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Machine learning algorithms are effective tools to reduce the number of engine dynamometer tests during internal combustion engine development and/or optimization. This paper provides a case study of using such a statistical algorithm to characterize the heat transfer from the combustion chamber to the environment during combustion and during the entire engine cycle. The data for building the machine learning model came from a single cylinder compression ignition engine (13.3 compression ratio) that was converted to natural-gas port fuel injection spark-ignition operation. Engine dynamometer tests investigated several spark timings, equivalence ratios, and engine speeds, which were also used as model inputs. While building the model it was found that adding the intake pressure as another model input improved model efficiency.

On- and off-road heavy-duty diesel engines modified to spark-ignition natural gas operation can reduce U.S. dependence on imported oil and enhance national energy security. Engine conversion can be achieved through the addition of a gas injector in the intake manifold and of a spark plug in place of the diesel injector. This paper investigated combustion characteristics and engine performance at several lean-burn operating conditions that changed spark timing, mixture equivalence ratio, and engine speed, using methane as NG surrogate.