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Faybishenko, Boris

Publications and source records attributed to Faybishenko, Boris.

6 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Soft interface instability and gas flow channeling in low-permeability deformable media

Understanding gas percolation through a clay layer or a shale formation is of great importance for the development of a geologic repository for nuclear waste disposal, a subsurface system for gas storage, and an engineering approach for hydrocarbon extraction from unconventional reservoirs. Gas injection experiments have revealed complex dynamic behaviours of gas percolation through water saturated compacted bentonite, characterized by a high breakthrough pressure, rapid breakthrough, a pressure/stress decay after the breakthrough, a relatively high migration rate, high-frequency periodic/nonperiodic variations in flow rate, stepwise rate reductions during relaxation, and low gas saturation over the whole process, all indicating channelling nature of the processes. Using linear stability analyses, we show that this channelling can autonomously emerge from the instability of the deformable interface between the injected gas and the compacted bentonite matrix driven by local stress concentration, pore dilation, and hydrologic gradient. Channel patterns formed would possess a fractal geometry. We further show that, once a percolating channel is established, the gas injected would percolate through the channel in a chain of gas bubbles, also due to the interface instability, resulting in periodic/chaotic variations in gas flow rate. Our work provides a unified explanation for key features observed for gas percolation in low-permeability deformable media. The work also suggests a possibility of designing an engineered barrier system for a nuclear waste repository that can have controllable gas release while limit water transport.

Bentonite↗
DOE OSTI2024

International Collaboration Activities in Geologic Disposal Research (FY24 Progress Report)

This report describes the FY24 status of international collaboration regarding geologic disposal research in the Office of Spent Fuel and Waste Science and Technology (SFWST) in the Department of Energy’s (DOE) Office of Nuclear Energy (NE). The mission of SFWST is to provide confidence in the safe long-term management of the nation’s spent nuclear fuel and high-level radioactive waste by reducing uncertainty and advancing technology for extended storage, transportation, and geologic disposal.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

QA/QC-ed Groundwater Level Time Series in PLM-1 and PLM-6 Monitoring Wells, East River, Colorado (2016-2022)

