Effect of increased fuel volatility on CDC operation in a light duty CIDI engine

Effect of increased fuel volatility on CDC operation in a light duty CIDI engine Fuel 194 (2017) 195–210 Contents lists available at ScienceDirect Fuel journal homepage www elsevier com/locate / fuel[.]

Fuel 194 (2017) 195–210 Contents lists available at ScienceDirect Fuel journal homepage: www.elsevier.com/locate/fuel Full Length Article Effect of increased fuel volatility on CDC operation in a light-duty CIDI engine M Groendyk ⇑, D Rothamer University of Wisconsin – Madison, United States DOE Great Lakes Bioenergy Research Center, United States a r t i c l e i n f o Article history: Received 24 August 2016 Received in revised form 29 November 2016 Accepted 15 December 2016 Keywords: Jet entrainment Physical properties Direct injection Biofuel Internal combustion engines Volatility a b s t r a c t Alternative diesel fuels derived from biomass can vary significantly in volatility compared to their petroleum-derived counterparts, and their appropriate utilization is contingent on their compatibility with existing engine infrastructure To investigate this compatibility, experiments were carried out to study the effect of fuel volatility on conventional diesel combustion (CDC) performance under a wide range of in-cylinder thermodynamic conditions at start of injection (SOI) Fuels of matched reactivity (i.e., cetane number (CN)) and varying volatility were produced by blending binary mixtures of 2,6,10trimethyldodecane (farnesane) and 2,2,4,4,6,8,8-heptamethylnonane, octane number primary reference fuels (PRF), and cetane number secondary reference fuels (SRF) Nine fuel blends were tested in total, consisting of volatility characteristics at reactivity levels Five engine operating conditions were utilized, ranging from 14.7–29 kg/m3 and 980–1120 K in-cylinder density and temperature at SOI Testing was performed in a single-cylinder GM 1.9 L diesel engine Only small differences in ignition delay (ID), incylinder pressure, and heat release rate (HRR) were observed between fuels of matched CN, regardless of their volatility An analysis of the spray breakup and mixture formation process indicated that there were only small variations in ambient air entrainment and jet temperature between fuel blends, in agreement with the observed combustion behavior Ó 2017 The Authors Published by Elsevier Ltd This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Introduction The increasing scarcity of petroleum resources coupled with the deleterious ecological effects of continued carbon dioxide emissions resulting from fossil fuel utilization present serious engineering challenges for industrialized nations Compounding this, the demand for heavy-duty transportation and aviation fuels traditionally provided by diesel-like middle-distillates is forecast to increase steadily over the coming years [1] Alternative fuels, particularly those derived from biomass, offer a compelling means to augment the existing petroleum fuel supply to meet this increase in demand while simultaneously satisfying ever tightening vehicle emissions standards [2–4] Hydrotreated renewable diesel (HRD) fuels, derived from oils and fats, and hydrogenated isoprenoids (such as farnesane), derived from fermentation, are of particular interest as they offer superior physical characteristics (such as a low cloud point) and emissions performance compared to fuels derived from trans-esterified oils [2,4] ⇑ Corresponding author at: University of Wisconsin – Madison, United States E-mail address: groendyk@wisc.edu (M Groendyk) The introduction of a new fuel into the supply stream offers its own set of challenges Petroleum-derived fuels are by nature a complex blend of molecular species, each contributing individually to the chemical and physical properties of the fuel as a whole In contrast, fuels derived from biomass are often composed of only a few, perhaps even a single chemical species This compositional difference makes matching the properties of an alternative fuel to its petroleum counterpart prohibitively difficult, and instead some allowance must be made for property differences Understanding exactly how much, and in which properties variation can be tolerated without adverse effects will allow for the greatest utilization of alternative fuels In particular, this study focuses on properties of interest to compression-ignition direct-injection (CIDI) internal combustion engines, and the fuel most commonly utilized in such combustion applications, diesel fuel This study is intended to expand upon work performed previously by the authors in a heavy-duty CIDI engine [5] In the previous study, the stock heavy-duty fuel injector was replaced by one with a smaller injector orifice hole diameter in order to eliminate spay-wall interactions and piston bowl wetting While this was useful for eliminating potentially complicating factors associated with the injection event, it is not representative of http://dx.doi.org/10.1016/j.fuel.2016.12.064 0016-2361/Ó 2017 The Authors Published by Elsevier Ltd This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) 196 M Groendyk, D Rothamer / Fuel 194 (2017) 195–210 typical diesel-engine operation In addition, the range of thermodynamic in-cylinder conditions in the previous study was limited and testing was only done at a single fuel reactivity level The present study significantly expands the range of in-cylinder temperature and density conditions and performs tests at multiple fuel reactivity levels In addition, the configuration of the engine used is representative of a stock, small-bore diesel engine, which will include the influence of spray-wall interactions under certain in-cylinder conditions 1.1 Background 1.1.1 Diesel combustion A discussion of the implications of varying the physical properties of a diesel fuel must begin with an understanding of the physical processes that diesel fuel must undergo to operate successfully in a CIDI engine Conventional diesel combustion (CDC) is typified by late direct fuel injection, whereby liquid fuel is injected at high pressure into the combustion chamber near the end of compression Due to the compression event, the chamber contents will be at elevated temperature and density when the fuel is introduced, allowing it to rapidly evaporate, form an ignitable mixture with the ambient oxygen, and begin to burn [6] The relevant physical changes the fuel must undergo are evaporation and mixing, and the fuel injection and resulting spray provide the mechanisms by which these changes come about Generally, CDC fuel injections can be thought of as mixing limited sprays, where the rate of ambient gas entrainment determines both the extent of mixing and the thermal state at any position A