Noise attenuation directly under the flight path in varying atmospheric conditions

Transcription

1 UNCLASSIFIED Nationaal Lucht- en Ruimtevaartlaboratorium National Aerospace Laboratory NLR Executive summary Noise attenuation directly under the flight path in varying atmospheric conditions Report no. NLR-TR Author(s) V. Sindhamani Report classification UNCLASSIFIED Date Spetember 2012 Knowledge area(s) Vliegtuiggeluidseffecten op de omgeving Descriptor(s) Noise Propagation Cabauw Measurements Weather Problem area In many countries, airport operations are constrained by noise restrictions to protect the people living in the vicinity of the airports. These restrictions help to create the desired eco-friendly environment by monitoring the noise levels using noise metrics such as L DEN or DNL. These noise doses (on a yearly basis) are typically expressed using noise contours plotted around an airport. The contours calculated are an approximation of the actual noise levels. In order to get a high quality result, the noise contours should be validated with the measurements. While doing so, the large gap between measured and calculated aircraft noise data directly under flight path was observed. This gap can be minimized by improving the accuracy of noise contour calculation methods. There are many factors that influence the accuracy of the noise contour calculations such as source variation, sound propagation path and receiver variation. The influence of these factors should be modelled and included in the noise contour calculations. Henceforth the UNCLASSIFIED

2 UNCLASSIFIED Noise attenuation directly under the flight path in varying atmospheric conditions main goal of this research is to improve the accuracy of noise contour calculation. Description of work In this research, one of the factors is considered that is the influence of weather conditions on sound propagation directly under the flight path. It is analysed and included in the noise contour calculations. In order to do so, an experiment setup was built at Cabauw by the NLR to isolate the influence of weather conditions on the sound propagation directly under flight path. The data obtained from this experiment was compared with standard noise contour prediction methods and correction methods were derived considering multiple weather parameters. Multi linear regression analysis was used to determine the correction factors which can be used to update existing noise contour calculation methods. In the end, the effectiveness of the correction methods is found by comparing it with measurements. Results and conclusions This research shows that the effect of varying temperature and humidity has a major influence on the noise attenuation directly under flight path in comparison with the variations in the other weather parameters. In the end, it was observed that the results found using the varying atmospheric conditions did not considerably improve the accuracy of the predicted noise levels. This implies that the varying atmospheric conditions do not have a significant impact on the deviations between the measured and the calculated noise data. Thus, the large deviations between the measured and the calculated noise data is to be sought in noise variations due to the aircraft itself. Applicability Currently noise contour calculation methods typically use fixed and average weather conditions. The results of this thesis show that noise contour calculations need to be done considering temperature and humidity to be dynamic factors as opposed to fixed parameters. The results of this research partially bridge the gap between the measured and calculated. This research paved the way for the further steps that need to be taken to address the main problem. It also recommends a fruitful step for further research that is the variations at the source of the noise should be obtained and be included in the noise modelling calculations as it influences the noise contour considerably. Nationaal Lucht- en Ruimtevaartlaboratorium, National Aerospace Laboratory NLR UNCLASSIFIED Anthony Fokkerweg 2, 1059 CM Amsterdam, P.O. Box 90502, 1006 BM Amsterdam, The Netherlands Telephone , Fax , Web site:

3 Nationaal Lucht- en Ruimtevaartlaboratorium National Aerospace Laboratory NLR NLR-TR Noise attenuation directly under the flight path in varying atmospheric conditions V. Sindhamani No part of this report may be reproduced and/or disclosed, in any form or by any means without the prior written permission of NLR. Customer National Aerospace Laboratory NLR Contract number Owner National Aerospace Laboratory NLR and TU Delft Division NLR Air Transport Distribution Limited Classification of title Unclassified September 2012 Approved by: Author V. Sindhamani Reviewer D.H.T. Bergmans, and M. Arntzen Managing department R.W.A. Vercammen. Date: Date: Date:

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5 Master thesis NLR-TR By Vivekanandhan Sindhamani 27 th September 2012 Prof. Dr. D. G. Simons Ir. M. Arntzen Ir. D.H.T Bergmans Graduating Professor First Supervisor Second Supervisor TU Delft TU Delft / NLR NLR 3

6 Noise attenuation directly under the flight path in varying atmospheric conditions Vivekanandhan Sindhamani 4

7 Preface This thesis titled Noise attenuation directly under the flight path in varying atmospheric conditions has been submitted in partial fulfilment of the requirements for the Degree of Master of Science in Aerospace Engineering, at the department of Air Transport & Operations (ATO) at Delft University of Technology in the Netherlands. First and foremost, I would like to thank my Professor Dr. D.G. Simons for giving me the opportunity to carry out my thesis work in the ATO department. I would like to sincerely express my gratitude to my first supervisor Ir.M.Arntzen for his extensive and unconditional support throughout my thesis. I am thankful to my second supervisor Ir. Dick Bergmans for sharing his valuable time and bringing out the professional in me. I am very grateful to all the NLR employees at the Department of Environment and Policy Support (ATEP) for including me in their team. I would also like to thank my internship supervisors Roel Hogenhuis and Sander Heblij for supporting me initially at the NLR.I would also thank Dr.ir.M.Voskuijl for accepting to be on the panel to judge my thesis. I would like to express my appreciation for my friend Varun Raman for proof reading my thesis and giving me constructive criticism. I would like to thank my friends Vishal, Sreenath, Satish kumar, Thilak and many others for their unwavering belief in me. Last, but definitely not the least, I am indebted to my parents and my girlfriend for their unconditional love and support. Delft, 20 August 2012 Vivekanandhan Sindhamani 5

