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Suggested Citation:"Summary." National Academies of Sciences, Engineering, and Medicine. 2009. Acoustic Beamforming: Mapping Sources of Truck Noise. Washington, DC: The National Academies Press. doi: 10.17226/14311.
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Page 1
Page 2
Suggested Citation:"Summary." National Academies of Sciences, Engineering, and Medicine. 2009. Acoustic Beamforming: Mapping Sources of Truck Noise. Washington, DC: The National Academies Press. doi: 10.17226/14311.
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Page 2
Page 3
Suggested Citation:"Summary." National Academies of Sciences, Engineering, and Medicine. 2009. Acoustic Beamforming: Mapping Sources of Truck Noise. Washington, DC: The National Academies Press. doi: 10.17226/14311.
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Page 3
Page 4
Suggested Citation:"Summary." National Academies of Sciences, Engineering, and Medicine. 2009. Acoustic Beamforming: Mapping Sources of Truck Noise. Washington, DC: The National Academies Press. doi: 10.17226/14311.
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Page 4
Page 5
Suggested Citation:"Summary." National Academies of Sciences, Engineering, and Medicine. 2009. Acoustic Beamforming: Mapping Sources of Truck Noise. Washington, DC: The National Academies Press. doi: 10.17226/14311.
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Page 5

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S U M M A R Y Heavy trucks are significant contributors to overall traffic noise levels. At highway speeds, the noise level produced by heavy trucks is about 10 dB greater than that of light vehicles. As a result, every heavy truck in the traffic flow contributes the same amount to the average noise level values as 10 light vehicles. Because of this significant contribution, a thorough understanding of the trucks as a noise source is crucial to the prediction and mitigation of traffic noise. Noise from heavy trucks originates from a variety of sources, which include exhaust stack outlet, muffler shell, exhaust pipes, engine block, air intake, cooling fan, tires, and aerodynamics. The relative contributions of these sources vary with vehicle type, oper- ating condition, and (for tire noise) the type of pavement. For modeling and abatement of traffic noise, the barrier performance of sound walls depends on the distribution of noise source heights. Because trucks contribute the tallest noise sources, highway noise walls are typically designed so the top of the exhaust stack is obscured from the receiver’s sight under the assumption that exhaust noise is a major source. The cur- rent treatment of truck noise for highway conditions is often simplistic, placing about 50% of the source strength at a height of 12 ft (3.7 m) and the other half at ground level, independent of vehicle speed or pavement type. A number of recent observations, however, challenge the current treatment of trucks and led to the need for new research. First, as a result of the federal regulations in the United States, truck noise levels have been incrementally lowered over the past few decades. In achieving this lower level of noise performance, engine and exhaust noise have been effec- tively addressed. From recent studies performed in Europe, tire–pavement noise has been found to have a much larger contribution, 63% to 84%, at highway speeds. A few studies have also shown that noise levels are strongly dependent on truck tire type. From research work related to the application of quieter pavements, reductions in traffic noise have been measured consistent with the reduction of tire–pavement source levels when pavement modifications have been made, which goes beyond what would be predicted based on the current treatment of truck noise as being split evenly between tire–pavement and ele- vated sources on trucks. For all these reasons, it is essential to have more information about truck noise sources than can be observed in typical passby measurements. The objective of this study was to use noise- source mapping techniques to accurately localize, identify, and quantify the noise sources on typical commercial trucks operating in the actual roadway environment. The scope of work for this project included design, development, experimental demonstration, and validation of a practical technology for truck noise-source localization and distribution under actual operating conditions. Acoustic Beamforming: Mapping Sources of Truck Noise 1

