Session: 04-38 Ammonia Combustion II

Paper Number: 154018

Optimization of a Secondary Air Injector for a Rich-Quench-Lean (RQL) Ammonia Combustor Using Computational Fluid Dynamics 

Ammonia has emerged as a promising medium for moving hydrogen around the globe due to its energy density, existing infrastructure for production, transportation, and storage, and multiple applications from fertilizer to power generation. However, one of the most significant challenges with ammonia combustion for power generation is the formation of nitrogen oxides (NOx) during combustion. Several combustion strategies have been developed to minimize the formation of NOx. One such strategy, known as rich-quench-lean (RQL), is a method that combusts NH3 in a fuel-rich environment, followed by a quick mix section and a lean burnout section. Rapid mixing of secondary air before lean burnout is thought to be important to minimize the formation of NOx. 

This work focuses on using previously developed methodologies and infrastructure to optimize the secondary air injection strategy to minimize the NOx formation in the lean burnout section of a RQL combustor. A Bayesian optimization (BO) method is used to parametrically vary the secondary air injector design, including the diameter, count, and angle over a constrained design space. The designs are evaluated using a non-reacting OpenFOAM model, with the objective function being the mixing time scale of the secondary air (modeled as a scalar). Hundreds of thousands of models are run on the National Energy Technology Laboratory's (NETL) Joule super computer.

In addition, a subset of designs were run with a reacting model to estimate NOx formation. A machine learning surrogate model is constructed that maps the results of the cheaper, non-reacting models to the expensive, reacting models. This mapping allows for the transformation of the mixing time scales to NOx emissions. Using this methodology, an efficient optimization of the secondary air injectors can be achieved with the objective of minimizing the formation of NOx.

Bench-marking of the algorithms and approaches is performed to understand how useful design processes like these are to the development of advanced energy systems. The most promising designs will be manufactured and tested in a small RQL burner setup, which is expected to lead to validation and insight into the practical usage of NH3 combustion for power generation.

Presenting Author: Justin Weber National Energy Technology Laboratory

Presenting Author Biography: Justin Weber is a general engineer in the Research and Innovation Center’s (RIC) Thermal Science Team. Justin is involved in numerous projects spread across both the computational and experimental domains, ranging from multiphase flow reactors to pressure gain combustion. His expertise in computational fluid dynamics (MFIX, Barracuda, OpenFoam), data analytics, machine learning, image processing, large language models, experimental diagnostics, and multiphase flow support multiple projects including chemical looping, gasification, direct air capture, pressure gain combustion, aerothermal heat transfer, and topology optimization. He received a BS in Mechanical Engineering from The Pennsylvania State University in 2009 and has been working at NETL since.

Authors:

Justin Weber National Energy Technology Laboratory
Kristyn Johnson May National Energy Technology Laboratory
Clinton Bedick National Energy Technology Laboratory
Douglas Straub National Energy Technology Laboratory
E David Huckaby National Energy Technology Laboratory