Browse Publications Technical Papers 2021-26-0265

Digital Approach for Dynamic Balancing of Three Cylinder Gasoline Engine Crank-Train 2021-26-0265

Because of ever increasing demand for more fuel efficient engines with lower manufacturing cost, compact design and lower maintenance cost, OEM’s prefer three cylinder internal combustion engine over four cylinder engine for same capacity, though customer demands NVH characteristics of a three cylinder engines to be in line with four cylinder engine.
Crank-train balancing plays most vital role in NVH aspects of three cylinder engines. A three cylinder engine crankshaft with phase angle of 120 degrees poses a challenge in balancing the crank train. In three-cylinder engines, total sum of unbalanced inertia forces occurring in each cylinder will be counterbalanced among each other. However, parts of inertia forces generated at No.1 and No. 3 cylinders will cause primary and secondary resultant moments about No. 2 cylinder. Conventional method of designing a dynamically balanced crank train is time consuming and leads to rework during manufacturing. Also, different vehicle models with a same engine can call for different crank-train options resulting in increased development time and efforts.
This paper discusses a numerical and digital approach for designing crank train of a three cylinder gasoline engine with dynamically balanced for any option required by vehicle. This approach eliminates the iterative process of prototyping. Multibody dynamic model of 1200 cc three cylinder gasoline engine crank train is developed with inertia properties of all child parts including crankshaft, piston and connecting rod. Reciprocating and rotating inertia forces with their moments were considered for respective child parts. Reasonable tolerances to achieve desired static and dynamic balance in production parts were determined by this approach. A sensitivity study to understand influence of counterweights on crankshaft balancing is also performed using this approach. All values and correlations developed in this approach are validated on physical engines.


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