Experimental study of premixed hydrogen enriched natural gas under an alternating-current (AC) electric field and application of support vector machine (SVM) on electric field assisted combustion

Fuel 258 (2019) 115934

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Experimental study of premixed hydrogen enriched natural gas under an alternating-current (AC) electric field and application of support vector machine (SVM) on electric field assisted combustion

T

Hao Duana, Zhijie Lib, Bo Wangb, Roopesh Kumar Mehrac, Sijie Luoc, Chuanguo Xud, ⁎ Baigang Sune, Xi Wange, Fanhua Mac, a

Department of Mechanical Design, School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, People’s Republic of China State Key Laboratory of Engine Reliability, Weichai Power Co., Ltd., Weifang 261061, People’s Republic of China State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100086, People’s Republic of China d China National Petroleum Corporation Jichai Co., Ltd., Jinan 250306, People’s Republic of China e Department of Energy and Power Engineering, School of Mechanical Engineering, Beijing Institute of Technology, Beijing, 100081, People’s Republic of China b c

A R T I C LE I N FO

A B S T R A C T

Keywords: Hydrogen enriched natural gas Lean burn Ionic wind effect Bi-ionic wind effect Support vector machine

Present research study explores the flame propagation and combustion characteristics of premixed hydrogen enriched natural gas under a 100 Hz low-frequency alternating-current (AC) electric field with various hydrogen blend ratios (0–100%) and fuel–air equivalent ratios (1.0/0.8/0.6) in a constant volume combustion chamber. Results show that: (1) With the application of the electric field, although the maximum pressure increases slightly, the mean flame propagation speed increases and the initial/main duration shortens apparently. At a hydrogen blend ratio of 20% and an equivalent ratio of 0.8, the mean flame propagation speed increases by 10.06%, and the initial/main duration shortens by 13.46%/10.72%. (2) The promotion effect of the electric field on the flame propagation and combustion of the fuel decreases gradually as the hydrogen blend ratio increases. There exists a maximum value of hydrogen blend ratio that electric field makes no difference to the combustion process and the value increases with the excess air ratio. The electric field almost has no effect on the pure hydrogen fuel. (3) Adding the AC electric field can improve the combustion of lean burn conditions more effectively than that of higher equivalent ratio conditions. Meanwhile, the support vector machine is applied for the predication of the flame propagation and combustion characteristics under electric fields. The prediction performance of the optimal model obtained by the SVM method for the maximum pressure is very accurate, while that of the mean flame propagation speed is relatively limited.

1 Introduction Fossil fuel is primary source of urban transportation and industrial power, which causes plenty of environmental pollution problems. It has become an important issue for modern combustion research to increase combustion efficiency along with reducing emissions. As an effective method to enhance the combustion process, electric field assisted combustion possesses obvious advantages of rapid and direct response. The method also can be controlled and adjusted easily, which has attracted the attention of many researchers all over the world. As the combustion process is essentially a redox reaction, it will produce charged particles such as positive and negative ions and free electrons. Because of restrictions of the combustion conditions within the flame, the charged particles can’t be combined into molecules in a short time, ⁎

so the flame will show some electrical properties. In this way, the electric field will force the charged particles (mainly positive ions) to collide with the neutral particles and transfer the momentum at the same time, which contribute to a large-scale directional migration behavior of ions and molecule, that is, the ionic wind effect [1]. In addition, the collision process under electric fields will also stimulate the accelerated oxidation of the nitrogen molecules, changing the chemical reaction rate and energy distribution of the relevant chemical reactions during the combustion, and then influence the entire combustion process, which is called the electrical-chemical effect [2–4]. 1.1 Effect of electric field on flame

Corresponding author. E-mail address: [email protected] (F. Ma).

https://doi.org/10.1016/j.fuel.2019.115934 Received 3 June 2019; Received in revised form 29 July 2019; Accepted 31 July 2019 Available online 08 September 2019 0016-2361/ © 2019 Elsevier Ltd. All rights reserved.

