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In this paper, an analytical procedure for prediction of shell radiated noise of air induction systems (AIS) due to engine acoustic excitation, without a prototype and physical measurement, is presented. A set of modeling and simulation techniques are introduced to address the challenges to the analytical radiated noise prediction of AIS products. A filter seal model is developed to simulate the unique nonlinear stiffness and damping properties of air cleaner boxes. A finite element model (FEM) of the AIS assembly is established by incorporating the AIS structure, the proposed filter seal model and its acoustic cavity model. The coupled acoustic-structural FEM of the AIS assembly is then employed to compute the velocity frequency response of the AIS structure with respect to the air-borne acoustic excitations.

Noise Vibration and Harshness (NVH) characteristics have become a key differentiator between “Good” vehicles and “Best-In-Class” vehicles. While all OEM's and most Tier 1 suppliers have on-site in-ground chassis dynamometers, a need was identified to design, develop and bring to market, a fully capable portable NVH full vehicle chassis system. The original concept entailed a device, which could be brought to the customer's location, be fully self contained, requiring no external power, and provide data acquisition using transducers that would not contact the vehicle. With traditional instrumentation taking several hours to install, non-contacting lasers would be used to provide significant timesaving, and prevent any possible damage to the vehicle from pinched wires. The new methodology should provide data acquisition in as little as 20 minutes. Analysis would be accomplished immediately following testing, with hard copies available before the next vehicle was ready to run.

Analytical methods are used extensively in the automotive industry to validate the feasibility of component and assembly designs and their dynamic behavior. Correlation of analytical models with test data is an important step in this process. This paper discusses the Finite Element model of an innovative Slip-in-Tube Propshaft design. The Slip-in-Tube joint (slip joint) poses challenges for its dynamic simulation. This paper discusses the methods of simulating the joint and correlating it to experimental results. Also, the Noise and Vibration (NVH) characteristics of the Slip-in-Tube Propshaft design. In this paper, a Finite Element model of the proposed propshaft is developed using shell and beam element formulations. Each model is verified to optimize the feasibility of using accurate and computationally efficient elements for the dynamic analysis.

The present work demonstrates the application of Design of Experiments (DOE) statistical methods to the design and optimization of a hydraulic steering pump for NVH performance. DOE methods were applied to RPM-domain data to examine the effect of several different factors, as well as the interactions between these factors, on pump NVH. Whereas most DOE analyses typically consider only a single response variable, the present work considered multiple response variables. Specifically, pump NVH performance curves for several pump rotational orders over a range of shaft speeds were analyzed. Thus, it was possible to determine the effect of the factors in question over the entire speed range of pump operation, rather than a single speed or setting. Statistical methods were applied to determine which factors and interactions had a significant effect on pump NVH. These factors were used to construct an empirical mathematical prediction model for NVH performance.

The present work discusses the application of multivariate statistical methods for the analysis of NVH data. Unlike conventional statistical methods which generally consider single-value, or univariate data, multivariate methods enable the user to examine multiple response variables and their interactions simultaneously. This characteristic is particularly useful in the examination of NVH data, where multiple measurements are typically used to assess NVH performance. In this work, Principal Components Analysis (PCA) was used to examine the NVH data from a benchmarking study of hydraulic steering pumps. A total of twelve NVH measurements for each of 99 pump samples were taken. These measurements included steering pump orders and overall levels for vibration and sound pressure level at two microphone locations. Application of the PCA method made it possible to examine the entire set of data at once.

The present work demonstrates the application of Design of Experiments (DOE) statistical methods to the design and the improvement of a hydraulic steering pump noise, vibration, and harshness (NVH) performance in relief. DOE methods were applied to subjective ratings to examine the effect of several different factors, as well as the interactions between these factors on pump relief NVH. Specifically, the DOE was applied to the geometry of the cross ports on a hydraulic relief valve to improve “whistle” noise in the pump. Statistical methods were applied to determine which factors and interactions had a significant effect on pump whistle. These factors were used to produce a more robust cross port configuration reducing whistle noise. Lastly, the final configuration was experimentally verified on the test apparatus and subjectively confirmed in vehicle-level testing.