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A new turbocharged 60° 2.7L 4V-V6 gasoline engine has been developed by Ford Motor Company for both pickup trucks and car applications. This engine was code named “Nano” due to its compact size; it features a 4-valves DOHC valvetrain, a CGI cylinder block, an Aluminum ladder, an integrated exhaust manifold and twin turbochargers. The goal of this engine is to deliver 120HP/L, ULEV70 emission, fuel efficiency improvements and leadership level NVH. This paper describes the upfront design and optimization process used for the NVH development of this engine. It showcases the use of analytical tools used to define the critical design features and discusses the NVH performance relative to competitive benchmarks.

An engine's radiated noise level is a very important attribute required for delivering customer satisfaction. Having an accurate radiated noise prediction capability during the planning, target setting, and initial design phases is critical to making the up-front decisions that enable the timely and cost efficient delivery of an engine that meets its radiated noise goals. This paper describes a simple radiated noise model that is based on a combination of regression modeling and simplified analytical modeling. The regression model uses measured data from multiple tests that can be broken down to noise sources such as mechanical, combustion, and accessory components. The simple analytical models are used to determine the parameters that the decomposed noise data is regressed against. The model developed in the paper is then compared to previous models suggested in the literature and to measured data from engines.