Acta Polytechnica doi:10.14311/AP.2017.57.0038 Acta Polytechnica 57(1):38–48, 2017 © Czech Technical University in Prague, 2017 available online at http://ojs.cvut.cz/ojs/index.php/ap PRELIMINARY STUDY ON COMBUSTION AND OVERALL PARAMETERS OF SYNGAS FUEL MIXTURES FOR SPARK IGNITION COMBUSTION ENGINE Rastislav Tomana, b, ∗, Marián Polónia, Andrej Chríbika a Institute of Transport Technology and Design, Faculty of Mechanical Engineering, Slovak University of Technology in Bratislava, Námestie Slobody 17, 812 31 Bratislava, Slovak Republic b Department of Automotive, Combustion Engine and Railway Engineering, Faculty of Mechanical Engineering, Czech Technical University in Prague, Technická 4, 166 07 Prague 6, Czech Republic ∗ corresponding author: rastislav.toman@fs.cvut.cz Abstract. This paper presents a numerical study on a group of alternative gaseous fuels – syngases, and their use in the spark-ignition internal combustion engine Lombardini LGW 702. These syngas fuel mixtures consist mainly of hydrogen and carbon monoxide, together with inert gases. An understanding of the impact of the syngas composition on the nature of the combustion process is essential for the improvement of the thermal efficiency of syngas-fuelled engines. The paper focuses on six different syngas mixtures with natural gas as a reference. The introduction of the paper goes through some recent trends in the field of the alternative gaseous fuels, followed by a discussion of the objectives of our work, together with the selection of mixtures. Important part of the paper is dedicated to the experimental and above all to the numerical methods. Two different simulation models are showcased: the single-cylinder ‘closed-volume’ combustion analysis model and the full-scale LGW 702 model; all prepared and tuned with the GT-Power software. Steady-state engine measurements are followed by the combustion analysis, which is undertaken to obtain the burn rate profiles. The burn rate profiles, in the form of the Vibe formula, are than inserted into the in-house developed empirical combustion model based on Csallner-Woschni recalculation formulas. Its development is described in the scope as well. The full-scale LGW 702 simulation model, together with this empirical combustion model, is used for the evaluation of engine overall performance parameters, running on gaseous fuel mixtures. The analysis was done on engine full load and stoichiometric mixture conditions only. Keywords: spark-ignition piston combustion engine; alternative gaseous fuel; syngas; combustion modelling. 1. Introduction Syngas or a ‘synthesis gas’ is a summarizing name for the gaseous fuel mixture containing combustible and relatively high inert gas content. The main com- bustible components of these fuels are always hydrogen (H2) and carbon monoxide (CO). Methane (CH4), car- bon dioxide (CO2) or nitrogen (N2) and other minor hydrocarbon constituents (ethane, propane, butane etc.) can then constitute the rest of the fuel mixture. The syngas is produced from various feedstock, for example, coal, biomass, organic waste, tar or natural gas, by a variety of production processes such as gasi- fication or pyrolysis [1–4]. The exact composition of a syngas is then given by its production process and feedstock used. Resulting syngas mixture is a solid alternative fuel in the field of power generation by cogeneration units. The topic of alternative gaseous fuels for the use in cogeneration units is of growing interest in recent years. An extensive research has been done to make the spark-ignition combustion engines fuelled by nat- ural gas (NG) competitive to diesel engines, with a special focus on exhaust gas emissions. Experimental studies dealing with the effect of exact NG composi- tion, lean burn conditions, different EGR levels, and the use of 3-way catalytic converters on the overall engine parameters and emissions were presented [5–7]. More recent experimental studies focus also on the combustion characteristics and emission effects of dif- ferent dilution gases on the NG engine under constant- conditions (load, speed and spark advance) stoichio- metric operation [8]. Nevertheless, the general trend is to use the experiments for a model validation pur- pose, and then to employ the model for a numerical investigation and optimization of ICE running in the cogeneration unit [9]. Regarding the syngas mixtures, the impact of the syngas composition on the com- bustion characteristics in the ICE [10], or in more detail on the laminar and turbulent flow