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About Predictivity for CO2 Capture Deployment

Overall Project Objectives

There is an ever increasing national awareness that an effective, sustainable energy future must include fossil-fuel utilization that deploys cost-efficient technologies (both new and retrofit) that significantly improve energy efficiencies and reduce CO2 emissions over existing electric power and thermal generating plants.

In order to make decisions about future energy options, many different stakeholders need to predict the consequences of various designs and build options. However, all stakeholders need confidence that the simulation is appropriately accurate. Specifically, stakeholders need to have available not only the predicted consequences of the decisions but to quantitatively know the uncertainties and errors in those predictions at full scale.

The overall objective of this work is to produce methods and tools that couple advanced simulation and computing with selected key experimental observations to accelerate the deployment of new, cost-effective technologies for increased efficiency and for capturing CO2. Work will be done with selected industrial partners to produce simulations with quantified predictivity that will be used by industry for the technological, economic and environmental consequences of two retrofit technology options for the large installed base of electric power and thermal energy: oxy-fuel technology and mineralization technology. Predictivity assumes that the simulation inherently holds the ability to extrapolate from a current knowledge base to future effects. To be useful the simulation must simultaneously provide the uncertainty in that estimate. This uncertainty arises from uncertain models and uncertain measurements, requiring a new level of collaboration between measurements and simulations. To rapidly implement a new technology requires quantified predictive capability for scaling and for site specific variations.

To ensure that these tools are useful to industry, these projects will not only demonstrate their applicability using academic software but we will also perform simultaneous predictions, verification, validation and uncertainty quantification with both the software developed at the University of Utah under previous NNSA funding (UINTAH and ARCHES) but also with commercially available software (CDAdapco's Star CCM+). All of the tools developed under this program will be made available over the internet under open source licensing from the University of Utah to all potentially interested users. This includes the simulation software (UINTAH, ARCHES, and any addon CCM+ components) and the V&V/UQ tools.

To use this level of simulation and modeling (with verification and validation uncertainty quantified) to more rapidly deploy oxy-fuel and mineralization retrofit technologies will require peta- to exa-scale computing capability.The demonstration of this capability and the tools developed to do so, will not only assist new CO2 capture technologies but will be applicable to many other simulation-based applications.

Predictivity for CO2 Capture Deployment