Search Results

Certification of vehicle noise emissions for passenger vehicles, motorcycles and light trucks is achieved by measuring external sound levels according to procedures defined by international standards such as ISO362. The current procedure based on a pass-by test during wide-open throttle acceleration is believed far from actual urban traffic conditions. Hence a new standard pass-by noise certification is being evaluated for implementation. It will put testing departments through their paces with requirements for additional testing under multiple ‘real world’ conditions. The new standard, together with the fact that most governments are imposing lower noise emission levels, make that most of the current models do not meet the new levels which will be imposed in the future. Therefor automotive manufacturers are looking for new tools which are giving them a better insight in the Pass-by Noise contributors.

The Transfer Path Analysis method is at the core of the Source-Transfer-Receiver approach to address noise and vibration problems. While originally developed for analyzing structure-borne noise transmission, its application range has been extended to airborne noise. Various frequency and time domain approaches have been developed with a focus of supporting specific design engineering problems. One such application is the source contribution analysis in the context of vehicle pass-by-noise. The upcoming changes in the pass-by noise regulation will not only require more complex tests in different conditions but most importantly, the new directive will force car manufacturers to further reduce the emitted noise levels of their vehicles.

One of the practical consequences of the development of low CO₂ emission cars is that many of the traditional NVH sound engineering processes no longer apply and must be revisited. Different and new sound sources, new constraints on vehicle body design (e.g., due to weight) and new sound perception characteristics make that the NVH knowledge built on generations of internal combustion-powered vehicles cannot be simply transferred to Hybrid and Electric Vehicles (HEV). Hence, the applicability of tools must be reviewed and extensions need to be developed where necessary. This paper focuses on sound synthesis tools as developed for ICE-powered vehicles. Because of the missing masking effect and the missing intake and exhaust noise of the Internal Combustion Engine (ICE) in electric vehicles, on one hand electric vehicles are quieter than traditional vehicles.

Vehicles with electrified powertrains are being introduced at an increasing pace. On the level of interior sound, one is often inclined to assume that NVH problems in EV have disappeared together with the combustion engine. Three observations demonstrate that this is not the case. First of all, only the dominant engine sound disappears, not the noise from tire, wind or auxiliaries, which consequently become increasingly audible due to the removal of the broadband engine masking sound. Secondly, new noise sources like tonal sounds from the electro-mechanical drive systems emerge and often have, despite their low overall noise levels, a high annoyance rating. Thirdly, the fact that engine/exhaust sounds are often used to contribute to the “character” of the vehicle leads to an open question how to realize an appealing brand sound with EV. Hybrid vehicles are furthermore characterized by mode-switching effects, with impact on both continuity feeling and sound consistency problems.

This paper presents a new time-domain source contribution analysis method for in-room pass-by noise. The core of the method is a frequency-domain ASQ model (Airborne Source Quantification) representing each noise generating component (engine, exhaust, left and right tyres, etc.) by a number of acoustic sources. The ASQ model requires the measurement of local FRF's and acoustic noise transfer functions to identify the operational loads from nearby pressure indicator responses and propagate the loads to the various target microphones on the sides of the vehicle. Once a good ASQ model is obtained, FIR filters are constructed, allowing a time-domain synthesis of the various source contributions to each target microphone. The synthesized target response signals are finally recombined into a pass-by sound by taking into account the speed profile of the vehicle.

Active suspension systems aim at increasing safety by improving vehicle ride and handling performance while ensuring superior passenger comfort. Good control of this active system can only be achieved by providing the control algorithm with reliable and accurate signals for the required quantities. This paper presents the design and development of a state estimator that accurately provides the information required by a sky-hook controller, using a minimum of sensors. The vehicle inertial parameters are estimated by an algorithm based on Monte Carlo simulations and anthropometric data. All state updating is performed using Kalman filters. The resulting performance enhancement has been proven during test drives.