velocities is prominent [11]. Apart from these experimental and numerical studies, the authors in [12] presented a com- bined experimental, numerical, and theoretical study on the engine performance running on four gaseous mixtures: two different syngas mixtures with compari- son to the NG and hydrogen. After the measurements, 38 http://dx.doi.org/10.14311/AP.2017.57.0038 http://ojs.cvut.cz/ojs/index.php/ap vol. 57 no. 1/2017 Preliminary Study on Combustion and Parameters of Syngas Fuel Mixtures Parameter Unit NG SG6 SG18 SG21 SG22 SG23 SG31 CH4 [vol%] 96.033 20 0 0 10 0 10 C2H6 [vol%] 2.036 0 0 0 0 0 0 C3H8 [vol%] 0.599 0 0 0 0 0 0 nC4H10 [vol%] 0.196 0 0 0 0 0 0 nC5H12 [vol%] 0.001 0 0 0 0 0 0 H2 [vol%] 0 20 10 20 20 30 25 CO [vol%] 0 30 30 20 10 10 20 CO2 [vol%] 0.283 25 10 10 10 10 20 N2 [vol%] 0.794 5 50 50 50 50 25 LHV [kJ/kg] 48 796 12 027 4 043 4 298 6 762 4 620 8 385 A/F Ratio [kg/kg] 16.96 3.66 1.01 1.12 2.08 1.26 2.49 Molar mass [kg/kmol] 16.74 24.42 27.01 24.41 23.22 21.81 23.52 Density [kg/m3] 0.696 0.998 1.104 0.998 0.949 0.892 0.961 H/C [1] 3.91 1.6 0.5 1.33 2.67 3 1.8 O/C [1] 0.01 1.07 1.25 1.33 1 1.5 1.2 Table 2. Basic physical and chemical properties of the used fuel mixtures. Unit Group 1 Group 2 Group 3 CH4 [vol%] 0–50 0–20 0–20 H2 [vol%] 10–50 10–30 5–45 CO [vol%] 20–40 10–30 10–50 CO2 [vol%] 25 10 20 N2 [vol%] 5 50 25 Table 1. Range of syngas groups composed with various inert gas concentrations (in vol%). combustion analysis was undertaken, with crank angle resolved in-cylinder turbulence and flame propagation plotted onto so-called ‘Bradley diagram’, to explain the variation of the engine performance parameters. Our own previous work in the facility of Slovak University of Technology in Bratislava was mostly fo- cused on the experimental research of NG based fuels, such as NG mixtures with hydrogen [13] or CO2 [14] – H2NG and CO2NG. However, here presented litera- ture on alternative gaseous fuels show many opportu- nities for a simulation too. The principal opportunity is that a well-tuned numerical model allows for the extensive optimization of ICE parameters, for exam- ple, ignition or valve timing, as it was the case in [9]. A numerical model may also guide the experiments. Other possibility is to employ the advanced predic- tive combustion models to model and study the ICE combustion process for different fuel mixtures [12]. Finally, the simulation tools also shorten the develop- ment experimental loops and subsequently reduce the time and costs of the research and real-life application of alternative fuels into the cogeneration units. Reca- pitulating all discussed benefits, we have decided to enhance our research activities with simulation tools. There are two main objectives of this paper. First objective is the preliminary analysis of the main com- bustion parameters for the different syngas mixtures, with a GT-Power combustion analysis model. This combustion analysis model processes the measured in-cylinder pressure data together with other impor- tant measured quantities. The second objective of this paper is to get an estimation of steady-state engine performance parameters powered by different stoichio- metric syngas mixtures under full load and different engine speeds (1200 to 2500 RPM sweep) by simu- lation. To meet this second objective a GT-Power full-scale simulation model of Lombardini LGW 702 engine was prepared and tuned, with a proper in-house combustion model. 1.1. Selected fuel mixtures In the early phases of our research on syngas mixtures, we determined thirty-five various syngas mixtures, based on the knowledge of real-life syngas produced industrially, with the aim to cover the widest range of possible mixture compositions. For a better classifi- cation of these compositions, the mixtures are sorted into three different groups by the inert gas concentra- tion: with 30, 45, and 60 volume percent of the inert gas. The rest are combustible components (Table 1). Group 1 contains seventeen various syngas mixtures with relatively small amount of inert gases. Group 2 contains only six mixtures and Group 3 twelve differ- ent mixtures. For the preliminary analysis, only six of the total number of thirty-five various syngas mixtures are pre- sented in this paper: one syngas from Group 1 and one from a Group 3 as a representative sample. From the Group 1 it is syngas 6 (further labelled SG6) and from Group 3 the SG31. In this paper, a Group 2 syngas mixtures are of a special interest, due to their very high inert gas content. Therefore, four