Daniel M. Tartakovsky

Daniel M. Tartakovsky

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Research Focus & Metrics

Academic Career Length

29Years (1998-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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22 February 2022 in Reviews of Geophysics
Verified
Journal Article
Open access
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HVHari Viswanathan
Hari Viswanathan
JAJonathan Ajo-Franklin
Jonathan Ajo-Franklin
Jens BirkholzerJens Birkholzer
Jens Birkholzer
JCJ. W. Carey
J. W. Carey
YGY. Guglielmi
Y. Guglielmi
DTDaniel M. Tartakovsky
Daniel M. Tartakovsky
Quantitative predictions of natural and induced phenomena in fractured rock is one of the great challenges in the Earth and Energy Sciences with far‐reaching economic and environmental impacts. Fractures occupy a very small volume of a subsurface formation but often dominate fluid flow, solute transport and mechanical deformation behavior. They play a central role in CO2 sequestration, nuclear waste disposal, hydrogen storage, geothermal energy production, nuclear nonproliferation, and hydrocarbon extraction. These applications require predictions of fracture‐dependent quantities of interest such as CO2 leakage rate, hydrocarbon production, radionuclide plume migration, and seismicity; to be useful, these predictions must account for uncertainty inherent in subsurface systems. Here, we review recent advances in fractured rock research covering field‐ and laboratory‐scale experimentation, numerical simulations, and uncertainty quantification. We discuss how these have greatly improved the fundamental understanding of fractures and one's ability to predict flow and transport in fractured systems. Dedicated field sites provide quantitative measurements of fracture flow that can be used to identify dominant coupled processes and to validate models. Laboratory‐scale experiments fill critical knowledge gaps by providing direct observations and measurements of fracture geometry and flow under controlled conditions that cannot be obtained in the field. Physics‐based simulation of flow and transport provide a bridge in understanding between controlled simple laboratory experiments and the massively complex field‐scale fracture systems. Finally, we review the use of machine learning‐based emulators to rapidly investigate different fracture property scenarios and accelerate physics‐based models by orders of magnitude to enable uncertainty quantification and near real‐time analysis.

1 January 2013 in Advances in Water Resources
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Journal Article
Closed access
DTDaniel M. Tartakovsky
Daniel M. Tartakovsky

18 March 2019 in Transport in Porous Media
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Journal Article
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DTDaniel M. Tartakovsky
Daniel M. Tartakovsky
MDMarco Dentz
Marco Dentz

1 January 2020 in SIAM Journal on Scientific Computing
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Journal Article
Closed access
HLHannah Lu
Hannah Lu
DTDaniel M. Tartakovsky
Daniel M. Tartakovsky
Dynamic mode decomposition (DMD), which belongs to the family of singular-value decompositions (SVDs), is a popular tool of data-driven regression. While multiple numerical tests demonstrated the p...

1 April 2020 in Journal of Computational Physics
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Journal Article
Closed access
HLHannah Lu
Hannah Lu
DTDaniel M. Tartakovsky
Daniel M. Tartakovsky

15 October 2020 in Stochastic Environmental Research and Risk Assessment
Verified
Journal Article
Closed access
PDF file
ZZZitong Zhou
Zitong Zhou
DTDaniel M. Tartakovsky
Daniel M. Tartakovsky