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9 Abstract Aircraft noise levels around the airports are limited by noise regulations in order to protect the inhabitants living in the vicinity of the airports. Hence, noise level predictions in the vicinity of the airport are a must. The noise level predictions are made based on the standard methods and are compared with the measured noise levels. While doing so, large deviations were observed between predicted and measured aircraft noise data directly under the flight path. These deviations may be attributed to the uncertainties in source noise levels, influence of atmospheric conditions and/or uncertainties at the receiver s end. In this thesis, the influence of the atmospheric conditions on sound propagation directly under the flight path was examined. In order to examine this phenomenon, an experiment setup was built by the NLR in the KNMI tower at Cabauw in The set-up was designed to measure simultaneously the varying atmospheric conditions and their influence on sound propagation directly under flight path. The data obtained from this experiment was compared with standard noise contour prediction methods and a correction method was derived considering multiple weather parameters. Multiple linear regression analysis was used to determine the correction factors which were used to update existing noise contour calculation methods. The results obtained correspond to an altitude of up to 100 meters. To determine the noise contours for altitudes above 100 meters, the results generated using the measured values up to a height of 100 meters were scaled. In the end, it was observed that the results computed using the newly formulated methods did not considerably improve the accuracy of the predicted noise levels. This implies that the varying atmospheric conditions do not have a significant impact on the deviations between the measured and the calculated noise data. Thus, the large deviations between the measured and the calculated noise data is to be sought in the noise variations due to the aircraft itself. 7

10 Contents 1 Introduction Problem definition Focus Steps Research questions Research approach Report outline 18 2 Framework Experimental setup Background Information 22 3 Inputs and Methods Acoustic data Weather parameters Statistical Analysis Correction method Scaling Method 33 4 Results and Discussion Seasonal effect of weather parameters on AGSP Influence of wind parameters at different frequency bands Effectiveness of different noise contour calculation methods Effectiveness of noise contour calculation methods at higher altitudes 48 5 Conclusion 51 6 Recommendations 52 References 53 Appendix A 55 Appendix B 61 8

11 Appendix C 66 Appendix D 70 Appendix E 72 9

12 List of terms and symbols The important terms and metrics used in this thesis are tabulated in this section. Terms Description 1/3 rd Octave bands 1/3rd Octave bands are used to analyse the broad-band frequency AGSP ANOMS ANP Database A-weighting A-weighted sound level, L A,i Airport noise contours Decibel KNMI NLR Noise Noise Power Distance (NPD) content of sound. It divides the frequency range into bandwidths. The ratio of two adjacent bandwidth centre frequencies is 2(1/3). Air-to-ground sound propagation path Airport Noise and Operations Monitoring System The international Aircraft, Noise and Performance database ( A standard frequency weighting or correction used to reflect the frequency response of the average human ear over a wide range of listening conditions. The unit of the A-weighted sound levels is displayed by dba or db(a) A-weighted sound pressure level, unit is db (A) where i is the frequency band (which ranges from 1 to 31 for 1/3rd octave bands with centre frequency from 10Hz to 10kHz). Lines on a map which represents equal noise exposure levels. Amplitude of the sound signal described on a logarithmic (Decibel) scale. Royal Netherland Meteorological Institute ( National Aerospace Laboratory, Amsterdam, the Netherlands ( ) Noise is defined as unwanted sound which results in annoyance, sleep disturbance or health problems during high and long exposures. The terms noise and sound are sometimes used interchangeably in this thesis. Noise event levels are tabulated as a function of perpendicular distance vertically below an airplane in steady level flight at a reference speed in a reference atmosphere, per aircraft and engine type. The data account for the effects of sound attenuation due to 10

13 Terms Sound Sound Attenuation Sound Pressure Level Sound Exposure Level L, overall A-weighted A sound level L A,max, maximum overall A-weighted sound level Relative Humidity (RH) Transmission loss (TL) Excess transmission loss (ExTL) Refraction Description spherical wave spreading (inverse-square law) and atmospheric absorption in SAE AIR-1845 Atmosphere [1]. Sound is a pressure variation in an elastic medium (i.e. liquid or gas). Sound energy propagates in an elastic medium by longitudinal wave motion which is sensible to ear. Decrease of sound energy along the propagation path. Difference in actual pressure in the sound wave to the standard reference pressure ( Pascal). The constant sound level which has the same amount of sound energy in one second as the original noise event; the standard single event descriptor is described in ISO The logarithmic summation of A-weighted sound energy. The resultant value depends on the spectrum Maximum value of LA over a period of time, The ratio of prevailing water vapour pressure in the air at a given temperature divided by saturated vapour pressure at that temperature times 100 percent [2]. The amount of attenuation of the sound energy due to air-to-ground sound propagation. The difference between the measured and calculated transmission loss (TL). This phenomenon describes the bending of sound waves when the wave passes from one medium to the other. 11