of them are covered: the SG18, the SG21, the SG22, and the SG23. The basic physical and chemical properties of these six fuel mixtures are summarised in Table 2. In 39 R. Toman, M. Polóni, A. Chríbik Acta Polytechnica LGW 702 Bore 76 mm Stroke 77.6 mm Cylinders 2; in line Engine displacement 704 cm3 Compression ratio 12 : 01 : 00 Valves per cylinder 2 IVO 340 °CA IVC 599 °CA EVO 147 °CA EVC 386 °CA Table 3. Tested engine main dimensions. our experimental and simulation study of syngas mix- tures, we use the NG distributed in Slovak Republic (96 % of methane) as a reference fuel mixture, with the real composition used during the measurements. A high inert gas content in syngas mixtures lowers the LHV, which should then lead to lower maximum pressures, temperatures, lower overall performance parameters, and high fuel consumption. Inert gases slow down the burning process, whereas hydrogen does the opposite. Thus, different burn duration is expected compared to the NG. The best performance properties should be reached by the SG6, because of its high LHV compared to the other syngas mixtures. 2. Experimental methods All the experimental data were acquired on a steady- state test bench in the facility of STU Bratislava on a small, two-cylinder, water-cooled, naturally-aspirated, spark-ignition engine Lombardini LGW 702 [15]. This engine was rebuilt from the compression ignition Lom- bardini variant LDW 702. Thus, it had to be fitted with an ignition system instead of fuel injectors and new lower compression ratio pistons. The combustion chamber has a simple geometry with a flat cylinder head and a centred piston bowl. The main geometri- cal parameters of LGW 702 are listed in Table 3 (the cam timing zero reference point is the firing TDC). A fuel mixture is prepared in a diffuser-type mixer. Our experimental test bench is equipped with an acquisition system measuring engine torque and speed, overall pressures and temperatures, air and fuel flows, together with the exhaust gas composition. The ICE is then equipped with one spark plug integrated piezo- electric pressure transducer, for the in-cylinder pres- sure measurement, together with a crank angle en- coder. Currently, we measure the pressure with a con- stant frequency of 66 kHz and the crank angle encoder only determines the TDC. In the future measurement, a full ‘Three-pressure-analysis’ (TPA) capability will be introduced into the test bench, together with more precise crank angle related pressure measurement. All fuel mixtures in this study were experimentally tested under full load and constant engine speed of 1500 RPM. The spark timing of 27 °CA BTDC and stoichiometric air-to-fuel ratio (λ = 1) were kept con- stant. The latter due to the use of the 3-way catalytic converter. 3. Numerical methods 3.1. Combustion analysis The combustion of fuel mixture in a combustion en- gine is best understood by studying the burn rate profile. A burn rate profile can be obtained from a measured in-cylinder pressure by a so-called ‘reverse run’ simulation method. In a ‘reverse run’ method, the in-cylinder pressure is an input for the analysis; the burn rate is then an output. In a normal simula- tion, the ‘forward run’, it is vice versa. These methods are also implemented in the GT-Power simulation tool. A great advantage of using the GT-Power is that both forward and reverse runs use the same approach of two-zone combustion modelling, without any simpli- fying assumptions on the side of thermodynamics or chemistry [16]. Then in the GT-Power, there are two approaches of how to use the ‘reverse run’ cylinder analysis. The first one: the ‘stand-alone burn rate calculation’ (1Cyl) uses only an in-cylinder measured pressure; together with a few basic cycle averaged results (as volumetric efficiency), engine cylinder geometry, cylinder wall temperatures, heat transfer model, and initial condi- tions. This model is not connected to any upstream or downstream 1D flow components, therefore, these ini- tial conditions represent the trapped conditions at the IVC and need to be estimated from measurement. The second approach is the already mentioned TPA, which also requires measured intake and exhaust pressures. The authors in [17] show, that the TPA approach is more accurate, since there is no need of parameter estimation: the trapped conditions at the IVC are directly simulated from the 1D flow components. Since, in our research facility, the TPA is not avail- able, we use a simple 1Cyl approach and for the initial- ization of the closed-volume