Predicting Soot Emissions in RQL Combustors Shaun Pitchers Department of Aeronautical and Automotive Engineering REFERENCES 1. https://climate.leeds.ac.uk/news/aviation- contributes-3-5-to-human-caused-climate-change 2. Randy L. Vander Wal, Vicky M. Bryg, and Chung- Hsuan Huang. Aircraft engine particulate matter: Macro- micro- and nanostructure by HRTEM and chemistry 3. Prem Lobo, et al. Comparison of standardized sampling and measurement reference systems for aircraft engine non-volatile particulate matter emissions. Journal of Aerosol Science, 145:105557, July 2020. ACKNOWLEDGEMENTS Thanks to Rolls-Royce for sponsoring this project and Professor Jon Carrott and Dr. Duncan Walker for their guidance and supervision. CONTACT INFORMATION Shaun Pitchers National Centre for Combustion and Aerothermal Technology (NCCAT) Department of Aeronautical and Automotive Engineering Loughborough University Leicestershire LE11 3TU UK [email protected] ABSTRACT INTRODUCTION RESULTS ❖ New manufactured aircraft engines will be required to control its non-volatile particulate matter (nvPM) emissions in terms of particle mass and number. ❖ This legislation has been motivated by the increasing amount of evidence that soot is deleterious to human health and contributes to global warming. ❖ This study focuses on using a low order Chemical Reactor Network model in order to identify the key chemical and physical parameters that cause carbon formation. From smoke to Nanoparticles ❖ Soot has been one of the pollutants that has been legislated since the 1980’s in the form of a filter paper test. ❖ The new nvPM standard set by the Committee on Aviation Environmental Protection (CAEP) moves away from primitive measurement techniques, and towards more sophisticated measurement systems that can report health and climate relevant parameters. ❖ Current engine tests using the sampling system as specified in the SAE’s AIR6241 has been conducted by P. Lobo et al (2020). The results show that there is a increase in the soot emissions at low thrust levels, the level engines run at near the vicinity of airports. ❖ V. Wal et al. in 2014 analysed the soot nanostructure at various thrust levels and concluded that the soot at low thrust has a distinctively different nanostructure to that at the high thrust levels. Concluding that the chemical pathways at which lead to the soot in the two cases are different. METHODOLOGY ❖ In order to analyse the chemical pathways that lead to soot, a chemical reactor network is used. This focuses on the complex chemistry and simplifies the fluid mechanics. ❖ The most complete chemical mechanism for Aviation Fuels is used, that includes a soot-sectional method and all of the soot precursor chemistry that is currently known. Consisting of approx. 25,000 reactions and 450 species. ❖ An LES calculation is used to breakdown the complex flow field into simple reactors to reduce the computation time. ❖ The model is being developed and compared to experimental data gathered at the NCCAT’s reacting test rig. ❖ A simple reactor network model was created using LES data and experimental inputs. The model results was then compared to experimental data gaseous emissions and temperature. Model Inputs • Effective areas from Airbox testing: • Injector, Heatshield, Cooling jets, Dilution Jets • Operating conditions • AFR • DP/P • Pin • Tin • Recirculation % • Will vary between different hardware but we can estimate it from looking at the results and then iterating. WHAT’S NEXT? ❖ Further work on the model is needed to capture the soot trends accurately. Advanced physical parameters such as: rate of axial mixing, local distribution of equivalence ratio, residence time distribution, and atomisation are being investigated. ❖ The final model will be used to identify changes to the combustors geometry in order to reduce the soot formation at low thrust levels. An experimental campaign will then be undertaken to validate the model. Sliced view of injector and combustor geometry Schematic of reactor network Comparison of combustion temperature and efficiency – Model vs Experimental
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Predicting Soot Emissions in
RQL Combustors Shaun Pitchers
Department of Aeronautical and Automotive Engineering
REFERENCES
1. https://climate.leeds.ac.uk/news/aviation-
contributes-3-5-to-human-caused-climate-change
2. Randy L. Vander Wal, Vicky M. Bryg, and Chung-
Hsuan Huang. Aircraft engine particulate matter:
Macro- micro- and nanostructure by HRTEM and
chemistry
3. Prem Lobo, et al. Comparison of standardized
sampling and measurement reference systems for
aircraft engine non-volatile particulate matter
emissions. Journal of Aerosol Science,
145:105557, July 2020.
ACKNOWLEDGEMENTS
Thanks to Rolls-Royce for sponsoring this project and
Professor Jon Carrott and Dr. Duncan Walker for their
guidance and supervision.
CONTACT INFORMATION
Shaun Pitchers
National Centre for Combustion and Aerothermal Technology (NCCAT)
Department of Aeronautical and Automotive Engineering
Loughborough University
Leicestershire LE11 3TU UK
ABSTRACT
INTRODUCTION RESULTS
❖ New manufactured aircraft engines will be required to control its
non-volatile particulate matter (nvPM) emissions in terms of particle
mass and number.
❖ This legislation has been motivated by the increasing amount of
evidence that soot is deleterious to human health and contributes to
global warming.
❖ This study focuses on using a low order Chemical Reactor
Network model in order to identify the key chemical and physical
parameters that cause carbon formation.
From smoke to Nanoparticles
❖ Soot has been one of the pollutants that has been legislated since
the 1980’s in the form of a filter paper test.
❖ The new nvPM standard set by the Committee on Aviation
Environmental Protection (CAEP) moves away from primitive
measurement techniques, and towards more sophisticated
measurement systems that can report health and climate relevant
parameters.
❖ Current engine tests using the sampling system as specified in the
SAE’s AIR6241 has been conducted by P. Lobo et al (2020). The
results show that there is a increase in the soot emissions at low
thrust levels, the level engines run at near the vicinity of airports.
❖ V. Wal et al. in 2014 analysed the soot nanostructure at various
thrust levels and concluded that the soot at low thrust has a
distinctively different nanostructure to that at the high thrust levels.
Concluding that the chemical pathways at which lead to the soot in
the two cases are different.
METHODOLOGY❖ In order to analyse the chemical pathways that lead to soot, a
chemical reactor network is used. This focuses on the complex
chemistry and simplifies the fluid mechanics.
❖ The most complete chemical mechanism for Aviation Fuels is used,
that includes a soot-sectional method and all of the soot precursor
chemistry that is currently known. Consisting of approx. 25,000
reactions and 450 species.
❖ An LES calculation is used to breakdown the complex flow field
into simple reactors to reduce the computation time.
❖ The model is being developed and compared to experimental data
gathered at the NCCAT’s reacting test rig.
❖ A simple reactor network model was created using LES data and experimental inputs. The model results
was then compared to experimental data gaseous emissions and temperature.
Model Inputs
• Effective areas from Airbox testing:
• Injector, Heatshield, Cooling jets, Dilution Jets
• Operating conditions
• AFR
• DP/P
• Pin
• Tin
• Recirculation %
• Will vary between different hardware but we can estimate it from looking at the results and then iterating.
WHAT’S NEXT?❖ Further work on the model is needed to capture the soot trends accurately. Advanced physical
parameters such as: rate of axial mixing, local distribution of equivalence ratio, residence time
distribution, and atomisation are being investigated.
❖ The final model will be used to identify changes to the combustors geometry in order to reduce the soot
formation at low thrust levels. An experimental campaign will then be undertaken to validate the model.
Sliced view of injector and combustor geometry
Schematic of reactor network
Comparison of combustion temperature and efficiency – Model vs Experimental
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