detailed discussion of the spray formation process will not be included here, instead readers are directed to the works of Reitz and Bracco [7], Naber and Siebers [8], and Siebers [9–11] for additional background on spray formation 1.1.2 The role of volatility in CDC While the implications of physical properties for spray phenomena have been well studied, the impact of spray behavior on the overall combustion event is somewhat more complex The variations in the conditions produced by the dispersion and vaporization of the spray are convolved with further variations in the chemical processes of ignition and combustion which follow Using computer simulations, Ra et al [12] were able to study the combustion of a diesel fuel in which individual physical properties were systematically varied The effects of combustion kinetics were isolated from the simulation by using the chemical kinetics of n-tetradecane for all of the simulated fuels Their results indicated that all of the 11 physical properties included in their spray breakup and droplet collision/evaporation models had a significant impact on the simulated combustion event In particular, they found that the density, vapor pressure, and to a lesser extent the heat capacities (both liquid and vapor) of the fuel exerted the greatest influence on combustion performance [12] A similar study by Kim et al [13] using n-dodecane for the baseline fuel and chemical kinetic mechanism, varied six fuel physical properties within ranges found among commonly used diesel fuel surrogates They found that at diesel-relevant conditions, varying liquid density, viscosity, vapor pressure, and heat capacity had significant impact on liquid penetration length, and that the liquid density and heat capacity variations had the most significant effect on ignition delay (i.e the length of time between injection and the start of combustion)[13] Both the results of Ra et al [12] and Kim et al [13] indicate that for kinetically identical fuels ignition can still be impacted by changes in the local conditions resulting from differences in fuel physical properties Both of these studies included volatility properties: vapor pressure (distillation curve), enthalpy of vaporization, and liquid and vapor heat capacities Both studies also showed some dependence of spray and or combustion behavior on these volatility properties For this study volatility can be defined as the thermal dependence of a fuel’s vapor-liquid equilibrium fractions According to both the mixing-limited jet vaporization model and to experimental observation, volatility plays a crucial role in determining the extent of liquid penetration into the cylinder [8,9,11,13] While this has obvious implications for cylinder-wall/ piston-bowl impingement, the effect of liquid length on subsequent spray mixing and other important combustion parameters (such as ignition delay) is not fully understood [14–16] Ra et al [12] predicted a large change in both ignition timing and peak pressure for kinetically identical fuels differing only in vapor pressure; however, the study by Kim et al [13] suggests that the effects of volatility are limited mainly to liquid penetration, and that ignition timing is more strongly affected by a fuel’s liquid density and heat capacity In addition to these computational studies, numerous experimental studies have been undertaken to assess volatility’s influence on combustion directly [5,14–22] Generally, the ignition delay of a fuel has been observed to be insensitive to its liquid length (and therefore its volatility), and is instead correlated with its cetane number [5,17,21,22] This correlation is not perfect however, and several investigations have noted deviations in fuel ignition behavior which can potentially be ascribed to volatility effects [14–16] Alternative fuels with volatilities that differ significantly from petroleum diesel are being seriously considered Alcohols, such as butanols for example, have received considerable attention as potential blend stocks, and are of significantly higher volatility than conventional diesel [23–25] Given the importance of ignition behavior for effective CIDI fuel utilization, the impact of volatility on ignition should be resolved, so that appropriate recommendations regarding the required volatility of alternative diesel fuels can be made 1.1.3 A word on the cetane number Ignition timing is of crucial importance for a fuel that is to be used in a compression-ignition engine [26,27] Engine load, emissions, and efficiency are all heavily dependent on combustion phasing, which in a CIDI engine is controlled by the injection timing and the reactivity of the injected fuel Reactivity, being a key factor in combustion phasing, is of critical importance, and must be quantified in some way The cetane number (CN) is a practical reactivity metric that is widely used to characterize diesel-like fuels The CN of a fuel is defined by ASTM standard D-613 [28], and is determined from tests performed in a specific engine by comparing the compression ratios necessary for fuels to achieve a specified ignition delay at a prescribed set of operating conditions The circumstances of the CN test differ significantly from modern diesel engine operation, and despite its popularity, appropriate use of the CN has been the subject of repeated inquiry [10,15–17,27,29–32] Therefore, the use of the CN for the present work will be discussed, and its applicability established before proceeding The present work is intended to provide insight into the physical processes of autoignition in a modern CIDI engine This kind of autoignition event includes both chemical and physical processes In order to accurately study variations in the physical processes only, the chemical portion of the auto-ignition should be held fixed to as high a degree as possible to eliminate it as a variable One method to attempt to accomplish this is to match CNs of the fuels to be tested The issue with this approach is that one would think that the CN is a characterization of the total auto-ignition process, and may include the influences of both chemical and physical M Groendyk, D Rothamer / Fuel 194 (2017) 195–210 properties Before CN can be employed as a metric of strictly chemical reactivity, more consideration is needed A number of studies have been undertaken utilizing primary reference fuel (PRF) blends, similar to those used in the current work, in homogeneous-charge compression-ignition (HCCI) engines For the current study, PRF refers to the reference fuels used for octane number (ON) testing, i.e., n-heptane and isooctane As the name suggests, HCCI utilizes a fully pre-mixed and prevaporized, homogeneous charge of fuel and oxidizer, and compression-induced heating to initiate combustion Such a strategy