cycle we use the trapped quantities from a full-scale engine model. In general, the error in a calculation of the burn rate from the measured in-cylinder pressure is always present. There are potential errors in the in-cylinder pressure measurement, initialization inputs estimation, or also from other sub-models, such as for the in- cylinder heat transfer coefficient calculation. In every combustion analysis run, the GT-Power reports some automatic ‘consistency checks’ that help to identify the source of the error. These ‘consistency checks’ are listed in [16] and we used them during our analysis to compensate the lack of the TPA or inaccuracies in our measurement. The most important of the ‘consistency checks’ is the ’Fuel energy (LHV) Multiplier’ (LHV Multiplier). When the LHV Multiplier value differs from unity, it means that the input energy in the simulation system is different from the needed one to follow the measured in-cylinder pressure curve. 40 vol. 57 no. 1/2017 Preliminary Study on Combustion and Parameters of Syngas Fuel Mixtures 3.2. Combustion modelling One of the objectives of this study is to estimate correctly the engine performance parameters under different engine speeds. But the output of the com- bustion analysis – a burn rate profile – cannot be universally used for different engine speeds, since the profile is deformed substantially with changed oper- ating conditions [18]. Various possibilities of either empirical or phenomenological combustion models can be used inside the GT-Power. Empirical models proposed in [18, 19] are based on an extensive measurement database. The authors used simple recalculation multipliers of significant burn points, dependant on engine speed, load, spark advance, λ, residual gas fraction, intake pressure, and temperature. Other option is the GT-Power’s predic- tive phenomenological turbulent combustion model EngCylCombSITurb (or SITurb), based on [20, 21]. Both the empirical and phenomenological options need a number of accurately measured engine operation points. The advantage of the empirical combustion models is that they are easy to implement, but their downside is that they work well only inside the tuned operating range. The SITurb model is even more de- manding on the side of the tuning process and input data, requiring the swirl and tumble characteristics of the intake valves, together with the complete com- bustion chamber geometry. However, the SITurb can then be used also for the reasonable combustion pre- diction outside of the tuned operating range. This was shown in [22] for the gasoline fuel. The SITurb was likewise successfully used for syngas combustion modelling in [12]. In our study, we have decided to build an empirical combustion model (Empirical model) by combina- tion of empirical Vibe formula model EngCylCombSI- Wiebe [23] xb = 1 − exp−a (α−α0 ∆α ) (1) and recalculation formulas proposed by Csallner and Woschni [24]. Csallner-Woschni formulas incorporate the effect of different engine operating point (engine speed, load, λ, spark-advance, and residual gas frac- tion) on the combustion characteristics. Vibe formula requires three important parameters to be set: Burn Duration (10–90 % MFB), Vibe Shape Exponent m (1), and CA50, so the crank angle at 50 % mass fuel burned (Figure 1: a comparison of three different gas mix- tures at full load, λ = 1 and 1500 RPM for illustra- tion). In our Empirical combustion model, the Vibe formula parameters – outputs from a single opera- tion point combustion analysis – are automatically adjusted by Csallner-Woschni formulas, to obtain a new steady-state operating point values. Regarding the Vibe Shape Coefficient, we assume its value to be constant for the particular syngas and the whole operating range. This simplification is also justified and used by the author of [25], who states that the Crank Angle [deg] -40 -30 -20 -10 0 10 20 30 40 50 60 M F B [ -] 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 CA10 CA50 CA90 NG@1500 SG18@1500 SG23@1500 spark Figure 1. Vibe curves and significant burning points for different fuel mixtures at constant spark advance and engine speed. change in the shape coefficient does not affect the overall result. It is worth to emphasise that the Csallner-Woschni recalculation formulas have not been verified for this preliminary analysis. However, they are necessary for our future simulation activities. 