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Page 9
Suggested Citation:"Section III - Key Project Findings." National Academies of Sciences, Engineering, and Medicine. 2012. Measurement of Gaseous HAP Emissions from Idling Aircraft as a Function of Engine and Ambient Conditions. Washington, DC: The National Academies Press. doi: 10.17226/13655.
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Suggested Citation:"Section III - Key Project Findings." National Academies of Sciences, Engineering, and Medicine. 2012. Measurement of Gaseous HAP Emissions from Idling Aircraft as a Function of Engine and Ambient Conditions. Washington, DC: The National Academies Press. doi: 10.17226/13655.
×
Page 10
Page 11
Suggested Citation:"Section III - Key Project Findings." National Academies of Sciences, Engineering, and Medicine. 2012. Measurement of Gaseous HAP Emissions from Idling Aircraft as a Function of Engine and Ambient Conditions. Washington, DC: The National Academies Press. doi: 10.17226/13655.
×
Page 11
Page 12
Suggested Citation:"Section III - Key Project Findings." National Academies of Sciences, Engineering, and Medicine. 2012. Measurement of Gaseous HAP Emissions from Idling Aircraft as a Function of Engine and Ambient Conditions. Washington, DC: The National Academies Press. doi: 10.17226/13655.
×
Page 12
Page 13
Suggested Citation:"Section III - Key Project Findings." National Academies of Sciences, Engineering, and Medicine. 2012. Measurement of Gaseous HAP Emissions from Idling Aircraft as a Function of Engine and Ambient Conditions. Washington, DC: The National Academies Press. doi: 10.17226/13655.
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Page 13

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9 Despite the complexities of the idle settings, an inverse relationship between emissions and fuel flow rate was observed. Figure III-1 shows the fuel flow rate as a function of test point conditions during the MDW 2009 tests (with a CFM56-7 engine). The operational categories in Figure III-1 are drawn from the nominal name given to each test point in the test matrix. The data points have been slightly offset horizontally within each category in order to better distinguish points that are very close in fuel flow rate. In addition to fuel flow rate, two other engine characteristics—fraction of maximum rotational fan speed (N1) and exhaust gas temperature (EGT)—were varied as a consequence of the test point conditions. N1 is related to the rotational speed of the fan in the turbofan engine and is reported in units normalized by the maximum rota- tional speed for that engine and reported as a percentage. N1 = 25% defines the 7% idle condition for the CFM56 fam- ily of engines. Figure III-2 shows the relationship between N1 and fuel flow and the effect of test conditions on exhaust gas temperature. At first glance the measurements suggest there are two disparate data groups that do not correlate well with fuel flow or EGT. Setting the data points with N1 greater than 23% aside, EGT generally increases with fuel flow (dark red at lowest fuel flow, orange and yellow at higher fuel flow). Because the engine bleed air demand is responsible for increasing fuel flow rate and there is no concomitant increase in the engine intake fan speed, relatively more combustion heat is available than at lower fuel flows, which increases the EGT. When the engine is set to produce a specified rotational speed (e.g., 25%), the outer fan speed ensures that the fuel- to-air ratio and the pressure in the combustor are scaled accordingly. The EGT is cooled with the increase in bypass air flow induced by N1. This underscores the complex relationships in the near-idle on-wing regime between fuel flow, N1, and EGT. Similarly, the combustor pressure and fuel-to-air ratio do not vary linearly when bleed air demand is introduced. The overall carbon dioxide (CO2) mixing ratio by volume at the engine exhaust plane is greater for the high bleed air demand con- dition than when N1 = 25%, which indicates a substantial shift in fuel-to-air ratio and thus combustion performance. The geometry of the turbomachinery is designed for maxi- mum performance at continuous-cruise state, where the majority of fuel is consumed, and the use of variable bleed air during ground idle operation further exacerbates any combustion inefficiency. The incorporation of variable bleed air demand into the test matrix employed in this study has required consideration of additional engine state parameters. The relationship that is depicted in Figure III-2 between fuel flow, N1, and EGT highlights the problem with assuming N1 (or thrust) varies linearly with fuel flow at idle. At this stage, we might be tempted to abandon this study in favor of more controlled combustion rig-based measurements. To do so, however, would fail to produce a meaningful capability to estimate HAP emissions for airport operations. As designed, this project was able to test 11 engines of the same model that were pulled from operational service. A mixture of newer and older engines was present in the test pool. We do not have detailed knowledge of each engine’s clearances, tolerances, or overall efficiency. For some engines, documented maintenance histories were unavailable. The tally of potential system inefficiencies is further complicated by the fact that bleed air demand involved systems that are not part of the engine (e.g., cabin packs). There were no a priori assumptions made as to which engine parameters would be needed for describing the emissions performance near idle. After examining the data tabulated in Appendix A and the detailed engine data recorded in the data recorder, fuel flow seems to best collapse the observed variability in emission indices for the speciated VOCs of interest. Several HAP compounds were quantified in this study and tended to exhibit highly correlated behavior, as will be shown later. Observed correlations among the measured S e c t i o n i i i Key Project Findings