This data set contains QA/QC-ed (Quality Assurance and Quality Control) water level data for the PLM1 and PLM6 wells. PLM1 and PLM6 are location identifiers used by the Watershed Function SFA project for two groundwater monitoring wells along an elevation gradient located along the lower montane life zone of a hillslope near the Pumphouse location at the East River Watershed, Colorado, USA. These wells are used to monitor subsurface water and carbon inventories and fluxes, and to determine the seasonally dependent flow of groundwater under the PLM hillslope. The downslope flow of groundwater in combination with data on groundwater chemistry (see related references) can be used to estimate rates of solute export from the hillslope to the floodplain and river. QA/QC analysis of measured groundwater levels in monitoring wells PLM-1 and PLM-6 included identification and flagging of duplicated values of timestamps, gap filling of missing timestamps and water levels, removal of abnormal/bad and outliers of measured water levels. The QA/QC analysis also tested the application of different QA/QC methods and the development of regular (5-minute, 1-hour, and 1-day) time series datasets, which can serve as a benchmark for testing other QA/QC techniques, and will be applicable for ecohydrological modeling. The package includes a Readme file, one R code file used to perform QA/QC, a series of 8 data csv files (six QA/QC-ed regular time series datasets of varying intervals (5-min, 1-hr, 1-day) and two files with QA/QC flagging of original data), and three files for the reporting format adoption of this dataset (InstallationMethods, file level metadata (flmd), and data dictionary (dd) files).QA/QC-ed data herein were derived from the original/raw data publication available at Williams et al., 2020 (DOI: 10.15485/1818367). For more information about running R code file (10.15485_1866836_QAQC_PLM1_PLM6.R) to reproduce QA/QC output files, see README (QAQC_PLM_readme.docx). This dataset replaces the previously published raw data time series, and is the final groundwater data product for the PLM wells in the East River. Complete metadata information on the PLM1 and PLM6 wells are available in a related dataset on ESS-DIVE: Varadharajan C, et al (2022). https://doi.org/10.15485/1660962. These data products are part of the Watershed Function Scientific Focus Area collection effort to further scientific understanding of biogeochemical dynamics from genome to watershed scales. 2022/09/09 Update: Converted data files using ESS-DIVE’s Hydrological Monitoring Reporting Format. With the adoption of this reporting format, the addition of three new files (v1_20220909_flmd.csv, V1_20220909_dd.csv, and InstallationMethods.csv) were added. The file-level metadata file (v1_20220909_flmd.csv) contains information specific to the files contained within the dataset. The data dictionary file (v1_20220909_dd.csv) contains definitions of column headers and other terms across the dataset. The installation methods file (InstallationMethods.csv) contains a description of methods associated with installation and deployment at PLM1 and PLM6 wells. Additionally, eight data files were re-formatted to follow the reporting format guidance (er_plm1_waterlevel_2016-2020.csv, er_plm1_waterlevel_1-hour_2016-2020.csv, er_plm1_waterlevel_daily_2016-2020.csv, QA_PLM1_Flagging.csv, er_plm6_waterlevel_2016-2020.csv, er_plm6_waterlevel_1-hour_2016-2020.csv, er_plm6_waterlevel_daily_2016-2020.csv, QA_PLM6_Flagging.csv). The major changes to the data files include the addition of header_rows above the data containing metadata about the particular well, units, and sensor description. 2023/01/18 Update: Dataset updated to include additional QA/QC-ed water level data up until 2022-10-12 for ER-PLM1 and 2022-10-13 for ER-PLM6. Reporting format specific files (v2_20230118_flmd.csv, v2_20230118_dd.csv, v2_20230118_InstallationMethods.csv) were updated to reflect the additional data. R code file (QAQC_PLM1_PLM6.R) was added to replace the previously uploaded HTML files to enable execution of the associated code. R code file (QAQC_PLM1_PLM6.R) and ReadMe file (QAQC_PLM_readme.docx) were revised to clarify where original data was retrieved from and to remove local file paths.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Meteorological and hydrological parameters for 17 locations of meteorological stations of the East River Watershed

The Data Package includes a set of csv files of input meteorological parameters for locations of 17 meteorological stations within the East River watershed. These meteorological datasets were downloaded from the (1) PRISM database--monthly precipitation, air temperature (minimum, mean, and maximum), vapor pressure deficit (minimum and maximum), and dewpoint temperature, and (2) NCEP/NCAR Reanalysis database – wind database. The datasets were used for calculations of the Potential Evapotranspiration (ETo), Actual Evapotranspiration (ET), Standard Precipitation Index (SPI) , Standard Evapotranspiration-Precipitation Index (SPEI) for the period from 1966 to 2021. The main research questions addressed are: the evaluation of the long-term temporal trends of climatic parameters, hierarchical clustering, and areal mapping/zonation of the East River watershed. Calculations were conducted in the Rstudio environment. The dataset additionally includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type.The input datasets were downloaded from (a) PRISM database (the Northwest Alliance for Computational Science and Engineering at the Oregon State University), and (b) NCEP/NCAR Reanalysis database.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Long-term missing value imputation for time series data using deep neural networks

We present an approach that uses a deep learning model, in particular, a MultiLayer Perceptron, for estimating the missing values of a variable in multivariate time series data. We focus on filling a long continuous gap (e.g., multiple months of missing daily observations) rather than on individual randomly missing observations. Our proposed gap filling algorithm uses an automated method for determining the optimal MLP model architecture, thus allowing for optimal prediction performance for the given time series. We tested our approach by filling gaps of various lengths (three months to three years) in three environmental datasets with different time series characteristics, namely daily groundwater levels, daily soil moisture, and hourly Net Ecosystem Exchange. We compared the accuracy of the gap-filled values obtained with our approach to the widely used R-based time series gap filling methods ImputeTS and mtsdi. The results indicate that using an MLP for filling a large gap leads to better results, especially when the data behave nonlinearly. Thus, our approach enables the use of datasets that have a large gap in one variable, which is common in many long-term environmental monitoring observations.

97 MATHEMATICS AND COMPUTING↗