3.3. Full scale engine simulation Lombardini LGW 702 GT-Power full-scale (Full) model includes all relevant engine cylinder and crank- train dimensions. Full model further includes mea- sured engine speed dependant friction losses of the whole LGW 702 assembly and measured necessary in- take/exhaust flow coefficients. The Empirical combus- tion model from the previous section is included in the Full model. The in-cylinder heat transfer coefficient calculation is carried out by WoschniGT model [16] that closely emulates the classical Woschni correlation without swirl, as described in [26]. The base Full model was tuned after the initial modelling. The tuning process was split into two steps. We focused the first step on the engine integral parameters: brake torque, BSFC, and volumetric effi- ciency. Full model results were adjusted to the set of the NG full load steady-state measurement data [27], which does not include the in-cylinder pressure curves. There was a small uncertainty on the exact NG com- position (arising from a possible grid gas composition variation that is controlled by a public regulation), but the spark timing and λ were known for each oper- ating point. Other combustion model parameters had to be reasonably estimated. Some of the component flow and heat transfer characteristics were manually adjusted to get an overall agreement with measured data (especially volumetric efficiency). Our require- ment was to reach the error values lower then ±5 %. This requirement was fulfilled (Figure 2). 41 R. Toman, M. Polóni, A. Chríbik Acta Polytechnica Minimized target function Xk Weight factor αk Optimal solution Average pressure difference 0.35 0.179 Maximum pressure difference 0.15 0.224 Average LHV multiplier error 0.35 0.005 Maximum LHV multiplier error 0.15 0.008 Table 4. Criterial function weight factor from Equation (3) and optimal solution values. Engine Speed [RPM] 1200 1400 1600 1800 2000 2200 E rr o r [% ] -4 -3 -2 -1 0 1 2 3 4 Brake Torque BSFC Volumetric Efficiency Figure 2. Errors on brake torque, the BSFC and vol- umetric efficiency prediction after first step calibration (simulation – measurement). The second Full model tuning step was focused on in-cylinder quantities, mainly in-cylinder pressure curve agreement with measurement and the already mentioned LHV Multiplier. We used a set of seven full load, λ = 1 operation points (engine speeds 1200- 2200 RPM), with a pure methane as a fuel. Methane properties are well known and were inserted into our model, together with measured in-cylinder pressure. Then, since the measurement errors and uncertainties are always present, we appointed three main possible sources: • effective Compression Ratio, which can be different from the geometrical compression ratio due to blow- by, wrong geometry estimation, or material wear; • Convection Multiplier from the heat transfer model is generally not a unity [16] and needs to be ad- justed; • TDC Error parameter, that introduces a possible inaccuracy in the measurement TDC determination. These three tuning parameters can be tuned man- ually, or as in our case: coupled to an external opti- mization software. We performed this multi-criterial optimization task using the genetic algorithm NSGA- II [28]. As an optimization criterion, four target func- tions from Table 4 were minimized. The pressure difference is evaluated by a simple formula (2) that compares the measured and simulated pressure curve. Crank Angle [deg] -100 -80 -60 -40 -20 0 20 40 60 80 100 120 140 160 P re s s u re [ b a r] 0 10 20 30 40 50 60 CH4@2200 sim CH4@2200 meas CH4@1200 sim CH4@1200 meas spark Figure 3. Comparison of measured and simulated pressure curves for the optimal solution. Other quantities, for instance, the mixture air ex- cess λ, were assumed as correctly determined, and therefore were not included into the multi-criterial optimization: ∆p = 1 αend −αstart ∫ αend αstart |pmeas −psim|dα. (2) The Pareto Frontier results set was then processed by a criterial function (3). The fraction Xk Xk,max represents the normalization, so that different functions Xk can be combined into a single equation: F = n∑ k=1 αk Xk Xk,rmmax . (3) Table 4 also shows the chosen weight factors αk of the four target functions and the values of the ‘optimal solution’. The ‘optimal solution’ determined the values for the three tuning parameters. Figure 3 displays the comparison of measured and simulated pressure curves of two operating points (methane; full load; λ = 1; 1200 RPM and 2200 RPM) of the ‘optimal solution’. In addition, some different weight factors were tested. However, if the weight factors ratio between the average and maximum errors is kept, the final ‘optimal solution’ is not affected by the weight factors change. 