Analysis and simulation of nonlinearities in noise attenuation model for a diesel engine block

Jiajun Cao, Purdue University


The purpose of this thesis is to understand the characteristics of noise attenuation of a diesel engine block, by means of setting up a structural attenuation model and exploring the impact of variations of the model, in terms of nonlinearities. A model motivated by a set of experimentally-determined attenuation measurements was constructed, validated and explored for the potential for sound power prediction, with the aid of simulation. Attenuation curves have long been considered an effective and straightforward method to understand the relationship between cylinder pressure and the corresponding noise radiation from an engine block. Preliminary measurements on a small single-cylinder diesel engine, however, suggest dependency of attenuation curves on injection parameters and operating conditions of the engine. Such dependency signals a possibility of inherent nonlinearities in the engine block attenuation and calls for a deeper investigation of the hypothesis. A mass-spring-damper-based model was developed from the averaged experimental attenuation measurements and served as an alternative to attenuation curves to represent the noise attenuation characteristics of the engine block. The hypothesis of inherent nonlinearities was tested with the model and simulation results demonstrated relevance of nonlinearities to several off-linear behaviors of experimentally-determined attenuation curves. Validation for the model was performed by simulation under different operating conditions, and consistent observations further justified the nonlinear hypothesis. Nonlinearities were also categorized to account for different behaviors of the attenuation model. Based on the model developed, sound power levels were predicted for a given set of cylinder pressures under different operating conditions. The simulation-model-based predictions demonstrated more reliability compared to the attenuation-curves-based prediction. Predictions among different models, linear or nonlinear, were compared and the impact of nonlinearities was analyzed. Judging the validity and accuracy of the predictions, however, requires further experimental measurements in the future.




Meckl, Purdue University.

Subject Area

Mechanical engineering

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