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As the interior sound levels in cabin compartments of passenger vehicles continue to get quieter, noises from various sources which previously were not objectionable can become an issue. One such source is the “slosh noise” from liquid movement within fuel tanks. Vehicle manufacturers, responding to the phenomena, have turned to their suppliers and worked with them to establish robust test and analysis methods to characterize the NVH performance of their fuel storage and delivery systems. Test facilities have recently made great advancements in the capability to measure and characterize “fuel slosh noise” in tanks. However, the industry today lacks standardized procedures to apply to the issue, including defining test parameters and analysis methods (both of which are complex because of the time-domain nature of slosh events).

Superior NVH performance is a key focus in the development of new powertrains. In recent years, computer simulations have gained an increasing role in the design, development, and optimization of powertrain NVH at component and system levels. This paper presents the results of a study carried out on a 4-cylinder in-line spark-ignition engine with focus on growl noise. Growl is a low frequency noise (300-700 Hz) which is primarily perceived at moderate engine speeds (2000-3000 rpm) and light to moderate throttle tip-ins. For this purpose, a coupled and fully flexible multi-body dynamics model of the powertrain was developed. Structural components were reduced using component mode synthesis and used to determine dynamics loads at various engine speeds and loading conditions. A comparative NVH assessment of various crankshaft designs, engine configurations, and in- cylinder gas pressures was carried out.

In this paper a design methodology for automotive heat exchangers has been applied which brings robustness into the design process and helps to optimize the design goals: as to maintain an optimal coolant temperature and to limit the vehicle underhood air temperature within a tolerable limit. The most influential design factors for the heat exchangers which affect the goals have been identified with that process. The paper summarizes the optimization steps necessary to meet the optimal functional goals for the vehicle as mentioned above. Taguchi's [1] Design for Six Sigma (DFSS) methods have been employed to conduct this analysis in a robust way.

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.

For vehicle Original Equipment Manufactures (OEMs), road noise inside the vehicle is an important aspect that contributes to the comfort and the sound quality image of the vehicle. Road noise inside a vehicle is a function of the source (tire design interacting with road surface) and of vehicle sensitivity functions. Road noise targets and tire targets are typically developed by characterizing the tire as a noise and/or vibration source and by characterizing the vehicle as a matrix of acoustic or structural paths(1). This paper focuses on the development of a simplified procedure for measurement of Noise Reduction (or acoustic vehicle sensitivity function) from tire patch to vehicle interior. Several procedures are available from either literature, vehicle manufacturers or software providers, which exhibit important differences regarding sound production, number and position of source and receiver microphones, or measured parameters (2).

A process to create a vehicle resistance curve based on airflow predictions using Computational Fluid Dynamics (CFD) simulation technique is presented. 1-dimensional engine cooling system simulation tool KULI is used to compute the coefficients of vehicle resistance curve. A full factorial Design of Experiment (DOE) established the relationship between the coefficients and the sum of absolute difference between KULI and CFD predictions. The NLPQL optimization routine is used to accurately predict the coefficients so that sum of absolute difference between KULI and CFD predictions is minimized.

Road-tire induced vibrations are in many vehicles determining the interior noise levels in (semi-) constant speed driving. The understanding of the noise contributions of different connections of the suspension systems to the vehicle is essential in improvement of the isolation capabilities of the suspension- and body-structure. To identify these noise contributions, both the forces acting at the suspension-to-body connections points and the vibro-acoustic transfers from the connection points to the interior microphones are required. In this paper different approaches to identify the forces are compared for their applicability to road noise analysis. First step for the force identification is the full vehicle operational measurement in which target responses (interior noise) and indicator responses (accelerations or other) are measured.

Design parameters for automotive components can be highly affected by the requirements imposed for vehicle pass-by compliance. The key systems affecting pass-by performance generally include the engine, tires, intake system, and exhaust system. The development of these systems is often reliant on the availability of prototype hardware for physical testing on a pass-by course, which can lead to long and potentially costly development cycles. These development cycles can benefit significantly from the ability to utilize analytical data to guide development of component-level design parameters related to pass-by noise. To achieve this goal, test and analysis methods were developed to estimate the vehicle-level pass-by performance from component level data, both from physical and/or analytical sources. The result allows for the estimation of the overall vehicle-level pass-by noise along with the contributions to the total and dominant frequency content from each of the key noise sources.

As an agricultural tractor OEM was moving a new tractor model from development into production, an objectionable cab “boom” was identified that was not present in the preproduction pilot -level tractors. The cab boom was identified as a low frequency tone causing an increase of 7 (dBA) over the level in the pilot tractors, which was deemed unacceptable. The process used by the tractor OEM engineering team to address this problem has been widely used and refined in the automotive industry, but it is relatively new in the agricultural/off-road vehicle industry. This paper describes the source-path-receiver approach that led to identifying the exhaust tip as the source and the vibro-acoustic coupling of a windshield structural mode with an acoustic cab cavity mode as the path of the boom event.

With the current focus of the automotive industry on improving fuel consumption, it is becoming increasingly more common to adapt current/existing vehicle platforms for integration with electric powertrains. This integration can have an impact on many areas of the vehicle development process, including noise and vibration performance. Alongside the understood benefits to fuel economy, electric powertrains can present many unique noise and vibration related development challenges which require specific attention, particularly for cases in which a conventional gasoline engine vehicle platform is used as a surrogate for the electric powertrain. In this paper, several of the potential noise and vibration development activities will be highlighted, including discussions on powertrain vibration, accessory noise and vibration, and acoustic package material development to deliver a refined noise and vibration experience to the customer.

