Solid-State Stoichiometry Pumping at the Flame Base on a Sandia Piloted Jet Burner
Oral-In-person · Withdrawn
Abstract
A novel method called Solid-State Stoichiometry Pumping is introduced to control local mixture composition and enhance flame stabilization in piloted methane–air jets. The concept relies on a solid-state oxygen pump embedded at the burner lip, which injects or withdraws oxygen through a controlled flux at the wall. This technique enables fine adjustment of the near-wall equivalence ratio without altering the overall inflow or pilot conditions. The "Solid-State Stoichiometry Pumping"approach was implemented numerically in OpenFOAM for the Sandia Flame D configuration using the detailed GRI-Mech 3.0 chemistry mechanism (53 species, 325 reactions) with mixture-averaged transport properties. The Solid-State Stoichiometry Pumping boundary was modeled as a Neumann condition for both species and enthalpy at the burner surface, with an applied oxygen flux in the range of 0–2.0×10−3 mol m−2 s−1. The simulations captured the coupled influence of the imposed flux on near-wall stoichiometry, radical generation, and heat-release dynamics. At an optimal oxygen flux of 1.2×10−3 mol m−2 s−1, the near-wall oxygen mass fraction increased by 8–10%, while radical concentrations of OH, O, and H rose by 10–18%. The local flame base advanced approximately 3–4 mm upstream, reducing the lift-off height from 16.2 mm to 12.8 mm—a 21% decrease. The maximum temperature near x/D≈0.8 increased from about 1820 K to 1920 K, and CO levels decreased by 8–12%, accompanied by a small NO rise of 4–7%. These results demonstrate that "Solid-State Stoichiometry Pumping" provides an effective, low-power mechanism for fine-scale mixture control and flame stabilization, enabling lean-limit extension and potential active ignition assistance in practical combustion systems without altering the global flow field.
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Publication: 1- Mousavi, M. Application of Solid-State Stoichiometry Pumping (SSSP) in NH₃–CH₄–H₂ Blends under MILD Combustion Conditions.
2- Mousavi, M. Supervised Learning–Adaptive Global Pathway Selection for NH₃–CH₄ Blends under MILD Conditions: Control of Reacting Flow Structure Using SSSP.
Presenters
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Mahmood Mousavi
- Embry-Riddle Aeronautical University