is entirely kinetically controlled and free of any influence of the physical properties which govern mixture formation These studies have shown that, for PRF blends and conventional diesel fuels, combustion phasing is strongly correlated with CN [30–32] As the combustion phasing is controlled entirely by the fuel chemistry under these conditions, it follows that CN is correlated to fuel chemistry for these types of fuels Janecek et al [33,34] have performed other HCCI studies using a fuel substitution technique to relate the reactivities of diesel-like fuels to blends of PRFs They report a strong correlation between CN of the diesel-like fuel and the composition of the PRF blend (the PRF number) that has matched combustion phasing - and therefore equivalent reactivity The correlation is applicable over a wide range of CN (20–75) and is insensitive to the particular HCCI operating conditions used This offers additional compelling evidence that CN primarily characterizes chemical reactivity, as PRF number is based on ON testing, which does not directly involve spray breakup and mixture preparation processes Based on these findings, for the circumstances of this study, CN has been deemed an appropriate metric of fuel chemical reactivity for PRFs and diesel-like fuels, and is applied as such 1.2 Objective The objective of this investigation is to ascertain the effect that large variations in fuel volatility of will have on CDC in a typical light-duty application indicative of current automotive diesel engines Ignition delay is used as a key metric, as it has strong influence on virtually all combustion parameters of importance Volatility is isolated as completely as possible from other physical properties of importance (such as liquid density) by utilizing fuels composed of carefully selected pure chemical species To facilitate the widest range of testing conditions, the test fuels were blended to produce a wide range of chemical reactivity, and then run in the engine under a wide range of intake conditions Methods 2.1 Fuels To investigate the effect of fuel volatility on ignition at various levels of reactivity, both volatility, and reactivity must be controlled as independently as is possible, across the widest feasible range with minimal changes to other important fuel characteristics The approach to this used binary blends where each of the two constituent compounds had approximately matched volatility, but substantially different reactivity (as quantified by CN) This allowed the reactivity of the blend to be adjusted by altering the relative proportions of its constituents without significantly impacting the volatility of the blend as a whole Care was also taken with respect to the chemical makeup of the blending components to ensure similar reaction chemistry was also achieved (e.g., similar stoichiometry, oxidation pathways, etc.) We have discussed our metric for reactivity but have not yet established our basis for the comparison of volatility Volatility is 197 a complex property, and it is difficult to fully quantify, but it can be thought of as the thermal energy required to vaporize a fuel Vapor pressure, boiling point, enthalpy of vaporization, and heat capacity all influence evaporation, and so are all components of volatility Generally for fuels however, volatility is used to refer to the temperature dependence of certain vapor-liquid equilibria (e.g boiling point, flash point, etc.) So, for the purposes of this study, the volatility of a fuel will be defined by its distillation curve as measured by test method ASTM D-86 [35] The distillation curve provides an approximate range of temperatures over which a sample of fuel is vaporized, not a single number, so direct comparison is not straightforward However, the use of binary blends whose components have very similar boiling points facilitates this comparison by flattening the distillation curve to a near constant value PRFs are a natural choice for this application, due to their structural similarity, availability, and the fact they are well studied in the literature In addition, their volatility is nearly identical, as shown in Table 1, meaning they can be blended in any proportion without affecting the distillation curve of the blend This volatility is significantly higher than that of even the most volatile portion of a typical middle-distillate fuel, which can be seen in Fig Finally, the constituent species of PRF-based blends, n-heptane and isooctane, are reported to have CN’s of 56 and 12.0–17.5 respectively [37] (as determined by ASTM D-613), which offers a reactivity range of practical interest to modern CIDI engines An appropriate counterpart for the PRF-based fuels will have similarly matched chemical composition but much lower volatility, ideally in the range of currently available diesel-like fuel Farnesane (FAR) or 2,6,10-trimethyldodecane, was selected as the high reactivity component of the low volatility binary blend; 2,2,4,4,6, 8,8-heptamethylnonane (HMN) was selected as the low reactivity component Both are saturated alkanes which differ primarily in their respective degrees of branching Both are also readily available, and reasonably well studied with respect to combustion processes; HMN is used as a primary reference fuel in ASTM D-613 and farnesane is used as a bio-derived diesel/jet fuel substitute [2,4,26,28,48] These species, like the PRFs, offer an approximately matched volatility and a comparable range of reactivities (from CN 58 for pure farnesane [26] to CN 15 for pure HMN [28]), however, as their higher molecular weight suggests, their volatilities are significantly lower and are within the range of typical diesel fuels The difference in distillation temperatures between PRF and FAR-HMNbased fuel blends (shown in Fig 1) is uniform and approximately 150 °C, indicating the magnitude of the volatility difference achieved Utilizing binary components of approximately matched boiling point provides fuels with a narrow boiling ranges In contrast, conventional diesel fuels, by virtue of their diverse chemical makeup, provide wide boiling ranges (typically >100 °C) The difference, for example, between a standard #2 diesel and the pure species blends of PRF and FAR-HMN is readily apparent in Fig The width of the boiling range should also be taken into account in an assessment of the volatility of a fuel, and so its effect was also studied Secondary reference fuels (SRF) for CN testing were selected to provide a third fuel blend which offered diesel-like volatility with respect to boiling range The SRFs are not pure chemical species, but instead consist of compositionally diverse petroleum distillates This allows SRF-based blends to vary in reactivity between CN 75.2 and 19.4 while maintaining a wide boiling