Acoustical Analysis of Active Control in the Server Room of a C7 Data Centers Colocation Facility

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1 Brigham Young University Acoustical Analysis of Active Control in the Server Room of a C7 Data Centers Colocation Facility Feasibility Report Advisor: Dr. Scott Sommerfeldt C7 Data Centers representative: Mike Maughan Group Leaders: Jesse Daily James Esplin Zach Collins Matthew Shaw June 21, 2010

2 Contents 1 Executive Summary 2 2 Introduction 2 3 Methods Data Collection Room Acoustic Measurements Noise in the Server Room Computational Simulations Governing Equations Computational Model Results Experimental Testing Methods Results Conclusion Summary Conclusions Recommendation Contact Information 18 1

3 Feasibility Report 1 Executive Summary C7 Data Centers provides colocation, virtualization and disaster recovery services and wanted to explore the possibility of applying active noise control technology to reduce ambient noise in the data center server room. Active noise control uses loud speakers and microphones to observe the acoustical environment to emit an inverse sound signal to cancel out the noise. Three methods were used to determine the feasibility of using active noise control inside the data center server room: measured acoustical data, computational modeling, and experimental testing. For the measured data, four measurement locations were considered: inside the data center entrance door, in front of a C7 Data Centers cage near the center of the room, in a hot row aisle and inside a cold row aisle. Two different computational simulations were performed, the first determined the effect of using a simplified best-case scenario model, and the second used a more complex model. Finally, an experimental test was constructed to determine if active noise control is feasible for the C7 Data Centers Lindon 5 server room. The culmination of our results indicates that active noise control over an extended region is not possible in the data center room in general. This is due to the random nature of the noise and the high modal density of the room itself. However, our results indicate that active noise control may be feasible inside the cold row aisle, given that modest control (2-4dB) was achieved in laboratory experiments. 2 Introduction C7 Data Centers has been proactive in seeking out new technologies to make the data center experience more enjoyable for their customers. Reducing ambient noise on the data center floor isnt necessarily a need, but more of an interest to see whether something minimally invasive could be done to counter-act the noise. The C7 Data Centers Lindon 2

4 5 data center, as is typical of data centers, has little acoustic absorption. The common, passive forms of acoustical absorption, such as foam or fiberglass panels were not employed as they contain materials which are highly flammable and give off particulates that could trigger the early warning fire detection systems. Active noise control (ANC) was considered as it uses loud speakers and microphones to observe the acoustical environment and then emits an inverse sound signal to attenuate the noise. Since active control does not use fibrous materials, it may be the ideal solution for reducing low frequency noise in the server room. To determine if active control is feasible, acoustic data of the server room was used in concert with computational models and experimental testing. 3 Methods Three methods were used to determine the feasibility of using active control in a C7 Data Centers server room: measured acoustical data, computational modeling, and experimental testing. Taking acoustical data of the server room yielded valuable information on the acoustic characteristics of the room and of the noise emitted by the servers and HVAC. The recorded noise was then used in computational models of the room. These model simulations were used to estimate the effect that active noise control might have on eliminating unwanted sound. Finally, an experimental active control mockup was made to determine if a physical solution was feasible and to validate or invalidate the computational model. 3.1 Data Collection Experimental data of the server room at C7 s Lindon 5 facility was taken to measure the acoustic characteristics of the room and of the noise emitted by the servers and HVAC. Four measurement locations were considered (see Fig. 1): by the entrance, in front of a C7 equipment cage, in a hot row and in a cold row. Room Acoustic Measurements All rooms reverberate sound. As sound reflects off walls and objects within the room, the sound decays until it cannot be heard. This decay of sound is called the reverberation time of a room. Reverberation time is found by emitting an impulsive sound (popping a balloon, firing a starting pistol, etc.) and measuring the time it takes for the sound to decay 60dB (decibels). This room acoustics measurement is called the T60 time. The Early Decay 3