42 vol. 57 no. 1/2017 Preliminary Study on Combustion and Parameters of Syngas Fuel Mixtures Figure 4. Schematic simulation procedure of a new syngas fuel mixture. Verification function Syngas set Average pressure difference 0.308 Maximum pressure difference 0.594 Average LHV multiplier error 0.022 Maximum LHV multiplier error 0.033 Table 5. Verification parameters from the syngas combustion analysis. 3.4. Simulation procedure The general simulation procedure of a new syngas mixture (or other gaseous mixture) is schematically represented on Figure 4. This procedure starts with a combustion analysis of the measured pressure data (pcyl). A simulation loop between the 1Cyl combustion analysis model and the Full model has to be iterated, until the combustion analysis results converge. This is because the 1Cyl model needs the initialization parameters from the Full model simulation, and these are affected by the combustion model parameters. It normally takes two to three manual iterations to reach the same result of the combustion analysis. Full model overall results have to be always com- pared against the measurements and also the 1Cyl simulated in-cylinder pressure and burn rate, to make sure everything is set up correctly. After that, the combustion analysis results are pre- pared to be used as an input for the Empirical com- bustion model. With the Empirical combustion model extended by the data from the new syngas mixture, the simulation or optimization of a new engine operating points on Full model can start. We used this partic- ular procedure in this presented project. Empirical combustion model was extended with the combustion analysis and then used for the simulation of the whole engine speed sweep (1200 to 2500 RPM), full load, and λ = 1. We carried out the spark-timing optimization for each operating point of all simulated mixtures in accordance with the aim of this project, which is to get the insight on maximum possible overall engine parameters. Figure 5. Burn Duration 10–90 % and SI Vibe Burn Exponent comparison of the 6 SGs with the NG fuel for the LGW 702 engine single-cylinder combustion analysis. 4. Simulation results 4.1. Combustion analysis results The syngas (and the NG reference) measurement set was measured right after the methane set, which was used for the model tuning. Therefore, the same TDC Error correction and other parameters were also used in the syngas combustion. Table 5 shows the verifica- tion functions from the 1Cyl combustion analysis on syngas set. Values in Table 5 are acceptable, although higher than those from Table 4 for the originally tuned methane set. This is probably caused by a relatively small set (seven operation points), that was used for the multi-criterial optimization, or other uncertainties in measurement. The 1Cyl combustion analysis determined the val- ues of the Vibe Shape Exponent and Burn Duration 10–90 % for each mixture (Figure 5). The values of the NG were checked with past research results and match them well. Nevertheless, the individual impact of the fuel mix- ture components and their interaction together in different mixtures has to be studied separately by a measurement and combustion analysis, which is not of interest in this presented paper. In the case of the analysis of the 6 different syn- gas compositions and natural gas, it is very hard to conclude the attribution of individual mixture com- ponents, as these can cancel out each other. The author in [19] shows a similar trend: the addition of H2 speeds-up the burning process and the inert gases N2 and CO2 have the opposite effect. The impact of CO2 is greater than the impact of N2 [8]. From the combustion analysis results (Figure 5) the effect of the H2/CH4 ratio, with other mixture components constant, for the SG22 and SG23 is visible: more H2 content over CH4 leads a to faster burn. The same can be said with H2/CO for the SG18 and SG21: again more H2 content leads to a faster burn. 43 R. Toman, M. Polóni, A. Chríbik Acta Polytechnica Engine Speed [RPM] 1200 1400 1600 1800 2000 2200 2400 2600 B u rn D u ra ti o n [ C A ] 10 15 20 25 30 35 NG SG6 SG18 SG21 SG22 SG23 SG31 Figure 6. Optimized CA50 position comparison of 6 SGs with NG fuel for LGW 702 engine. Engine Speed [RPM] 1200 1400 1600 1800 2000 2200 2400 2600 C ra n k A n g le A T D C [ d e g ] 6 8 10 12 14 16 18 NG SG6 SG18 SG21 SG22 SG23 SG31 Figure 7. Burn duration 10-90 % MFB comparison of the 6 SGs with the NG fuel for the LGW 702 engine. 