14 Metrics Units Description L DEN db(a) Day-evening-night level DENL, a noise index adopted by the European Commission which penalizes evening noise by 5dB and night-time noise by 10dB. SPL db Sound pressure level DNL db(a) Day-night average sound level is primary metric adopted by Federal aviation administration. It is a 24- hour average noise level. A 10 db penalty is applied to night-time (10:00 p.m. to 7:00 a.m.). SEL db(a) Sound Exposure Level 12

15 1 Introduction The world is moving towards an eco-friendly environment by reducing various forms of pollution. One of the single biggest challenges confronting us today is the mitigation of noise pollution. Increase in aircraft movements have resulted in increasing noise pollution. This alarming increase in noise levels has resulted in noise reduction becoming a top environmental and political priority. In many countries, airport operations are already constrained by noise restrictions to protect the people living in the vicinity of the airports. These restrictions help to create the desired ecofriendly environment by monitoring the noise levels using noise metrics such as LDEN or DNL. These noise doses (on a yearly basis) are typically expressed using noise contours plotted around an airport. The contours calculated are an approximation of the actual noise levels. Firstly in this chapter, the problem will be explicitly defined. Subsequently, earlier research and knowledge gaps are discussed. Finally the general framework and the steps of this master thesis are discussed. 1.1 Problem definition As mentioned above, noise contours are the most widespread means used to determine the noise levels. Prediction methods that are used to calculate noise contours in the vicinity of airports are the ECAC.Doc.29 [3] or Integrated Noise Model (INM) [4]. In order to get high-quality results, noise contours are validated with measurements. But it is seldom possible to match the hundreds of noise measurements made in the vicinity of airports with the theoretically computed values. In order to improve the accuracy between aircraft noise measurements and predictions, the assumptions made in the prediction models and the corresponding inaccuracies have to be addressed. The important factors that influence the accuracy in the prediction models include: Source (aircraft): o Aircraft type o Aircraft weight o Aircraft setting (thrust, flaps etc.) o Directionality of sound emission (from engine, airframe, landing gear, etc.) Propagation through the atmosphere: o Distance o Atmospheric effects (rain, wind, turbulence, etc.) o Obstructions (shielding) 13

16 Receiver: o Ground effects o Measurement aspects In-depth research, focused on these factors will improve the quality of the noise contours. This thesis focuses on one of the factors the noise attenuation directly under the flight path in varying atmospheric conditions with the ultimate aim of partially bridging the gap between measured and calculated aircraft noise. The gap between measured and calculated aircraft noise levels are attributed to two main reasons; the uncertainty in the measurements and the errors in aircraft noise modelling. Today s precision measurement equipment ensures that a deviation in the measurements in the order of ±0.5 dba [5] can be achieved. This illustrates that the large deviations between measured and calculated results is due to deficiencies in the theoretical calculations. Therefore the uncertainty in the measurements is not questioned in this research. In Figure 1, Miller et al., [6] shows predicted sound exposure levels (SEL) of 21,166 departures of eight common aircraft types (predicted using INM) against actual measurements of those same operations made by noise monitoring system at the Minneapolis Airport. Differences in the order of +/- 15dB between predicted and observed SEL s are common. The magnitude and bias of the difference are reduced when they are combined to estimate long term integrated yearly doses (LDEN). Figure 1 Predicted versus measured SELs for 20,000+ individual aircraft departures Fidell et al. [7] quoted from Miller et al. [6] state that especially at long ranges and small angles of incidence the errors are in the order of 3-4 db (95% bound on data). They also compared Day- Night-Average Levels (DNL) of two years, measured at 29 locations in major airports and 14

17 predictions were made by INM of the DNL value at the same locations. The standard deviation for 95% of the difference between monitored and predicted levels was about 4 db. After their review, they suggested that such errors in prediction are minimal compared to prediction errors associated with inadequate representation of flight trajectory, flight operational information, the seasonal variation, meteorological condition, and runway use that lead to very different noise exposures in airport neighbourhoods. The similar difference between the measured and calculated noise levels of individual events are mentioned in report CDV-NLR-TNO in January 2006 [8]. 1.2 Focus As mentioned, this thesis is about the noise attenuation directly under the flight path. From here on, this path is referred to as air-to-ground propagation. In contour calculations this path is taken from a Noise Power Distance (NPD) table. For a given power setting and distance between the source and the receiver, the noise value is taken (e.g. interpolated) from the table. Directly under the flight path implies a sound angle of incidence greater than 60 degrees. At smaller elevation angles a lateral correction is applied. The air-to-ground attenuation directly under the flight path is embedded in the NPD tables and includes the inverse square law together with a correction for atmospheric absorption under standardised atmospheric conditions (SAE AIR-1845 Atmosphere). In 2008 Okada et al. [9] & [10] studied the influence of atmospheric conditions on air-to-ground sound propagation by considering the air temperature, humidity and air pressure. They found that atmospheric absorption (function of temperature and humidity) tends to increase with increasing altitudes, and hence sound exposure levels (SEL) for aircraft decreases with increasing altitudes. Also the variation in winter was higher than that in summer as shown in [9]. Figure 2 Calculated A-weighted SPLs near the ground at horizontal distances (---- calculated values under the standard atmospheric conditions 25 C, 70%) [9] 15

