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Journal Articles International Journal of Engine Research Year : 2020

Engine combustion network special issue


The Engine Combustion Network (ECN) is an experimental and modeling collaboration dedicated to the improvement of Computational Fluid Dynamic (CFD) modeling, particularly at engine-relevant conditions occurring at high temperature and pressure where quantitative experimental data is sparse. Beginning in 2009, a working group established an internet data archive library (centrally at http://ecn.sandia.gov/) and directed experiments at specific injector and ambient gas conditions pertinent to those in engines. With the generous donation of fuel injectors by Robert Bosch LLC and Delphi Technologies the voluntary international working group agreed to perform experiments at the same target conditions, so named "Spray A" for the first diesel target and "Spray G" for the first gasoline target. The massive dataset generated and archived has become a serious focal point for CFD model improvement, and diagnostics have advanced to provide more quantitative results. To date, we estimate that over 75 different diagnostics have been performed by more than 20 institutions at Spray A conditions. And more than 30 institutions have performed CFD of Spray A using improved models, with results shared openly at ECN workshops. We also know that many more in the engine industry use the ECN archive to evaluate their own CFD and design practices, ultimately producing cleaner and more fuel-efficient engines. The rationale for participating voluntarily in the ECN may be compared to that of a cycling peloton, as shown in Figure 1. Engine combustion is complex, requiring substantial effort to understand the effect of certain variables or modeling assumptions. A researcher proposing a new diagnostic (e.g. #76) can save tremendous energy if building upon the previous 75 diagnostics, rather than repeating all of these diagnostics individually. Likewise, new modeling ideas are generated by contemplating a wide range of assumptions or results, without necessarily writing and debugging each version of the code on your own. Like cyclists enjoying the slipstream produced by riders at the front of the peloton, these researchers move quickly and advance faster. Soon, they push forward to the front of the group, making unique discoveries that rapidly advance the science of engine combustion, at a pace that would be impossible if working on their own. The need to work closely and precisely together increases according to the difficulty of the problem or if there is a lack of resources to pursue the problem. Cyclists would call this a strong headwind. We recognize that engine combustion research faces strong headwinds at the moment, increasing the need to work together efficiently.


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hal-02447372 , version 1 (21-01-2020)



Lyle M Pickett, Gilles Bruneaux, Raul Payri. Engine combustion network special issue. International Journal of Engine Research, 2020, 21 (1), pp.11-14. ⟨10.1177/1468087419882247⟩. ⟨hal-02447372⟩


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