4.2. Full model results The final Full model simulations of the fuel mixtures SG6, SG18, SG21, SG22, SG23, SG31, and the NG reference on the LGW 702 engine were carried out in a range of engine speeds from 1200 to 2500 RPM, at full load, λ = 1, and optimal CA50. The optimized CA50 position for each fuel mix- ture is shown on Figure 6. For higher engine speeds, the position of combustion centre shifts slightly to- wards the TDC. The Empirical combustion model automatically adjusts Burn Duration for each mix- ture (Figure 7), prolonging the Burn Duration with growing engine speed. The values at 1500 RPM are matching the trends from Figure 5: the SG21 and SG23 show faster burn in the whole RPM region; the SG18, SG22, and SG31 slow burn; and the SG6 with the NG are balanced in the middle. Figure 8 shows the comparison of the IMEP. As expected, the IMEP values, in general, for all the Engine Speed [RPM] 1200 1400 1600 1800 2000 2200 2400 2600 IM E P [ b a r] 6 6.5 7 7.5 8 8.5 9 9.5 10 NG SG6 SG18 SG21 SG22 SG23 SG31 Figure 8. IMEP comparison of the 6 SGs with the NG fuel for the LGW 702 engine. Engine Speed [RPM] 1200 1400 1600 1800 2000 2200 2400 2600 IS F C [ g /k W h ] 100 200 300 400 500 600 700 800 NG SG6 SG18 SG21 SG22 SG23 SG31 Figure 9. ISFC comparison of the 6 SGs with the NG fuel for the LGW 702 engine. SGs are below the NG, for instance, for the SG6, it is approximately 1.2 bar, but for the Group 2 SG18, SG21, and G23 with the lowest LHV, it is around 3 bars, so more than 30 %. The obvious increment of the ISFC for syngas mix- tures compared to the NG is displayed in the following Figure 9. Values of the ISFC for the NG are below 200 g/kWh; for the SG22 it is around 285 g/kWh (+45 %); for the SG21 already 535 g/kWh (+180 %), and the most extreme case is the SG18 with a difference of more than +400 %, compared to the NG. The figures for the IMEP and the ISFC (Figure 8 and Figure 9), show the indicated values, that are directly derived from the engines p-V diagram. The IMEP and the ISFC are independent on the ICE operating losses (friction, etc.), therefore, they can be considered as an appropriate relative measure of engine overall operation parameters. The indicated efficiency (indicated work per cycle over input energy 44 vol. 57 no. 1/2017 Preliminary Study on Combustion and Parameters of Syngas Fuel Mixtures Engine Speed [RPM] 1200 1400 1600 1800 2000 2200 2400 2600 In d ic a te d E ff ic ie n c y [ p e rc e n t] 36.5 37 37.5 38 38.5 39 39.5 NG SG6 SG18 SG21 SG22 SG23 SG31 Figure 10. Indicated Efficiency comparison of the 6 SGs with the NG fuel for the LGW 702 engine. Engine Speed [RPM] 1200 1400 1600 1800 2000 2200 2400 2600 B ra k e T o rq u e [ N m ] 20 25 30 35 40 45 NG SG6 SG18 SG21 SG22 SG23 SG31 Figure 11. Brake torque comparison of 6 SGs with NG fuel for LGW 702 engine. in fuel [16]) itself is shown on Figure 10. In general, the indicated efficiency differences between the mixtures are not significant. Figure 11 with the brake torque output illustrates what torque values can be expected during experimental testing on the engine test-bench. Here, the impact of the fuel LHV parameter on the brake torque output is visible: the low LHV syngas mixtures also show low brake torque output. Regarding the cycle data, there are three important values: maximum cycle pressure (Figure 12), maxi- mum cycle temperature (Figure 13), and maximum cycle pressure crank angle position (Figure 14). These three figures also show the importance of the fuel LHV parameter: the low LHV syngas mixtures also show low maximum cycle pressure and lower maximum cycle temperature. With the higher fuel LHV, it is vice versa. For the NOx production, it is crucial that for the SG fuels, the maximum cycle temperature values are Engine Speed [RPM] 1200 1400 1600 1800 2000 2200 2400 2600 P re s s u re [ b a r] 42 44 46 48 50 52 54 56 NG SG6 SG18 SG21 SG22 SG23 SG31 Figure 12. Maximum Cycle Pressures comparison of the 6 SGs with the NG fuel for the LGW 702 engine. Engine Speed [RPM] 1200 1400 1600 1800 2000 2200 2400 2600 M a x im u m T e m p e ra tu re [ K ] 2150 2200 2250 2300 2350 2400 2450 2500 NG SG6 SG18 SG21 SG22 SG23 SG31 Figure 13. Maximum Cycle Temperature comparison of the 6 SGs with the NG fuel for the LGW 702 engine. lower than for the NG (Figure 13). Thus, a lower tendency to produce NOx is expected. Finally, the maximum cycle pressure position (Fig- ure 14) shows the effect of the CA50 optimization and a different Burn Duration for each fuel mixture: the values are shifting towards the TDC with grow- ing engine speed, accordingly to the optimized CA50 position. 