5 Figure 1: Measurement locations inside the server room. Time (EDT) is the T60 time calculated by curve fitting the decay curve between 0 and -10dB. Reverberation time (or Early Decay Time, EDT) measurements were taken at three locations (the entrance, the C7 cage and the cold row ) to determine the acoustic characteristics of the room. At the entrance to the server room, eight microphones and an energy density probe were set up to measure the T60 time of the room. A twelve sided loudspeaker (dodecahedron) was used to ensure that sound was emitted spherically. The dodecahedron loudspeaker was placed 25 away from the center of the energy density probe and driven with a swept sine wave. The Electronic and Acoustic System Evaluation and Response Analysis (EASERA) was used to measure the EDT for all eight microphones. The microphones were then moved to the cold row and the EDT s were measured again. Table 1 shows the Early Decay Times (EDT) of the server room as a function of frequency. The higher the early decay time, the more of a problem a particular frequency range is for the room. In Table 1, the first two columns show the EDT for the entrance to the server room with two different source positions (Entrance 1 and Entrance 2). The next two columns show the remaining EDT data (the C7 Cage and the Cold Row ). 4

6 Table 1: Early Decay Times (EDT), times in seconds. Frequency Entrance 1 Entrance 2 C7 Cage Cold Row File Reference L1-S1-R1 L1-S2-R1 L2-S3-R2 L4-S4-R3 100Hz Hz Hz Hz Hz Hz Hz Hz Hz Hz Hz Hz kHz kHz An EDT of 3.20 seconds was measured for the 100Hz band by the C7 cage, which indicates that 100Hz is a reverberant frequency for the C7 Data Centers server room. At 8000Hz, all of the EDT s are below 0.6 seconds, meaning that those frequencies are not highly reverberant. Noise in the Server Room Noise is generally defined as unwanted sound. In C7 Data Centers server room, noise can be defined as the sounds emitted by the many computer fans and HVAC in the server room. These sources emit sound over a wide range of frequencies due to the turbulent air flow produced by the fans. Since fans operate at discrete frequencies, certain frequencies have more power than others. Eliminating these frequencies would greatly reduce the amount of noise present in the server room. Measurements of the noise in the server room were taken at three locations. These locations were chosen for being high foot traffic areas for both technicians and visitors to the data center. The first location was in front of a C7 equipment cage in the main aisle of the server room. The second location was in a hot row, an aisle between cabinet rows where the hot air coming out of the servers is directed. Lastly, noise was also recorded in a cold row (see figure 3), an enclosed aisle between cabinet rows that contains the cold air and directs it into the server cabinets. Figure 2 shows the sound power recorded at the 5

7 Figure 2: Power spectra of unfiltered C7 Data Center server room noise Figure 3: Measurements taken from inside the cold row entrance to C7 s Lindon 5 facility server room. These noise data were later used as input for computational models and experimental testing. 3.2 Computational Simulations The server room can be computationally modeled to determine the feasibility of using active noise control. This model can be used not only to determine the feasibility of active noise control but also to determine ideal locations and configurations for speaker installation. The model will consist of primary sources emitting the recorded noise taken in the C7 Data Centers Lindon 5 server room and control sources trying to actively control the noise. Governing Equations The computational model is based on fundamental acoustic principles. The server noise was approximated by several point sources emitting fan noise. The wave equation models the emission of sound pressure waves with a point source that can be defined as 6