Acoustic performance of vehicle engines is a real challenge for powertrain design engineers. Quiet engines are required to reduce noise pollution and satisfy pass-by noise regulations, but also to improve the driving comfort. Simulation techniques such as the Boundary Element Method (BEM) have already been available for some time and allow predicting the vibro-acoustic response of engines. Although the accuracy of these simulation techniques has been proven, a challenge still remains in the required computation time. Given the large amount of speeds for a full engine run-up and the need to cover a large frequency range, computation times are significant, which limits the possibility to perform many design iterations to optimize the system. In 2001, Acoustic Transfer Vectors (ATV) [1] have been presented to adequately deal with multiple rpm. The ATV provide the acoustic response for unit surface velocities and are therefore independent from the engine's actual surface vibrations.

15 years of NVH applications make Transfer Path Analysis (TPA) appear a commodity tool. But despite the fact that TPA is today successfully used in a large variety of applications in automotive and mechanical industries, its main bottleneck remains the huge measurement time to build the full TPA model. This paper presents a new TPA method that provides a good compromise between path accuracy and measurement time. The method is also referred to as OPAX. The key idea of OPAX is the use of simplified parametric load models with limited number of model parameters. The main advantage of this is that one should measure only a small amount of FRF data to identify the operational loads. This drastically reduces measurement time and efforts. In addition to this, the OPAX method does not require mount stiffness data and allows a simultaneous identification of structural and acoustic paths.

Despite the fact that Transfer Path Analysis (TPA) is a well known and widely used NVH tool it still has some hindrances, the most significant being the huge measurement time to build the full data model. For this reason the industry is constantly seeking for faster methods. The core concepts of a novel TPA approach have already been published in a paper at the ISMA 2008 Conference in Leuven, Belgium. The key idea of the method is the use of parametric models for the estimation of loads. These parameters are frequency independent as opposed to e.g. the classical inverse force identification method where the loads have to be calculated separately for each frequency step. This makes the method scalable, enabling the engineer to use a simpler model based on a small amount of measurement data for quick troubleshooting or simply increase accuracy by a few additional measurements and using a more complex model.

Since its first publication in the beginning of the eighties, Transfer Path Analysis (TPA) has evolved into a widely used tool for noise and vibration troubleshooting and internal load estimation, and this for single source as well as multivariate problems. One of the main bottlenecks preventing its even more widespread use in the actual vehicle development process is the test time to build the full data model, requiring not only in-operation tests but also extensive Frequency Response Function tests. As a consequence, several new approaches have appeared over the past years attempting to circumvent this limitation, such as Fast and Multilevel TPA and Operational TPA. The latter method attracts quite some attention as it only requires operational data measured at the path references and target locations.

The increasing contribution of electronic and mechatronic content to the vehicle value requires rethinking the vehicle design and engineering processes. The present paper describes an approach hereto, based on virtual and physical prototype testing of heterogeneous systems. A key element is the integration between 3D geometry-Based Models (FE, MBS) and 1D multi-physics system-theoretic models for simulating complex devices (hydraulics, actuators, specific sensors) and processes (combustion, thermal, flow). Embedding control laws paves the way to Model-ln-the-Loop (MIL) and Software-In-the-Loop (SIL) concepts. By linking the virtual models to hardware systems on a physical test-bench, the static and dynamic performance of the rest of the vehicle system (suspension, body…) can be represented, enabling “Hardware in the Loop testing” (HIL). The “Vehicle-in-the-Loop” (VIL) validation finally allows the evaluation of all system dependencies and system interconnections.

This paper presents an on-line, order-based roughness approach for vehicle engine sounds. This new algorithm reconstructs the sound envelope per critical band in an analytical way from the order amplitudes, phases and frequencies. The most time-demanding operations of the classical roughness models are no longer needed, rendering the algorithm extremely fast and applicable in real-time. Another interesting characteristic of this new algorithm is the unique link which is established between the roughness and the engine order components of the sound. Sound engineers can easily identify which order components need to be modified to reduce a roughness problem.

A key element to bring research advances on intelligent materials to industrial use is that the product CAE models must support such solutions. This involves modeling capabilities for intelligent material systems, sensor and actuator components, control systems as well as their integration in system-level application designs. The final result will then be a multi-attribute optimization approach integrating noise and vibration performance with reliability, durability and cost aspects. As no single integrated solution will fulfill all requirements of the various material and control approaches, the focus of the research is on the use, combination and extension of existing codes and tools.

