Daimler Calculation of Optimal Damping Placement in a Vehicle Interior

7/29/2019 Daimler Calculation of Optimal Damping Placement in a Vehicle Interior

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Calculation of Optimal Damping Placement in a

Vehicle Interior

Craig BirkettDaimler Trucks North America, LLC

4747 N Channel AvePortland, OR 97217

Poh-Soong TangDieter Featherman

Altair Engineering, Inc

1820 E Big Beaver RoadTroy, MI 48083

Originally published at the 2010 Noise-Con in Baltimore, Maryland, US

www.altairproductdesign.comcopyright Altair Engineering, Inc. 2012


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www.altairproductdesign.com

Copyright Altair Engineering, Inc., 2011 2

1.0 Introduction

One of the most difficult jobs of a NVH Analyst is to sift through a seemingly endless set ofresults and find the key conclusions that will improve a design. Different assumptions anddifferent subsets of data can give very different conclusions. This paper compares acousticresults calculated for a Class 8 heavy duty truck cab to choose an optimal configuration ofdamping material. The design was evaluated for structure and air-borne inputs, but onlystructure-borne inputs are considered in this paper.

Complementary tools were applied to the problem of determining damping material layout.Candidate locations for damping material were identified by summing A-weighted velocitiesover the frequency range of interest. This method had the advantage of giving a single resultthat pictorially shows the active areas of the cabin. It also did not require the user to lumpareas into discrete panels which may limit any optimization efforts by their choices.

Second, an automatic optimization was performed using structural inputs to determine the

optimal damping treatment from the candidate damping patches given weight constraints. Thisoptimization method had the advantages of being much more automated and could workdirectly to minimize sound levels at a number of response points.

Results were combined with vehicle dynamometer tests and contributed to the final noisepackage for the vehicle. The study was significant because it compared various practicalmethods of optimizing a vehicle interior.

2.0 Background

2.1 Assumptions

The following limitations were applied to the optimization process. First, the phasing of theinputs and results was neglected when possible when interpreting the results. Besides havingthe advantage of greatly simplifying the process, it is felt that this led to a more robust design.Many papers have shown that even parts with tightly controlled production process show awide variation in acoustic response from vehicle to vehicle and from test to test.1,2 This resultsuggests that vibrations should be combined in an RMS sum, assuming that they areincoherent above a boom frequencies. Although a single vehicle may not fit this assumption,an ensemble of vehicles does above some cut-off frequency.

In the automated optimization process, phase information in inherently considered. Finalconclusions can be adjusted in the end by considering the sensitivity information or fromlooking at results from an aggregate of several optimization studies.

Following in this line of thinking, a generic input was developed to represent a vibrationenvelope, developed from Wide Open Throttle Run-Ups (WOT). This input spectrum is shownin Figure 1 for the drivers side front cab mount. This was created by applying a FourierTransform to an entire 20 second WOT. The resulting curve was further smoothed to eliminatesharp frequency peaks as shown in Fig 1.


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The baseline cab was modeled with minimal damping materials added, but using materialdamping of assemblies measured in previous SEA studies.3 The authors felt this gave a morerealistic model of the untreated cab and helped add realism to the structural impact ofdamping materials.

Of course, these calculations represent only part of the noise entering the vehicle. Any designrecommendations must also consider effects of higher frequency structural vibrations,absorption effects, and air-borne paths through panels, gaps in insulation and seals. Effectson sound quality must also be kept in mind and are beyond the scope of this paper.

2.2 Optimization BackgroundNumerical optimization of coupled structural acoustic problems has many challenges. One ofthe main limitations was the required computation times for real engineering applications wasexpensive. This is because acoustic performance of complex structures is typical calculatedusing finite element (FE) analysis software, and the coupled structural acoustic FE analysis isbased on frequency response analysis that span over a range of frequencies. As a result, the

coupled structural acoustic FE analysis itself requires intensive computation. Since theacoustic performance is normally evaluated over a range of frequencies, the optimization hasto be performed over a range of frequencies as well instead of a scalar objective function. Thiscoupled with the iterative nature of numerical optimization made the FE analysis basedstructural acoustic optimization slow when compared to the design cycle.