8 2 ˆp 1 c 2 2 ˆp t 2 = Q 0(t)δ(r r 0 ), (1) where Q 0 (t) is the volume velocity of the point source. The pressure ˆp is the pressure field in the volume and can be characterized as the sumed response of all the point sources. ˆp = N max N=1 q N ψ N (2) The variable q n represents all n point sources that must be solved in the matrix, (k0 2 k2 1 )C 11 + D 11 D 12 q 1 ˆQ 0 ψ D 21 (k0 2 k2 2 )C D 22 q 2 (r 0) = ˆQ 0 ψ2 (r 0) or where k i is the acoustic wave number for each source and A Q = B, (3) C mn = V ψ mψ n d 3 x = Λ mn δ mn (4) D mn = (β β I )ψmψ n da (5) s Referring back to Eq. (2), the variable ψ n represents the eigenfunction solutions in Cartesian coordinates which are defined as, ( ) ( ) ( ) mπ nπ lπ ψ N = cos x cos y cos z. (6) L x L y L z The variables L x, L y, L z are the dimensions of the room. In Eq. (3), the A matrix can become very large. As the A matrix grows, solving for the q N s becomes increasingly difficult. To simplify the solution process, the off-diagonal terms in the A matrix can be deleted, effectively decoupling the A matrix and making the matrix far easier to solve. This code has been used previously with success for smaller rooms. 7

9 Figure 4: Attenuation of the noise in the cold row using the root-sum-squared technique. Computational Model Results Two different computational simulations were performed. The first was to determine the effect of using a best-case scenario. The second model created was a more reasonable control method that used a more complex model. The first simulation was performed to estimate the maximum achievable potential energy reduction in the cold rows of the server room. The dimensions of the simulated cold row were m. Fifteen thousand modes were used, and control was simulated every 20Hz from 100Hz to 1kHz. The disturbance sound field was simulated by a uniform random distribution of 100 sources throughout the cold row, each with random phase and amplitude. Seven control sources were used, the first at the center of the room. The other six were 1m from the center of the room in the shape of a cube. The control field for each case was simulated and recorded. The optimum controller was found for each speaker by minimizing the sum of squared pressures at 36 simulated locations, uniformly distributed around the center of the cold row. The covered region extended from the center approximately 1m each direction in length, 0.55m each direction in height, and 0.6m each direction in width. The db attenuation spectrum achieved using control speakers one-at-a-time was recorded. The seven attenuation spectra were combined using a root-sum-squared averaging technique. The resulting attenuation achieved over the frequency range studied is shown in Fig. 4. 8

10 Figure 5: Attenuation of the noise in the cold row using global potential energy. As can be seen, control effects in the range of 2-6dB are achievable below 200Hz. As frequency increases, attenuation remains steady at 1dB. This level of control required 7 simulated speakers and 36 simulated microphones. While this is infeasible in real life, it does give some idea of the limits of achievable attenuation. Next, the cold row was modeled again by controlling the global potential energy throughout the entire cold row (see Fig. 5). Global levels drop by 1-3dB below 200Hz, and about 0.5dB above 200Hz. While this indicates that large-area control is infeasible, it also shows that spillover problems are not likely very serious. For the second computational model, a diffuse sound field was created in a room with slightly absorbent walls. All four previously mentioned locations within the data center were considered, each at a number of different frequencies. Table 2 shows the frequencies tested at each location: For locations 1-3, the size of the room was m. The size of the room at location 4 was m. For each measurement location and frequency, a variety of different control methods were attempted. The number of primary noise sources varied between 1 and 3 while the secondary control sources were varied between 1 and 6 sources. For each control method, the attenuated pressure field was found at three orthogonal 9

11 Table 2: Frequencies tested at each location. Entrance C7 cage Hot row Cold row 356Hz 360Hz 305Hz 360Hz 500Hz 457Hz 455Hz 457Hz 540Hz 540Hz 588Hz Figure 6: 360Hz, 1 primary source, 1 control source in the x, y and z planes. planes cutting through the sensor. The attenuated field was found by minimizing the squared pressure at the sensors and by minimizing the energy density at the sensors (see Figs. 6-8, positive values indicate attenuation). The circles in the figures indicate error sensor locations. By controlling squared pressure, the field directly around the sensors is attenuated dramatically, but there is not a large region of control. When energy density is minimized at the sensors, there is more of a global attenuation, but there are still many locations where the sound level is boosted. Preliminary runs of the model were not encouraging. After 80 hours of computation, none of the models had produced any results. This was due to the fact that the solver was unable to converge and solve the large matrices in the code. To eliminate this problem, the code was changed to uncouple the point sources. This uncoupling allowed the model to solve for most frequencies (lower frequencies were more likely to converge). 10