range Also, the SRF fuel blends are directly correlated to CN (as they are themselves CN reference fuels) which offers a direct comparison to the CN test for the pure component blends Given the CN range of the selected pure species components, reactivity targets: CN 35, 45, and 55 were selected and appropriate splash blends of PRF, SRF, and FAR-HMN were produced for each PRF blending was done according to the ON-to-CN correlation of Kalghatgi [49] 198 M Groendyk, D Rothamer / Fuel 194 (2017) 195–210 Table Summary of fuel properties for the blending components utilized [36,37,26,38–42,28,43–47] Property Fuel component CN [–] q [kg/m3] NBP [°C] m [cSt] (Temp [°C]) LHV [MJ/kg] MW [kg/kmol] H/C ratio [–] AFRst [–] DHvap [kJ/kg] Cp;liq @298 K [J/kg K] r [dyne/cm] %Saturates %Olefins %Aromatics y ⁄ n-Heptane Isooctane Farnesane HMN SRF-T SRF-U #2 Diesel 56.0 687⁄ 98.3 0.496 (40) 44.5 100.23 2.28 15.13 359.17 2.24 21.6 100 0.0 0.0 12.0–17.5 691⁄ 99.3 0.547 (50) 44.3 114.26 2.25 15.09 307.19 2.08 20.6 100 0.0 0.0 58.0 770y 252 2.95 (40) 43.6 212.47 2.13 14.93 219.8 2.03 25.2 100 0.0 0.0 15.0 784y 240 3.08 (40) 43.9 226.50 2.13 14.92 231.4 2.03 24.4 100 0.0 0.0 75.2 794⁄ 202–348 2.238 (40) 43.2 197.90à 2.03 14.78 – 2.07 22.7 90.5 1.5 8.0 19.4 783⁄ 152–278 1.145 (40) 43.6 148.70à 1.95 14.67 – 2.04 25.9 76.5 2.6 21.0 45.0 857⁄ 189–345 2.70 (40) 42.1 193.40à 1.78 14.41 – 1.89 – 65.0 5.0 30.0 Average molecular weights determined from D-86 distillation data, see Ref [40] Density data at 293 K Density data at 288 K 350 300 Temperature [ºC] The three properties exhibiting the largest deviation from diesel fuel are viscosity, volatility, and molecular weight Our analysis will show that viscosity does not strongly impact the physical character of the sprays utilized in this study, as they are within the fully atomizing regime Similarly, molecular weight can have no direct influence on the spray on a macroscopic scale, and therefore is considered of secondary importance to the macroscopic physical properties to which it is related, such as liquid density This leaves volatility (with a minimum 48% difference in distillation temperature for PRF-based fuels relative to diesel) as the physical property of largest variation As such, the combustion performance of the fuels selected should provide a clear indication of any volatility-driven phenomena, even in the presence of complicating factors A full summary of relevant physical and chemical properties of the blend components can be found in Table Cert Diesel FAR-HMN PRF CN 35 SRF CN 45 SRF CN 55 SRF 250 200 150 100 2.2 Engine setup 20 40 60 80 100 % Recovered [-] Fig Volatility of representative fuel blends used in this study Note the variation in the SRF curves due to compositional variation and the large difference between PRF and FAR-HMN-based blends FAR-HMN blending was done by volume according to CN weighted average assuming ideal mixing as described in ASTM D-613, and the author’s previous work [5,28] SRF blending was also done by volume, per the manufacturer’s instructions For CN 45, emissions certification #2 diesel fuel (Haltermann 2007 EPA tier II cert diesel) was also tested as a reference case Unfortunately, it is impossible to alter the physical fuel properties completely independently of one another due to the strong correlation between molecular weight and properties like volatility, viscosity, density, and H/C ratio for a given molecular structure Despite careful selection of the blending components, notable differences in other properties of importance were unavoidable The magnitude of the variation in these properties is shown relative to the #2 diesel reference fuel in Fig In addition to the volatility differences: density, heat capacity, surface tension, and viscosity have significant deviations, and their effects on the spray breakup process must be considered in the analysis of the experimental results The engine used in this study was an instrumented singlecylinder version of a General Motors/Fiat JTD 1.9-L light-duty diesel engine In-cylinder pressure data were collected using a Kistler 6125A pressure transducer with a Kistler 5010 charge amplifier A 0.25-degree resolution crank angle encoder affixed to the crankshaft was used to time data acquisition Three hundred cycles of pressure data were acquired at each test condition and used for analysis The dimensions and configuration of the engine are summarized in Table Generally, the configuration was intended to be as similar as possible to a stock engine Fueling was provided by a Bosch CRI 2.2 direct injector equipped with a hole 140-lm tip, pressurized with a commonrail (CR) fuel pump Return fuel from both the CR and the injector bleed was passed through a brazed-plate fuel-to-water heat exchanger and recycled to the high-pressure pump to limit fuel consumption Fuel was supplied to the CR system as needed by a gal stainless steel canister, which was pressurized to 80– 100 kPa (gauge) with nitrogen Fueling rate was metered using a Coriolis mass-flow meter prior to introduction to the CR loop Injection duration was varied for each fuel to compensate for differences in energy density Injected energy was held constant for all fuels tested, however, the range of energy densities among the test fuels was small, so only small adjustments to injection duration were needed An injection pressure of 100 MPa was used for all fuels and conditions studied 199 M Groendyk, D Rothamer / Fuel 194 (2017) 195–210 Table Relevant engine geometry and operating parameters σ 298 Cp (@ ] m /s ν [m /cm] [dyne /m ] ρ [kg ] /kg-k k) [kJ LHV g] [MJ/k [-] AFR st H tio /C [-] mol] CN 35 PRF FAR-HMN SRF [kg/k MW -80 -40 40 Percent Difference Engine geometry Base engine Compression ratio Displacement [L] Bore [mm] Stroke [mm] Intake valve closure Exhaust valve opening Piston bowl type Swirl ratio GM 1.9 L Diesel 16.5:1 0.477 82 90.4 132° 112° Stock, re-entrant 1.5 Injector geometry Number of holes Orifice diameter [lm] Included spray angle Injection timing Injection duration [ms] Engine load [Bar IMEPg] Injected fuel energy [J] Injection pressure [MPa] 140 144° 5° 0.6 6.6–7.6 830 100 (a) ] m /s ν [m m] yne/c σ [d /m ] ρ [kg ] /kg-k k) [kJ Cp (@ g] [MJ/k LHV [-] AFR st H/C MW r -] atio [ mol] CN 45 PRF FAR-HMN SRF [kg/k -80 -40 40 Percent Difference (b) ] m /s ν [m m] yne/c σ [d /m ] ρ [kg ] /kg-k k) [kJ Cp (@ g] [MJ/k LHV [-] AFR st H MW tio [ /C -] mol] CN 55 PRF FAR-HMN SRF Fuel blends were run systematically by blend type and pseudorandomly by CN The fuel system was designed to minimize trapped volume to facilitate drainage and purging when fuels were exchanged Great care was taken to minimize fuel crosscontamination by utilizing a 2-stage purge process First the lowpressure side of the fuel system (return fuel heat