136 citations · 640 across the 30 of their papers we have counts for
30 papers
Impacts of permeability heterogeneity and background flow on supercritical CO2 dissolution in the deep subsurface
Scott K. Hansen, Yichen Tao, Satish Karra
Motivated by CO2 capture and sequestration (CCS) design considerations, we consider the coupled effects of permeability heterogeneity and background flow on the dissolution of a su…
The FluidFlower International Benchmark Study: Process, Modeling Results, and Comparison to Experimental Data
Bernd Flemisch, Jan M. Nordbotten, Martin Fernø +30
Successful deployment of geological carbon storage (GCS) requires an extensive use of reservoir simulators for screening, ranking and optimization of storage sites. However, the ti…
Predictive Scale-Bridging Simulations through Active Learning
Satish Karra, Mohamed Mehana, Nicholas Lubbers +12
Throughout computational science, there is a growing need to utilize the continual improvements in raw computational horsepower to achieve greater physical fidelity through scale-b…
AdjointNet: Constraining machine learning models with physics-based codes
Satish Karra, Bulbul Ahmmed, Maruti K. Mudunuru
Physics-informed Machine Learning has recently become attractive for learning physical parameters and features from simulation and observation data. However, most existing methods…
A Comparative Study of Machine Learning Models for Predicting the State of Reactive Mixing
B. Ahmmed, M. K. Mudunuru, S. Karra +2
Accurate predictions of reactive mixing are critical for many Earth and environmental science problems. To investigate mixing dynamics over time under different scenarios, a high-f…
PFLOTRAN-SIP: A PFLOTRAN Module for Simulating Spectral-Induced Polarization of Electrical Impedance Data
B. Ahmmed, M. K. Mudunuru, S. Karra +3
Spectral induced polarization (SIP) is a non-intrusive geophysical method that is widely used to detect sulfide minerals, clay minerals, metallic objects, municipal wastes, hydroca…
Physics-Informed Machine Learning Models for Predicting the Progress of Reactive-Mixing
M. K. Mudunuru, S. Karra
This paper presents a physics-informed machine learning (ML) framework to construct reduced-order models (ROMs) for reactive-transport quantities of interest (QoIs) based on high-f…
Large-scale Inversion of Subsurface Flow Using Discrete Adjoint Method
Shu Wang, Satish Karra, Daniel O'Malley
Sensitivity analysis plays an important role in searching for constitutive parameters (e.g. permeability) subsurface flow simulations. The mathematics behind is to solve a dynamic…
Towards Solving the Navier-Stokes Equation on Quantum Computers
Navamita Ray, Tirtha Banerjee, Balasubramanya Nadiga +1
In this paper, we explore the suitability of upcoming novel computing technologies, in particular adiabatic annealing based quantum computers, to solve fluid dynamics problems that…
Multilevel Graph Partitioning for Three-Dimensional Discrete Fracture Network Flow Simulations
Hayato Ushijima-Mwesigwa, Jeffrey D. Hyman, Aric Hagberg +5
We present a topology-based method for mesh-partitioning in three-dimensional discrete fracture network (DFN) simulations that take advantage of the intrinsic multi-level nature of…
Branching of Hydraulic Cracks in Gas or Oil Shale with Closed Natural Fractures: How to Master Permeability
Saeed Rahimi-Agham, Viet-Tuan Chau, Huynjin Lee +7
While the hydraulic fracturing technology, aka fracking (or fraccing, frac), has become highly developed and astonishingly successful, a consistent formulation of the associated fr…
Using Machine Learning to Discern Eruption in Noisy Environments: A Case Study using CO2-driven Cold-Water Geyser in Chimayo, New Mexico
B. Yuan, Y. J. Tan, M. K. Mudunuru +8
We present an approach based on machine learning (ML) to distinguish eruption and precursory signals of Chimayó geyser (New Mexico, USA) under noisy environments. This geyser can b…
Estimating Failure in Brittle Materials using Graph Theory
M. K. Mudunuru, N. Panda, S. Karra +5
In brittle fracture applications, failure paths, regions where the failure occurs and damage statistics, are some of the key quantities of interest (QoI). High-fidelity models for…
Reduced-Order Modeling through Machine Learning Approaches for Brittle Fracture Applications
A. Hunter, B. A. Moore, M. K. Mudunuru +9
In this paper, five different approaches for reduced-order modeling of brittle fracture in geomaterials, specifically concrete, are presented and compared. Four of the five methods…
Unsupervised Machine Learning Based on Non-Negative Tensor Factorization for Analyzing Reactive-Mixing
V. V. Vesselinov, M. K. Mudunuru, S. Karra +2
Analysis of reactive-diffusion simulations requires a large number of independent model runs. For each high-fidelity simulation, inputs are varied and the predicted mixing behavior…
Quantum Algorithm Implementations for Beginners
Abhijith J., Adetokunbo Adedoyin, John Ambrosiano +31
As quantum computers become available to the general public, the need has arisen to train a cohort of quantum programmers, many of whom have been developing classical computer prog…
Modeling flow and transport in fracture networks using graphs
S. Karra, D. O'Malley, J. D. Hyman +2
Fractures form the main pathways for flow in the subsurface within low-permeability rock. For this reason, accurately predicting flow and transport in fractured systems is vital fo…
CHROTRAN: A mathematical and computational model for in situ heavy metal remediation in heterogeneous aquifers
Scott K. Hansen, Sachin Pandey, Satish Karra +1
Groundwater contamination by heavy metals is a critical environmental problem for which in situ remediation is frequently the only viable treatment option. For such interventions,…
Regression-based reduced-order models to predict transient thermal output for enhanced geothermal systems
M. K. Mudunuru, S. Karra, D. R. Harp +2
The goal of this paper is to assess the utility of Reduced-Order Models (ROMs) developed from 3D physics-based models for predicting transient thermal power output for an enhanced…
Sequential geophysical and flow inversion to characterize fracture networks in subsurface systems
M. K. Mudunuru, S. Karra, N. Makedonska +1
Subsurface applications including geothermal, geological carbon sequestration, oil and gas, etc., typically involve maximizing either the extraction of energy or the storage of flu…
Large-scale Optimization-based Non-negative Computational Framework for Diffusion Equations: Parallel Implementation and Performance Studies
J. Chang, S. Karra, K. B. Nakshatrala
It is well-known that the standard Galerkin formulation, which is often the formulation of choice under the finite element method for solving self-adjoint diffusion equations, does…
A Model for Tracking Fronts of Stress-Induced Permeability Enhancement
K. C. Lewis, Satish Karra, Sharad Kelkar
Using an analogy to the classical Stefan problem, we construct evolution equations for the fluid pore pressure on both sides of a propagating stress-induced damage front. Closed fo…
A model for the degradation of polyimides due to oxidation
Satish Karra, K. R. Rajagopal
Polyimides, due to their superior mechanical behavior at high temperatures, are used in a variety of applications that include aerospace, automobile and electronic packaging indust…
Diffusion of a fluid through a viscoelastic solid
Satish Karra
This paper is concerned with the diffusion of a fluid through a viscoelastic solid undergoing large deformations. Using ideas from the classical theory of mixtures and a thermodyna…