MANTENIMENT DE LA LÍNIA CEL·LULAR.

In the final stage of this study we extend the adaptive system with the accelera- tion signals from the front suspension aiming to improve the estimation for the tyre cavity. From a first glimpse, it is evident that the synthesised road spectra are very similar to the measured ones, as low levels of error are obtained in fig- ures 5.6(a)-5.6(d). Moreover, the error spectra are closer now to the ones from the coherence calculation as figures 5.7(a)-5.7(d) illustrate. This indicates that this is almost an optimal acceleration combination, as the simulation resulted into a low estimation error.

(a) Left hand front microphone responses. (b) Right hand front microphone responses.

(c) Left hand rear microphone responses. (d) Right hand right microphone responses.

Figure 5.6: Comparison between measured road noise spectra, synthe- sised/estimated with LMS and the difference between them (error signal). −: Road noise. −: Estimated front and rear subframe mount contribution. −:

Estimation error.

(c) Left hand rear microphone responses. (d) Right hand right microphone responses.

Figure 5.7: Comparison between the estimated attenuation between the co- herence limit and multireference adaptive filtering. −: Multiple coherence. −:

Adaptive filtering error.

5.8

Summary

We performed a novel virtual synthesis of road noise by implementing an adaptive multireference LMS algorithm for system identification between the sensors at the vehicle’s axles and the microphones mounted at the headrests. The main task of this simulation was to accurately synthesise road noise based on the most coherent structural locations of road noise. We managed to verify the origin of the road rumble, which is the rear axle and tyre cavity is mainly transmitted from the suspension arms at the front axle.

This method of synthesis of the structure-borne sound field highlights the sensitivity of the acceleration signals placed at the sources or at axle locations, when exposed at high levels of road excitation, which may offer sufficient esti- mation of the main road resonances. In terms of ARNC this method is useful is a useful starting tool that offers a first look at acceleration signals frequency content, since they can improve the performance of the adaptive algorithm for specific road noise bands depending on their location.

Active Road Noise Control

A significant step prior to application of a real-time controller on the vehicle in- volves the and evaluation of the controller’s performance at several accelerometer locations and directions. In this chapter we determine the fundamental adaptive algorithms for multichannel active noise control. We then, compare the reduc- tions we achieved at different reference accelerometer locations and directions. This approach can allow to understand how locations that are essential in NVH road noise analysis, such as TPA-based methods are related to the performance of a multichannel controller, which uses the same input accelerometer locations as TPA. This could potential be advantageous, in case specific locations of the vehicle are known for their significant contribution to structure-borne road noise from TPA or other road noise NVH methods as the installation of the controller can be faster and more robust in terms of the reference sensor location. In this simulation study we examine the performance of a multichannel controller based on the geometry of the sensitive structural parts, in order to reveal the relation between the directions that are used in TPA methods. The common ground of ARNC and TPA is that both aim to synthesise accurately road noise, but with different input signals. In particular, TPA methods use force signals as inputs, whereas ARNC acceleration signals. However, both methods require measure- ment locations at several points on the suspension or other axle parts that are usually causing or allowing low frequency vibrations. On that basis, we will in- vestigate most of the parts that are usually found in TPA analysis, in order to understand the physical relationship between the reference acceleration signals and the ARNC operation.

6.1

Chapter Outline

Twelve three dimensional accelerometers at twelve locations at front and rear axle were used to built up an ARNC model. In first section 6.2 the theory of multichannel adaptive feedforward controller is derived, in order to introduce the famous filtered reference LMS algorithm that is later used in the simulation study and also in the real-time system in chapter 7. Following that in section

6.4 the results of the causal time domain simulation with the use of vehicle data are analysed for estimating the potential performance of the ARNC for various combination of locations at the front and rear axles. In particular road data at high speed (100 km/h) obtained on coarse chip asphalt road are used deliberately as at this speed airbourne contributions from as wind and road noise may start to influence the microphone signals, thus the performance of the controller. However, the vibration levels are high on the accelerometer side thus most of the structure-borne sources that act on the vehicle are exciting during this drive. As a result it is a realistic scenario for simulating the controller with most of the disturbances been active and discussing the limitations of the adaptive cancelling algorithm in the last section6.4.