FUEL-FLEXIBLE GASIFICATION-COMBUSTION TECHNOLOGY FOR PRODUCTION OF H2 AND SEQUESTRATION-READY CO2

Rizeq, George ; West, Janice ; Frydman, Arnaldo ; Subia, Raul ; Zamansky, Vladimir ; Wiltowski, Tomasz ; Miles, Tom ; Springsteen, Bruce

National Energy Technology Laboratory (U.S.)

关键词: Coal Gasification;    Hydrogen Production;    Combustion;    08 Hydrogen;    01 Coal, Lignite, And Peat;   

DOI  :  10.2172/794295
RP-ID  :  FC26-00NT40974--06
RP-ID  :  FC26-00NT40974
RP-ID  :  794295

美国|英语

来源: UNT Digital Library

【 摘 要 】

Further development of a combustion Large Eddy Simulation (LES) code for the design of advanced gaseous combustion systems is described in this sixth quarterly report. CFD Research Corporation (CFDRC) is developing the LES module within the parallel, unstructured solver included in the commercial CFD-ACE+ software. In this quarter, in-situ adaptive tabulation (ISAT) for efficient chemical rate storage and retrieval was implemented and tested within the Linear Eddy Model (LEM). ISAT type 3 is being tested so that extrapolation can be performed and further improve the retrieval rate. Further testing of the LEM for subgrid chemistry was performed for parallel applications and for multi-step chemistry. Validation of the software on backstep and bluff-body reacting cases were performed. Initial calculations of the SimVal experiment at Georgia Tech using their LES code were performed. Georgia Tech continues the effort to parameterize the LEM over composition space so that a neural net can be used efficiently in the combustion LES code. A new and improved Artificial Neural Network (ANN), with log-transformed output, for the 1-step chemistry was implemented in CFDRC's LES code and gave reasonable results. This quarter, the 2nd consortium meeting was held at CFDRC. Next quarter, LES software development and testing will continue. Alpha testing of the code will continue to be performed on cases of interest to the industrial consortium. Optimization of subgrid models will be pursued, particularly with the ISAT approach. Also next quarter, the demonstration of the neural net approach, for multi-step chemical kinetics speed-up in CFD-ACE+, will be accomplished.