Blar i Department of Energy and Petroleum Engineering (TN-IEP) på forfatter "Evje, Steinar"
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A pore-to-core scale investigation of hysteresis for CO2 storage processes using a discrete-domain method
Engarnevis, Nazila (Master thesis, 2022)In this thesis we have studied a discrete-domain method that is implemented to demonstrate the immiscible, two-phase displacements of two fluids, namely CO2 and water (or brine) in an originally water-saturated reservoir ... -
A Three-Phase Model for Tumor Cell Migration
Urdal, Jone (Masteroppgave/UIS-TN-IEP/2018;, Master thesis, 2018-06)Flow of interstitial fluid (IF) has proven to have a significant effect on the migration of cancer cells through tissue due to the tumor cells ability to sense flow by secreting chemokines that convect in the flow direction ... -
Analysis of Wired Drill Pipe data while using the Controlled Mud Level technology
El Demerdash Ali, Omar (Master thesis, 2021)Managed Pressure Drilling (MPD) methods are used to drill wells with narrow pressure margins in a safe, efficient, and economical way. Controlled Mud Level (CML) is a dual-gradient MPD technique which is found effective ... -
A compressible viscous three-phase model for porous media flow based on the theory of mixtures
Qiao, Yangyang; Evje, Steinar (Peer reviewed; Journal article, 2020)In this paper we focus on a general model to describe compressible and immiscible three-phase flow in porous media. The underlying idea is to replace Darcy’s law by more general momentum balance equations. In particular, ... -
Fluid-sensitive migration mechanisms predict association between metastasis and high interstitial fluid pressure in pancreatic cancer
Nævdal, Geir; Rofstad, Einar K; Søreide, Kjetil; Evje, Steinar (Peer reviewed; Journal article, 2022)A remarkable feature in pancreatic cancer is the propensity to metastasize early, even for small, early stage cancers. We use a computer-based pancreatic model to simulate tumor progression behavior where fluid-sensitive ... -
Identification of Nonlinear Conservation Laws Using Symbolic Neural Networks
Qing, Li (PhD Theses;731, Doctoral thesis, 2023)Nonlinear dynamical systems are omnipresent in nature, commonly seen in many disciplines such as physics, biology, chemistry, climate science, and engineering. In this thesis, we introduce several new ideas by integrating ... -
Mathematical and Numerical Modelling of Lithium Battery
Bandara, Yasas (Master thesis, 2021)Application of lithium-ion batteries have increased in recent years due to their high energy density, low weight and smaller form factor. Machine learning algorithms are used in lithium-ion battery management systems due ... -
Mathematical modeling of cancer cell migration - from in vitro to in vivo
Waldeland, Jahn Otto (PhD thesis UiS;511, Doctoral thesis, 2020-05)Tumors has been the object of computational model studies for nearly five decades. The early models considered simple tumor growth based on nutrients, whereas models now can simulate from microscale gene expressions in ... -
On the numerical discretization of a tumor progression model driven by competing migration mechanisms
Qiao, Yangyang; Li, Qing; Evje, Steinar (Peer reviewed; Journal article, 2021)In this work we explore a recently proposed biphasic cell-fluid chemotaxis-Stokes model which is able to represent two competing cancer cell migration mechanisms reported from experimental studies. Both mechanisms depend ... -
Ostwald ripening of trapped gas bubbles in porous media : A pore-scale perspective
Singh, Deepak (PhD thesis UiS;747, Doctoral thesis, 2024)Ostwald ripening of gas bubbles is a thermodynamic process for mass transfer, which is significant for both foam-enhanced oil recovery and underground gas storage. During recovery, it can impact the macroscopic efficiency. ... -
Solving Nonlinear Conservation Laws of Partial Differential Equations Using Graph Neural Networks
Li, Qing; Geng, Jiahui; Evje, Steinar; Rong, Chunming (Peer reviewed; Journal article, 2023)Nonlinear Conservation Laws of Partial Differential Equations (PDEs) are widely used in different domains. Solving these types of equations is a significant and challenging task. Graph Neural Networks (GNNs) have recently ...