On the influence of cyclic variability on surface noise contribution analysis of internal combustion engines. (March 2018) - Acri, A.; Nijman, E.; Klanner, M.; Offner, G.; Corradi, R.
Highlights: The acoustic radiation of a combustion engine is investigated. The acoustic field is efficiently calculated with the Wave Based Method. A surface contribution analysis is used to characterize the radiating surfaces. The influence of uncertain excitations on the acoustic radiation is analysed. The robustness of the SCA with respect to the excitation variability is tested. Abstract: Practical mechanical systems often operate with some degree of uncertainty. The uncertainties can result from poorly known or variable parameters, from uncertain inputs or from rapidly changing forcing that can be best described in a stochastic framework. In automotive applications, cylinder pressure variability is one of the uncertain parameters that engineers have to deal with when designing and analyzing internal combustion engines. The characterization of acoustic radiation patterns of internal combustion engines is a challenging task required for the purpose of effective noise reduction. In this paper the influence of cylinder pressure cyclic variability on the assessment and ranking of the different radiating engine surfaces is investigated. A surface contribution analysis (SCA) within a wave based method (WBM) framework is adopted for the assessment of noise radiated from different vibrating surfaces of an engine structure. The method adopted consists in the decomposition of the boundary conditions of the WBM model, assuming the linearity of the vibrational problem associated to the generation of the vibrations on the structure surface, for which the superposition principle is valid. In order to investigate the cyclic variability of cylinder pressure, a Monte Carlo approach is adopted. Starting from measured cylinder pressures that exhibits cyclic variability, random Gaussian distribution of the equivalent force applied on the piston is generated. The results obtained from this analysis are used to derive correlations between cyclic variability and statistical distribution of the results. The statistical information derived can be used to advance the knowledge of the WBM and SCA applications when uncertain inputs are considered.