Multi-Disciplinary Engineering Design ConferenceKate Gleason College of Engineering

Rochester Institute of TechnologyRochester, New York 14623

Project Number: 11227

FORMULA EXHAUST ACOUSTIC TUNING

Kyle Desrosiers/ Project Manager (ME) Bradley Fiedler (EE)

Christopher VanWagenen (EE) Greg Wodzicki (EE)

May 13, 2011

ABSTRACT

To be the best formula team in the nation, all the components of the racecar and the members must work together. The RIT FSAE Formula team has become one of the elite teams because of the chemistry the members have and the highly designed components of the car.

A problem that seems to be recurring was the exhaust and the sound level that was being emitted. To be able to compete, the sound level from the exhaust has to be below a certain level. The SAE team has been forced to throw more and more glass pact into the muffler to reach this level and compete.

To eliminate this problem of putting more glass pact in, a proposed Active Noise Cancellation (ANC) system was devised to replace the entire muffler. The ANC system should be more cost effective, weigh less or equal to, and be within the current measurements of the current muffler.

INTRODUCTION

The exhaust system for the RIT FSAE team has caused delays numerous times and limits the time the FSAE team has to practice and race. While there is a maximum for the sound level to race and the RIT team meets that rule regularly, every time the racecar is driven, the glass pact used

in the muffler deteriorates and moves within the muffler, which causes more noise to come out of the exhaust. When the RIT team cannot meet the maximum sound level, they will pack in more glass pact to quiet the exhaust. More glass pact translates to more weight to the racecar which will slow it down. Also, adding more glass pact would create more back pressure which would decrease the performance of the engine. The idea of an ANC system replacing the muffler gives hope that the exhaust system is at a constant weight, provides the necessary sound reduction, and maintain optimal engine performance. Having the necessary sound reduction provides more time for practicing and racing, and without any concerns about losing performance from the exhaust, the RIT FSAE team can concentrate on other components of the car.

The goal of an ANC is to use a processed sound from a speaker to physically cancel a source sound. A perfect ANC system would diminish the source noise to zero dB. The system uses a DSP control board to input the source noise and outputs a near perfectly inverted signal. A speaker is driven to provide the process sound to cancel the source sound. With the source noise and the processed sound from the output of the DSP merging together by a piping system, the result should be a noticeable reduction in sound. This

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sound reduction will be drop the engine exhaust to below the FSAE ruling.

NOMENCLATURE

ADC – Analog to Digital ConverterANC – Active Noise Cancellation, using speakers to actively cancel noise without any absorptionDAC – Digital to Analog ConverterdB – Decibels, units to measure sound levelsDSP – Digital Signal ProcessorFIR – Finite Impulse Response, type of algorithm FSAE – Formula SAE, collegiate formula car racingHz – Hertz, unit of frequencyLMS – Least Mean Square, type of algorithm filter used in the ANC systemPVC – Polyvinyl chloride, widely used plastic in construction and pipingRIT – Rochester Institute of TechnologySAE – Society of Automotive EngineersSPL – Sound Pressure LevelTI – Texas Instruments, electronic company

OVERVIEW

After starting the research of ANC systems, the topics were split up within the team. One group was devoted to researching about signal processing and the other group was devoted to sound properties. With this research, the groups followed their path with the building and testing stages of the project. The group that researched signal processing concentrated on the DSP and the algorithm needed, and the group that researched sound properties concentrated on building the design of the ANC system and collected the data.

As the groups expanded their knowledge in their specific area, calibrations occurred to design and build the final ANC system. Even though the main goal was to have a fully functional ANC system to replace the muffler exhaust on the FSAE racecar, the research proved that building a reliable and functional system within twenty-two weeks was near impossible. It was decided then that the end product should be more of a proof of concept. This was going to be the first step towards a fully functional ANC system for a FSAE car.

Figure 1 displays the basic flow chart of an ANC system using two microphones.

Figure 1: Simplified flow chart of an ANC system

DESIGN PROCESS

Top-down/Bottom-up Design

Top-down and bottom-up designs are methods used by companies worldwide for their own projects. The top-down method starts with specific design need and then works around this need to design smaller components. The bottom-up method starts with the basic information to design a system. Usually the bottom of a project is research or the mental knowledge, whereas the top is the application stage of the project. Both methods were used while devising the ANC system. The research at the beginning of the project helped sort out the parts needed to build an ANC system, whereas building a basic system that would work helped with the physical design of the system.

