Experimental investigation on alternative fuel combustion performance using a gas turbine combustor

Applied Energy 238 (2019) 1530–1542

Contents lists available at ScienceDirect

Applied Energy journal homepage: www.elsevier.com/locate/apenergy

Experimental investigation on alternative fuel combustion performance using a gas turbine combustor

T

⁎

Lukai Zhenga, , James Cronlyb, Emamode Ubogub, Ihab Ahmedb, Yang Zhangb, Bhupendra Khandelwalb a b

Nanjing Institute of Technology, Nanjing, Jiangsu 211167, People’s Republic of China Department of Mechanical Engineering, The University of Sheffield, Sheffield, South Yorkshire, United Kingdom

H I GH L IG H T S

effects of varying fuel properties on combustion performance have been studied. • The lean blowout and low nvPM emissions appear mutually exclusive. • Optimised of Derived Cetane Number a feasible method of optimising Lean Blowout. • Modification on the fuel aromatic molecular structure is required to optimise nvPM. • Investigation • Novel imaging detection technique validated for use in combustion research.

A R T I C LE I N FO

A B S T R A C T

Keywords: Alternative fuels Lean blowout Emissions Optical diagnostic

In order to ensure reliable combustion performance and low emissions profiles for prospective alternative fuels, this study was conducted in order to evaluate the impact of fuel properties and their composition on lean blowout performance and emission characteristics of various fuels. Significant contributions to knowledge have been achieved using visual images of the flames at the occurrence of lean blowout (LBO) that were captured by a high-speed camera and analysed to develop a new model for the evaluation of the LBO of current and future alternative fuels. The results show that high-density and high aromatic content fuel has the potential to produce higher soot formation. In addition, a low lean blowout equivalence ratio and low soot propensity is unlikely to be achieved at the same time by simply altering the value of fuel properties. It was discovered that the Derived Cetane number was observed to have a considerable effect on improving the LBO performance without inducing heavier soot emissions. An analysis of the impact of the fuel aromatic content (type and proportion) presents a hypothesis that not all aromatics species produce the same levels of particulate matter emissions. This work provides a comprehensive data foundation, analytical concepts and potential research techniques for the application of expeditious fuel screening tools to assess the combustion behaviour of current fuels, and also facilitates the potential for further operational fuel development. The visualisation method used in this work could contribute towards developing applications for flame detection in fuel performance assessment.

1. Introduction Significant developments have been made in recent years in modern aeronautical gas turbines [1] and on the use of alternative fuels [2,3]. Alternative fuels have the potential to provide a more diverse energy supply and reduced environmental pollution impact. With the prosperity of the alternative fuels sector, the combustion behaviour of a diverse range of fuels cannot be anticipated based on the previous

⁎

research concentrated and developed specifically on petroleum-derived jet fuel [4]. Zhang et al. [5] conducted a review of the latest developments in alternative jet fuels, highlighting the importance of fundamental research into fuel properties for combustion, which can potentially provide guidance for fuel development and feasibility assessment for aerospace applications. Much effort has been devoted to understanding alternative fuel combustion behaviour, including the ignition [6–8], emissions [9–13],

Corresponding author. E-mail address: [email protected] (L. Zheng).

https://doi.org/10.1016/j.apenergy.2019.01.175 Received 16 April 2018; Received in revised form 14 October 2018; Accepted 18 January 2019 0306-2619/ © 2019 Elsevier Ltd. All rights reserved.

Applied Energy 238 (2019) 1530–1542

L. Zheng et al.

Nomenclature DFCD PM LBO LII FAR SCR DCN ϕLBO ρ η γ TFP

LSP KD10 − 50 KD50 − 90 ΔH MWavg H Ar N-P I-P C-P R2 k IPK

Digital Flame Colour Discrimination Particulate Matter Lean Blowout Laser-Induced Incandescence Technique Fuel Air Ratio Sooty flame Concentration Ratio Derived Cetane Number LBO Equivalence Ratio density (Kg/m3) viscosity (cst) Surface Tension (mN/m) Flash Point (°C)

combustion efficiency [14] flame extinction [15,16] and instability [17]. The Particulate matter (PM) emissions have received considerable attention in recent years, predominantly because it closely associates with adverse impacts on health [18]. There is significant agreement within the research community in terms of fuel specific properties, the proportion of aromatic content of fuel is a primary reason for the generation of PM or smoke [19–21]. Alternative feedstock and production pathways are now available that comprise comparatively low proportions of aromatics [22,23], including but not limited to synthetic paraffinic kerosene (SPK), Fischer-Tropsch-derived fuels (FT), and gasto-liquid (GTL). Aromatics are important however, for the swelling of seals [24] in fuel systems and also increase the specific energy of a given fuel due to the higher gravimetric density in comparison to other forms of hydrocarbon [25]. In a recent study, Buffi et al. [26] demonstrated that lower soot formation can be observed with an increase of hydrotreated renewable Jet fuel blend percentage. A similar observation was been obtained by Xue et al. [23] through experimental work with six alternative jet fuels. Irrespective of aromatic content, Lobo et al. [27] also suggested that the hydrocarbon type, molecular size, smoke point and varying bulk properties of aviation fuels will affect combustion performance as well as soot formation. Roquemore and Litzinger [28] found that blended fuel with high initial and end boiling points can contribute to high PM. Calcote and Manos [29] developed an equation using a threshold sooting index (TSI) to rate the soot formation tendency based on the smoke point and molecular weight of the fuel. This model was later developed upon by Yang et al. [30], who claimed that the TSI increased with the smoke point and molecular weight. For pollutant emissions reduction, a candidate fuel has to narrow the stability limits and attain a satisfactory lean blowout performance [2]. The blow off events raise engine maintenance costs and may potentially prove fatal in an aircraft engine, particularly during landing [31]. Accordingly, the LBO performance is one of the major factors under considerations for fuel evaluations [32]. The parameters related to the combustor configuration and combustion conditions have a large impact on the LBO limit [33], including inlet flow velocity, equivalence ratio, bluff body geometry, inlet temperature, and stratification [34]. Based on Lefebvre’s theory [35], the LBO fuel air ratio is mainly dependent on the combustor geometric, thermo-fluidic, and fuel property terms, as shown in Eq. (1). The effects of the candidate fuel properties on the combustion performance are investigated in this study.