5. Conclusions This paper presented a preliminary simulation study of the engine performance and combustion parameters running on six different syngas mixtures compared to natural gas. A great part of the paper was focused on the numer- ical methods used for this preliminary analysis. Two different models – a closed-volume combustion analy- sis 1Cyl model and a Full model – were prepared and 45 R. Toman, M. Polóni, A. Chríbik Acta Polytechnica Engine Speed [RPM] 1200 1400 1600 1800 2000 2200 2400 2600 C ra n k A n g le A T D C [ d e g ] 6 8 10 12 14 16 18 NG SG6 SG18 SG21 SG22 SG23 SG31 Figure 14. Maximum cycle pressure position com- parison of the 6 SGs with the NG fuel for the LGW 702 engine. used, with a special emphasis on the accurate com- bustion modelling, which was done with an in-house Empirical model based on Vibe and Csallner-Woschni formulas. This methodology expands our previous ex- perimental work and goes in line with current research trends on alternative fuels. The combustion analysis of the measured data using the 1Cyl model showed some dependencies of the burn duration and the burn profile shape on the mixture composition: • hydrogen content in the fuel mixture affects the burn duration positively, speeding up the burning process; the inert gases act in opposite, slowing the process down; • for the Group 2 mixtures, the effect of H2/CH4 (the SG22 and SG23) and H2/CO (the SG18 and SG21) ratios on the burn duration is evident: more hydrogen means a faster burn; • however, in order to get more accurate conclusions on burn duration trade-offs or the Vibe shape ex- ponent, it is necessary to carry out a more detailed study on individual mixture components. The results from the combustion analysis were used subsequently in the Empirical combustion model for the simulation of the LGW 702 operation on various syngas mixture compositions. To make use of the sim- ulation model capabilities, we carried out the CA50 position optimization for each and every engine oper- ating point. The obtained results from the simulation can be summarized in the following conclusions: • all of the syngas mixtures show deteriorated overall performance and economy parameters compared to the NG, due to a high inert gas content and low LHV; • the most favourable results are shown by the Group 1 syngas SG6 that also has the lowest inert gas content; the worst results are for Group 2, the syngas SG18, SG21, SG22 and SG23; • regarding the IMEP values, the SG6 shows 11 % drop to the NG and the SG23 shows approximately 30 % drop to the NG values; ISFC difference varies from +45 % for SG22 to +400 % for SG18, compared to NG; • a favourable result for all syngas mixtures is the decrease of the maximum cycle temperature, which leads to a lower NOx production. All of these results are promising, but our future work should focus on the refinement of our simulation models and especially on the Empirical combustion model verification. Generally speaking, this study provides a framework for the future studies of the alternative fuels performance parameters. Here pre- sented methods will be applied on the whole range of thirty-five syngas mixtures. The measurements of those are already underway. List of symbols a Vibe duration coefficient [1] m Vibe shape coefficient [1] p pressure [bar] α crankshaft angle [deg] λ air excess [1] CA50 50 % fuel burned [CA deg] T temperature [deg, K] MFB mass fraction burned [1] IMEP indicated mean effective pressure [bar] ISFC indicated specific fuel consumption [g/kWh] F criterial function [1] αk weight factor [1] Xk minimized function [1] LHV lower heating value [kJ/kg] Acronyms 1Cyl single-cylinder combustion analysis model Full full scale engine model EVC exhaust valve closes EVO exhaust valve opens IVC inlet valve closes IVO inlet valve opens SI spark ignition engine TDC top dead center NG natural gas SG syngas CH4 methane molecule H2 molecular hydrogen N2 molecular nitrogen O2 molecular oxygen CO carbon monoxide CO2 carbon dioxide NOx mono-nitrogen oxides (NO and NO2) 46 vol. 57 no. 1/2017 Preliminary Study on Combustion and Parameters of Syngas Fuel Mixtures Acknowledgements This work was supported by the Slovak Research and Development Agency under Contract-No. APVV-0015-12 and Contract-No. APVV-14-0399 and was also supported by the Scientific Grant Agency under the Contract No. VEGA 1/0017/14. 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