In recent years, many techniques in model reduction and eigensolver, were developed forstructural acoustic analysis. For example, using Component Mode Synthesis (CMS) for fluidstructure external super elements reduced structural acoustic analysis computation time.4

Also AMLS (Automatic Multi-Level Sub-structuring) algorithm used in eigenvalue analysisgreatly reduced this part of the solution time. These factors combined with improved structureoptimization algorithms made a full fledged acoustic optimization feasible within the design

process.

3.0 Automatic Optimization

3.1 Optimization MethodologyIn this paper, an acoustic optimization was carried out for a Class 8 heavy duty truck cab(Figure 2) Using the commercial optimization software package, OptiStruct. The truck cabmodel consisted of 560,000 structural elements and 273,000 acoustic elements. A structure-borne Wide Open Throttle (WOT) spectrum was used as enforced acceleration input at thecab mounts and a coupled structural acoustic modal frequency response analysis wasperformed. The objective of the acoustic optimization was to obtain an optimum damping

material placements that provided maximum acoustic improvement to the truck cab. Theacoustic pressure at drivers ear location, which was obtained from the coupled structuralacoustic finite element analysis, was used to quantify the acoustic improvement. A-weightingfilter was applied to the acoustic pressure result before it was used in the optimization. The A-weighting filter reduced the acoustic pressure at low frequencies, which correspondedapproximately to how humans perceive sound. Such characteristic was also desired inacoustic optimization, as noisy low frequencies would derail the optimization if left untreated.


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To find the optimum damping material placement, a set of 44 pre-defined panels wereconsidered (see Figure 3). In this case, the word panel refers to a structural model of thedamping material. The gauges of these panels were used as design variables in thisoptimization. The optimum damping material configuration was achieved by allowing the

gauges of these panel to vary within a set of discrete thicknesses of .01 to 2 mm. If the gaugeof certain panels reached the minimum limit during the optimization, it was considered that nodamping material was required for those panels. On the other hand, the optimization wouldfind the right gauges for panels which required damping material.

3.2 Observations of Optimization ProblemOne of the difficulties encountered was that there was only a modest difference in interiorsound level was predicted when comparing the untreated cab with a fully treated version. Seefigure 4. In this case the untreated cab had some natural damping due to plasticcomponents, carpet, fasteners, sealant etc. The model also kept mastic panels where it wasrequired for other nonacoustic reasons such as the outer door skin. The maximum dampedcab had thick damping on every candidate panel and would never be used in production.

The acoustic optimization in this paper was defined to minimize the maximum acousticpressure of drivers ear given a limited damping material weight. The minimize maximumresponses feature served as a function to lower the overall magnitude of acoustic pressurecurve over an interested frequency range. From figure 4 it is apparent that there were anumber of peaks which at various points in the optimization process could become dominant.This resulted in an erratic objective function and could lead to many local minimum in theoptimization process.

3.3 Optimization ResultsThe acoustic optimization problem was defined as minimize the maximum acoustic pressureat drivers ear location subjected to damping material weight constraint. Different weight limits

were tried to investigate different trends and options of damping material placement. Thefollowing section shows the results of 2 different optimization runs, one with 5 kg of dampingmaterial added and one with 10 kg of material added. Figures 5 and 6 show these results.

The most noticeable thing from these two results were that the damping locations changeddramatically when the mass constraint was changed from 5 kg to 10 kg. This illustrates theuser can find many local minimum in a damping optimization and not achieve a clear direction.This feature was also apparent when reviewing the sensitivity information. Since panelsensitivities varied with each frequency and at different steps in the optimization, they did notclearly show a direction to take.

The results can be expanded by adding additional operating conditions (such as highway

cruise with dominant road inputs) and to additional response points (passenger, bunk sleeper)which only gives further variations in optimal damping treatment.

The total run time for both optimizations was approximately 13 hours with 8 CPUs on Linuxclusters.

4.0 Velocity Sums


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4.1 MethodologyA more manual method to optimized damping material placement was also attempted. Thiswas inspired partially by test methods which measure surface velocities on a cab makeconclusions based on the active panels of the cab. Velocities are combined over all

frequencies so that a single plot can show the overall hot-spots of the panels. This was foundto give an excellent graphical tool for discussing with designers what can be done to adddamping to the cab. Then the best candidate damping solutions could be evaluated withcoupled structural acoustic analyses.

