Work unlocks new avenue for research at Sandia’s Combustion Research Facility

New insights about how to understand and ultimately control the chemistry of ignition behavior and pollutant formation have been discovered in research led by Sandia. The discovery eventually will lead to cleaner, more efficient internal combustion engines.

“Our findings will allow the design of new fuels and improved combustion strategies,” said Nils Hansen, Sandia researcher and lead author of the research. “Making combustion cleaner and more efficient will have a huge impact, reducing energy use around the globe.”

The work, which focuses on the chemical science of low-pressure flame measurements, is featured in the Proceedings of the Combustion Institute and was selected as a distinguished paper in Reaction Kinetics for the 37th International Symposium on Combustion. Authors include Nils, Xiaoyu He, former Sandia intern Rachel Griggs and former Sandia postdoctoral fellow Kai Moshammer, who is now at the Physikalisch-Technische Bundesanstalt in Germany. The research was funded by the DOE’s Office of Science.

Creating a massive dataset of flames and fuels

The team combined the output from carefully controlled measurements on a wide range of fuels into a single categorized and annotated data set. Correlations among the 55 individual flames involving 30 different fuels were then used to reduce the effects of common uncertainty sources, identify inconsistent data and disentangle the effects of the fuel structure on chemical combustions pathways that lead to harmful pollutants. An initial analysis considered relationships among peak concentrations of chemical intermediates that play a role in molecular weight growth and eventual soot formation.

Nils said that, to his knowledge, this is the first time that researchers have looked at these possibilities. By identifying inconsistencies, the new methods ultimately should lead to better models for understanding combustion. Typically, well-controlled experiments help validate computer models to understand the combustion process and to develop new combustion strategies.

Data from low-pressure premixed flames are typically used to validate chemical kinetic mechanisms in combustion. These detailed mechanisms then provide the basis for understanding the formation of pollutants and predicting behavior for combustion applications.

Historically, research papers reported data from a single flame or a few flames, along with one new mechanism for a specific fuel. However, the approach pioneered by Nils’ team paves the way for measuring a large number of flames and publishing numerous mechanisms that are not usually cross-validated with other data and mechanisms.

Nils compares the discovery to the unearthing of an old artifact. Very few conclusions can be drawn from a single artifact. However, piecing together thousands of similar artifacts creates a more complete historical picture.

“Our work reveals information typically hidden in the ensemble of low-pressure flame data,” Nils said. “For example, useful targets for model validation can be gleaned from a database with more than 30,000 data points.”

Analyzing flames

After analyzing 55 individual flames involving 30 different fuels, researchers found that correlated properties provide new validation targets accessible only when examining the chemical structures of a wide set of low-pressure flames.

Nils said the comprehensive chemical kinetic models for combustion systems increasingly are used as the basis for engineering models that predict fuel performance and emissions for combustor design. These models are often poorly constrained because of the large set of model-input parameters, but synchrotron-based single-photon ionization mass spectrometry measurement, pioneered in DOE’s Gas Phase Chemical Physics program, has created an unprecedented surge of detailed chemical data.

Traditionally, only sets of data from a single fuel were analyzed together to obtain a chemical mechanism. Combining and analyzing these datasets together and using informatics tools lead to better species-specific validation targets, including individual kinetic processes that need to be studied further. Quantifying correlations improves species prediction, resulting in more reliable models with broader applicability and more rigorous uncertainty limits.

The research took advantage of the detailed flame chemistry data available because of species detection and characterization breakthroughs spawned due to investments by DOE’s Basic Energy Sciences in synchrotron photoionization mass spectrometry and data science.

Long-term benefits

The work eventually will help to assemble more accurate chemical mechanisms for describing combustion processes, Nils said.

 “Our goal is to better understand and ultimately control the chemistry of ignition behavior and pollutant formation,” he said. “Subsequently, this will lead to clean and efficient internal combustion engines.”

Nils said that his team’s findings unlock an entirely new avenue for research at Sandia’s Combustion Research Facility. 

 “Applying data science and machine-learning tools extracts even more information from large datasets,” he said. “Our work has opened the gate wide to show that data science can be applied to combustion research.”

The data from the study is available to the wider combustion community to support the development of new data science extraction techniques.