This paper discusses the requirement for CAE methods to properly take into account the variabilities and uncertainties that characterizes design input properties without leading to oversized structures. Optimizing the structural behaviour while taking into account expected variability and uncertainty in the structure and its model, requires the adoption of a reliability-based design optimization approach. This paper starts with an overview of the problem of simulation uncertainty. The key focus is then on the description of the most commonly used methods and enabling tools for reliability analysis and reliability based design optimization. The theory is illustrated by real automotive design problems.

This paper presents the results of an experimental test campaign carried out on a city bus powered by serial hybrid power train. The driveline system combines an Internal Combustion Engine with a battery pack and two electric motors. Tests were aimed at identifying the salient signal characteristics of the noise spectra recorded during operating conditions and to assess the acoustic comfort in the passenger compartment. Transfer Path Analysis technique was applied to identify airborne and structure borne vibro-acoustic loads, to measure transfer functions linking source locations to target locations and to estimate the internal vibro-acoustic comfort in operating conditions.

Traditionally, vibration analysis in operating conditions (on the road or on a bench) had to be combined with experimental modal analysis in controlled laboratory conditions in order to understand the modal behaviour of the structure. This requires additional measurements, costs and time. However, in many applications, the real operating conditions may differ significantly from those applied during the modal test and hence the vibration modes from the modal test might not be representative for the active modes in operation conditions. The need for a capability of doing a modal analysis on data from operating conditions is obvious. Over the last years, several modal parameter estimation techniques have been proposed and studied for modal parameter extraction from output-only data. Each method needs to make a number of assumptions and has some limitations.

One of the scientific fields where, for already more than 20 years, system identification plays a crucial role is this of structural dynamics and vibro-acoustic system optimization. The experimental approach is based on the “Modal Analysis” concept. The present paper reviews the test procedure and system identification principles of this approach. The main focus though is on the real problems with which engineers, performing modal analysis on complex structures on a daily basis, are currently confronted. The added value of several new testing approaches (laser methods, smart transducers…) and identification algorithms (spatial domain, subspace, maximum likelihood,..) for solving these problems is shown. The discussed elements are illustrated with a number of industrial case studies.

Traditionally, vibration analysis in operating conditions (on the road or on a bench) had to be combined with experimental modal analysis in controlled laboratory conditions in order to understand the modal behaviour of the structure. This requires additional measurements, costs and time. However, in many applications, the real operating conditions may differ significantly from those applied during the modal test and hence the vibration modes from the modal test might not be representative for the active modes in operation conditions. The need for a capability of doing a modal analysis on data from operating conditions is obvious. Over the last years, several modal parameter estimation techniques have been proposed and studied for modal parameter extraction from output-only data. Each method needs to make a number of assumptions and has some limitations.

Experimental identification of structural dynamics models is usually based on the modal analysis approach. In the classical modal parameter estimation approach, the baseline data which are processed are Frequency Response Functions measured in laboratory conditions. However, in many applications, the real operating conditions may differ significantly from those applied during the modal test. Hence, the need arises to identify a modal model in operational conditions. This issue is even more complicated by the fact that in most cases, only response data are measurable while the actual loading conditions are unknown. Therefore, the system identification process will need to base itself on output-only data.

The subjective quality of sounds is a topic of increasing importance in the automotive industry. The first consideration is to describe the perceptual characteristics of this quality by means of jury tests or appropriate metrics. Once a NVH problem is determined in terms of an appropriate Sound Quality description, an in-depth analysis of the underlying physical phenomena must be made and engineering solutions newel to be proposed and validated This involves: • the detailed analysis of the signal structure in the time, frequency and order domain and identifying the signal Components Critical to the relevant sound quality dimension • the Correlation of the critical signal components to specific sources noise or vibration transmission paths and vibro-acoustic system characteristics. Ultimately this should lead to the prediction of the effect of feasible modifications in sound quality terms through the use of engineering models.