Smoke Point (°C) Distillation Slope 10–50% recovery Distillation Slope 50–90% recovery Net Heat Combustion Average Molecular Weight Hydrogen Content (wt%) Aromatic Content (wt%) Normal Paraffin Content (wt%) Iso-Paraffin Content (wt%) Cyclo-Paraffin Content (wt%) Correlation Coefficients Gradient of The Linear Trend Line Iso-paraffinic Kerosene

total combustor air employed in primary-zone combustion; ṁ A : air mass flow; P3 : combustor inlet pressure; T3 : temperature of combustor inlet value; D0 : initial mean drop size of fuel spray, m λ eff : effective value of evaporation constant m2 / s ; LCV :lower calorific value of fuel (J/kg); x: a constant to be determined experimentally. Lefebvre and Ballal [36] also pointed out the impact of fuel on stability, ignition and combustion efficiency. It was concluded that lean blowout limit changes more with fuel physical bulk properties which impacts atomisation, as compared to fuel molecular chemical properties. Considerable efforts have been made to analyse the effect on LBO behaviour. Jansohn et al. [37] studied the impact of gaseous fuel variability on LBO using turbulent, lean, premixed combustion. It was observed that introduction of up to 50% hydrogen can increase the lean blowout performance of methane by up to 22%. Similar behaviour was also observed in a study by Schefer [38]. In another study using a realistic gas turbine combustor [39], liquid fuel effects on lean blowout were presented in addition to their LES simulations results. It was observed that different fuels give different LBO performance. Rock et al. [40] studied the impact of fuel properties on LBO performance at different inlet temperatures using a liquid fuel combustor. It was found that fuels with properties of greatest radiation losses have the best blowout performance. Fuel effects on lean blowout using a well-stirred reactor were investigated [41], during which it was observed that different types of fuels have an impact on combustion efficiency, blowout limits and emissions. Lieuwen et al. [42] and Noble et al. [43] studied the impact of gaseous fuel flexibility on flame stability, blowout and ignition. It was observed that hydrogen enrichment has a significant impact on flame stability, blowout and ignition. Due to the intrinsic transient nature of the LBO process, many complicated diagnostic systems have been developed to provide realtime monitoring of these quantifiable dynamic characteristics. The preliminary LBO prediction relied on pressure drops or fluctuations in the combustion chamber, as claimed by Snyder et al. [44], and Lucenko [45] et al. Later on, the fast diagnostic techniques, like Piezo-electric transducers [46], tunable diode laser [47], planar laser induced fluorescence (PLIF) [48] acoustic and optical monitors [49] have been used for the LBO’s signature description. The optical approach used based on narrow or broadband-filtered, high-speed colour emissions imaging constitutes a suitable alternative, non-intrusive diagnostic way, to determine valuable dynamic combustion parameters [50,51]. Gentz et al. [52] has used the optical imaging method to conduct a deep study of autoignition behaviour. Flame luminosity is one of the main and easily observable features whereby each flame has its own spectrum of luminosity that can be used to characterise the combustion process [53]. The flame colour segment method has been utilised to characterise the instantaneous flame behaviour of LBO in this study. Experimental testing and analysis are essential to understand fuel chemical and physical properties’ effect on the overall performance of a combustor under different operating conditions, leading to further

(1) where Vpz : combustion zoon volume in the primary zone; fpz : fraction of 1531

Applied Energy 238 (2019) 1530–1542

L. Zheng et al.

detail of the combustor, with photographs showing the internal and external view of the experimental hardware. Fuel is supplied to the combustor using an electronically controlled fuel system. The fuel supply bomb is pressurised using a regulated head filled with nitrogen and provides a steady fuel pressure. Fuel exiting the bomb is then controlled by a twin series (coarse and fine) of air-actuated needle valves and an air-actuated fuel shut-off solenoid. Each of the air-actuated fuel control values and the air-fan motor speed controller are controlled using an LV analogue voltage controller at a frequency of approximately 5 Hz via a NI SCXI chassis and control PC. Data from the atmospheric pressure airline orifice flow meter and the fuel flow Coriolis meter is read into this low-speed system and is displayed on the screen for monitoring by the test engineer. The operating parameters for the LBO and stable conditions were determined empirically in the effort to generate a measurable range of PM. The air mass flow rate was set as 200 g/s for both conditions. At stable burning condition, the fuel flow rates were 1.8 g/s The PM number concentration was measured by differential mobility spectrometer (DMS 500). For LBO detection, once the combustor reached a steady state, all other operating conditions were maintained, while the fuel flow rate was gradually reduced at 0.0001 g/s until flame blow-out occurred. The fuel reduction rate was controlled using LabView. Meanwhile, the control computer recorded all parameters. To identify blow-out, the combustor upstream pressure and exhaust gas temperature were observed to have a sudden drop. Once the LBO point was identified, the above procedures were repeated at least ten more times to ensure consistency. The Photron-SA4 high-speed colour camera was employed for the optical data collection. In order to provide optical access to the burner, a quartz window was mounted on the exhaust duct, facing the combustor chamber axially. The camera was placed at the exhaust of the combustion chamber, and the images were acquired at the tail of the flame through the exhaust vent. Images of the sooty flame profile were captured at both LBO point and at stable burning conditions. The shutter speed was 1/1000 s for LBO condition.

areas of interest under which further alternative fuel research can proceed. However, the majority of the studies on performance of alternative fuels concentrate on emissions performance [23], whereas the physical properties and molecular composition on the overall performance of alternative fuels has not been studied in detail. There is also no outcome that gives an order of importance to direct an improved plan for future fuels. The combustion phenomena studies still largely resort to numerical simulation [54]. The use of actual engine hardware for investigating these effects is also not available in literature. The project aims to gain a better understanding of the effects of alternative jet fuels on engine combustion [55] to provide guidance and support when formulating and developing new jet fuel products. In this study, the impact of fuel properties and composition on LBO, sooty flame tendency and particulate matter have been investigated in order to provide detailed statistical analysis of combustor performance while using different fuels. The optical combustion monitoring in association with high-speed imaging provides new insights on the tendencies of soot formation at extreme lean burning situations. The analysis and research method contribute to the development of the jet fuel screening tool and combustion monitoring.

2. Experimental set-up and procedures A schematic of the experimental configuration utilised for this campaign can be observed in Fig. 1. It contains an air supply system, a fuel supply system, and an optic data acquisition system. In the air supply system, air is delivered to the combustor from an atmospheric pressure fan through a process line, which has been designed and manufactured according to the industrial design standard BS EN ISO 5167. This line is capable of measuring air mass flow with an uncertainty of ± 2% of the measured value. The atmospheric pressure, fan-drive motor, is controlled by a speed controller, and provides analogue control of the fan speed subject to the execution of customwritten LabVIEW code. The Tay combustor research rig was provided by Rolls Royce, which was designed to house a single can combustor, air blast type fuel injector, igniter, and fuel spray nozzle. The configuration provides a platform to investigate the performance of the Tay combustor at various flight conditions. Fig. 2 provides a diagrammatic illustration of the

Fig. 1. Experimental apparatus of Tay combustor. 1532

Applied Energy 238 (2019) 1530–1542

L. Zheng et al.

Fig. 2. Tay combustor configuration.

3. Methodology

blend, PM emissions measurements were repeated thrice to measure experimental precision.