Figure 2: Diagram of the top-down bottom-up methods

Figure 2 is a diagram of the top-down bottom-up methods. This is the basic path the team took to have a finished ANC system.

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Needs

For the first eight weeks of the project, it was not clear what the needs were for the customer. Through the beginning of research and calibration with the customer, the needs for the customer changed several times. The evolving requirements delayed the progress of the project and forced the team to adjust to the changing environment. It was not until research was done and discussions between the team and customer had occurred that the needs were finally established.

Specifications

The specifications are derived from the needs and wants from the customer. Once the needs for the customer were sorted out, the specifications were clearly defined. When everyone agreed that the needs of the customer could not be accomplished within twenty weeks, the project team provided their own specifications to fit with the realistic goal of the project.

Primary Design

The main design we are using for the ANC system is using PVC piping connected to two speakers. The two pipes from the speakers are then merged into one using a wye connection. The wye connection provides enough interface between the two sound waves that the emerging waves become one single sound wave. Since the sound waves are emerging as the primary signal source is processed and outputted, the inverted sound would physically cancel a percentage of the primary sound. Two microphones were placed in the piping, one near the primary source and the second near the single opening, to allow the DSP to capture the noise and process it for the secondary speaker.

Alternative Design

The second option for an ANC system design was to use a dipole box. The idea of using a dipole box would be that it would work better than a wye connection if the source was an engine and decrease the probability the speaker would be damaged from the intense temperature and pressure from the exhaust gases. After testing and recording the data, the dipole box seemed to work, but not as well as the wye connection. The dipole

box created dead spots because the two sound waves were not exiting as a single wave. Similarly to the primary design, there would be two microphones placed near the primary source and the opening, and would be used as the inputs for the DSP.

Feasibility Assessment

As the weeks passed on the project, it was more and more evident that a four-person group of student engineers could not complete the task as initially described in twenty-two weeks. Though the primary goal was to be able to use an ANC system on the FSAE team, the actual goal became to have a functional ANC system in any size. It was realized that a functional ANC system for an automobile still has years of research, testing, and new technologies before a group of engineering students can manufacture one.

PRELIMINARY DESIGN

Hardware

Since the project was mainly split between hardware development and software development, the hardware had to be picked out as early as possible so the software could be implemented. Most of the hardware parts were easily accessible from nearby stores, i.e. RadioShack or Lowes, or the turnaround was relatively quick.

One model of microphone was selected to the two microphones used within the piping of the ANC system. Though the selection process was fairly easy, the discussion about using different microphones occurred throughout the testing of the system.

The important hardware portions that had to be selected were the speakers and the DSP. The size of the speaker determined the frequency range for possible cancellation. From data collected from the lawn mower engine and noise cancellation of pure harmonic tones, the range for which the best cancellation occurs below 800 Hz. A ten inch speaker was selected as the best fit speaker for noise cancellation.

The DSP selection had to be the most important part of the project because if it was too slow or could not process the algorithm that was needed, then there would be no sound cancellation.

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Software

The C5505 DSP was provided with sample code for many different applications. One of these applications was an active noise cancellation system for headphones, much like the systems used by Bose and other audio companies. This was the basis of our ANC algorithm program, and provided a necessary foundation to ease the programming. The algorithm used in the headphone app is similar to that used for the general noise cancellation but differs in some key application that limits its viability for other programs. The software suite, Code Composer Studio is free for the C5505 and comes with the other DSP.

ENGINEERING MODEL

Hardware

The task of choosing an appropriate DSP for this project was difficult. Texas Instruments manufactures two large, multiple-channel development kits based on two different DSPs, with one focused on a very wide range of applications, and the other specifically designed for audio processing. Since this project consisted entirely of audio processing, filtering, and algorithm design, the TI TAS3108EVM2 evaluation module was selected.

Figure 3: TAS3108EVM2 evaluation kit

This module contains 8 inputs and outputs, and also comes with a graphical design tool to make programming easier. Since most DSPs are programmed using assembly language, this tool was embraced as a quicker and simpler way to implement any required filters and the LMS

algorithm. However, after several days of experimentation and communication with TI, it became apparent that the graphical programming tool was much less useful than we had hoped. Only simple FIR filters, volume controls, and basic mathematical functions could be performed. Additionally, there was no option to program in a high-level language such as C, and the only way to program the DSP was to use assembly language for the entire program.

