MFIX-DEM Phi: Performance and Capability Improvements Towards Industrial Grade Open-source DEM Framework with Integrated Uncertainty Quantification
[1];
- Arizona State Univ., Tempe, AZ (United States). School of Computing, Informatics, Decisions Systems Engineering (SCIDSE)
- Arizona State Univ., Tempe, AZ (United States). School for Engineering of Matter, Transport and Energy (SEMTE)
- Arizona State Univ., Tempe, AZ (United States)
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Two major challenges hinder the effective use and adoption of multiphase computational fluid dynamics tools by the industry. The first is the need for significant computational resources, which is inversely proportional to the accuracy of solutions due to computational intensity of the algorithms. The second barrier is assessing the prediction credibility and confidence in the simulation results. In this project, a multi-tiered approach has been proposed under four broad activities to overcome these challenges while addressing all of the objectives outlined in FOA-0001238 through Phases 1 and 2 of the project. The present report consists of the results for only Phase 1, which was the funded performance period. From the start the project, all of the objectives outlined in FOA were addressed through four major activity tasks in an integrated and balanced fashion to improve adoption of MFIX suite of solvers for industrial use. The first task aimed to improve the performance of MFIX-DEM specifically targeting to acquire the peak performance on Intel Xeon and Xeon Phi based systems, which are expected to be one of the primary high-performance computing platforms both affordable and available for the industrial users in the next two to five years. However, due to a number of changes in course of the project, the scope of the performance improvements related task was significantly reduced to avoid duplicate work. Hence, more emphasis was placed on the other three tasks as discussed below.The second task aimed at physical modeling enhancements through implementation of polydispersity capability and validation of heat transfer models in MFIX. An extended verification and validation (V&V) study was performed for the new polydispersity feature implemented in MFIX-DEM both for granular and coupled gas-solid flows. The features of the polydispersity capability and results for an industrially relevant problem were disseminated through journal papers (one published and one under review at the time of writing of the final technical report). As part of the validation efforts, another industrially relevant problem of interest based on rotary drums was studied for several modes of heat transfer and results were presented in conferences. Third task was aimed towards an important and unique contribution of the project, which was to develop a unified uncertainty quantification framework by integrating MFIX-DEM with a graphical user interface (GUI) driven uncertainty quantification (UQ) engine, i.e., MFIX-GUI and PSUADE. The goal was to enable a user with only modest knowledge of statistics to effectively utilize the UQ framework offered with MFIX-DEM Phi to perform UQ analysis routinely. For Phase 1, a proof-of-concept demonstration of the proposed framework was completed and shared. Direct industry involvement was one of the key virtues of this project, which was performed through forth task. For this purpose, even at the proposal stage, the project team received strong interest in the proposed capabilities from two major corporations, which were further expanded throughout Phase 1 and a new collaboration with another major corporation from chemical industry was also initiated. The level of interest received and continued collaboration for the project during Phase 1 clearly shows the relevance and potential impact of the project for the industrial users.
- Research Organization:
- Arizona State Univ., Tempe, AZ (United States)
- Sponsoring Organization:
- USDOE Office of Fossil Energy (FE)
- Contributing Organization:
- Lawrence Livermore National Laboratory (LLNL)
- DOE Contract Number:
- FE0026393
- OSTI ID:
- 1439328
- Report Number(s):
- 2018-001
- Country of Publication:
- United States
- Language:
- English
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