3.1. PM emissions measurements 3.2. Optical flame morphology analysis For particle size distribution measurement, a combustion differential mobility spectrometer (DMS 500) was employed for the experiment, as this instrument can provide real-time measurement of soot particle size distribution [56–58]. The exhaust sample was directed through a corona discharge charger, where particles gain charges according to their size; thereafter, they are transported to a classifier. On application of high voltage to the central electrode, the charged particles are deflected toward the electrometer rings. Particles with a higher charge/lower drag will be deflected further and land on an electrometer ring closer to the sample inlet. As charged particles land on the metal electrometer rings, their charge will flow to the ground via an electrometer amplifier. This amplifier is capable of measuring small currents caused by groups of particles landing on the metal rings, and this forms the basis of particle detection. The average number-weighted particle mobility diameter (Dp) distribution, n(Dp) = dN/dlogDp, for each size bin recorded, was used to generate a particle size distribution, which was then integrated to produce the total number concentration per volume of the exhaust. The soot particles collected at the exhaust vent can be delivered to the analyser through a five-meter sample line. For each candidate

The digital flame colour discrimination (DFCD) approach developed by Huang and Zhang [59,60] shows that the instantaneous flame combustion performance can be directly represented in flame colour. The optical detection method and imaging post-processing provide a non-intrusive and alternative approach for monitoring the instantaneous soot formation behaviour of liquid fuel combustion [50,51,61,62]. In Fig. 3, sample flame images of Jet A, JP5, and JP8 demonstrate the LBO behaviour during the last 0.1 s of appearance of the flame, in which set the moment of the last appearance of flame as the 0 s and before that are counted as negative seconds. The bluish flame area has been enhanced five times for the facilitation of visual observation. The two-dimensional flame image can be considered the integration of flame luminosity along the axis of the combustion chamber. For each fuel, the flow pattern of the bluish and yellowish luminosity generally exhibited highly complex turbulence in a ring shape, with a clockwiseswirl motion encircling the central recirculation zone. More dynamic flame details of the blends at blow-out point can be observed in video clip. Soot emissions are a primary field of interest for fuel evaluations of

Fig. 3. Sample images of different types of fuels during the last 0.1 s of LBO. 1533

Applied Energy 238 (2019) 1530–1542

L. Zheng et al.

air pollution contribution from combustion. Sooty flames consist of unburned carbon particles which have been heated up, manifested by the bright yellow colour owing to the incomplete combustion. Hence, the sooty flame area ratio is a direct indicator of the soot emission and insufficient burning of the fuel components. As shown in the Fig. 3, the sooty flame area and intensity varied for different candidate fuels. This indicates that the fuels still undergo different levels of incomplete combustion, even at very lean conditions. The combustion performance relies on the properties and composition of the fuels in question. Consequently, further quantitative analyses will be conducted on the sooty flame profile analysis in order to assess fuel variability on soot forming propensity. Owing to the limitation of the imaging position, the spatial distribution of the sooty flame cannot be detected. Through the observation of axial integration of flame luminosity in combustion chamber, the combination analysis of the sooty flame area ratio and local soot concentration profile can compensate for the deficiency of observing position. The sooty flame region and blue chemiluminescence included region can be identified and separated based on the image post-processing method introduced by Wang et al. [63]. The blue flame of the captured images was filtered within the hue value band ranging from 180° to 252°, while the sooty flame was filtered within the range of 10–70°. The soot radiation concentration can be analysed based on the integrated luminosity via the method introduced by Huang and Zhang [64], which was successfully applied to various combustion performance evaluations [62,65]. They suggested that the ‘R intensity’ in the hue rang of 0–70° can expresses the soot-induced digital coloration and represents the local distribution of the soot concentration. The 2D contour plot of soot concentration result is shown in Fig. 4. The soot concentration rate displayed in the colour bar ranges from 0 to 255, in arbitrary units, and varied colour from dark blue to red. The red regions in a contour represent the highest soot concentration. According to the recorded 2000 images in the last 2 s before blowout, the sooty flame concentration ratio (SCR) for one image is defined by Eq. (2) and calculated using MatLab. The 2000 relevant flame images for each fuel between 0.000 s and −2.000 s before blowout has been considered to analyse the soot concentration profile. Each fuel shows significantly different properties just at the blow-off point and has reasonable comparability. For each fuel, the effective image data has been collected and calculated 5 times to ensure reliability and repeatability. 70°

SCR= ∑ Rhue × hue = 0° 70°

=

∑ Rhue × hue = 0°

4. Candidate fuels The data for the candidate fuels properties and chemical composition evaluated in this study are shown in Table 1. The properties considered in the study include the density, viscosity, DCN, flash point, and the net heat of combustion. The effects of changes in the chemical composition have been evaluated in terms of their content of aromatic, n-paraffin, i-paraffin, and c-paraffin constituents. Naming convention for all the fuels tested has been kept same as for National Jet Fuel Combustion Program (NJFCP), this is to help readers to cross refer other published literature. 5. Results and discussion The nine candidate fuels were tested under two combustion conditions, namely at LBO and stable burning conditions. In regard to the LBO condition, the combustion performance of the candidate fuels was analysed from two aspects, namely, by considering the LBO equivalence ratio and sooty flame concentration ratio. The PM concentration of each fuel was measured at stable conditions. 5.1. The performance of the candidate fuels In this section, the absolute data of each candidate fuels for the combustion performance of PM emissions, LBO and the sooty flame profile at LBO will be discussed and benchmarked using fuel A2 (Jet A). The PM number concentration of each fuel is the integrated result of the soot particle size distribution and presented in Fig. 5(a). From the graph, high aromatic fuel (C8) shows the highest PM number concentration. Highly branched Iso-paraffinic kerosene (C1) and Low DCN Fuel (C4) have the value of PM numbers concentration lower than 2 × 106/cm3 , especially for C4 displaying the overall lowest PM emissions. Both of the two fuels have aromatic content less than 0.5% by volume, with noticeably negligible presence in C1. This suggests, in keeping with previous literature, that lower aromatic content is important for lower PM emissions with regard to number of particles. The equivalence ratio at the LBO (ϕLBO ) of each fuel was calculated according to the FAR directly measured by the Coriolis mass flow meter at the blow-off point. The value of each point plotted in Fig. 5(b) is the average of ten times records to ensure reliability and repeatability. From the column graph, C1 blows off at the highest ϕLBO of all of the candidate fuels. Whereas, JP-5 (A3) and high cyclo-paraffin fuel (C7) have relative best performance on ϕLBO . This is especially notable for A3, as it features the lowest ϕLBO at less than 0.0195. Sooty flame concentration ratios of the candidate fuels have been calculated to evaluate the sooty flame profile at the LBO condition and the results are shown in Fig. 5(c). It clearly shows that A3 features the highest SCR, followed by C8. Comparing to the other tested fuels, C4

Sooty flame region Whole flame region

Pixel No . of sooty flame Pixel No . of Whole flame area

(2)

Fig. 4. Soot concentration contour plot of different types of sample fuels at last 0.1 s of LBO. 1534

