An Adaptive Chemistry Approach to Modeling Emissions Performance of Gas Turbine Combustors, Phase I

Metadata Updated: May 2, 2019

In this proposed SBIR project, we seek to implement the Adaptive Chemistry methodology in existing CFD codes used to investigate the emissions performance of gas turbine engine combustors. We will demonstrate the feasibility of integrating Adaptive Chemistry algorithms to current CFD codes. We will also further develop the Adaptive Chemistry method to take advantage of species reduction enabling even larger CPU speedups. The value of the technique is enhanced predictive capability and computational efficiency of existing CFD codes for reacting flows such as gas turbine engine combustion systems. The successful completion of this project will produce the first CFD numerical code that is able to model detailed chemical kinetics as well as fluid dynamics. The end results allow the user to easily and transparently control the balance between computational efficiency and solution accuracy.

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Public: This dataset is intended for public access and use. License: U.S. Government Work

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Metadata Created Date August 1, 2018
Metadata Updated Date May 2, 2019

Metadata Source

Harvested from NASA Data.json

Additional Metadata

Resource Type Dataset
Metadata Created Date August 1, 2018
Metadata Updated Date May 2, 2019
Publisher Space Technology Mission Directorate
Unique Identifier TECHPORT_6464
Maintainer Email
Public Access Level public
Bureau Code 026:00
Metadata Context
Metadata Catalog ID
Schema Version
Catalog Describedby
Datagov Dedupe Retained 20190501230127
Harvest Object Id 6cd14e32-a8b0-4ab6-bad7-928e0cd9732f
Harvest Source Id 39e4ad2a-47ca-4507-8258-852babd0fd99
Harvest Source Title NASA Data.json
Data First Published 2008-08-01
Homepage URL
Data Last Modified 2018-07-19
Program Code 026:027
Source Datajson Identifier True
Source Hash badb72e12cd94df5ee44abe36e55560c8e07149c
Source Schema Version 1.1

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