exchanger, fuel reservoir, and CR pump lines) are fully drained of fuel and purged with N2 Second, after the reservoir is charged with the new fuel blend, the high-pressure side (CR, injector, high pressure fuel lines) return lines are diverted from returning to the low-pressure side, and the CR pump is jogged This runs fuel from the low pressure side, through the high-pressure side and out of the system, where it is collected and its volume is measured Once 2 the trapped volume of the high-pressure side has been collected, the system is closed by returning the high-pressure side return lines to their original flow pattern Dry intake air was supplied to the engine through a surge tank from the dedicated building compressed-air line Intake temperature was maintained using electronically controlled heaters, and air mass flow rate was controlled using choked-flow orifices The exhaust back pressure was maintained at 10 kPa below intake pressure for all conditions investigated This was done to minimize the amount of exhaust residuals in-cylinder This also allowed a wider range of in-cylinder temperatures at start of injection (SOI) to be achieved Exhaust gas emissions measurements were performed with a Horiba 5-gas emissions bench, Horiba flame ionization detector (FID) hydrocarbon analyzer, and an AVL-415 smoke meter Together the emissions bench and the FID measured the concentrations of CO, CO2, O2, NOx, and unburned hydrocarbons Emissions data were very similar for the different fuels except for the expected differences in CO2 concentrations due to the different H/C ratios [5] Only soot emissions showed significant differences and will be reported A schematic showing the major components of the air and fuel systems is shown in Fig [kg/k 2.3 Operating conditions -80 -40 40 Percent Difference (c) Fig Comparison of relevant physical properties of PRF, FAR-HMN, and SRF-based fuel blends relative to the #2 diesel reference fuel Comparison is made for binary mixtures of different reactivities (a) CN 35 (b) CN 45 (c) CN 55 Test conditions for the study were selected to provide a wide cross section of in-cylinder conditions that might be encountered in automotive or heavy-duty diesel engines Temperature and density of the cylinder contents at SOI characterize the operating condition, as these are the parameters which govern spray breakup and ignition phenomena Of particular interest to this study are 200 M Groendyk, D Rothamer / Fuel 194 (2017) 195–210 Fig Schematic diagram of the engine laboratory See Table for the relevant specifications 35 1200 30 1150 200 1100 1050 25 1000 150 20 950 900 15 SOI Temperature [K] 10 300 350 2.4 Data analysis techniques 850 100 Intake Pressure [kPa] 250 SOI Density [kg/m ] performance Intake air temperature and pressure were varied independently to achieve temperatures and densities in the range of 980–1120 K and 14.6–29 kg/m3, respectively It was decided to test only the extremes of the possible conditions to assess the overall magnitude of any global trends This sparse testing matrix consisted of points, at the extreme combinations of temperature and density, and one intermediate condition to provide information on the shape of the trend between them (see Fig and Table 3) In all cases, the engine was run at a speed of 2100 RPM and was fired for at least h prior to any data collection to ensure that the engine had reached a stable thermal condition, and that any residual fuel not eliminated during purging had been flushed out of the fuel system A portion of the conditions matrix was repeated multiple times to determine data repeatability PRF and SRF-based fuels were inexpensive enough to allow for and measurements, respectively, taken on different days at each condition HMN was prohibitively expensive, even at the modest fuel flow rate required of the light-duty engine; FAR-HMN data presented in the this paper represent only a single trial for each test condition The repeatability for the other fuels at equivalent conditions provides insight into the day-to-day variability and give an approximate measure of the uncertainty in the FAR-HMN results Injection duration was selected such that ignition occurred prior to end of injection for the lowest CN at Condition (lowest temperature and density) This criterion established the fueling rate for the entirety of the study, as fuel energy was maintained for all conditions and fuels These conditions resulted in a wide range of ignition delay (from 200 to 800 ls), as well as a variety of pressure and heat release characteristics This is exemplified in the pressure and heat release results shown in Fig 5(a) for the SRF CN 45 blend at all operating conditions, and in Fig 5(b) for SRF blends of all reactivities at Condition Despite their differences, Conditions and resulted in almost identical ignition delay and heat release, making their results difficult to distinguish in the following analysis 400 450 Intake Temperature [K] Fig A map of the operating conditions utilized in this study The color scale indicates temperature at SOI in Kelvin and the contours indicate density at SOI in kg/m3 The axes correspond to the intake conditions used to achieve them (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) operating conditions resulting in low in-cylinder temperature and density, where the volatility property differences are anticipated to have the largest influence on the combustion process and engine Ignition delay (ID) is one of the characteristics of central interest for this study However, determining the ignition delay from incylinder pressure measurements is not always straightforward Numerous ID determination techniques have been described in the literature, each with merits and drawbacks For the present study, the ID determination technique of Groendyk and Rothamer [5] is used This approach determines the start of combustion (SOC) from raw in-cylinder pressure data by locating the onset of rapid pressure rise associated with pre-mixed burn Ignition delay is then derived from the difference between SOI and SOC timing Readers are directed to the work of Murphy and Rothamer [21,22] for a more detailed discussion of ID determination in engines In addition to ID data, cylinder pressure and heat release rate (HRR) data will be presented and discussed Pressure data are presented in both the raw as acquired state and with low-pass filter- Table Running conditions Condition Intake parameters Est SOI conditions Temperature [K] Pressure [kPa] (absolute) Temperature [K] q [kg/m3] 305 346 400 425 454 112 200 161 113 212 980 987 1060 1100 1120 16.1 29.0 21.3 14.7 27.1 201 M Groendyk, D Rothamer / Fuel 194 (2017) 195–210 50 400 Pressure [Bar] 100 350 300 250 80 200 60 150 40 100 SOI 20 50 SRF: CN45 0 -10 10 20 Crank Angle [ º ] 30 