Applied Energy 238 (2019) 1530–1542

L. Zheng et al.

Table 1 Properties of candidate fuels. Fuel Name

A1

A2

A3

C1

C3

C4

C5

C7

C8

Fuel description

JP8 C10.8H21.8 779.9 3.5 25.8 39.2 42.0 28.5 25

Jet A C11.4H22.1 803.2 4.5 28 48.3 48.0 22 29

JP5 C11.9H22.6 826.8 6.5 28.4 48.8 60.0 20 26

Highly Branched IPK C12.6H27.2 759.7 5.00 24.7 17.1 49.5 34.5 4.4

High Viscosity C12.8H25.3 807.7 8.00 27.7 47.0 66.0 25.2 25.2

Low DCN C11.4H24.8 759.2 3.87 25 28.0 46.0 37.2 10.3

Flat Boiling Range C9.7H18.7 768.9 1.96 26.1 39.6 44.0 21.4 1.1

High Cyclo-Paraffin C12.1H23.9 817.1 6.53 28.7 43.1 64.0 — 20

High Aromatic C11.6H21.4 823.2 4.84 28.9 42.7 56.0 — 24

50 − 90 KD ΔH MWavg

45

39

26

41.1

14.8

26.8

1.8

28

37

43.10 152

43.01 159

43.02 166

43.82 178

43.29 180

43.81 162

43.01 135

43.30 170

42.91 160

H (wt%) Ar (wt%) Normal Paraffin (wt%) Iso-Paraffin (wt%) Cyclo-Paraffin (wt%) Mono-aromatics (wt%) Di-aromatics (wt%) Cyclo-aromatic (wt%)

14.4 13.41 26.82 39.69 20.08 10.859 1.059 1.490

13.9 18.66 20.03 29.45 31.86 12.896 2.332 3.434

13.4 20.59 13.89 18.14 47.38 10.369 1.335 8.883

15.34 0 0 100.00 0 0 0 0

14.05 13.61 9.17 45.19 31.72 6.841 0.966 5.805

15.33 0.39 0.23 98.94 0.43 0.271 0.014 0.104

13.93 30.68 17.66 51.58 0.07 30.676 0.000 0.000

14.4 4.88 3.30 29.51 62.31 2.458 0.274 2.148

13.6 27.31 13.72 20.99 37.97 18.191 1.982 7.141

3

ρ kg/m η (cst) γ (mN/m) DCN TFP (°C) TSP (°C) 10 − 50 KD

Fig. 5. (a) PM number concentration at stable condition, (b) equivalence ratio and (c) SCR at LBO of candidate fuels. 1535

Applied Energy 238 (2019) 1530–1542

L. Zheng et al.

and C1 barely generate a sooty flame at LBO negligible SCR. It indicates they have the best performance of sufficient burning and lowest soot formation propensity. To have a better evaluation of the alternative fuel performance in comparison with Jet A, the results of all the fuels have been benchmarked against A2 using the calculation shown Eq. (3) and the results are shown in Fig. 6. The positive value indicates the candidate fuel has a higher result than A2, namely, a higher PM number, higher ϕLBO and higher sooting propensity. It indicates the fuel has worse performance than A2 on that aspect, which has been highlighted with red border in the plot. Whereas the negative value means it performs better than Jet A.(See Fig. 7.)

%Diff =

exceeded nearly 7%. Only A3 and C7 can sustain burning at a lower equivalence ratio with 4% and 1.5%, respectively. Fig. 6(c) shows the sooty flame concentration behaviour of each fuel at blowout point benchmarked with Jet A. The fuel with positive value indicates it has larger sooting tendency than A2 from the flame visualisation analysis. It can easily be observed that A3, C3, C5, and C8 blowout with heavier sooty concentration ratio than Jet A. A1 exhibited similar performance as Jet A. The remaining three candidate fuels burn cleaner. Moreover, the sooty flame concentration ratio of C1 and C4 show around 80% less than Jet A. It's also worth noting that only C7 performs better than Jet A (A2) in all ratings. 5.2. Fuel impacts on combustion performance

Resultfuel − ResultA2 ResultA2

(3) As shown from the results above, the fuels with different physical properties and chemical composition exhibit various performances during combustion. In this section, the effects of each fuel property on the combustion performance of the LBO equivalence ratio (ϕLBO ), the soot profile at lean blow out point (SCR) and PM emissions will be qualitatively analysed. It aims to provide a general perspective of the impacts of the fuel properties and compositions on these measured results. To identify the correlation between the fuel properties and combustion performance, the Forest regression method has been employed

Fig. 6(a) shows the percentage difference in PM emission produced by different fuels as compared to Jet A. From the results, it can be clearly observed that majority of the tested fuels produce lower PM emissions as compared to Jet A. Only A3 and C8 have 20% and 60% higher PM number concentration then Jet A. While, burning the fuels of C1 and C4 can reduce the PM emissions by as much as 80%. The comparison of LBO performance is shown in Fig. 6(b). Most of the fuels blowout at a higher ϕLBO than A2, which means it exhibits worse blowout resistance behaviour than A2. The highest one C1

Fig. 6. Different percentage of (a) PM number concentration (b) equivalence ratio and (c) SCR of candidate fuels comparing with A2. 1536

Applied Energy 238 (2019) 1530–1542

L. Zheng et al.

Fig. 7. Feature importance for LBO, SCR and PM emission with the Random Forest Regression Method.

LBO performance, DCN becomes the most negligible factor for soot particle formation. These figures only provide the indication of the importance rank, but the judgment on the positive or negative effect of these parameters on the LBO and soot formation performance should be assisted by the linear regression results.

to evaluate the relative importance of fuel properties. In addition, the linear regression algorithm was applied to find out these parameters have a positive or negative effect on the combustion emission performance. 5.2.1. Integrated regression prediction The Forest Regression Method [66] is a statistical approach to evaluate the importance of fuel properties on the combustion performance. The rank in Fig. 5 implies the degree of the relative importance factor from significant to none. For LBO prediction, DCN occupies the absolute leading factor with the relative importance more than 50%. The other considered factors shared the rest portion with all less than 10% or nearly negligible. This is in agreement with the conclusion claimed by Burger et al. [67,68]. For both of the soot tendency profile and PM emissions prediction, the aromatic content was found to be the most important factors. The hydrogen content, iso-paraffin content and density were also considered found to be, albeit less so. In contrast to

5.2.2. Individual linear regression analysis The variability in combustion performance is the determined by a multitude of factors. The above statistical analysis provides a general view to predict the combustion performance with those integrated properties. In this section, the influence of individual properties of the fuel will be further investigated, with considering the positive or negative effect on the combustion performance and to what extent. The results have direct meaning for the fuel development with manipulating the highlighted single property of fuel and improve the fuel performance.