12 Figure 7: 457Hz, 2 primary sources, 2 control sources in the x, y and z planes. Figure 8: 540Hz, 3 primary sources, 6 control sources in the x, y and z planes. 3.3 Experimental Testing Methods An experimental test was constructed to determine if active noise control was feasible for C7 Data Centers Lindon 5 server room. There are two possible locations for employing ANC in C7 s Lindon 5 server room: the server room in general, and the cold rows. Ideally, the experimental ANC would be conducted in rooms of similar dimensions and room reverberation times for both the server room and the cold rows. However, rooms with similar characteristics without extraneous noise were not available at BYU. For simplicity, normal classes and labs were used to test ANC. While these experimental results were not identical 11

13 to those that would be produced in the actual locations, the results yielded valuable insight into whether or not ANC was possible. The first step in determining if ANC is possible was to try to control a simple sine wave. As a sine wave only has a single frequency, it is the easiest signal to control. If the ANC failed to control a sine wave then there was little possibility of controlling a more complex signal such as white noise. The experiment was conducted in U186C in the Eyring Science Center at Brigham Young University (room dimensions 4m x 4m x 4m). The experimental setup consisted of two Mackie loudspeakers. The first loudspeaker acted as the source, while the second loudspeaker was used as the control. A two-dimensional, four channel energy density sensor (a sensor that measures both pressure and velocity instead of just pressure) was used as the error sensor. A 1 4 microphone was used to monitor the effectiveness of ANC. The microphone was moved away from the reference energy density sensor to measure how ANC performed as a function of distance. Results A sine wave was first controlled to ensure the ANC system was functioning correctly. Two frequencies were controlled: 100Hz and 250Hz, and the resulting pressure was monitored at distances of 12.7cm, 25.4cm, 50.8cm, and cm from the error sensor. The results can be seen in Table 3. For the 100Hz tone at 12.7cm, active noise control attenuates the fundamental tone nearly 30dB, and the third harmonic by 20dB. The 100Hz tone at 25.4cm was attenuated 10dB for the fundamental but was boosted 10dB for the third harmonic. The overall sound pressure level of each of these tests can be seen in Figs. 9 and 10. For the 100Hz tone, ANC was able to attenuate the overall signal out to a distance of about 40cm (see Fig. 9). However, for the 250Hz signal, ANC was only able to attenuate the source signal out to a distance of 20cm before the signal is actually boosted by as much as 4dB (see Fig. 10). These results are typical of what can be expected for ANC in a room of similar dimensions. 12

14 Figure 9: Overall sound pressure level for 100Hz sine wave as a function of distance. Figure 10: Overall sound pressure level for 250Hz sine wave as a function of distance. The noise from the server room was then output as the source to be controlled. The noise was controlled at distances of 1cm, 12.7cm, 25.4cm, 50.8cm, and cm. The results can be seen in Fig. 11. It can be seen that for all distances, ANC was able to attenuate the main tone at 40Hz by several db. Above 100Hz ANC contributes very little, neither attenuating nor boosting the signal. These results were then processed to visualize the 13

15 Table 3: ANC results for sine waves at 100Hz and 250Hz. Results measured at 12.7cm, 25.4cm, 50.8cm, and cm. 100Hz 250Hz cm 50.8cm 26.4cm 12.7cm overall sound pressure level (see Fig. 12). The ANC results for the noise from the server room were promising: 2-4dB of attenuation was achieved out to a distance of 120cm (see Fig. 11). Three comments need to be made about these results. 1. The experimental setup used only a single source to output the recorded noise from 14