18 Even before this particular study, Okada et al. [11] statistically investigated the influence of atmospheric absorption on environmental noise propagation (traffic, constructions, airconditioning system in building and birds) instead of aircraft noise propagation. They conducted an experiment for a period of one year (208 days of valid measurements) by measuring the Sound Pressure Level (SPL) and meteorological data simultaneously. Ambient noise levels and meteorological data measured at every second were averaged over 10 minutes. They found that the effects of atmospheric absorption are larger in the winter than in the summer. They analysed that the correlation between atmospheric absorption and SPL become high when the observation time is short, meaning that the correlation obtained for an hour is higher when comparing with those obtained for day and night or a whole day. As a result of their study, it has been found that the effect of atmospheric absorption on environmental noise propagation is large and cannot be neglected in sound spectrum analysis of the total noise in residential areas. Although Okada identified the influence of a varying atmosphere on air-to-ground attenuation, their validation is based on noise simulations with measured weather data, not noise measurements. Thereby varying wind and turbulence conditions were not taken into account leaving mismatches with measurements partly unexplained. 16

19 1.3 Steps To include dynamic atmospheric conditions into noise contour calculations one should first know what the real impact of the atmosphere on sound propagation is and what data is available to estimate the differences due to the effect of the atmosphere on sound propagation. If no significant improvement is found, then there is no need to improve the noise contour calculations. The first step involves measuring the aircraft noise and determining if any trends are found while plotting the weather parameters against measured noise levels. However the variation of aircraft noise measurement also includes aircraft settings inclusive of noise variations from the source. Thus it is meaningless to measure the aircraft noise directly [NLR-CR ]. Therefore, an experimental setup should be designed to isolate the effect of varying atmosphere on air-toground sound propagation. Therefore the National Aerospace Laboratory (NLR) in the Netherlands decided to explore this aspect in detail. In 2010 the NLR started to assess the effect of atmosphere on aircraft sound propagation from air-to-ground and built a set-up in the KNMI tower at Cabauw. The set-up was designed to eliminate all other influences but the varying atmosphere, while determining the sound attenuation. If a trend is found between the weather parameters and measured noise levels, then the next step is to formulate a correction method to improve noise contour calculations. With the aim of carrying out this thesis in a structured manner, the aforementioned steps are formatted into research questions. A research approach was developed to answer the research questions in an orderly manner. 1.4 Research questions The steps mentioned above were framed into three research questions, 1. Can a trend be determined for varying atmospheric conditions using real noise measurement on the air-to-ground propagation path? 2. If a trend is found, can it be incorporated into the noise contours calculations? 3. Does the formulated correction method significantly improve the noise contour calculations? 17

20 1.5 Research approach To develop initial steps for this thesis, an overview of the experimental setup and initial assessment done by the NLR is briefly explained. Secondly, further assessment of varying atmospheric conditions on sound propagation is done to answer the first research question. The results of the assessment are formulated into a correction method and incorporated in the noise contour calculations to answer the second research question. Finally, the latter method and standard noise contour method are compared to answer the third research question. The results are expected to provide a solution to the problem defined earlier. 1.6 Report outline Figure 3 below shows the build-up of this report, Figure 3 Report Outline 18

21 2 Framework In this chapter, the elements required for the development of the initial steps of this thesis are described. Firstly the experimental setup built by the NLR is described. Subsequently the initial assessment done by the NLR and the associated measurements taken are discussed. 2.1 Experimental setup In this section, the requirements of the experimental setup, selection of the location for the experiment, and an overview of the setup and a description of each part are discussed in detail Requirement for the setup The purpose of the experiment is to isolate the effect of varying atmosphere while determining airto-ground sound attenuation. The requirements to that end are listed below, 1. The sound spectrum used for the experiment should be similar to aircraft flyover noise. 2. The air-to-ground sound propagation directly underneath the flight path should be considered. This means that the elevation angle of the sound propagation path should be higher than 60 degrees. 3. The noise should be measured at the ground level. Simultaneously, weather parameters should be measured along the sound propagation path. 4. The variation in the sound source should be eliminated. 5. The effect of background noise should be eliminated from the measurements. 6. The effect of ground reflections should be eliminated from the measurements.(in accordance to Annex 16 [12]) 7. The emitted noise levels should stay within the accepted noise levels to avoid disturbances to the local residence around the experimental site Selection of the location The location of the setup is at Cabauw where the KNMI has a measurement tower for meteorological experiments. The tower is the third tallest tower in the Netherlands (213 m high) and is shown in Figure 4. It is located in the western mid part of the Netherlands. The location is shown in the Appendix A.1. This tower was chosen as it satisfies the main requirements of the experimental setup mentioned earlier due to its height and availability of instruments for measuring weather parameters along the air-to-ground path. 19

