1.  155 South 1452 East Room 380  Salt Lake City, Utah 84112  1-801-585-1233 This research was sponsored by the National Nuclear Security Administration under the Accelerating Development of Retrofitable CO2 Capture Technologies through Predictivity program through DOE Cooperative Agreement DE-NA0000740 Todd Harman Department of Mechanical Engineering University of Utah Isaac Hunsaker Department of Chemical Engineering University of Utah

2. Year 1: Proof-of-concept design for solving the RTE using RMCRT within ARCHES/UINTAH. Year 2: Demonstration of a fully-coupled problem using RMCRT within ARCHES/UINTAH. Scalability demonstration. Year 3: Full burner scale V&V/UQ demonstration using RMCRT.

3. Reverse Monte-Carlo Ray Tracing is an all-to-all method. All geometry information and radiative properties for the entire computational domain must reside in each processor’s memory*. Very restrictive For example: 3 double precision arrays on a 500^3 domain = 3 Gbytes * Xiaojing Sun. Reverse Monte Carlo Ray-Tracing For Radiative Heat Transfer in Combustion Systems. PhD Dissertation, University of Utah, 2009.

4. Use data from coarse regions/levels in the RMCRT calculations. (3 double precision arrays on a 250^3 domain = 375 Mbytes) Utilize the multi-level infrastructure in Uintah to project data from fine to coarse levels. What is a Level? Collection of patches all with the same cell spacing. Extents are determined by user or developer Levels are overlapping. Communication between levels with refining and coarsening operators.

5. Coarsen operator: Mass/volume weighted or summation of fine data

6. Refine Operator:

7. CFD: Always computed on the fine level. RMCRT: 2 Level: RMCRT on a coarse level, CFD on a fine level. “Data Onion”: RMCRT & CFD on fine level, data pulled from other coarse levels.

8. 2 Levels

9. Advantages: Simple Little to no heavy lifting, AMRICE, AMRMPM, AMRMPMICE already using multi-level code. Previous work suggests that this may be sufficient for pool fires*. Disadvantages: Accuracy (maybe). Gautham Krishnamoorthy. Predicting Radiative Heat Transfer in Parallel Computations of Combustion. PhD Dissertation, University of Utah, 2005.

10. N-Levels

11. 1D User inputs: Number of levels Refinement ratio between levels Step size Size of fine level patch Coax UCF into mimicking a composite mesh that moves.

12. Advantages: Improved accuracy over 2 levels Disadvantages: Increased complexity Coarsening data multiple times More expensive.

13. Initial development in a light weight test-bed component. Advantages Rapid testing of 2-level and data onion scheme. No disruptions to ARCHES users. Scalability studies focus on RMCRT tasks. Encapsulate verification code, no pollution in ARCHES Forces RMCRT tasks to be portable. Increased developer productivity from faster recompile times.

14. Implemented a lightweight multi-level test-bed component. Implemented pseudo CFD and ray tracing tasks. (RMCRT not quite ready) Implemented the 2 level scheme with pseudo CFD & ray tracing tasks. Implemented the data onion scheme, with pseudo CFD and ray tracing tasks.

15. Implementation of RMCRT tasks within the test-bed. (single level and single patch) 80% Complete: Implementation of coarsening & refining code 75% Complete: Improve portability of RMCRT tasks. 50-70% Complete: Integration of RMCRT tasks within ARCHES (single level and single patch)

16. RMCRT tasks with multiple patches. Verification of RMCRT tasks on a single patch. Scalability studies of RMCRT tasks on a single level. Implement 2-L RMCRT in test-bed. Accuracy versus refinement ratio studies Scalability Studies Implement 2-L RMCRT in ARCHES. Implement RMCRT with a data onion in the test-bed. Accuracy versus number of levels, refinement ratio, size of fine patch. Scalability Studies

17. On track for meeting year 1 deliverable: “Proof-of-concept design for solving the radiative transport equation using RMCRT within the ARCHES/UINTAH framework.”