40 Pressure [Bar] 80 160 140 120 60 100 80 40 60 40 SOI SRF: Condition3 20 0 10 20 Crank Angle [ º ] 30 Apparent Heat Release Rate [J/CAD] 180 35 45 55 -10 Order Fit 30 46 20 44 10 42 40 -10 -4 -2 Crank Angle [ º ] CN 20 th 48 -6 200 100 15 Pressure [Bar] 40 Raw Data Apparent Heat Release Rate [J/CAD] 120 Apparent Heat Release Rate [J/CAD] Test Condition 40 Fig (a) CN 45 SRF blend pressure and HRR data for all test conditions, note the closely matched HRR for Conditions and (b) Pressure and HRR data from Condition for SRF blends of each CN selected for testing ing The filter was designed to minimize passband ripple by utilizing a wide transition band with Gaussian roll off Passband and stopband edges were fixed at 1650 Hz and 7500 Hz respectively, and a stopband attenuation of 10 dB was specified for all pressure data that was filtered However, analysis was generally carried out on raw pressure data to avoid the inclusion of any filter artifacts HRR was calculated from the pressure data (both raw and filtered) and engine geometry using the 1st law balance given by Gatowski et al [50] To detect any differences in mixture preparation as the result of physical differences between the fuels, close scrutiny was applied to pre-ignition phase (i.e., the period between SOI and main ignition) of the cycle This proves problematic for the analysis of heat release data, due to the low levels of HRR in this region, and the susceptibility of HRR calculation to noise and artifacts introduced by conventional noise-reduction techniques [5,21] However, the specificity of region of interest allows for the application of an Fig HRR data for CN 35 PRF at Condition calculated using both raw pressure data, and pressure data that has been conditioned using the polynomial fit noisereduction technique alternative method of noise reduction A high-order polynomial is fit to a small section of pressure data, offering good agreement with measurements and a noise free derivative for the calculation of heat release Provided the resolution of the pressure data is high and the fitting window is carefully selected to eliminate the ringing that follows ignition, the technique is quite effective and provides a clear HRR trace for comparison free of any filtering artifacts, as shown in Fig This technique was applied to all HRR comparisons in the pre-ignition region Results and discussion 3.1 Ignition delay Average ignition delays for each condition were determined from the mean of the ignition delays of the individual cycle pressure data Fig 7(a) shows the average ignition delays for all fuels at the conditions tested as a function of fuel blend CN The ignition delay results show the expected sensitivity to cylinder temperature and density at SOI [5,51] ID was observed to decrease with increasing CN and the sensitivity of ID to CN at reduced CN is increased for all conditions This is most apparent at conditions conducive to longer ID As mentioned earlier, the similarity of the ignition behavior observed at Conditions and makes them difficult to discern in Fig 7(a) When the results are viewed individually by condition (Figs 7(b) and 8) the agreement between the fuels can be seen more clearly Results for the CN 45 binary fuel blends are also compared to the #2 diesel reference fuel at each condition Good agreement between the diesel and the blended fuels provides a check on the SRF composition (which serves as a known reactivity reference), and the ignition delay determination technique Uncertainty in the ID measurements are presented based on 95% confidence in the mean ID determined from repeated trials at identical conditions The maximum uncertainty occurred at Condition and was 51 ls The average uncertainty in the ID data was 15.9 ls Uncertainty in individual measurements was also investigated, but found to be small (2 ls) relative to the variability in repeated trials Uncertainty is not presented for FAR-HMN data, as no repeat trials were performed, as previously discussed 202 M Groendyk, D Rothamer / Fuel 194 (2017) 195–210 900 Condition Condition Condition Condition Condition FAR-HMN PRF SRF 800 600 800 Ignition Delay [μs] Ignition Delay [μs] Condition1 Condition4 400 Conditions 2&3 Conditions FAR-HMN PRF SRF Cert Diesel Condition 700 600 200 500 35 40 45 Cetane Number [-] 50 55 35 40 45 50 Cetane Number [-] 55 Fig (a) Ignition delay for all fuels and conditions tested Error bars shown on SRF and PRF data are 95% confidence intervals based on and repeated measurements respectively (b) Detailed view of ID for all fuels at Condition 1, individual data points are shown for each replicated run, and the dashed lines connect the averages of the replicated data The shortest ignition delays were observed at Condition 5, which had both the highest in-cylinder temperature and density of all the conditions studied Ignition delays for Condition also showed the least sensitivity to CN, as was expected Energy transfer to the fuel jet under these conditions is sufficiently rapid that even fuels of widely varying activation energy will overcome their energy barriers within a comparable interval following injection This leads to the characteristic reduction in sensitivity to CN In contrast, Condition shows the longest ignition delays due to its comparatively cool, low density, cylinder conditions at the SOI Sensitivity of ID to CN is much greater, varying by more than 200 ls from CN 35 to 55, compared to the roughly 50 ls variation seen at Condition over an identical CN range Under these conditions differences between the fuel blends become significant for certain CNs It was observed that the PRF-based fuel blends for both CN 35 and 45 experienced shorter ignition delay compared to both FAR-HMN and SRF-based blends of comparable reactivity, while at CN 55, ignition delay for the PRF blend was comparable to the SRF-based blend FAR-HMN-based blends showed a consistently longer ID compared to the PRF-based blends at Condition Interestingly, agreement between the SRF and FAR-HMN-based blends trends opposite of the SRF and PRF-based fuel blends, and the best agreement is found at the lowest CN The reasons for this behavior are not clear at present For all fuel reactivities tested PRF-based blends ignited approximately 50 ls in advance of FARHMN-based blends at Condition 1, which is a small but potentially statistically significant difference With the exception of Condition 1, no statistically significant differences in ID were observed for any of the fuel blends at the conditions tested 3.2 Pressure and heat release Cylinder pressure and apparent heat release rate data are consistent with the ignition delay results The major features of the pre-ignition, ignition, and mixing controlled combustion events are well matched at each condition at a