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A new math-based piston noise model is proposed. It is based on a EHD (elastohydrodynamic) piston lubrication and secondary dynamic analysis. The model predicts the force on the bore wall as a function of time, which is then transformed to the frequency domain. The dynamic calculations of the model were validated against a closed form solution for a simple 1-D model. The model was applied to a production V8 engine. Predictions of the model compared well with block acceleration measurements for the same engine both in relative magnitude and frequencies. Effects of changing clearance and oil supply were also investigated using the model.

Minimum bearing oil film thickness (MBOFT) was measured in both a main and a connecting-rod bearing of a production V-6 engine using the total capacitance method (TCM). MBOFT was measured at 1500 rpm and at three different engine loads (64, 128, and 192 Nm). The oil sump temperature was controlled at 100°C. Five engine oils were tested (SAE grades 5W-20, 20W-20, 5W-30, 10W-30, and 20W-50) with emphasis given to the SAE 5W-30 and 10W-30 oils. MBOFT was also calculated using a computer code. The absolute minimum of the MBOFT (MBOFTmin, closest approach between the journal and the bearing) increased with increasing values of a Sommerfeld parameter (viscosity/load) and this dependence was similar in both the main and the connecting-rod bearings. But, for the main bearing the MBOFTmin values showed a higher dependence on the Sommerfeld parameter than those for the connecting-rod bearing. Similar results were obtained with the theoretical calculations of the MBOFT_min values.

Comparisons are made between friction predictions of the FLARE (Friction and Lubrication Analysis of Reciprocating Engines) computer code and experimental data for the purpose of validating FLARE. An in-line four-cylinder engine under motoring conditions was selected for doing the experiments. Three major friction producing subassemblies were considered: piston assembly, crankshaft main bearings, and valve train. A Taguchi-type L16 matrix was used for the piston assembly, while an L8 matrix was used for the valve train. A traditional approach (varying one parameter at a time) was used for crankshaft main bearings. The agreement between experimental measurements and FLARE predictions, for all the cases studied, is very good. The match is closest for the valve train, followed by the crankshaft main bearings and piston assembly. In addition, trends and effects of changing design parameters are predicted correctly by FLARE.