2Findings Current noise-source identification techniques were reviewed and assessed for their suitability for the type of measurements necessary in the study. Traditional truck noise-source measurement methods—such as component wrapping, removal, and substitution—are labor intensive and can realistically be performed only on a relatively small number of vehicles in controlled tests. The sound intensity method, involving a special probe placed near different source regions of the vehi- cle, is typically applied either in laboratory settings or in controlled near-field measurements but cannot be employed in uncontrolled situations. Another method used for sound source identifi- cation is near-field acoustic holography. It employs an array of microphones that is also typically used in stationary, laboratory test conditions or is suspended along side of the tested vehicle. However, the microphone array must be physically as large as the source region of interest and cannot be employed in uncontrolled passby applications with moving vehicles. The more compelling method of localizing noise sources is acoustic beamforming, which uses a stationary array of microphones to focus measurement on a specific point near the actual source and to scan this focus point electronically over the object of interest. The measurements can be performed in the far field, and the associated computer algorithm can account for both the mov- ing source and Doppler frequency shift, as well as provide tracking of the vehicle by evaluating several time periods during the passby, thus increasing the accuracy of the result. Beamforming techniques have been used for localizing noise sources on trains and automobiles and in other applications. However, several issues had to be resolved before this technique could be employed for the truck application, including the spatial resolution for the low frequencies of interest for truck noise, the optimal array dimensions and configuration, the number of array microphones needed, as well as a few practical application concerns. Based on the analysis of existing research and current practices, as well as the study team’s expe- rience in the preceding truck noise studies, beamforming was found the most appropriate noise source mapping method for truck application and was further developed and optimized for this application in the current study. As the result of the development, an experimental 70+ microphone elliptical array was designed and fabricated for truck testing. The elliptical shape was found optimal because vertical resolution of the noise source image is more important than horizontal resolution for traffic noise barrier design and also because of certain inherent horizontal resolution of the noise source image during a passby. The microphone array parameters assuring the optimal performance were selected for implementation in the array hardware. Associated beamforming software was developed and implemented using a computerized data acquisition system. The experimental microphone array and noise-source mapping technique was validated through proof-of-concept testing. It was performed at low-speed and high-speed truck testing facilities for a representative sample of trucks with different characteristics to validate the mea- surement system performance and verify its parameters. Multiple sound distribution images were obtained during the testing for truck noise sources—such as engine compartment, tires, and exhaust—in the effective frequency range from approximately 250 to 2000 Hz. The test results confirmed that the developed system provided adequate noise mapping and localization for typical noise sources on various trucks, both stationary and moving with the speed up to 50 mph (80 km/h). With a few minor modifications, the measurement system was further applied to quantify noise sources for a wide range of trucks under actual road conditions on an in-service highway. One hundred vehicle passbys (heavy and medium trucks, and buses) were recorded using the mea- surement system in a single day of data acquisition. Individual vehicle speed during passbys var- ied from 55 to 70 mph (88 to 112 km/h). Figure S-1 shows the microphone array setup for the roadside noise measurements.

3Figure S-1. Microphone array setup for roadside noise measurements. Figure S-2. Tire noise dominates in this truck passby: (a) source image and (b) vertical distribution of the overall A-weighted sound levels. (a) (b) The results of these measurements for 59 heavy truck passbys and 4 medium truck passbys were analyzed in detail, with acoustic images and spatial noise source distributions obtained to define and interpret the noise source levels and sound paths from the engine, tires, exhaust, and other body components. Individual trucks, as well as statistical metrics for the truck passbys, were eval- uated. Figure S-2 shows a typical acoustic image of a truck with prevailing tire noise (a) and a ver- tical distribution of the overall A-weighted sound levels measured during the truck passby (b). The results of the study indicate that, for majority of the measured trucks (55 heavy and 4 medium trucks), the highest noise levels were generated close to the pavement at approximately 0.6 m (2 ft) elevation. For these trucks, the measurement data did not reveal significant noise sources at the height of vertical exhaust stacks. Four of the heavy trucks measured (7.3%) exhibited significant noise generation in the area of the vertical exhaust stack at about 3.6 m (12 ft) above the road surface, with the sound levels exceeding that produced by the tire–pavement interaction at low frequencies (below 500 Hz). Figure S-3 shows an acoustic image of a truck with the exhaust noise dominating the tire noise (a) and a vertical distribution of the overall A-weighted sound levels measured during the truck passby (b). The study also showed that statistical vertical distributions (mean and maximum) of truck noise sources can be effectively simulated by a simple system of two uncorrelated sources located