The influence of an electric field on a flame was first explored by

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Nomenclature

PSO GA R rh Sh Sa p pmax dp/dt tid/tmd C q g MAPE Theil IC

Symbols DC/AC HCNG THC CNG CFM HCCI SVM H Φ PTFE RMS U

Direct-current/Alternating-current Hydrogen enriched compressed natural gas Hydrocarbon Compressed natural gas Coherent flame model Homogeneous charged compression ignition Support vector machine Hydrogen blend ratio Fuel-air equivalent ratio Polytetrafluoroethylene Root mean square RMS voltage

Particle swarm optimization Genetic algorithm Correlation coefficient Flame radius Flame propagation speed Mean flame propagation speed Cylinder pressure Peak pressure Pressure rise rate Initial/Main combustion duration Penalty factor kernel Function width (σ; denoted by q) Insensitive band loss function (ε; denoted by g) Predicted mean absolute percentage error Hill inequality coefficient

in flame. Sakhrieh [16] researched the effect of electric field on the flame of methane/air seven-hole Bunsen flame at high pressures, and found that the concentrations of pollutants such as CO, NO and NO2 depend on the ratio of the applied voltage amplitude to the pressure. Meanwhile, after applying the electric field, the CO emission is reduced by 90% while the NOx is increased by 20%-25%. Zhang [17] observed the non-premixed methane/air flame combustion and NO emissions under high-frequency AC electric fields (f = 10 kHz, U = 0–4 kV). The results concluded that the response of the flame to the voltage depends on the voltage magnitude. The flame deformation, the emission of CO and NO, and the luminescence intensity of chemiluminescent materials OH* and CH* are all found to change non-monotonically with the voltage amplitude.

Brande [5] in 1814. His research reported that the flame propagation speed varied substantially under an electric field. From then on, a great deal of experimental and theoretical investigations were carried out in this field, proving that electric fields can improve the flame propagation and enhance the combustion process effectively. Jaggers [6] found that the flame propagation speed of premixed methane/air or ethylene/air mixtures was significantly increased when direct-current (DC), alternating-current (AC) or high-frequency transverse electric fields were implemented to the vertical tubes filled with the blends. They believed that the electrons can get enough energy from an electric field to force the neutral molecules and radicals to jump from lower stage to a higher oscillation state by collision, which also increases the activation particle concentration, although the amplitude of applied electric field is far less than the breakdown voltage. Vage [7] studied the effects of DC electric fields on the flame temperature, radiant heat flux and flame propagation speed of premixed CH4/O2/N2 laminar Bunsen flame. The results showed that the flame propagation speed is significantly enhanced with the deformation of the flame front at a high-strength electric field, which is attributed to the field induced flame stretching caused by the ionic wind effect. Won [8,9] explored the effects of DC and AC electric fields on the laminar jets propane flame, and discovered that the flame propagation rate increases approximately linearly with the AC and DC electric fields. In addition, the enhancement effect of the coaxial jet flame propagation speed has a good correlation with the electric field intensity. The effect of electric fields on flame stability has also been explored in detail. Calcote [10] reported that the electric field will strongly affect the stability of the flame, and the effects of electric fields with different polarities are also not the same. The experimental results of Yagodnikov [11] indicate that the stability of propane flame can be enhanced by applying horizontal or vertical electric fields. The effect of electric fields on the flame stability of premixed methane/air seven-hole Bunsen flame under high pressure also illustrates that the electric field can enhance the stability of the lean burn flame and the effect increases with the initial pressure [12]. Kim [13] represented the effect of AC electric field frequency on the rise and blowout velocity of the propane/ air injection flame, concluding that AC electric fields can extend the stable flame range, and the fields of different amplitudes and frequencies show various influences on the flame stability. In addition, electric fields have a considerable impact on the combustion characteristics, such as heat release, combustion limit and emissions. Saito [14] observed that applying electric fields can induce great deformation of the flame surface, and the field polarity can influent the flame deformation and soot emissions. Vatazhin [15] studied the extraction of carbon particles from a laminar propane flame by using a DC electric field, and results showed that the carbon particles are precipitated by the electric field, which results in no soot formation