16 the server room. In reality, there will be many sources of noise in the server room and in the cold rows. 2. The experimental setup used only one control source to implement ANC in the room. Since this was just an experimental mockup, only one control source was attempted. If ANC were implemented in the server room, multiple control sources could be used to perform ANC. 3. The aspect ratio of the test room was approximately one. This means that the modal density in the room increases rapidly with the room dimensions. Rooms having high modal densities are more difficult to control. The cold rows have a much higher aspect ratio (long and thin), meaning that they will have fewer modes present in the room. 4 Conclusion 4.1 Summary Three aspects of engineering were considered in characterizing the C7 Data Centers Lindon 5 server room noise: taking experimental data, running computational simulations, and mockup testing. The first step was taking the acoustic data present at the server room. These data were crucial in determining what frequencies contained the most energy and were integral for use in the computational models and the experimental mockups. Data were taken in 4 locations in the C7 Data Centers Lindon 5 server room, including data that were taken in the cold rows. Computational simulations were performed to model the use of active noise control in the server room. The models used were originally designed by Buye Xu for modeling modal analysis in an enclosed 3-D space. This model was used to simulate both the cold rows and the entire server room. Initially, the solvers in the code struggled to converge. The codes were modified to decouple all the point sources, which makes the simulations less realistic, but simplified the matrices generated during the simulation. Multiple frequencies were tested with multiple control sources. Results were not encouraging. Computational results show that almost no control is possible in the server room or in the cold rows. Finally, an experimental mockup was set up to attempt active noise control in a controlled laboratory setting. A sine wave was output as a trial signal to test the active noise control system. Once a sine wave was controlled in a laboratory setting, the recorded noise from the C7 Data Centers Lindon 5 server room was output through a loudspeaker. This recorded signal was then controlled in the lab using ANC with some attenuation achieved. 15

17 Figure 11: ANC results for server room noise. Results measured at 1cm, 12.7cm, 25.4cm, 50.8cm, and cm. 16

18 Figure 12: Overall sound pressure level for server room noise as a function of distance. 4.2 Conclusions Data were taken at C7 Data centers Lindon 5 facility. These were used to calculate the early decay times of the main server room and the cold rows. Results show a significant amount of sound power below 500Hz and EDT s between seconds depending on location. These data were also used in both the computational models and the experimental testing of active noise control. While useful, the computational model had some severe drawbacks. The rooms input into this model were on the order of a few meters to several dozen meters. The code was run on BYU s supercomputer, Mary Lou, but after 80 hours of computation time, none of the coupled codes were able to run to completion. Only after decoupling the code were a few of the codes able to run to completion for a few select frequencies. The results of these simulations indicate that ANC is not effective. However, these results are not in full agreement with the results of the experimental testing done in the lab. The experimental testing done using the measurements from the C7 Data Centers Lindon 5 server room indicate that active noise control is able to attenuate the noise by 2-4dB and may therefore be a viable option. These results were verified by first controlling a simple sine wave. Given the difficulties encountered with the computational code, the experimental testing is considered to yield the more reliable results. 17

19 4.3 Recommendation The initial plan for implementing active noise control in the C7 Data Centers Lindon 5 facility was at two locations, in the server room, and in the cold rows. The cumulation of our results indicates that active noise control will not be effective in the main area of the server room (this includes the Entrance and the C7 Cage area). This is due to the random nature of the noise and the high modal density present in the main room. However our results indicate that active noise control may be possible in the cold rows. Since active noise control was possible in the laboratory experiments, it can be said that similar control within the cold rows may be feasible, but more elaborate experiments should be performed to ensure that active noise control can yield sufficient acoustic control. 5 Contact Information Dr. Scott Sommerfeldt 1 Dean, College of Physical and Mathematical Sciences phone: scott Jesse Daily Graduate student phone: Mike Maughan C7 Data Centers Contributing Students: Zach Collins, Jesse Daily, James Esplin, Jarom Giraud, David Krueger, Dan Manwill, Matt Shaw, Brad Solomon, Dan Tengelsen, Alan Wall, Buye Xu 1 Corresponding author 18

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