10 emission indices for several VOC species are actually more precise than the capability to reproduce low-fuel-flow engine states. Although some important differences in the VOC profile have been uncovered in this study, the same trend will generally be present in other HAP species when data for a particular HAP or VOC are evaluated. Figure III-3 presents all measured formaldehyde emission indices, from every test conducted when the ambient air was lower than 0°C in 2009 and 2010 at MDW, plotted versus fuel flow for 11 different CFM56-7B24 combustors. Of all param- eters recorded in the digital flight data records, fuel flow best linearizes the trend in emission index. Similar patterns are observed in the other specific hydrocarbon measurements, including ethene, acetylene, and 1,3-butadiene. III.1 Dependence of VOC on Fuel Flow Near Idle The ICAO engine certification databank (ICAO 2006) defines the idle condition as 7% of rated thrust. An ICAO engine entry specifies the engine combustor and the thrust rating for each particular model certified for use on commer- cial aircraft. In some cases, the combustor technology is not drastically different for entries in the databank with different thrust ratings. Because the ICAO definition of idle is relative to maximum thrust, if the engines using the same combustor designs can be identified there may be preexisting data that can address the dependence of VOC emissions on fuel flow for near-idle operations (DuBois and Paynter 2006). Examination of the tabulated total UHC emission indices should provide a qualitative guide for the UHC emissions- related performance to compare with the trend observed in Figure III-3. Figure III-4 shows a selected portion of the ICAO emissions databank for total hydrocarbons. The blue data entries in the Figure III-2. N1 fan speed versus fuel flow rate. The data points are colored by exhaust gas temperature. The small data points are the warm-up test points. The data circled in blue represent points where de-icing technologies were enabled. Figure III-1. Fuel flows resulting from the named engine condition in the near-idle test matrix (GI = ground idle). These tests were conducted at ambient temperatures between 265K and 271K.

11 EI HC column are the UHC emission indices at idle thrust setting (7% thrust). The black entries labeled with black arrows are from combustors with similar characteristics. When the entry with the highest fuel flow/lowest emission index is used to normalize each entry, we can compare the relationship of the UHC emissions index with absolute fuel flow for the CFM56-7Bxx family of engines to the data collected in the tests conducted for this project. Essentially, the exercise described in Figure III-4 uses the ICAO databank to form a generalized UHC dependence on fuel flow at near-idle condition. This procedure will be used to account for the engine-to-engine variability observed in absolute UHC emission indices for the on-wing engine tests in order to draw out whatever trend may exist in the UHC emission index with fuel flow. This procedure is also used to directly examine any systematic coupling between fuel flow and specific VOC emission indices. The construct described in Figure III-4 is referred to in this document as the near-idle fuel flow dependence. Formally, the slope of this assumed line carries units of s kg-1, and is the slope parameter in the following expression. HC HC Fuel Flow FF slope s kg Fuel Fl ( ) ( ) = +    7 1% ow FF−[ ]7% In this case the fuel flow is the independent variable, and the result is effectively a multiplicative factor that can project the UHC emission index to fuel flows lower than the fuel flow at 7% thrust (FF7%) in the expression above. III.2 Test Results for Engine SA012 The test results for the aircraft engine “SA012” in Appendix A are plotted in Figure III-5 using the normalization procedure described in Figure III-4. The unnormalized data is depicted in Appendix A as Figure A01-E12b, and the three apparent ratios of the emission index (at a given fuel flow rate) relative to the measured emission index at the N1 = 25% fuel flow are tabulated in the legend. The uncertainties in the slopes are twice the standard error of the fitting procedure. This process can be extended to the entire dataset for all engines and all VOC species. III.3 Ensemble Result for Fuel Flow Dependence The entire dataset has been analyzed for each VOC follow- ing the procedure described in the preparation of Figures III-4 and III-5. The histogram of all these results has been plotted in Figure III-6. This result forms the basis of the fuel flow dependence that will be carried forward to a near-idle emis- sions index model described later. The goal of this project is to quantify HAP emissions from idling aircraft as a function of engine and ambient conditions. Despite the complexities of working with on-wing engines and the various bleed air operational states, the result depicted in Figure III-6 sug- gests an increase in the VOC emissions index with reduced fuel flow. The histogram for all the VOC project data fit to a Figure III-3. Formaldehyde emission index versus fuel flow. Figure III-4. ICAO emissions performance databank analysis. The upper table shows selected columns and rows from the ICAO emissions performance databank. The inset graph depicts the results of an analysis scheme that addresses the relative UHC emission index increase caused by small changes in fuel flow for idling engine operation.