22 Figure 4 KNMI-Meteorological measurement tower, Cabauw Overview of the setup Mic 3 M Mic Mic 5 Mic Top-view (a) Isometric view (b) Figure 5 Schematic diagram of the experimental setup The setup consists of a loudspeaker (see Appendix A.2 for loud speaker specifications) which is positioned at 100 meters above the ground at one side branch of the tower pointing 240 from North as shown in Figure 5 (a). The loud speaker is programmed to transmit an hourly audio signal. The emitted sound is recorded by five microphones which are positioned directly under and 25 20

23 meters away in the directions of 40, 130, 220 and 310. The emitted sound is recorded by a microphone placed 1 meter in front of the speaker and this is used as the reference sound. (As shown in Figure 5 (b)) (See Appendix A.3 for types of microphones used). Simultaneously, the atmospheric parameters like wind speed, wind direction, temperature, humidity and their variation are measured at different heights. A broadband audio signal of 15 seconds is emitted every hour by the loudspeaker. With the aim of capturing the background noise, the recording is started a few seconds prior to the loudspeaker emissions and it is stopped a few seconds post the emission Description of the setup In this section the individual parts of the setup are explained. Sound Source: The sound power output of an aircraft flyover is usually distributed over a wide range of frequencies. The aircraft sound contains equal power within a fixed bandwidth at any centre frequencies which has similar characteristics of broadband noise. The loud speaker is programmed to transmit an audio signal of broadband noise (frequency ranges from 250 Hz to 4000 Hz) every hour mimicking an aircraft flyover. The extent to which the loudspeaker mimics the aircraft flyover noise is shown in Appendix A.4.Thus the speaker spectrum is assumed to represent the aircraft spectrum. Air-to-ground path: To focus only on the air-to-ground sound propagation path, the ground microphones are placed directly under the loud speaker and 25 meters away in four different directions. By doing so, the elevation angle of sound propagation is 75 as shown in Figure 5 (b) and subsequently the sound is measured in four different directions. Weather parameters: The weather parameters are measured by the KNMI at different heights ranging from 10m to 200m above ground level. Simultaneously, the emitted sound is recorded by the NLR experimental setup. The weather data retrieved from the KNMI and acoustic data recorded are stored in a database. The retrieval method of the weather and acoustic data together with the database formulation are explained in Appendix A.3.1. Source variation: To eliminate the variation at the source itself, a reference measurement is placed at a distance of one meter below the speaker. The position of the reference measurement is shown in Figure 5 (b). 21

24 Background Noise: To reduce the effect of background noise, the overall A-weighted sound level measured at the ground should be greater than 60dB (A), which is 10 db higher than the average background noise. Since the loud speaker produces a noise of 102 db (A), it is placed at 100 meters above the ground. The reduction in the noise level due to spherical spreading is equal to 40 db (A). If the reduction of sound due to weather parameters is assumed to be less than 2dB (A) then the initial noise level at the ground is about = 60 db (A). Ground reflection: The effect of ground reflection has influence on the noise measurement. The ground effects are defined as the difference between the measured sound pressure level and the measured sound pressured level in free field conditions. The physical and geometrical explanation of ground effect was explained in chapter 8 of reference [2]. In this experiment the emitted sound is recorded under full ground reflection conditions (i.e. measuring flush using a 40 cm metal plate laying on sand foundation, as described by ICAO Annex 16 [12]). Therefore the receiver is placed directly on a full reflecting surface (shown in A.3.1). This results in the difference in path length between the direct and reflected signal becoming zero. Under this condition, both direct and reflected signals arrive in phase and interfere constructively. To eliminate full ground reflection, the theoretical amplification of 6 db is subtracted from the recorded noise levels for all the frequencies. 2.2 Background Information The above described experimental setup was operational from 1st of August The data collected until April 2011 was used initially by the NLR to assess the effects of the atmosphere on sound propagation. In this section, an overview of initial assessment results and steps taken in this thesis to continue NLR s work are explained Initial assessment The initial assessment work was carried out by Dick Bergmans, Michel Arntzen and Wim Lammen at the NLR. The figures used in this section are taken from their conference paper [13]. The results of the initial assessment were based on the visualization of the data showing the variation in AGSP under dynamic atmospheric conditions. Here, the AGSP is quantified as the transmission loss which is denoted by R. The transmission loss is the subtraction of the overall A- weighted sound level recorded on the ground from overall A-weighted sound level (LAeq) recorded by a reference microphone at 100m height. 22