Fig. 8. Relative importance rank for the fuel performance of LBO, SCR and PM emissions. 1537

Applied Energy 238 (2019) 1530–1542

L. Zheng et al.

Effect index = k × R2

The linear regression algorithm has been applied for each fuel property under consideration. For ease of comparison, all the results data and fuel properties data have been normalised between 0 and 1, which indicates the lowest to the highest value along the X-axis and Yaxis. The gradient of the linear trend line is k. The ‘−’ symbol indicates that the combustion performance decreases with increases of the fuel property values, while the ‘+’ symbol means that the performance result has increased. The coefficient of determination (R2) has been introduced to demonstrate feature fitting correlation. Higher R2 indicates this property has a strong linear relationship between this property and performance. The rank of the relative importance for the combustion performance of LBO, SCR and PM is shown in Fig. 8. It can notice that DCN shows the highest linear fitting correlation with the LBO performance. LBO barely depends on the aromatic content. For the PM emissions at the stable condition and the sooty tendency profile at the lean blowout point, ΔH and aromatic content rank as the top two correlating properties for the generation of soot emissions, which further underlines that the PM is strongly related to aromatic content. The impact of DCN on PM emissions can be considered negligible. The results are in a good agreement with the conclusion computed via the Forest regression method. As discussed above, the large absolute k value indicates the impact degree of the property on the combustion character. The ‘+’ and ‘−’ symbol present the positive and negative tendency of the effect on the performance. Beside of the magnitude of k, the different correlation index of these properties should be taken into consideration. Therefore, the integrated effect index of each fuel property has been calculated by Eq. (4).

(4)

For the purpose of facilitating the comparison, the Effect index of the fuel properties on the combustion performance has been incorporated in Fig. 9. Large absolute Effect index indicates a considerable impact of the property on the combustion character. The positive ‘+’ and negative ‘−’ values present increasing and decreasing tendencies of the effect on performance. Illustratively, they indicate that with increasing the value of each fuel property, different levels of positive or negative impacts on the LBO and emission performances will occur. These tendencies are clearly shown in the column diagram, whereby the fuel properties have different levels of impacts on the ϕLBO and SCR at LBO conditions, and PM emissions at stable conditions. Aromatic content and density have the largest ‘+’ effect index on SCR and PM emission, which indicates fuel with higher aromatic content and density tends to cause more soot formation. Oppositely, the fuel with higher hydrogen content can help the fuel to decompose more efficiently and with comparatively less soot. DCN and distillation slope from 10% to 50% have the most significant impact to resist blowout. This is consistent with studies by Colket et al. [69] and Won et al. [4]. They concluded that fuel with high DCN have a better capability to resist blowout. Braun-Unkhoff [70,71] hypothesised that it could be caused by the shifting of n-paraffin/iso-paraffin ratio. There is no noticeable impact of viscosity, flash point, Iso-paraffin content and mole weight on either of the three performances. Moreover, all the fuel properties exert a consistent effect on the SCR and on the PM emissions. This indicates that the results from high-speed imaging and DMS 500 are highly correlated with each other. Furthermore, it finds that the LBO equivalence ratio and the soot formation always elicit opposite responses for all the fuel properties. For

Fig. 9. Impacts of fuel properties on the combustion performance. 1538

Applied Energy 238 (2019) 1530–1542

L. Zheng et al.

accordance with the method introduced in the previous section, the combustion performance character has been normalised in the range of 0 to 1. In the contour plot, the highest value of the character of 1 is presented using a red colour, while the lowest value of 0 is shown in blue colour. Most of the candidate fuels are concentrated in locations close to the iso-paraffin axis, as there is no fuel with a comparatively high normalparaffin content in terms of mass. Therefore, in terms of the available results based on the data analyses shown in Fig. 10(a), the high ϕLBO values concentrate at the high iso-paraffin corner, which indicates that the fuels with high iso-paraffin content are easily blown out at high fuel/air fractions. Fig. 10(c) and (d) provides a clear view on the fuel composition effect on the sooty flame area ratio and PM. As shown in the results, the high value of these two criterions concentrate at the high cyclo-paraffin corner, which suggests that the fuels with high cyclo-paraffin content are prone to the generation of more soot irrespective of the applied condition. The aromatic content of fuels is primarily responsible for the generation of particulate matter (PM). However, not all aromatics produce the same levels of smoke and emissions. Therefore, further investigations have been conducted on the aromatics species. The term aromatic refers to a wide range of individual molecular species with a number of different features, including but not limited to varying levels of saturation, number of cyclical structures and the presence of varying types and numbers of functional alkyl groups. The NJFCP research

example, the fuel with higher density may induce higher PM emissions, but lower the LBO. It suggests that by manipulating individual fuel properties, the expected low LBO equivalence ratio and low soot emission cannot be achieved at the same time. Consequently, a compromise could be made to optimise the combustion performance at LBO. However, it is worth noting that the Cetane number has the best effect on the LBO equivalence ratio and a negligible effect on soot formation. Therefore, the DCN content manipulation can be developed into a feasible method for the optimisation of the engine under LBO conditions operation without increasing soot formation. In addition, the statistical analysis above provides a new model for the development of the jet fuel screening tool and help to enrich its database.

5.2.3. Fuel chemical composition effects Kerosene is composed of various molecular forms of hydrocarbons. The composition is dependent on the origin of crude oil, the processing used, and storage type and duration. Therefore, in-depth knowledge is required as to the specific composition of an alternative jet fuel and its impact on overall performance and emissions profile [22]. Fig. 10(a) shows the 3D ternary diagram of fuel distribution for aromatic, normal, iso-, and cyclo-paraffin, as a fraction of the entire fuel content by mass. Each corner of the ternary presents 100% content for one of the constituents and 0% content for all the others. The 2D contour plots present the fuel’s performance. From the upper elevation view of the 3D ternary plot, the aromatic content is not presented. In

Fig. 10. Ternary diagram showing the fuel composition effects on the combustion performance. 1539

Applied Energy 238 (2019) 1530–1542

L. Zheng et al.

community has sub-categorised aromatic content for each fuel by utilising comprehensive two dimensional gas chromatography using the following nomenclature [22]. mono-aromatics (mono cyclical unsaturated alkyl-benzenes such as Toulene, Xylene), Di-aromatics (alkylnaphthalenes featuring binary unsaturated cyclical rings) and cycloaromatics (polycyclic molecules featuring a saturated and unsaturated ring as primary features, such as Tetralin and Indan). The same naming conventions have been used in this work for consistency with the established literature. The content by mass of the aromatics types for each candidate fuel is provided in Table 1. Fig. 11 shows a combined column/line plot of different aromatics types composition content and PM emission for nine fuels. The results clearly demonstrate the relation between aromatics content percentages and PM emissions from gas turbines. The fuels of C1 and C4 with trace amount of aromatics produced the lowest amount of PM emissions. On the contrary, the fuels of C8 generated the highest PM emissions followed by A3, but their aromatic content was not the highest. It is interesting to note that C5 contains the largest amount of aromatic content, but the PM emissions value located at the average level. This phenomenon could be explained as the aromatics in C5 is mainly composed of mono-aromatics, while the aromatics of A3 and C8 are consisted of a plurality of aromatics types. Rather than claiming that aromatics is the main cause of soot formation, it is better to say diaromatics and cyclo-aromatics more precisely. Owing to their inherently complex and stable chemical structures, di-aromatics and cyclo-aromatics could be relative difficult to decompose effectively, and lead to the formation of soot. The results demonstrate the feasibility of manipulating the type and content of the aromatics and DCN to achieve better combustion performance. For future fuel development, attention should be paid to the type of aromatic hydrocarbons rather than the removal of aromatic hydrocarbons.