4near the pavement and at the exhaust stack elevation. These results can be used for developing a model for the truck noise propagation. Conclusions Overall, the results of the study validated the beamforming measurement technique in the truck noise application. It is confirmed that the measurement system developed in the course of the proj- ect performed effectively in mapping and localizing typical noise sources for stationary and mov- ing trucks in actual road conditions in a wide frequency range from 250 to 2000 Hz. Sound distribution images and maps obtained during truck passbys permitted examination of the time histories and spatial distributions of sources, as well as an analysis of the noise paths from the engine, exhaust, muffler, tires, and other body components for various trucks. Statistical analysis of the vertical distribution of noise sources indicated that for the majority of truck passbys measured at highway speeds on an in-service highway, tire–pavement interaction was the dominant source generating sound close to the pavement. A small proportion of heavy trucks, however, exhibited significant noise generation in the area of the vertical exhaust stack, dominating at low frequencies and elevations around 3.6 m (12 ft). These results are in general agreement with the conclusions of the California Department of Transportation study that used a commercial beamforming microphone array. The two studies provided essentially similar results in terms of sources identified, their relative contributions, and lack of higher elevation sources except in a few cases. For the noise prediction modeling purposes, the current study indicated that a simple system of two uncorrelated sources, one located near the pavement and another at the exhaust stack eleva- tion, can generally be used for simulating statistical vertical distributions (mean and maximum) of truck noise sources. Recommendations Based on the key findings and conclusions for the project, the following future research and testing needs can be identified: Figure S-3. Exhaust noise dominates tire noise in this truck passby: (a) acoustic image and (b) vertical profile of overall A-weighted sound levels. (a) (b)

• Conduct nationwide roadside truck noise measurements on a wider range of pavement types in multiple states to establish a new truck noise source database (source height distributions and spectral content) for traffic noise models. Additional analysis of subsampling of the full beamforming microphone array may be necessary, based on the data obtained in the current project, to expand the technique, simplify the array, and speed up data collection for wider scale measurements at a practical degree of effort. • The noise source distributions for trucks obtained in the current study, although based on a relatively small sample of the truck population, can be applied (if deemed appropriate) as interim source height adjustments to the reference emission levels in the FHWA Traffic Noise Model. • Traffic noise prediction models updated using the noise source distributions obtained in the current study can serve as a resource for state and federal agencies to examine the effective- ness of highway noise mitigation strategies, such as the use of quieter pavements or barrier design. • Novel information obtained for noise source distributions on trucks, as well as the measure- ment technique developed in the course of the study, are recommended to truck manufactur- ers for further studies of potential source- or path-targeting treatments. • The beamforming measurement technique developed during the course of the study is also recommended for use in the analysis of noise-generating mechanisms and noise abatement measures for automobiles, buses, and motorcycles, as well as other noise sources such as con- struction equipment, etc. • The approach developed in the study can be used for exploring in greater detail the effects of varying pavement types and terrains on noise generated by different vehicle types, including the effects on vertical noise source distribution and tire–pavement interaction. 5

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TRB’s National Cooperative Highway Research Program (NCHRP) Report 635: Acoustic Beamforming: Mapping Sources of Truck Noise explores the acoustic beamforming technique in an attempt to pinpoint and measure noise levels from heavy truck traffic. The beamforming technique uses an elliptical array of more than 70 microphones and data acquisition software to measure noise levels from a variety of noise sources on large trucks—including the engine, tires, mufflers, and exhaust pipes.

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