12 Gaussian profile, depicted in Figure III-6 as the dashed-dot line, asserts a central value and Gaussian half width of -51±17 (s kg-1), assuming the data are described by normal distribution. Whether this is a good assumption may not be known without measuring a much larger sample; however, it at least provides a metric for gauging the width of the distribution. The two dominant VOC compounds by mass—ethene and formaldehyde—are characterized by average and standard deviation values of -60±16 and -49±10 (s kg-1). The measured VOC/fuel flow dependence compares favorably with that determined for the UHC emission indices derived from the ICAO databank of -54±4 (s kg-1) as depicted in Figure III-4. The width of this distribution will be revisited after a sum- mary of the error analysis associated with the emission index measurements. III.4 Systematic Error and Fuel Flow Dependence The plume sampling methodology has a known bias of +2% (see Appendix A). Typically, the precision error in any single determination of an emissions index is much less than 5% Figure III-5. Speciated VOC emissions data normalized to the N1 = 25% test result. The data for this aircraft engine is SA012 and can be found in Appendix A. Ethene (circles), formaldehyde (squares), and benzene (triangles) have been plotted and fit as a function of fuel flow. Figure III-6. Histogram of the measured dependence of VOC emission on fuel flow for all engines and speciated VOCs measured in the study. The dashed-dot line is a Gaussian fit to the distribution. O cc ur re nc es

13 (see data tables, Appendix A). In this formalism, because ratios are used, the systematic error in the measurement of each specific hydrocarbon actually drops out. For example, if the proton transfer for reaction mass spectrometry (PTR-MS) measurement of styrene is systematically high by 25%, because the emission index is being divided by the emission index taken with the same test hour, the bias in this measurement will essentially cancel when the instrument is operating within its linear dynamic range. Overall uncertainty estimates for the analytical instru- ments used in this study are discussed in Appendix E. An assessment of analytical instrument contribution to error, in the context of determining the near-idle fuel flow depen- dence of the emissions index, yields 5%. In this work, the emission index for CO2 has been assumed to be a constant 3160 g kg-1. This assumption is good to 3%. Furthermore, this work assumes that all fuel carbon is in the form of CO2, which introduces a bias of up to 5%. Taken together, the combination of analytical instrument error and estimated analysis methodology uncertainty indicates an overall sys- tematic uncertainty of 12%. III.5 Fuel Flow Dependence and Variability Based on the dominant VOCs as well as the ICAO UHC databank results, this study recommends a near-idle fuel flow dependence of -51 s kg-1 for the CFM56-7B engine variants. The overall uncertainty in this recommended central value of the near-idle fuel flow dependence parameter is ±23%. This has been estimated by combining the overall systematic uncertainty in the measurements of emission index with the average spread observed in the fuel flow dependence param- eter for formaldehyde and ethene. The overall spread, how- ever, of observed variability in this parameter when consider- ing the entire distribution of VOCs measured in this study is large (see Figure III-6). The variability in the observations can be characterized by the 95% confidence limit. Twice the Gaussian half width of the distribution would imply a range of -86 to -18 s kg-1. We attribute the width of this distribution to genuine engine-to-engine and intra-engine state variability. A cursory examination of the test results in Appendix A reveals consistent patterns for all VOC and CO emission indices for a single test. This test pools the results from several engines, installed on different airframes, with operational parameters characterized by different on-board sensors, oper- ating with different bleed air requirements, and with differing maintenance histories. This project assumes that each of these factors contributes to the observed width in the distribution of near-idle fuel flow dependences. The variability in these data should not be confused with error. In a model estimate of airport HAP emissions, the number of aircraft source engines will be much larger than the 11 studied here. If the influence of engine-to-engine variability effectively forms a normal distribution of idle fuel flow emissions dependence, then the uncertainty from this source of error in the tabulation of total airport emissions can be characterized by the uncertainties in the central value and the width of the emissions distribution. For these data, the uncertainty in the central value is much less than the width of the distribution.

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TRB’s Airport Cooperative Research Program (ACRP) Report 63: Measurement of Gaseous HAP Emissions from Idling Aircraft as a Function of Engine and Ambient Conditions is designed to help improve the assessment of hazardous air pollutants (HAP) emissions at airports based on specific aircraft operating parameters and changes in ambient conditions.

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