25 First, the weather parameters such as temperature and humidity, measured at 10m height were compared with the measured transmission loss at each microphone position. For the Microphone positioned at 310, the variation is shown in Figure 6.. Figure 6 Variations in Temperature and Humidity The variation in temperature and humidity shows no clear trend with the variation in the transmission loss. Similarly, variation of other weather parameters such as wind velocity, wind direction and turbulence (parameterization of turbulence is explained in Appendix A.5) were considered individually. Even then, no clear correlation was found between weather parameters and transmission loss. Finally to express the importance for further research, the variation between predicted and measured transmission loss were illustrated by Figure 7. Figure 7 Differences between theoretical values vs. measured distribution at position 310 º (Solid red line represents the predicted reference; dashed magenta line represents the measured mean) 23

26 The theoretically computed transmission loss was found based on spherical wave spreading and atmospheric absorption (method prescribed in ISO ). The absorption was calculated based on local temperature and humidity. The measured transmission loss was subtracted from the predicted transmission loss and shown using blue dots). No scientific explanation could be given for the variation seen in Figure 7. However, the authors recommended a statistical approach with different parameterization to assess the effects of atmosphere on sound propagation and use the complete dataset of an entire year for analysis. Apart from the above recommendations, the filtering of the dataset should be improved to remove the invalid data points for the analysis. This initial set of results, its limitations and recommendations have paved the way for this research. From the initial assessment results, limitations and recommendations, the subsequent steps for this research are constructed and are described in the block diagram given below. Figure 8 Steps undertaken 24

27 3 Inputs and Methods The description of the set-up and the main objectives of the thesis are explained in the previous chapters. From the description of setup, it is perceived that large sets of audio recordings and weather parameters are collected for this thesis. There are several ways to analyse the collected data. In this chapter the input required for the analysis and the analysis methods are explained in the context of the objectives. 3.1 Acoustic data As mentioned in the previous chapter, acoustic data is extracted from audio recordings. There are several ways to do this. The extraction method used in the initial assessment (section 2.2.1) has drawbacks; therefore the extraction method has been changed in the current assessment to overcome the drawbacks. The difference between the extraction methods used in initial (section 2.2.1) and current assessment are explained in appendix B.1. In the end, the acoustical data are censored in a way to exclude the data points with high background noise. Any disturbances are left out (as good as possible). An automated process has been defined to censor the acoustical data and can be found in the appendix B.2. The audio signal desired from the speaker is broadband (white) noise. The emitted sound, however, depends on the speakers specifications. As the speaker is made for speech purposes this changes the broad band noise characteristics. The selection of frequency bands used for the analysis is depicted in Figure 9. Figure 9 A-weighted sound levels at 1/3rd octave bands (Centre frequency ranges from 250 Hz to 4 khz) 25

28 The loudspeaker was programmed to emit the audio signal whose frequencies ranges from 250 Hz to 4 khz. In the above figure it is shown that the sound levels between 500 Hz to 3150Hz (represented in blue lines) contain most energy. The sound levels at 4 khz (represented in green lines) and the sound levels between 250 Hz to 400Hz (represented in red lines) do not contain as much sound energy. The variation in the sound energy is mainly due to the emitted sound spectrum of the loudspeaker as shown in Appendix A.4 (Figure 27). In further analysis the frequency bands range from Hz are the ones considered, since they contain enough sound energy. 3.2 Weather parameters In addition to the acoustical data, weather parameters are part of the analysis. In this section, the selections of weather parameters are explained. The weather parameters used to represent varying atmospheric conditions are temperature, humidity, wind speed, wind direction, turbulence, wind speed gradient and temperature gradient (gradients w.r.t to altitude). In this thesis, however, only five were considered and provided by the KNMI. All weather parameters provided by the KNMI are mentioned in Appendix A.5. From that, the weather parameters measured at 10m height are used for the analysis. The 10 metres height was deliberately chosen. The KNMI uses this height to measure common weather conditions at different sites. Data at this height is therefore widely available throughout the Netherlands (e.g. around Amsterdam Airport Schiphol). This availability has a practical benefit, especially when weather variations (i.e. findings of this research) are aimed to be incorporated into the noise contour calculations for different kind of airports. The selected weather parameters are listed below, 1. Temperature 2. Humidity 3. Wind speed 4. Wind direction 5. Turbulence The temperature and humidity is represented by a single acoustical parameter called atmospheric absorption. The absorption is function of temperature and humidity. This is calculated using the method prescribed by Society of Automotive Engineers Aerospace Recommend Practice 866A (SAE ARP 886A) [1]. The mean wind speed (WS) and turbulence (the standard deviation of wind speed over 10 minutes) parameter are used in the same format as provided by the KNMI and is defined in Appendix A.5 26

29 Table A- 1. Nevertheless, the wind direction is used in a different format. The wind can be split up into upwind and down wind direction with respect to the sound propagation. The conversion of wind direction parameter provided by the KNMI to the respective format used in this thesis is explained schematically using Figure 10. The upwind condition is that when wind is blowing against the sound propagation direction and downwind condition is that when wind is blowing in the direction of the sound propagation. Figure 10 Wind direction parameter The wind direction parameter is denoted as WR and is dimensionless. The wind direction parameter w.r.t to sound propagation direction is calculated using the formula given in (3.9), (3.9) Here, Figure 10. For downwind conditions, WR value ranges from 0 < WR < 1. For upwind conditions, WR value range from -1 < WR < Statistical Analysis In this section, the need and selection of a statistical method to analysis the influence of the atmosphere on sound propagation is briefly explained. As mentioned in the previous chapter, the results of the initial assessment made by the NLR couldn t draw a clear scientific explanation on the given problem. Nevertheless, one of the 27