Fig. 12. Correlation between PM concentration number and SCR.

of these hot soot particles can reflect fuel particles emissions to some extent. This indicates that the imaging sooty monitoring method can be considered as a mutual inspection, supplement and support tool for PM measurement. This novel visualisation method can be future developed for the real time engine monitoring to help making the future air travel cleaner and safer. 6. Conclusion An experimental investigation has been carried out to evaluate the fuel properties and composition effects on the LBO performance and PM emissions on a Rolls-Royce Tay single can gas turbine combustor. This study focussed on a comparative analysis showing the importance and positive or negative impact of fuel properties on combustion performance using a statistical analysis method and a novel imaging technique. The main research findings and conclusion can be summarised as follows:

5.3. Correlation of the PM emissions and sooty flame profile. It is also interesting to note that, on the whole, there were similarities between the sooty concentration amplitude for each fuel computed by the imaging data and PM number concentration measured by DMS 500. To determine the relevance between sooty flame profile at the LBO condition and PM emissions, the linear regression analysis of the consistency rate between these two results is shown in Fig. 12a. In general, SCR was in good agreement with PM number concentration with R2 = 0.92. This can be explained by the fact that typically the soot in a flame becomes incandescent fined particles, due to the heat of the flame. The spectral band of the optical soot radiation follows the thermal radiation theory [72], which sees the high soot concentration area exhibit an identifiable orange-yellow colour. Therefore, luminosity

• Highly Branched iso-paraffinic kerosene (C1) and Low derived ce-

•

tane number (C4) fuel with low aromatic content generates the lowest PM emissions and soot concentration ratio, but LBO occurred at comparatively higher equivalence ratios. High aromatics content, high cyclo-paraffin and high-density are the primary three correlations for fuel with high the sooting tendency and PM generation. Different species and types of aromatics could lead to different level of soot emissions. Comparing the PM results from di-aromatics and

Fig. 11. PM size as a function of the distribution of particle counts. 1540

Applied Energy 238 (2019) 1530–1542

L. Zheng et al.

•

•

cyclo-aromatics, Alkyl-benzenes may not be the correlating factor for significant soot formation. Therefore, potential future fuel development could devote interest to controlling aromatic type content instead of removing aromatics, thus, achieving the possibility of 100% drop-in fuel application on the jet engine rather than proportional blends. LBO performance and soot formation tendency follow opposite trends with increasing value of the fuel properties. This indicates that a low LBO equivalence ratio and low soot propensity may be mutually exclusive when attempting to manipulating an isolated fuel parameter. LBO can be optimised by increased DCN without promoting soot formation. Therefore, DCN can be developed into a feasible method for the optimisation of the engine under LBO conditions of operation. The sooty profile acquired via high-speed imaging and DMS 500 correlate well with each other. It indicates that the imaging sooty monitoring method can be considered as a mutual inspection, supplement and support tool for PM emissions measurement. This novel visualisation method can be developed for future engine monitoring to promote the future air travel cleaner and safer.

Mechanical Engineers. 2018. p. V04AT04A011-V04AT04A011. [9] Khandelwal B, Ubogu E, Akram M, Blakey S, Wilson CW. Experimental analysis on emission production and performance of stressed 100 % SPK, stressed Fully Formulated Synthetic Jet Fuel and Jet A-1 in a small gas turbine engine. 11th Int. Energy Convers. Eng. Conf.. 2013. [10] Kinsey JS, Timko MT, Herndon SC, Wood EC, Yu Z, Miake-Lye RC, et al. Determination of the emissions from an aircraft auxiliary power unit (APU) during the alternative aviation fuel experiment (AAFEX). J Air Waste Manag Assoc 2012;62:420–30. https://doi.org/10.1080/10473289.2012.655884. [11] Khandelwal B, Wijesinghe CJ, Sriraman S. Effect of alternative fuels on emissions and engine compatibility. Energy Propuls. Springer; 2018. p. 27–50. [12] Zheng L, Ling C, Ubogu EA, Cronly J, Ahmed I, Zhang Y, et al. Effects of alternative fuel properties on particulate matter produced in a gas turbine combustor. Energy Fuels 2018. https://doi.org/10.1021/acs.energyfuels.8b01442. [13] Khandelwal B, Wijesinghe CJ, Sriraman S. Effect of Alternative Fuels on Emissions and Engine Compatibility. In: Energy Propuls., Springer, 2018. p. 27–50. [14] Şöhret Y, Turan Ö, Hikmet Karakoç T. Analysis of combustion efficiency for turbofan engine combustor using MATLAB. Int J Eng Technol 2015;7:86–90. https:// doi.org/10.7763/IJET.2015.V7.772. [15] Kumar K, Sung C-J, Hui X. Laminar flame speeds and extinction limits of conventional and alternative jet fuels. Fuel 2011;90:1004–11. [16] Zheng L, Ahmed I, Ubogu E, Zhang Y, Khandelwal B. Evaluation of the lean blowout limit of alternative fuels in a gas turbine combustor. Proc. ISABE 2017, International Society of Air-breathing Engines. 2017. [17] Wijesinghe CJ, Khandelwal B, Impact of alternative fuel on gas turbine noise, vibration and instability, in: AIAA Scitech 2019 Forum, 2019. p. 240. [18] Lobo P, Hagen DE, Whitefield PD, Raper D. PM emissions measurements of inservice commercial aircraft engines during the Delta-Atlanta Hartsfield Study. Atmos Environ 2015;104:237–45. [19] Saffaripour M, Zabeti P, Kholghy M, Thomson MJ. An experimental comparison of the sooting behavior of synthetic jet fuels. Energy Fuels 2011;25:5584–93. https:// doi.org/10.1021/ef201219v. [20] Botero ML, Mosbach S, Kraft M. Sooting tendency of paraffin components of diesel and gasoline in diffusion flames. Fuel 2014;126:8–15. https://doi.org/10.1016/j. fuel.2014.02.005. [21] Witkowski D, Kondo K, Vishwanathan G, Rothamer D. Evaluation of the sooting properties of real fuels and their commonly used surrogates in a laminar co-flow diffusion flame. Combust Flame 2013;160:1129–41. [22] Braun-Unkhoff M, Kathrotia T, Rauch B, Riedel U. About the interaction between composition and performance of alternative jet fuels. CEAS Aeronaut J 2016;7:83–94. https://doi.org/10.1007/s13272-015-0178-8. [23] Xue X, Hui X, Singh P, Sung CJ. Soot formation in non-premixed counterflow flames of conventional and alternative jet fuels. Fuel 2017;210:343–51. https://doi.org/ 10.1016/j.fuel.2017.08.079. [24] Silverman B. Effects of high aromatic aviation fuel on sealant systems; 1980. doi:https://doi.org/10.4271/800881. [25] Chen K, Liu H, xia Z. The impacts of aromatic contents in aviation jet fuel on the volume swell of the aircraft fuel tank sealants. SAE Int J Aerosp 2013;6. https://doi. org/10.4271/2013-01-9001. 2013–01-9001. [26] Buffi M, Valera-Medina A, Marsh R, Pugh D, Giles A, Runyon J, et al. Emissions characterization tests for hydrotreated renewable jet fuel from used cooking oil and its blends. Appl Energy 2017;201:84–93. https://doi.org/10.1016/j.apenergy.2017. 05.104. [27] Lobo P, Christie S, Khandelwal B, Blakey SG, Raper DW. Evaluation of non-volatile particulate matter emission characteristics of an aircraft auxiliary power unit with varying alternative jet fuel blend ratios. Energy Fuels 2015;29:7705–11. https:// doi.org/10.1021/acs.energyfuels.5b01758. [28] Roquemore WM, Litzinger TA. The science of emissions from alternative fuels. Air Force Research Lab Wight-Patterson AFB United States; 2017. [29] Calcote HF, Manos DM. Effect of molecular structure on incipient soot formation. Combust Flame 1983;49:289–304. https://doi.org/10.1016/0010-2180(83) 90172-4. [30] Yang Y, Boehman AL, Santoro RJ. A study of jet fuel sooting tendency using the threshold sooting index (TSI) model. Combust Flame 2007;149:191–205. https:// doi.org/10.1016/j.combustflame.2006.11.007. [31] Chaudhari RR, Sahu RP, Ghosh S, Mukhopadhyay A, Sen S. Flame color as a lean blowout predictor. Int J Spray Combust Dyn 2013;5:49–65. https://doi.org/10. 1260/1756-8315.5.1.49. [32] Ruslan M, Ahmed I, Khandelwal B. Evaluating effects of fuel properties on smoke emissions. ASME Turbo Expo 2016 Turbomach. Tech. Conf. Expo. 2016. https:// doi.org/10.1115/GT2016-56791. V04AT04A046. [33] Mukhopadhyay A, Chaudhari RR, Paul T, Sen S, Ray A. Lean blow-out prediction in gas turbine combustors using symbolic time series analysis. J Propuls Power 2013;29:950–60. https://doi.org/10.2514/1.B34711. [34] Chakravarthy SR, Sampath R, Ramanan V. Dynamics and diagnostics of flameacoustic interactions. Combust Sci Technol 2015;189:1–20. https://doi.org/10. 1080/00102202.2016.1202938. [35] Lefebvre AH. Fuel effects on gas turbine combustion - ignition, stability, and combustion efficiency. J Eng Gas Turbines Power 1985;107:24–37. https://doi.org/ 10.1115/1.3239693. [36] Lefebvre AH, Ballal DR. Gas turbine combustion: alternative fuels and emissions. CRC Press; 2010. [37] Boschek E, Griebel P, Jansohn P. Fuel variability effects on turbulent. Lean Premixed Flames at High Pressures 2007:373–82. https://doi.org/10.1115/ GT2007-27496. [38] Schefer RW. Hydrogen enrichment for improved lean flame stability. Int J