At each node a plate element we have

Take the dot product with the element normal vector, n, to get

Then calculate the magnitude

So now we have the peak velocity of the element at frequency fi. calling it V e (fi) for simplicity.If the inputs to the FEA simulation are rms accelerations or velocities, then the resultingsurface velocities will also be rms quantities.

We want to sum the velocities over all frequencies to get a single rms velocity at eachelement. For this study the sums were done over the entire frequency band and then also in1/3 octave sums for more insight as to the problem frequencies.

In the process of doing the sums, A-weighting factors were applied to ensure the lowerfrequency velocities did not dominate the higher frequency velocities. Sensitivity weightingfactors might also be included at this point.

Finally we convert the rms velocity at each element to a dB scale by using


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4.2 Velocity Sum ResultsIdeally, this process would be applied before the automated optimization process, since it canhelp in selecting the candidate panels for damping. However, in this case the velocity sumwork was completed after the optimization runs. As stated in the introduction, these

calculations were made on a cab with a minimum of damping material, so that areas thatrequire damping material are apparent.

Figure 7 shows the velocity sums from one simulation. It was found that the areas whichneeded damping generally agreed between the two methods. The areas selected for dampingby the automated process tended to have high values in the velocity sum plot, but there wasnot a one to one correspondence. Of course, the velocity sum plots as shown here neglectedphase information and acoustic sensitivity to the occupants ears. So it would not be expectedto yield the same results.

Velocities were also measured on an operating cab using laser vibrometry. But again a one-toone comparison was difficult. The same input conditions were not used and it was not

practical to even obtain the same cab design for the measurements. Also access to somepanels such as the floor was difficult and added limitations. For these reasons a comparisonbetween the test and the simulation was not practical.

5.0 ConclusionsTwo processes were described that provided a practical approach to optimizing dampinglocations in a truck cab or automotive vehicle.

The objective function of the maximum SPL peaks due to structure-borne noise was found tobe erratic and easily subject to finding local minimums. Comparision of the simulation resultswith various constraints for damping material demonstrated this feature. When using an

automated optimization routine it is best to run the simulation numerous times to look for arobust solution and explore different local minimums.

The velocity sum method showed promise to provide additional understanding of the cabvibration environment and to help in selecting local choices in candidate damping panels. Itwas also found to be an excellent tool to use for discussions of the overall noise treatmentplan. Conclusions from this method were similar to those made from multiple optimizationsimulations.

The approach outlined of evaluating the interior acoustics for a range of conditions in thiscase an entire engine run-up was found to be an effective tool for making decisions of theacoustic treatment of the vehicle.

6.0 AcknowledgementsThe authors would like to thank Phil Murray and Alex Gorodisher of Daimler Trucks, N.A. LLCfor providing measurements of the input vibration spectrum from the WOT Run-up. We alsowould like to thank Bineka Kristanto of Altair for his contribution in programming the velocitysum calculations.


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7.0 References1. Vehicle-to-Vehicle NVH Performance Variance, Robert M. Shaver, Kuang-Jen J. Liu

and Michael G. Hardy, Chrysler, LLC.2. Kompella, M., and Bernhard, R., Measurement of the Statistical Variation of

Structural-Acoustic, Characteristics of Automotive Vehicles, SAE 931272, 1993.3. Arnaud Charpentier, Craig Birkett, Manuel Sanchez and Vivian Dias, Modeling

Airborne Noise Transmission in a Truck Using Statistical Energy Analysis, SAE Noiseand Vibration Conference, 2007.

4. OptiStruct V10.0 On-line Manual, Altair Engineering, 2009.


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Fig. 1 Wide Open Throttle Envelope - Chassis-Side Vibration at Driver's Front CabMount

Fig. 2 Cab FEA Model, Some Panels Removed


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Fig. 3 Panels Selected for Optimization Process

Fig 4 Comparison of Untreated and Maximum Treatment PredictionsApproximately 2 dB difference in structure-borne noise over the frequency range


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Fig 5. Damping Results for 5kg Study, Red = Thickest Damping, Blue = Thinnest

Fig 6. Damping Results for 10 kg Study, Red = Thickest Damping, Blue=Thin, Grey=None


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Fig 7. RMS Velocity Sums for WOT Envelope Inputs, dBA Scale

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Daimler Calculation of Optimal Damping Placement in a Vehicle Interior