The sound quality of a prototype series hydraulic hybrid passenger vehicle powertrain was analyzed. Different sound quality metrics were evaluated to determine which one correlated best with the subjective assessment of sound quality, and a desired sound quality target was developed. Next, the effect of the design of the hydraulic powertrain components on sound quality was analyzed. Two extreme options were analyzed: “stiff” systems with a hard drive shaft or short fluid hoses, and “soft” systems with a soft drive shaft or long fluid hoses. Experimental results from these systems are presented in the paper. Finally, design recommendations were made to achieve the best sound quality of the hybrid hydraulic powertrain, and therefore maximum customer satisfaction.

The role of NVH test development has changed from addressing a system-level NV concern late in the design cycle (firefighting) to having well established NV optimized test procedures in place. One way this is achieved is by leveraging the information gained during troubleshooting of current product to improve the future product development process for noise and vibration. Today, most NV groups/laboratories use optimized test procedures for creating accurate, consistent, and efficient test results. This still requires expertise to post-process data, compute targets and interpret results to guide product development. This step is often overlooked and, in recent years, due to the lack of NV expertise of “younger” labs (typically in non-automotive industries) or of more established labs affected by the economic downturn (early retirements, lay-offs, especially in the automotive industry) there has been a growing need for automated post-processing “intelligent” procedures.

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.

With the advent of quieter powertrain and improved cabin acoustic sealing, there is an increased focus on noise generated in the HVAC unit and climate control ducting system. With improved insulation from exterior noise sources such as wind & road noise, HVAC noise is more perceptible by the occupants and is a key quality indicator for new generation vehicles. This has increased the use of simulations tools to predict HVAC noise during the virtual development phase of new vehicle programs. With packaging space being premium under the instrument panel, changes to address noise issues are expensive and often impractical. The current methodology includes the best practices in simulation accumulated from prior aero acoustics validation studies on fans, ducts, flaps and plenum volume discharge. The paper details the acoustic noise generation and propagation in the near field downstream of an automotive HVAC unit in conjunction with ducting system.

A unique Matlab-based coded engineering software tool (Time-Frequency Analyzer Core®) was developed that allows users to process acquired time data to help in identifying sources and paths of noise and vibration (in the experience of the authors). The Time-Frequency Analyzer Core (TFAC) software does not replace commercial off the shelf software/hardware NV specific tools such as modal analysis, ODS, acoustic mapping, order tracking, etc., rather it aims at providing basic, yet powerful data inspection and comparison techniques in a single software tool that facilitates drawing conclusions and identifying most effective next steps. The features and advantages of using this software tool will be explained, along with a description of its application to a few different cases (automotive and off highway/agricultural).

As the demand for Sound Quality improvements in vehicles continues to grow, robust analysis methods must be established to clearly represent end-user perception. For vehicle sounds which are tonal by nature, such as transmission or axle whine, the common practice of many vehicle manufacturers and suppliers is to subjectively rate the performance of a given part for acceptance on a scale of one to ten. The polar opposite of this is to measure data and use the peak of the fundamental or harmonic orders as an objective assessment. Both of these quantifications are problematic in that the former is purely subjective and the latter does not account for the presence of masking noise which has a profound impact on a driver's assessment of such noises. This paper presents the methodology and results of a study in which tonal noises in the presence of various level of masking noise were presented to a group of jurors in a controlled environment.

For the development of a self-propelled crop spraying machine, a hybrid experimental and analytical Source-Path-Contribution (SPC) approach is utilized by a leading agricultural equipment manufacturer. The objective is to predict noise and sound quality in the cab before prototypes are assembled, so that dB(A) and SQ targets can be assessed early on and better specifications sent to suppliers to achieve these vehicle-level targets. The experimental SPC task is conducted on the current crop sprayer model, which has the same cab but different engine, transmission and hydraulics than the new model. A hybrid FE-SEA model of the current cab is developed and run at load cases derived from test data. The SEA approach is needed to evaluate the effect of cab acoustic treatments, which are not accounted for in the SPC experimental model. Contributions to in-cab noise for the current sprayer are estimated from both experimental and analytical SPC.

Source-path-contribution (SPC) analysis, or transfer-path-analysis, is a test based method to characterize noise and vibration contributions of a complex system. The methodology allows for the user to gain insight into the structural forces and acoustic source strengths that are exciting a system, along with the effects of the structural and acoustic paths between each source and a receiver position. This information can be utilized to understand which sources and/or paths are dominating the noise and vibration performance of a system, allowing for focused target cascading and streamlined troubleshooting efforts. The SPC process is widely used for automotive applications, but is also applicable for a wide range of product types. For each unique application the basic SPC principles remain constant, however best practices can vary for both measurement and analysis depending on the type of system being evaluated.

One method of understanding the general mechanical response of a complex system such as a vehicle, a human surrogate, a bridge, a boat, a plane, etc., is to subject it to an input, such as an impact, and obtain the response time-histories. The responses can be accelerations, velocities, strains, etc. In general, when experiments of this type are run the responses are contaminated by sample-to-sample variation, test-to-test variability, random noise, instrumentation noise, and noise from unknown sources. One common method of addressing the noise in the system to obtain the underlying response is to run multiple tests on different samples that represent the same system and add them together obtaining an average. This functionally reduces the random noise. However, if the fundamental response of each sample is not the same, then it is not altogether clear what the average represents. It may not capture the underlying physics.