The work contributes to provide a data foundation, analytical concepts and research technique for the application of expeditious fuel screening tools to assess the combustion behaviour. The results should contribute to operational fuel development for jet engine combustion. The visualisation method could contribute towards developing applications for flame detection in fuel performance assessment. Acknowledgment This work was funded by the US Federal Aviation Administration (FAA) Office of Environment and Energy as part of the CLEEN Programme under the FAA Award Number: DTFAWA–10–C–00006. Some of the fuels for this work were funded by the US FAA Office of Environment and Energy as a part of the NJFCP Program. Any opinions, findings, and conclusions, or recommendations, expressed in this material are those of the authors and do not necessarily reflect the views of the FAA, or other NJFCP and CLEEN sponsors. This work has been supported by University of Sheffield’s Low Carbon Combustion Centre. Appendix A. Supplementary material Supplementary data to this article can be found online at https:// doi.org/10.1016/j.apenergy.2019.01.175. References [1] Parker R, Lathoud M. Green aero-engines: technology to mitigate aviation impact on environment. Proc Inst Mech Eng Part C J Mech Eng Sci 2010;224:529–38. https:// doi.org/10.1243/09544062JMES1515. [2] Blakey S, Rye L, Wilson CW. Aviation gas turbine alternative fuels: a review. Proc Combust Inst 2011;33:2863–85. https://doi.org/10.1016/j.proci.2010.09.011. [3] Khandelwal B, Roy S, Lord C. Effect of novel alternative fuels and compositions on vibrations of a gas turbine engine. 50th AIAA/ASME/SAE/ASEE Jt. Propuls. conf., American Institute of Aeronautics and Astronautics 2014. https://doi.org/10.2514/ 6.2014-3410. [4] Won SH, Veloo PS, Dooley S, Santner J, Haas FM, Ju Y, et al. Predicting the global combustion behaviors of petroleum-derived and alternative jet fuels by simple fuel property measurements. Fuel 2016;168:34–46. https://doi.org/10.1016/j.fuel. 2015.11.026. [5] Zhang C, Hui X, Lin Y, Sung CJ. Recent development in studies of alternative jet fuel combustion: progress, challenges, and opportunities. Renew Sustain Energy Rev 2016;54:120–38. https://doi.org/10.1016/j.rser.2015.09.056. [6] Sforzo B, Wei S, Seitzman JM. Non-premixed ignition of alternative jet fuels. 55th AIAA Aerosp. Sci. Meet. 2017. p. 1–10. https://doi.org/10.2514/6.2017-0147. [7] Rye L, Wilson C. The influence of alternative fuel composition on gas turbine ignition performance. Fuel 2012;96:277–83. https://doi.org/10.1016/j.fuel.2011.12. 047. [8] Ahmed I, Zheng L, Ubogu EA, Khandelwal B. Evaluation of Impact on Lean Blowout Limit and Ignition Delay While Using Alternative Fuels on Gas Turbine Combustor. ASME Turbo Expo 2018 Turbomach. Tech. Conf. Expo., American Society of

1541

Applied Energy 238 (2019) 1530–1542

L. Zheng et al.

[39]

[40]

[41]

[42]

[43]

[44] [45] [46] [47]

[48]

[49] [50]

[51]

[52]

[53]

[54] [55]