30 recommendations is to follow a statistical approach with different parameterization for the further assessment. Generally the statistical methods are mainly used as a tool to find an influence of one variable on the others. Therefore, the statistical approach is considered as one of the worthy step to start with the analysis Selection of the statistical method In the process of selecting a statistical method, first the definition and categories of statistics are briefly explained. After that, one of the categories is chosen based on the type of the solution required for a problem. The complete breakdown of the categories of general statistical analysis is shown in Figure 11. Figure 11 Categories of statistics The two main broad categories of statistics are, 1. Descriptive Statistics 2. Inferential statistics The descriptive statistics allows a researcher to organize and summarize the information. The inferential statistics allows a researcher to draw a conclusion about data. In this thesis, the latter category is needed to draw a conclusion about the influence of atmosphere on AGSP. 28

31 The inferential statistics category is further subdivided into two main types, 1. Estimation 2. Hypothesis testing Estimation statistics are used to make estimates about the values based on the given sample data. Hypothesis testing statistics are used to make a statistical inference to check whether or not data supports a particular hypothesis. In this thesis, the quantity of the atmospheric influence needs to be found. For that, the estimation statistics needs to be used to estimate the quantity of atmospheric influence on AGSP. Further down, estimate statistics is subdivided into two types, 1. Parameter estimation 2. Confidence interval Parameter estimation is used to describe the relationship between variables in a population. Confidence interval is used to indicate a reliability of an estimate. In this thesis, the solution to the problem is to formulate a relation between the atmospheric parameters and the acoustic parameters. To be sure that a trend is found, the formulated relation should have a high reliability. Therefore estimation statistics is selected for the analysis in this thesis. Eventually, there are many mathematical models developed based on the estimation statistics method The primary statistical models developed based on estimation statistics are linear-regression models and multi-linear regression models. In precise, a linear-regression model is used to analyse the relationship between two individual variables. The multi-linear regression model is used to analyse the relationship between an individual variable and a group of variables. The relationship between an individual acoustic parameter and individual weather parameters needs to be found. Similarly, the relationship between an individual acoustic parameter and group of weather parameters needs to be found. Hence, linear-regression analysis and multi-linear regression model is selected and used. The high level statistical models are not considered for the selection process to avoid complications. The definition, formulation and description of the estimation statistics and multi linear regression model are given in Appendix B.1. In line with one of the main research questions, the results of the statistical analysis are used to formulate a correction method. This is explained in the next section. 29

32 3.4 Correction method In this section, the description of the correction method used to estimate the transmission loss along AGSP for a slant distance of 100m height is explained Categorisation The dataset was divided based on seasonal variation prior to the statistical analysis. The effects of seasonal variation were determined by dividing the year into four common seasons and the data was collected for the following periods given in Table 1. Table 1 Seasonal periods Seasons Periods Number of Valid data points Autumn 01-Sep-10 to 30-Nov Winter 01-Dec-10 to 28-Feb Spring 01-Mar-11 to 31-May Summer 01-Jun-11 to 31-Aug (Total) Formulation of equation for a single frequency band The estimation method to determine the transmission loss in the Cabauw set-up is similar for all the one third octave bands ranging from 500 Hz to 3150 Hz. Thus, the calculation of the transmission loss at a single one-third octave band is explained in the following paragraphs. The standard method to calculate general transmission loss includes spherical spreading and atmospheric absorption at standard temperature and humidity condition according to SAE ARP- 866A and it is given in (3.10). (3.10) 30

33 Where, GTL General transmission loss f Centre frequency of 1/3 octave band (Hertz) Sp Transmission loss due to spherical spreading (db) α Atmospheric absorption coefficient (db/m) in SAE AIR-1845 Atmosphere r Distance between the source and the receiver Based on the above, the transmission loss would theoretically always be the same in the Cabauw set-up. However there are variations between the measured and calculated transmission loss. This variation is assessed by estimating the seasonal excess transmission loss (difference between measured and calculated transmission loss) due to weather parameters excluding temperature and humidity. Atmospheric absorption has been excluded in the estimated formulation as they have been accounted for in the standard formula. The format of the equation for excess transmission loss is given in (3.11). The coefficients of the weather parameters are not the same for all seasons as the analysis is carried out for each season individually. (3.11) Where, ExTL Excess transmission loss f Centre frequency of 1/3 octave band (Hertz) n Season number (n = 1, 2, 3, 4) WR Wind direction (value ranges from -1 to 1) WS Wind Speed (m/s) Tur Turbulence factor (standard deviation of wind speed for 10 min) B1 to B4 are the found coefficients of weather parameters using statistical analysis. The final transmission loss is composed of spherical spreading, atmospheric absorption at varying temperature and humidity and the excess transmission loss found by statistical analysis. (3.12) 31

