Multiphase porous medium systems arise routinely in natural and engineered systems and span applications in the geosciences, process engineering, and the biomedical field. However, traditional models for understanding these systems suffer from a lack of connection to the microscale, where the physics are better understood, than at the macroscale, where the models are formulated and solved. Miller’s team seeks to bridge these two scales by using leadership-class computing to develop a new generation of multiphase flow models for porous medium systems.
A prior INCITE allocation resulted in a rigorous derivation and computational confirmation of a hysteretic-free equation of state involving capillary pressure that applies to both equilibrium and dynamic conditions; the establishment of the precision in thermodynamically constrained averaging theory (TCAT) variables as a function of domain size; a new evolution equation for the geometric orientation tensor; a promising approximation for the velocity of the fluid–fluid interface; the formulation of evolution and constraint equations for mean and Gaussian curvature; and substantial progress on computational confirmation of the new theoretical developments. In this project, the team will develop a macroscale simulator implementing the new TCAT model and paving the way for rigorous evaluation and validation of the entire model and not just the component parts individually. The researchers anticipate that this work will hasten the development of a new generation of TCAT-model-based simulators yielding higher fidelity simulation of multiphase porous medium systems than is possible using current approaches.
|Source||Hours||Start Date||End Date|
|DOE INCITE PROGRAM||5,000||2020-01-01||2020-12-31|
|DOE INCITE PROGRAM||340,000||2020-01-01||2020-12-31|
|DOE INCITE PROGRAM||2,500||2019-10-02||2019-12-31|
|DOE INCITE PROGRAM||61,000||2019-01-01||2019-12-31|
|DOE INCITE PROGRAM||1,500,000||2019-01-01||2019-07-31|
|DOE INCITE PROGRAM||5,000||2019-01-01||2019-12-31|
|DOE INCITE PROGRAM||18,600,000||2019-01-01||2019-07-31|