Astronautics 2007. https://doi.org/10.2514/6.2007-770. [56] Symonds JPR, Reavell KSJ, Olfert JS, Campbell BW, Swift SJ. Diesel soot mass calculation in real-time with a differential mobility spectrometer. J Aerosol Sci 2007;38:52–68. [57] Rye L, Lobo P, Williams PI, Uryga-Bugajska I, Christie S, Wilson C, et al. Inadequacy of optical smoke measurements for characterization of non-light absorbing particulate matter emissions from gas turbine engines. Combust Sci Technol 2012;184:2068–83. https://doi.org/10.1080/00102202.2012.697499. [58] Ubogu EA, Cronly J, Khandelwal B, Roy S. Determination of the effective density and fractal dimension of PM emissions from an aircraft auxiliary power unit. J. Environ. Sci. 2018. https://doi.org/10.1016/j.jes.2018.01.027. [59] Huang H, Zhang Y. Flame colour characterization in the visible and infrared spectrum using a digital camera and image processing. Aerospace 2008;085406. https://doi.org/10.1088/0957-0233/19/8/085406. [60] Huang HW, Zhang Y. Digital colour image processing based measurement of premixed CH 4 + air and C 2 H 4 + air flame chemiluminescence. Fuel 2011;90:48–53. https://doi.org/10.1016/j.fuel.2010.07.050. [61] Botero ML, Mosbach S, Akroyd J, Kraft M. Sooting tendency of surrogates for the aromatic fractions of diesel and gasoline in a wick-fed diffusion flame. Fuel 2015;153:31–9. https://doi.org/10.1016/j.fuel.2015.02.108. [62] Jiotode Y, Agarwal AK. Endoscopic combustion visualization for spatial distribution of soot and flame temperature in a diesohol fueled compression ignition engine. Energy Fuels 2016;30:9850–8. https://doi.org/10.1021/acs.energyfuels.6b01585. [63] Wang Y, Zheng L, Woolley R, Zhang Y. Investigation of ignition process from visible to infrared by a high speed colour camera. Fuel 2016;185:500–7. https://doi.org/ 10.1016/j.fuel.2016.08.010. [64] Huang HW, Zhang Y. Dynamic application of digital image and colour processing in characterizing flame radiation features. Meas Sci Technol 2010;21. https://doi.org/ 10.1088/0957-0233/21/8/085202. 085202–. [65] Mortazavi H, Wang Y, Ma Z, Zhang Y. The investigation of CO2 effect on the characteristics of a methane diffusion flame. Exp Therm Fluid Sci 2018;92:97–102. https://doi.org/10.1016/j.expthermflusci.2017.11.005. [66] Segal MR. Machine learning benchmarks and random forest regression; 2004. [67] Burger V. The influence of fuel properties on threshold combustion in aviation gas turbine engines; 2017. [68] Burger V, Yates A, Viljoen C. Influence of fuel physical properties and reaction rate on threshold heterogeneous gas turbine combustion; 2012. p. 63–71. https://doi. org/10.1115/GT2012-68153. [69] Colket M, Zeppieri S, Dai Z, Hautman D. Fuel research at UTRC. Multi-Agency Coord. Counc. Combust. Res. 5th Annu. Fuel Res. Meet. Livermore, California, Sept. 2012. p. 17–20. [70] Hui X, Kumar K, Sung CJ, Edwards T, Gardner D. Experimental studies on the combustion characteristics of alternative jet fuels. Fuel 2012;98:176–82. https:// doi.org/10.1016/j.fuel.2012.03.040. [71] Jeyashekar N, Ph D, Muzzell P, Sattler E, Hubble N, Antonio S. Lubricity and derived cetane number measurements of jet fuels, alternative fuels and fuel blends interim report. San Antonio, U.S.: Force Projection Technologies; 2010. [72] Goulay F, Schrader PE, Michelsen HA. Effect of the wavelength dependence of the emissivity on inferred soot temperatures measured by spectrally resolved laser-induced incandescence. Appl Phys B 2010;100:655–63. https://doi.org/10.1007/ s00340-010-4119-2.

Hydrogen Energy 2003;28:1131–41. https://doi.org/10.1016/S0360-3199(02) 00199-4. Esclapez L, Ma PC, Mayhew E, Xu R, Stouffer S, Lee T, et al. Fuel effects on lean blow-out in a realistic gas turbine combustor. Combust Flame 2017;181:82–99. https://doi.org/10.1016/j.combustflame.2017.02.035. Rock N, Chterev I, Emerson B, Seitzman J, Lieuwen T. Blowout sensitivities in a liquid fueled combustor: Fuel composition and preheat temperature effects. Proc. ASME Turbo Expo. 4A–2017 2017. p. 1–11. https://doi.org/10.1115/ GT201763305. Blust JW, Ballal DR, Sturgess GJ. Fuel effects on lean blowout and emissions from a well-stirred reactor. J Propuls Power 1999;15:216–23. https://doi.org/10.2514/2. 5444. Lieuwen T, McDonell V, Petersen E, Santavicca D. Fuel flexibility influences on premixed combustor blowout, flashback, autoignition, and stability. J Eng Gas Turbines Power 2008;130:011506. https://doi.org/10.1115/1.2771243. Noble DR, Zhang Q, Shareef A, Tootle J, Meyers A, Lieuwen T. Syngas mixture composition effects upon flashback and blowout; 2006. p. 357–368. https://doi. org/10.1115/GT2006-90470. Snyder TS, Rosfjord TJ. Active gas turbine combustion control to minimize nitrous oxide emissions; 1998. Lucenko M, Vanderleest RE, Onge KJS. Method and apparatus for detecting burner blowout, Patent# 5581995; 1996. Lee JG, a Santavicca D. Experimental diagnostics of combustion instabilities. Prog Astronaut Aeronaut 2005;210:481–529. Li H, Zhou X, Jeffries JB, Hanson RK. Active control of lean blowout in a swirlstabilized combustor using a tunable diode laser. Proc Combust Inst 2007;31(II):3215–23. https://doi.org/10.1016/j.proci.2006.07.006. Chaudhuri S, Kostka S, Renfro MW, Cetegen BM. Blowoff dynamics of bluff body stabilized turbulent premixed flames. Combust Flame 2010;157:790–802. https:// doi.org/10.1016/j.combustflame.2009.10.020. Nair S, Lieuwen T. Acoustic detection of blowout in premixed flames. J Propuls Power 2005;21:32–9. https://doi.org/10.2514/1.5658. Ng WB, Clough E, Syed KJ, Zhang Y. The combined investigation of the flame dynamics of an industrial gas turbine combustor using high-speed imaging and an optically integrated data collection method. Meas Sci Technol 2004;15:2303–9. https://doi.org/10.1088/0957-0233/15/11/016. Zheng L, Faik A, Zhang Y. Flame colour analysis for the droplet combustion of water-in-diesel emulsions. In: 12th Int. Conf. Heat Transf. Fluid Mech. Thermodyn., Malaga, Spain; 2016. p. 212–7. < https://edas.info/web/hefat2016/titles. html#F > . Gentz G, Gholamisheeri M, Toulson E. A study of a turbulent jet ignition system fueled with iso-octane: Pressure trace analysis and combustion visualization. Appl Energy. 2017;189:385–94. https://doi.org/10.1016/j.apenergy.2016.12.055. Nori V, Seitzman J. Evaluation of chemiluminescence as a combustion diagnostic under varying operating Conditions. 46th AIAA Aerosp. Sci. Meet. Exhib. 2008. p. 1–14. https://doi.org/10.2514/6.2008-953. Chong CT, Hochgreb S. Spray flame structure of rapeseed biodiesel and Jet-A1 fuel. Fuel 2014;115:551–8. Edwards T, Colket M, Cernansky N, Dryer F, Egolfopoulos F, Friend D, et al. Development of an experimental database and kinetic models for surrogate jet fuels. 45th AIAA Aerosp. Sci. Meet. Exhib., American Institute of Aeronautics and

1542