34 3.4.3 Equation formulation The following steps, briefly explains the process of finding the above mentioned final transmission loss equation: 1. By using multi-linear regression analysis, equation (3.12) was found by analysing the excess transmission loss and the combination of weather parameters for three different microphone positions and each season. The microphone positions (310, 130, 40 ) were considered as they were the only ones which provided sufficient data points. Total numbers of equations are, 4 (seasons) 3 (Microphone positions) = 12 equations 2. Next, the equations for three microphone positions are reduced to one general equation for each season by taking the mean of respective weather parameter coefficients. It reduces the number of equations to 4. This approach is used because the difference between the coefficients B1 and B2 of microphones is negligible. The effect of turbulence is independent of direction. As mentioned earlier, turbulence is represented as variation in wind speed, it doesn t dependent on the wind direction. The influence of these weather parameters are discussed in the next chapter Formulation of equations for different frequency bands In the previous step, the transmission loss for a single 1/3 rd octave band from 100m altitude to ground level was calculated using four different equations representing the seasonal variation. The same methodology was carried out to formulate the equation for the other third octave bands whose centre frequency range from 500 to 3150Hz. For some 1/3rd octave bands, the coefficients (like B1, B2, B3, and B4) of weather parameters are unacceptable (insignificant). Those coefficients are interpolated between the adjacent one third octave bands that are significant. 32

35 3.5 Scaling Method The NASA report of 1975 [14] on the variation of the excess attenuation has been instrumental in the analysis of the excess transmission loss at heights above 100meters. It is determined that the excess attenuation is more pronounced near the earth's surface than at higher altitudes. Figure 12, taken from [15] shows the altitude dependence of the excess attenuation schematically. The data point in this curve at a particular altitude indicates the excess attenuation coefficient at that height. The gradient of the curve varies inversely with the height up to a height h o from the ground and remains virtually constant above that altitude. The value of h o was provided as approximately 200 m (600 ft) i.e. the excess attenuation prominently changes below this altitude. Figure 12 Schematic diagram of variation of excess attenuation with altitude (dimensionless) The graph shown above was utilized to obtain the equation which explains the variation of the excess transmission loss with altitude. The equation representing the linear portion of the graph was already explained in the NASA report. The equation of exponential part was not available and has been determined from the graph. The equation found is given in (3.13). (3.13) 33

36 The excess transmission loss for AGSP at 100 meters was available from the results of the experiment. In order to scale the excess transmission loss (at 100 meters) for different altitudes, there are two conditional equations that need to be used. Conditional 1 equation (3.13) is used to scale the ExTL up to 200 meters altitude. Condition 2 equation (addition of linear portion, explained in NASA report) is used to scale the ExTL above 200 meters altitude. The scaling of excess transmission loss for AGSP (above 100 meters) is explained in the next section. Condition 1 (Altitude below 200m): ; dba (3.14) ) ExTl = excess transmission loss at specified altitude (dba) Y (d) = Numeric excess transmission loss value from the graph (dimensionless) X (d) = Numeric altitude value from the graph (Dimensionless) The above equation is valid up to an altitude of 200 meters. For altitudes above 200 meters condition 2 is applicable. Condition 2 (Altitude above 200m): The residual attenuation coefficient shown below was obtained from the NASA Report and it is equal to the slope of the linear variation. Thus as mentioned earlier this value has added to the equation of the exponential section of the graph. (Condition 1) For d > 200m; = 200m; Residual attenuation coefficient 34

37 (3.15) (3.16) Y (d ) is obtained using condition 1. The results and the discussion of the multi linear regression analysis are explained in the next chapter. 35

38 4 Results and Discussion In this chapter, the results of the statistical analysis performed for this thesis are presented. The first and second sections focus on the seasonal effect of weather parameters and its dependence on the frequency of the AGSP at a height of 100 meters. The third and fourth sections focus on the effectiveness of the devised noise contour calculation method. The dataset used for the analysis is collected from the setup (described in chapter 2) between August 2010 and Dec Seasonal effect of weather parameters on AGSP As mentioned in the previous chapter, the weather parameters considered for the analysis are temperature, relative humidity, wind speed, wind direction and turbulence. The seasonal effect of weather parameters is studied by analysing its effects on a single 1/3 rd octave band (2000 Hz). The effect of weather on the other 1/3 rd octave bands (ranges from 500 Hz to 3150 Hz) is not considered, in order to study only the seasonal effect of weather parameters on transmission loss for AGSP. In section 4.2, the influence of weather parameter on different frequency bands are studied. Although the analysis is carried out on the entire dataset, this section focuses on the results obtained from microphone position 5 (shown in Figure 5a). Figure 13 shows the correlation coefficients found using the linear regression analysis for a transmission loss of 2000Hz at Microphone position 5 versus individual weather parameters. Microphone position 4 is excluded as it provided insufficient data points. The correlation coefficients found using linear regression analysis for the Microphone position 2 and 3 are not shown. The values found between the individual weather parameters and transmission losses measured at the microphone positions were insignificant for certain seasons. Figure 13 Seasonal effect of weather parameters (Microphone position 5) 36