This study presents an innovative approach for sustainable and cost-effective groundwater monitoring. This approach takes advantage of recent advances in various technologies: (1) in situ autonomous sensors, (2) big data analytics, and (3) parallel high-performance computing for flow and reactive transport modeling. In situ sensors are used to periodically measure the key variables (such as pH, redox potential, electrical conductivity, and groundwater level), which control contaminant mobility and the plume spatial and temporal distribution. Based on a limited number of groundwater sampling, the data analytics methods-data mining and machine learning-allow us to identify and quantify the correlations between the in situ-measured variables and contaminant concentrations, and also to detect significant changes associated with the plume mobility. In addition, a state-of-the-art parallel numerical flow and reactive transport simulator Amanzi, and uncertainty quantification software Agni are used to provide an improved physical and mechanistic understanding of the contaminated groundwater system behavior, and to predict the long-term plume distribution for optimizing and adapting the monitoring strategy. Amanzi and Agni were developed as part of the Advanced Simulation Capability for Environmental Management (ASCEM) program of the DOE Office of Environmental Management. Such modeling is critical, particularly, for assessing the impact of climate change and associated hydrological shifts. The developed approach is expected to significantly reduce the groundwater sampling frequency and associated cost. In addition, the real-time information on plume mobility serves as an early warning system, improving the resiliency of contaminated or potentially contaminated sites. We demonstrate this approach using as an example the Savannah River Site (SRS) F-Area, where groundwater is contaminated by various radionuclides, including uranium, tritium and technetium. (authors)

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DOE OSTI2017

A Multiscale Bayesian Data Integration Approach for Mapping Air Dose Rates around the Fukushima Daiichi Nuclear Power Plant - 17166

This paper presents a multiscale data integration method to estimate the spatial distribution of air dose rates in the regional scale around the Fukushima Daiichi Nuclear Power Plant. We integrate various types of datasets, such as ground-based walk and car surveys, and airborne surveys, all of which have different scales, resolutions, spatial coverage, and accuracy. This method is based on geo-statistics to represent spatial heterogeneous structures, and also on Bayesian hierarchical models to integrate multiscale, multi-type datasets in a consistent manner. The Bayesian method allows us to quantify the uncertainty in the estimates, and to provide the confidence intervals that are critical for robust decision-making. Although this approach is primarily data-driven, it has great flexibility to include mechanistic models for representing radiation transport or other complex correlations. We demonstrate our approach using three types of datasets collected at the same time over Fukushima City, Japan: (1) coarse-resolution airborne surveys covering the entire area, (2) car surveys along major roads, and (3) walk surveys in multiple neighborhoods. Results show that the method can successfully integrate three types of datasets in a consistent manner, and create an integrated map (including the confidence intervals) of air dose rates over the domain in high resolution. (authors)

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DOE OSTI2017

Applications Of The Advanced Simulation Capability For Environmental Management (ASCEM) - 17253

Three applications were completed using the Advanced Simulation Capability for Environmental Management (ASCEM), supported by the U.S. Department of Energy's Office of Environmental Management. These analyses were performed for the Savannah River Site (SRS) F-Area, the Los Alamos National Laboratory (LANL) Site, and the Nevada National Security Site (NNSS). At the SRS F-Area, ASCEM simulations were used to help develop cost-effective, long-term monitoring approaches for science-based site closure and management. The simulation results were used to develop a mechanistic understanding of correlations between contaminant concentrations and master variables that can be used for monitoring. At LANL, the ASCEM simulator Amanzi was integrated with a LANL-based decision support tool for the LANL onsite program. A saturated-zone site model focused on a key area of interest was developed and calibrated to measured data. This model was applied to quantify uncertainty and provide decision support to inform chromium remediation efforts. To support assessment of groundwater contamination at NNSS, ASCEM was used to calibrate hydrologic properties and flow directions using water levels observed under steady-state conditions and during four different pump tests on Pahute Mesa. The flow fields generated from these pump tests will be used to simulate transport pathways from underground nuclear tests to compare with field observations and to test alternative conceptual models of transport, which will ultimately facilitate better placement of monitoring wells. (authors)

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DOE OSTI2017

Integrated Modeling and Monitoring Technologies for Environmental Resiliency in Nuclear Energy

Environmental concerns pose the biggest challenge for the development of the nuclear energy industry, often preventing or limiting the construction of new plants and radioactive waste disposal facilities. There are currently numerous sites in the world at which surface and groundwater are contaminated by nuclear weapon production and testing, as well as due to nuclear power plant accidents. Many of those sites remain unused or inhabited primarily due to public concern. Improving scientific and technical capabilities to develop environmental resiliency is critical not only to address existing contamination, but also to build public confidence and support for nuclear energy. One of the obstacles in remediation is to tackle a large volume of contaminated soil and groundwater with relatively low concentrations. Although many contaminant removal technologies have developed (e.g., pump-and-treat systems), they are often expensive and not cost-effective for low-concentration radionuclide contamination. Although soil removal is often the most effective, it produces a large amount of waste that has to be stored securely for a long period of time or treated at the special treatment facilities. Transporting a large amount of waste may also produce greenhouse gases and disrupt local communities. Recently, the concept of 'sustainable remediation' has been proposed to improve a remediation system as a whole, including the consideration of cost effectiveness, reduction of greenhouse emission, waste production and energy usage. Rather than just focusing on removing contaminants, the sustainable remediation highlights the use of passive remediation technologies to immobilize radionuclides along with the application of monitored natural attenuation and longer institutional control. However, such passive technologies and monitored natural attenuation require the increased burden of proof to ensure that those immobilized contaminants would not compromise the long-term public health. Although contaminant transport modeling and risk assessments are always performed, they sometimes fail due to the lack of understanding the contaminant transport proposes at the time of the risk assessment or due to some unexpected processes. Monitoring technologies are critical to ensure the long-term stability of such remediated sites, and also to address the public concerns. Recent advances in in situ sensor technologies and spatially-extensive mapping technologies (such as unmanned aerial vehicles, airborne mapping, geophysics) have improved monitoring of contamination over space and time. In addition to new sensing devices, data management and data analytics technologies have been expanded to manage and process large spatial and temporal datasets. Such technologies can be used to reduce monitoring cost significantly, and to facilitate long-term monitoring. In this study, we demonstrate our data analytics technologies for two nuclear-related contaminated sites: (1) air dose rate monitoring in Fukushima prefecture, and (2) groundwater monitoring at the Savannah River Site F-Area. We are developing the spatial and temporal data integration methodologies to integrate various spatially extensive data and time-series data for improving long-term monitoring. Such monitoring technologies and experiences at the existing contaminated sites can be transferred to build a better monitoring system for nuclear facilities, such as nuclear power plants or nuclear waste storages.

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