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The noise radiated from the snorkel of an air induction system (AIS) can be a major noise source to the vehicle interior noise. This noise source is typically quantified as the snorkel volume velocity which is directly related to vehicle interior noise through the vehicle noise transfer function. It is important to predict the snorkel volume velocity robustly at the early design stage for the AIS development. Design For Six Sigma (DFSS) is an engineering approach that supports the new product development process. The IDDOV (Identify-Define-Develop-Optimize-Verify) method is a DFSS approach which can be used for creating innovative, low cost and trouble free products on significant short schedules. In this paper, an IDD project which is one type of DFSS project using IDDOV method is presented on developing a robust simulation process to predict the AIS snorkel volume velocity. First, the IDDOV method is overviewed and the innovative tools in each phase of IDDOV are introduced.

A turbocharger is currently widely used to boost performance of an internal combustion engine. Generally, a turbocharger consists of a compressor which typically is driven by an exhaust turbine. The compressor will influence how the low frequency engine pulsation propagates in the intake system. The compressor will also produce broad-band flow induced sound due to the turbulence flow and high frequency narrowband tonal sound which is associated with rotating blade pressures. In this paper, a practical simulation procedure based on a computational fluid dynamics (CFD) approach is developed to predict the flow induced sound of a turbocharger compressor. In the CFD model of turbocharger compressor, the unsteady, moving wheel, detached eddy simulation (DES) approach are utilized. In this manner, both the broad-band and narrow-band flow induced sound are directly resolved in the CFD computation.

Taguchi method is a technology to prevent quality problems at early stages of product development and product design. Parameter design method is an important part in Taguchi method which selects the best control factor level combination for the optimization of the robustness of product function against noise factors. The air induction system (AIS) provides clean air to the engine for combustion. The noise radiated from the inlet of the AIS can be of significant importance in reducing vehicle interior noise and tuning the interior sound quality. The porous duct has been introduced into the AIS to reduce the snorkel noise. It helps with both the system layout and isolation by reducing transmitted vibration. A CAE simulation procedure has been developed and validated to predict the snorkel noise of the porous ducted AIS. In this paper, Taguchi's parameter design method was utilized to optimize a porous duct design in an AIS to achieve the best snorkel noise performance.

Two finite element optimization techniques are presented for minimizing automotive engine air induction structural radiated noise and mass. Air induction systems are generally made of thin wall plastic which is exposed to high levels of pulsating engine noise. Weak air induction system walls vibrate excessively creating noise that can be heard by the driver. The conventional approach is to add ribs (many times through trial and error) which increase part weight or by adding “kiss-offs,” which restrict air flow. The finite element optimization methods considered here are shape optimization and topometry optimization. Genesis, a fully integrated finite element analysis and optimization package by Vanderplaats Research & Development, was used to perform finite element optimization. Choice of optimization method is primarily dependent on several factors which are appearance, part interference and flow restriction requirements.

The air induction system (AIS), which provides clean air to the engine for combustion, is very important for engine acoustics. A practical CAE procedure to predict AIS inlet noise is presented in this paper. GT-Power, a commercially available software program can be used to simulate the engine performance and predict air induction noise. The accuracy of GT-Power is dependent on many variables, such as: proper duct discretization size, proper number of flow splits to model the air box and the capturing of the correct resonator geometry for tuning frequency. Since GT-Power is based on a 1D assumption, several iterations need be performed to model the complex AIS components, such as, irregular shaped air box, resonator volume, porous ducts and perforated pipes. Because of this, the GT-Power AIS model needs to be correlated to test data using transmission loss data.