After discussion with TI, it was recommended that our team switch to a VC5505 low-power DSP. This development module is shown in Figure 3. This processor included only one stereo input and output, which meant that only a single ANC system could be driven at one time; this was not a problem, as a modular system was being designed for using the system with different sources. This DSP also came with teaching materials, including a very basic headphone ANC program, to get us started. The processing speed and sampling rate of this smaller, low-power DSP were much lower than the TAS3108EVM2, but did not cause a significant delay due to processing time, and the entire board could be powered from a single USB port, eliminating the need for an external power supply. Extensive documentation, a huge code base, and PowerPoint learning materials made programming this DSP a much more reasonable task.

Figure 4: VC5505 DSP Evaluation Kit

Software

Programming the DSP to perform active noise cancellation was somewhat difficult. Due to the speed requirements of this type of system, high-level programming languages cannot be used (upon compiling, the program will be too slow). This limits the programming language to assembly, which is very difficult to follow and comprehend. Sample code provided with the kit implemented a single-input ANC system designed for headphones. Using this code as a starting point, we were able to

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create a system that takes two input signals (a primary microphone and secondary microphone), performs LMS calculations on these signals, and outputs an appropriate signal to cancel the primary noise source. The LMS algorithm was programmed in assembly language for maximum speed and minimum delay in the system, while the main program (mapping inputs, performing FIR low-pass filtering, outputting status) was programmed in C. A sampling rate of 48 kHz was used to eliminate aliasing in the microphone signals and preserve as much of the original sound quality as possible.

To improve the accuracy of the LMS algorithm and reduce processing complexity, a low-pass filter was designed and implemented. Previous testing showed almost no cancellation of pure tones above 1 kHz, so a 1 kHz Hamming-window low-pass filter was used to significantly reduce high-frequency components detected by the two microphones. A filter size of 51 taps was used, enough to produce a reasonable high-frequency attenuation but not too large as to slow down the processing of the DSP. This filter was applied directly to both inputs before passing to the algorithm. The filter itself was designed using FDATool in MATLAB, which creates the appropriate number of FIR filter coefficients based on several specifications such as necessary stop band attenuation and cutoff frequency.

The goal of this algorithm is to create an out-of-phase signal based on the primary microphone and detected error and drive the output of the system (sound detected at the error microphone) to zero. This “adaptive filter” must process the signal and create its opposite as quickly as possible, before the wave front from the primary source reaches the coupling joint that combines the primary and cancellation sources. In practice, this was not a problem, as the total processing time was on the order of nanoseconds and the time for the wave front to reach the junction was a few milliseconds.

Figure 5 displays how the algorithm is implemented within the ANC system. X(k) is the inputted sound from the source and e(k) is the produced sound from the DSP outputted to the speaker. The LMS algorithm would process the two signals (primary and the output) and implement the resulting signal to the controller, C(z).

Figure 5: Functional block diagram of the algorithm used in the ANC system

EXPERIMENTAL SETUP

For the initial testing to determine if ANC is possible in practice, two smaller computer speakers were connected to PVC piping and merged in a wye connector with a single output. Using a waveform generator, one speaker outputted a sine wave harmonic tune while the other speaker outputted an inverted sine wave of the same frequency as the other speaker. Different frequencies were tested for their noise cancellation capabilities at different distances from the speakers. The data that was collected was then analyzed through spectral analysis. Through these tests, there was a noticeable increase in sound reduction as the frequency of the sine wave decreased, and as the frequency approached and passed 1 kHz, any noise cancelling effect was not noticeable, which was expected from the research.

As the project continued, the same test was used as a preliminary test when using the ten-inch speakers that were selected. The next step in testing was to include a DSP, but without microphones.

Figure 6: Model of the fully assembled ANC system

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Figure 6 is the basic design of the fully assembled ANC system being used. The DSP and microphones are not in Figure 9. It is also the same design used for the preliminary testing with the small computer speakers and the selected ten-inch speakers.

Two additional microphones were installed at equal distance from the wye intersection in order to test for output accuracy and delay. The difference in signal structure shows the accuracy of the algorithm and the viability of the design. The delay is seen when the difference in identical signals from the two microphones is found.

The system was also tested in a dipole setup. This allowed the setup to be removed from possibly harmless environmental hazards in the primary path. At a disadvantage, full wave superposition does not occur and not all spots are accurately attenuated. Microphone placement also moves outside the device and requires precise placement.

