The multiple coherence of the four acceleration signals can be used to estimate the performance of a multichannel ARNC system without including the con- straint of causality [Oh et al.(2002)]. This means that the physical delays of the secondary paths transfer functions and the electronic latency of the controller are not included in this type of prediction and only the coherency between the input-output signals is taked into account. The reduction as a funtion of fre- quency of an active road noise controller is usually estimated with the use of the multiple coherence between the accelerometer signals and a microphone signal in the cabin as follow
R(f) =−10log(1−γ2m:k(f)) (4.4) Tables 4.1 and 4.2 present the predictions for the four optimised accelerometer positions and for twenty accelerometer signal obtained from the main paths of structure-borne road noise.
Left hand front Right hand front Left hand rear Right hand rear Speed [km/h] ARNC dB(A) ARNC dB(A) ARNC dB(A) ARNC dB(A)
60 3.05 3.03 3.04 3.04
100 3.02 3.00 3.02 3.03
Table 4.1: Predicted ARNC performance for each of the four microphone at the headrests. The calculation is based on the four reference acceleration
signals for the frequency range of 0-500 Hz.
Left hand front Right hand front Left hand rear Right hand rear Speed [km/h] ARNC dB(A) ARNC dB(A) ARNC dB(A) ARNC dB(A)
60 3.27 3.26 3.29 3.29
100 3.26 3.26 3.28 3.28
Table 4.2: Predicted ARNC performance for each of the four microphone at the headrests. The calculation is based on twenty reference signals for the
frequency range of 0-500 Hz.
By comparing the two tables it can be noticed that there is a very small improvement from four to twenty accelerometer signals as reference inputs. This is contradictory to what has been found in the past for other vehicles as an increase in the number of reference acceleration signal significantly improved the reduction [Mackay and Kenchington (2004)]. On the other hand, Bernhard has that four acceleration signals may result to 7 dB reduction for a Japanese sedan [Heatwole et al. (1993)]. This variation in the unconstrained prediction of the active reduction implies that there is a strong dependency between the estimated ARNC performance and the type of vehicle. In our case the luxury vehicle happens to have relatively high levels of refinement compared to typical high volume vehicles, which explains the fact that theoretically it can achieve more that 3.2 dB(A) reduction.
In our case the vehicle under investigation has relatively good structure- borne road noise performance especially for the front seats as we discovered in the analysis of the vibro-acoustic FRFs in section 2.5.3. As a result the ARNC system can provide a certain amount of improvement in this vehicle, since from the coherence analysis looks like that only two structural road noise sources are actually acting on the vehicle. Hence, sufficient control at the road noise resonances can be achieve also with a low number of accelerometer sensors.
4.4
Summary
Based on road noise measurement data, this chapter has investigated the most coherent transfer paths of noise. It was found that the contributions from the front axle are mostly related to tire cavity noise and also to a midfrequency resonance at 290 Hz. On the other hand, the rumble noise (100-120 Hz) is caused due to structural vibrations coming from several points at the rear axle. Two subframe mount locations from the front and two from the rear axle were selected to calculate the multiple coherence and evaluate the performance of a feedfoward ARNC controller. Reduction around 3 dB(A) at each headrest microphone are predicted, which is a good indication that the accelerometers are placed at sensitive structural points of the car that provide high levels of vibrations related to the interior road noise.
Road noise synthesis
The coherence analysis of the vibro-acoustic paths in the previous chapter re- vealed that there are two main locations at the rear axle that are responsible for the road rumble between 90-120 Hz. As for the tire cavity band between 180-220 Hz the primary paths originate from the front axle of the vehicle. In this chapter we will demonstrate that these locations can be used to artificially synthesise the interior road noise with multireference adaptive filtering in the time domain. This technique is widely used in the identification of impulse responses of physical systems. In our case we determine the impulse responses of each NTF between the reference sensors and a microphone in the cabin in the time domain without the electro-acoustic paths between the loudspeakers and the microphones, which we will use later to model the ARNC system. The main target of this artificial synthesis of road noise is to highlight the contributions from the front and rear axle with the use of adaptive filtering instead of frequency domain methods, such as OTPA, which require matrix inversion of the CPSD matrix of the acceleration signals. In our case, we make use of adaptive filtering to identify the systems between the acceleration and sound pressure signals. This type of approach is a special case of adaptive noise cancellation that does not include the effects of the acoustic secondary paths. Therefore this can reveal the potential reduction with a given set of sound and vibration signals acquired from the vehicle without the effect of delays that are inherent in the secondary path impulse responses and also in the hardware of the controller. To summarise, this study aims to offer an impression of how close is the performance of adaptive filtering algorithms are to the attenuation, which is computed non-causally in the frequency domain with the use of multiple coherence function.
5.1
Chapter outline
In section we start with the introduction of the famous multiple reference LMS algorithm that is used in section 5.3 In section 5.4, 5.5, 5.7 and 5.7 the main structural inputs into the vehicle’s body from the subframe are used for synthe- sising their vibro-acoustic contributions.