“Scaling” the Challenges in Subsurface Simulations

Integrating biogeochemical models across multiple time and spatial scales

Timothy D. Scheibe (project webpage)
Pacific Northwest National Laboratory

Important biogeochemical processes (e.g., microbial respiration) are best understood and defined at very small scales, typically ranging from molecular to cellular to small batch and column experimental scales (with time scales of minutes to days). However, the scales at which we are interested in predicting phenomena (e.g., fate and transport of contaminants in aquifers) are very large, typically ranging from meters to kilometers (with time scales of months to years or even centuries). This problem is aggravated by the variability of natural subsurface properties that exist across the broad spectrum of spatial and temporal scales. Recent experimental research has revealed important details about the physical, chemical, and biological mechanisms involved in these processes at a variety of scales ranging from molecular to laboratory scales. However, it has not been practical or possible to translate detailed knowledge at small scales into reliable predictions of field-scale phenomena important for environmental management applications.

From a computational standpoint, this problem is manifested in the fact that numerical grids cannot simultaneously cover large (e.g., field-scale) domains and achieve extremely high (e.g., pore-scale) resolution. In simple terms, we cannot computationally solve problems at the scale of tens of meters (let alone kilometers) while explicitly resolving pore and grain structures, even if we had characterization data to populate such a model. A large assortment of numerical simulation tools have been developed, each with its own characteristic scale. However, integration of these tools into a coherent multi-scale modeling framework has not been seriously attempted in the subsurface modeling field (although other disciplines have made significant advances in this area). Furthermore, most applications of the available simulation tools do not utilize high-performance computational facilities because of the investment required to parallelize and optimize performance of codes, the complexities of data management and visualization, and other barriers (perceived or real) between the computational and domain sciences.

This project will customize and apply existing multiscale hybrid modeling methodologies to subsurface science, in the context of high-performance computing, to advance both scientific understanding and predictive capability that is applicable to field-scale problems.

Biogeochemical reactions occurring at very small scales often control the subsurface mobility of contaminant metals and radionuclides but are poorly simulated at the field scale. Hybrid modeling methodologies adapted for use on high performance computers that address issues of scale will be developed and evaluated against benchmark data to provide more accurate descriptions of subsurface processes controlling the mobility of contaminants at DOE facilities.

Science Application: Groundwater Reactive Transport Modeling and Simulation

Project Title: Hybrid Numerical Methods for Multiscale Simulations of Subsurface Biogeochemical Processes

Principal Investigator: Timothy D. Scheibe
Affiliation: Pacific Northwest National Laboratory

Project Webpage: http://subsurface.pnl.gov

Participating Institutions and Co-Investigators:
Idaho National Laboratory - Paul Meakin, George D. Redden
Oak Ridge National Laboratory - Scott C. Brooks
Pacific Northwest National Laboratory - Timothy D. Scheibe (PI), Bruce Palmer, Karen Schuchardt, Alexandre M. Tartakovsky
University of California at San Diego - Daniel M. Tartakovsky

Funding Partners: Office of ScienceOffice of Advanced Scientific Computing Research, and Office of Biological and Environmental Research

Budget and Duration: Approximately $1.1 million per year for four years 1

Other SciDAC groundwater efforts



1Subject to acceptable progress review and the availability of appropriated funds

 


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