RESULTS AND INTERPRETATION

Prior to completion of the DSP, it was important to test the physical set up of the system to make sure sound waves were converging in a desirable and effective way to cancel each other. The following graphs show results for the Wye connector setup using small computer speakers playing pure tones. The speakers are perfectly out of phase from one another and as such provide the optimal (ideal) reduction value we could expect out of a perfect ANC algorithm.

Figure 7: Results from preliminary testing at 200 Hz

Figure 8: Results from preliminary testing at 1000 Hz

As can be seen in the graphs provided, as the fre-quency to be attenuated increases closer to 1000 Hz, the attenuation from the ANC system greatly reduces to almost nothing. At 200 Hz, reduction achieved is roughly 30 dB. These testing results prove that the processed signal will not have a no-ticeable effect on a sound that approaches and sur-passes 1000 Hz in frequency.

Prior to receiving the DSP, an algorithm was created in MATLAB to simulate the functionality and feasibility of using the algorithm for an ANC system. After some debugging, the results of the simulations came out to be surprisingly excellent. These results boosted the moral of the team and the guides, and gave hope that the finished product will have similar results. Figure 9 displays those MATLAB results. While the MATLAB results were for ideal conditions, the actual results using the hardware were not expected to be exactly like the simulations. This is because of the non ideall hardware factors that cannot be taken into consideration easily in simulation.

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Figure 9: MATLAB simulations of the DSP algorithm

The first tests that included a DSP used a signal directly from a media player (laptop, mp3 player, etc.) and outputted the inverted of that signal. Using a Sound Pressure Level (SPL) meter, the noise level being emitted was observed to be lower, but was not as low as expected. Just listening to the emitted sound, it was evident the sound reduction was in the bass area (low frequency range). This proved two hypotheses; ANC was possible when using a DSP, and lower frequencies are easier to attenuate.

The next step of adding two microphones within the ANC system proved to be a challenge. Before testing, it seemed like cheap microphones would be a good choice to use because they use the same element as more expensive microphones and were a fraction of the costs, but the resulting sound from the two speakers discouraged this choice.

To debug the resulting sound when using two microphones, two more microphones were placed equal distances away from the wye connection. The signals from the two additional microphones were monitored when the testing was repeated.

CONCLUSION

The final design of the ANC system is the first step towards applying an ANC system for the exhaust of the RIT FSAE team. Though the project was unsuccessful in completing an ANC system that could be used as an exhaust, the ANC system was able to cancel out sounds at certain frequencies. It was the starting point for future projects towards an ANC system. The whole project also was a teaching tool for possible real world events and displays the knowledge and experience of the future engineers that have worked to complete it.

RECOMMENDATIONS

The customer(s) needs to be clearer on what the desired product should be when the project is in its beginning stages. The fact that the direction of the project changed a few times within the first eight weeks delayed the amount of knowledge that was obtained about ANC systems and DSP boards.

Having mainly electrical engineering students worked well while designing an ANC system, but having a student engineer with more experience with programming and in different languages would have been helpful. Electrical engineering does not require as much programming as computer or software engineering. A more experienced programmer would have had more success using and figuring out the software, language, and algorithm that was used with the DSP boards.

ACKNOWLEDGMENTS

The team would like to express its appreciation to those who made contributions to this project.

The team would like send out their deepest gratitude to Professor George Slack and Professor Ed Hanzlik for guidance and support through the whole project experience.

Thanks to Texas Instrument for donating both DSP boards and helping the team with their software and algorithm and Cenco for donating audio equipment that is essential for ANC systems.

Thanks to Dr. Dorin Patru and Dr. Vincent Amuso for helping the team with their expertise on DSPs and algorithms.

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REFERENCES

General[1]http://paws.kettering.edu/~drussell/Demos/superposition/superposition.html[2]http://personal.cityu.edu.hk/~bsapplec/natureof1.htm

Texas Instruments[3]http://focus.ti.com/docs/toolsw/folders/print/tas3108evm2.html[4]http://focus.ti.com/docs/toolsw/folders/print/tmdx5505ezdsp.html

Signal Processing [5] Elliot, Stephen, “Signal Processing for Active Control” 2001, American Press.[6] Kuo, Sen M. and Morgan, Dennis R., “Active Noise Control Systems” 1996, John Wiley & Sons, Inc.

Miscellaneous[7]http://ausweb.scu.edu.au/aw06/papers/refereed/pennell/paper.html

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