given cetane number, regardless of fuel blend Fig 9(a) shows pressure data for the CN 45 fuels at Condition as an example Onset of rapid pressure rise is well matched between all fuels, as is peak pressure, ringing intensity, and chamber pressure through expansion Calculated heat release rates for this same condition show a corresponding degree of similarity, and, in addition, indicate that cylinder cooling during the pre-ignition phase due to fuel vaporization and heating is comparable despite the increased volatility of the PRF blend Only small differences in HRR and pressure can be identified for any given condition, but some are present Most notably, there is a slight increase in the rate of pressure rise for the PRF blends compared to either the SRF or FAR-HMN blends for most of the conditions tested This increase is reflected in the HRR as a slight increase in peak HRR during the premixed-burn spike This effect is not observed at Condition for fuels of CN 35 however, as shown in Fig 9(b) As previously indicated by the calculated ignition delay, Condition resulted in PRF-based fuels igniting in advance of both FARHMN and SRF-based blends of CN 35 Inspection of the pressure and HRR data for this condition (shown in Fig 9(b)) supports this conclusion, clearly indicating the advanced onset of rapid pressure rise for the PRF-based blend relative to the other two However, this level of analysis offers no additional insight into how this advanced ignition came about Fig 10 offers a closer inspection of the HRR data for Condition 1, focused specifically on the preignition region Here again though, even under close scrutiny, HRR is comparable for all fuel blends regardless of CN The lack of distinction among the fuel blends at a given condition is interesting given the expected influence of the physical properties on the spray breakup process and the large differences in fuel volatility Therefore, a detailed analysis of the physical processes relevant to ignition was carried out for the specific conditions used in this study to shed light on this interesting result 203 M Groendyk, D Rothamer / Fuel 194 (2017) 195–210 500 450 FAR-HMN PRF SRF Cert Diesel 450 400 Condition 400 350 300 Ignition Delay [μs] Ignition Delay [μs] 500 200 200 55 35 600 40 45 50 Cetane Number [-] 55 400 550 FAR-HMN PRF SRF Cert Diesel 350 FAR-HMN PRF SRF Cert Diesel 500 Condition 300 Condition 450 400 Ignition Delay [μs] Ignition Delay [μs] 300 250 40 45 50 Cetane Number [-] Condition 350 250 35 FAR-HMN PRF SRF Cert Diesel 250 200 350 150 300 100 35 40 45 50 Cetane Number [-] 55 35 40 45 50 Cetane Number [-] 55 Fig Ignition delay for all fuels and conditions tested Individual data points are shown for each replicated run, and the dashed lines connect the average of the replicated data 3.2.1 Physical considerations Based on the computational models of Ra et al [12], Kim et al [13], and the jet entrainment model of Naber and Siebers [8], liquid density, heat capacity, enthalpy of vaporization, volatility, viscosity, and surface tension can be expected to impact the spray and combustion in the following ways:  Changes in volatility will lead to changes in the liquid penetration length  Changes in viscosity and surface tension will lead to changes in spray breakup  Changes in liquid density will lead to changes in air entrainment and mixture preparation  Changes in heat capacity and heat of vaporization will lead to changes in the axial temperature distribution within the jet Based on the observations made in the current study (that fuels having substantial differences in these properties but matched chemical ignition delay result in the same ignition delay and combustion performance) these effects must either be small in magnitude, or have offsetting effects Each of these potential impacts are discussed in turn Liquid Length: Using Siebers [11] liquid length scaling law, and available thermodynamic data, the liquid length of the pure species blends was estimated for each condition Liquid lengths are summarized in Table and are depicted relative to the piston bowl in Fig 11(b) to allow direct comparison between PRF and FARHMN-based fuels Complete thermodynamic data for farnesane were unavailable, so the properties of pure HMN were used instead for these calculations As expected, the lower volatility FAR-HMN-based fuel blends are consistently calculated to have a significantly longer liquid M Groendyk, D Rothamer / Fuel 194 (2017) 195–210 120 100 60 50 40 20 0 -10 -5 10 Crank Angle [ º ] 15 20 PRF FAR-HMN SRF 80 400 300 60 SOI 200 40 100 20 0 -10 -5 10 Crank Angle [ º ] 15 Apparent Heat Release Rate [J/CAD] SOI 80 Apparent Heat Release Rate [J/CAD] 100 Pressure [Bar] 150 PRF FAR-HMN SRF Diesel Pressure [Bar] 204 20 Fig (a) Pressure data for the CN 45 blend of each fuel at Condition 3, representative of the global combustion trends Note that pressure rise, phasing, and magnitude of peak pressure are well matched for all three fuels Note also the slight increase in pressure rise rate for PRF relative to the other fuels (b) Pressure data for the CN 35 blend of each fuel at Condition 1, the longest observed ID Note the slightly advanced onset of rapid pressure rise for PRF in agreement with the calculated ignition delay penetration length compared to the PRF-based fuel blends The difference is greatest at Condition (55%) and least at Condition (48%) but appears to be relatively insensitive to the operating condition The overall magnitude of the liquid penetration length correlates strongly with ambient density, with the longest penetrations predicted at Condition and the shortest at Condition While this without question represents a substantial difference between the PRF and FAR-HMN-based fuels with regards to spray formation, Kook and Pickett, and Dumitrescu et al [16,52] have shown that liquid length itself does not impact ignition or combustion directly Liquid penetration only becomes relevant to combustion in the event of wall impingement or piston bowl wetting Even at Condition the maximum liquid penetration of FAR-HMN-based fuel is calculated to be well short of impingement These differences then, while significant, are not expected to have influenced ignition or combustion for the conditions used in this study Spray Breakup: Spray breakup regimes are generally delineated by dimensionless parameters which characterize the relative influence of fluid-dynamic forces within the jet: the Weber number, defined in reference to the ambient fluid into which the fuel is injected (We) and the Ohnesorge number (Oh) For the purposes of a fuel injection event, the We and Oh can be defined as: We ¼ Oh ¼ qa U d r pffiffiffiffiffiffiffi We Re ð1Þ ð2Þ where qa is the ambient air density, U is the injection velocity of the fuel, d is the mean droplet diameter (which was estimated to be on the order of 10 lm), r is the surface tension of the fuel, and Re is the Reynolds number Injection velocity was estimated using the Bernoulli equation assuming a pressure drop of 100 MPa through the injector For all conditions the We was of order 103 and the Oh was of order 101 which indicates a spray well within the atomization regime [7] Simultaneously high We and low Oh indicate that inertial forces dominate both surface tension and viscous forces in the spray Therefore, the effect of changes in r or m will not significantly alter the character of the fuel sprays used in this study, and will have limited influence of combustion as a result Experimental evidence of this is provided by Hiroyasu and Arai [53], who found that jet spreading angle is insensitive to fuel viscosity in the atomization regime It is reasonable then that even the large differences in viscosity between PRF, FAR-HMN, and SRF-based fuels are not observed to have any significant impact on ignition or combustion Entrainment: For an atomizing spray the jet spreading angle has been shown to be dependent only on the liquid to ambient density ratio [7,9] Naber and Siebers [8] have shown that the spreading angle, together with principles of conservation of mass and momentum, is sufficient to calculate the rate at which air is entrained in a steady, non-vaporizing jet as it propagates Momentum flux was equivalent for all the fuel jets in this study, as it is dependent on injection pressure only, so any variation in jet spreading angle among the test blends directly correlates to variation in air mass entrainment This correlation is complicated somewhat by the phase change of the fuel which can affect the jet boundaries independently of ambient gas entrainment, however, it still offers a reasonable metric for mixture preparation The variation in fuel density between the blends utilized in this study is not especially large (PRF-based blends are on average 10% less dense than FAR-HMN-based counterparts) but is potentially significant In addition to liquid penetration length, Siebers [9] also provides a correlation for determining jet spreading angle as a function of fuel density and ambient density, the results of which are also summarized in Table As with liquid penetration length, the difference in spreading angle between PRF and FAR-HMNbased fuel blends is consistent (0.34° on average) with the PRFbased fuel blends having the larger spreading angle Unlike liquid penetration however, the magnitude of the difference is quite small (2%) To assess the impact of this difference on charge preparation, the cross sectionally-averaged equivalence ratio was calculated for each fuel as a function of axial position An example case is shown in Fig 11(a) for Condition 205 M Groendyk, D Rothamer / Fuel 194 (2017) 195–210 40 Pressure [Bar] 30 46 20 44 10 42 40 -10 -6 -4 -2 40 PRF FAR-HMN SRF 48 30 46 20 44 10 42 40 -10 -6 -4 Crank Angle [ º ] 40 46 20 44 10 42 40 -10 48 30 46 20 44 10 42 40 -10 -6 -4 40 46 20 44 10 42 40 -10 Crank Angle [ º ] 6 40 PRF FAR-HMN SRF 48 Pressure [Bar] Pressure [Bar] 30 2 30 46 20 44 10 42 40 Apparent Heat Release Rate [J/CAD] 48 0 50 Apparent Heat Release Rate [J/CAD] PRF FAR-HMN SRF -2 -2 Crank Angle [ º ] 50 -4 40 Crank Angle [ º ] -6 PRF FAR-HMN SRF Pressure [Bar] Pressure [Bar] 30 Apparent Heat Release Rate [J/CAD] 48 -2 50 Apparent Heat Release Rate [J/CAD] PRF FAR-HMN SRF -4 -2 Crank Angle [ º ] 50 -6 Apparent Heat Release Rate [J/CAD] 48 50 Apparent Heat Release Rate [J/CAD] PRF FAR-HMN SRF Pressure [Bar] 50 -10 -6 -4 -2 Crank Angle [ º ] Fig 10 [TOP] CN 35 Pressure and HRR data for all fuel blends at Condition shown (a) raw (b) conditioned using polynomial fit [MIDDLE] CN 45 pressure and HRR data for all fuel blends at Condition shown (c) raw (d) conditioned using polynomial fit [BOTTOM] CN 55 pressure and HRR data for all fuel blends at Condition shown (e) raw (f) conditioned using polynomial fit 206 M Groendyk, D Rothamer / Fuel 194 (2017) 195–210 that the majority of phase transition occurs near the fuel’s critical point, where vaporization enthalpy approaches zero Ignition chemistry is strongly dependent on temperature, so any variation in local temperature is likely to impact the ignition delay for sprays that are otherwise equivalent A comprehensive comparison of heat capacity for the fuels used in this study is difficult, due to the dependence of heat capacity on temperature, and to the limited availability of thermodynamic data No data for liquid heat capacity could be found for farnesane at any temperature, so the group contribution estimation technique of Ru˚zˇicˇka and Domalski [38] was applied The temperature range over which this technique is appropriate is narrow, so comparison between fuels could only be made near room temperature While this is not ideal, the temperature dependence of heat capacity in non-cyclic saturated hydrocarbons is relatively uniform from species to species, and the value at room temperature should characterize fuels accurately on a relative basis Similar to farnesane, the multicomponent fuels used in this study did not have thermodynamic data available, so heat capacity was also estimated Due to their unknown molecular structure, group contribution estimation was not possible, so a correlation was used to relate the distillation curve to heat capacity for both the SRFs and the diesel fuel [40] The PRF-based blends used in this study were found to have liquid heat capacity 6–10% greater than that of their FAR-HMNbased counterparts SRF-based blends are also of higher heat capacity compared to FAR-HMN-based blends, but only by 1– 1.5% A 10% increase in fuel heat capacity is functionally identical to a 10% reduction in energy entrainment with respect to local jet temperature, so it is reasonable to expect that the temperature of a PRF jet will be cooler at any axial location than an equivalent FAR-HMN or SRF jet This level of temperature reduction is potentially relevant for ignition chemistry however, no major differences are observed in either the ignition delay, or the pressure and HRR data It is important to note that PRF and FAR-HMN jets are not equivalent, as demonstrated in the discussion of entrainment PRF-based blends are expected to experience on average 8% greater Table Summary of calculated spray parameters Condition Fuel Liquid length [mm] Jet spread angle h [°] FAR-HMN PRF 17.9 10.1 14.2 14.5 FAR-HMN PRF 13.4 7.8 15.8 16.2 FAR-HMN PRF 14.1 8.4 15.0 15.3 FAR-HMN PRF 16.4 9.7 14.0 14.3 FAR-HMN PRF 11.8 7.2 15.6 16.0 The PRF-based blend is expected to consistently have a slightly lower local equivalence ratio compared to either FAR-HMN or SRF-based blends, however the magnitude of the difference is again small (