1.2 Development of HCNG Hydrogen enriched compressed natural gas (HCNG) is a multicomponent substitute gas fuel obtained by mixing natural gas with hydrogen in a certain proportion, and has been researched for nearly 20 years as a new kind of internal combustion engine alternative fuel. Attempts to apply HCNG to automobile engines have been made in the United States, Canada, Ireland, Australia, the European Union and other countries and regions. The greatest benefit is that the ratio of NOx emission concentration in the exhaust gas of the HCNG fueled internal combustion engine can be reduced to approximately 50% compared with that of compressed natural gas. Using HCNG fuel can also reduce the concentration of hydrocarbons (THC) and CO emissions and improve the CO2 emission reduction [18–21]. In 2000, Sierens [22] explored the combustion emissions of HCNG mixtures with 0%, 10% and 20% hydrogen blend ratios in an 8-cylinder engine. The experiments results demonstrated that the HCNG fuel has lower emission with lean burn technology. Ortenzi [23] researched the influence of the hydrogen blend ratios and control strategies on the economy and emissions on a compressed natural gas (CNG) engine by using the ECE15 test cycle. It was observed that with the theoretical air fuel ratio and recalibration control parameters, the emission level is lower than that of the CNG engine, and the lowest emissions are found at a hydrogen blend ratio of 10%. Meanwhile, NOx and CO emissions decreased in lean burn conditions, although the HC emission is relatively higher. In 2017, the performance and emissions of an HCNG fueled SI engine with a high compression ratio had also been reported as a reference for commercial engines, and the paper concluded that the HC and CO emissions of the engine can reach the Euro VI standard [24]. Except for engines, the characteristics of HCNG fuel have also been researched in constant volume combustion vessels in order to further explore the mechanism of the combustion enhancement with hydrogen addition [25–28]. Meanwhile, some chemical kinetic software such as 2

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1.3 Intelligent algorithm

CHEMKIN is applied for researching the mechanism of the combustion enhancement with hydrogen addition. Huang [29–32] obtained the flame propagating photos of premixed combustion and direct-injection combustion by using constant volume chambers and schlieren photographic technique. After analyzing the mole fractions of OH, O, CH4, CO and CO2 and so on in the flame front, it was found that the mole fractions of major species (CH4, CO and CO2) decrease while the mole fractions of OH and O increase as hydrogen is blended. The enhancement of the spark ignition of natural gas with hydrogen addition can be ascribed to the increase of the H, OH and O mole fractions in the flames. Cordiner [33] simulated the combustion process of a spark ignition engine fueled with CNG and HCNG of 15% hydrogen blend ratio via the engine 3D simulation software KIVA-3V by embedding the modified CFM (Coherent Flame Model) combustion model in 2007. The laminar flow flame propagation velocity of the fuels with different hydrogen blend ratios, air–fuel equivalent ratios, unburned temperatures and pressures was calculated by using the open-source chemical kinetics solving software CANTERA. The research illustrated that the CFM combustion model is suitable for CNG and HCNG with a 15% hydrogen blend ratio. Elkelawy [34] used the HCCI (Homogeneous Charged Compression Ignition) engine model in the chemical kinetics software CHEMKIN to simulate the combustion process of an HCCI engine fueled of HCNG, concluding that the ignition time of spontaneous combustion is advanced by hydrogen addition and it can make the mixture more easily ignited at lower initial temperatures and lean burn conditions. Wang [35] built a multi-dimensional CFD model coupled with detail reaction kinetics based on CFD software and reaction kinetics software to study the combustion process in an engine fueled with HCNG. Detail reaction mechanism was used to simulate the chemistry of combustion and a combustion model considering the turbulent mixing effects was also applied in their work. The results show that the cylinder pressure from simulation has a good agreement with the experimental data. Besides, CO and NOx emissions are well predicted by the model in a wide range.

Flame propagation and combustion characteristic parameters are of great importance in the combustion research of fuels. In the past hundreds years, kinds of experimental and numerical simulation methods were proposed and applied for calculating the parameters mentioned above. For example, the flame propagation speed could be investigated by various combustion experimental test bench such as Bunsen flame [36,37] and constant volume combustion bomb [38,39]. Meanwhile, the simulation calculation tools such as CHEMKIN can also be applied to simulate complex chemical reactions in the combustion process [40,41]. Nowadays, with the development of the computer technology, it is possible to use intelligent algorithms to predict various parameters during combustion process based on the basic experimental data. Intelligent algorithms, such as simulated annealing algorithm, genetic algorithm, fuzzy theory, neural network, etc., are all based upon simulations of natural processes, aiming to solve some complex engineering problems. Support vector machine (SVM) method, put forward based on the structural risk minimization (SRM) principle, is no doubt one of the most useful intelligent algorithms. In 1992, an optimal boundary classifier was proposed by Boser, marking the establishment of the SVM method prototype [42]. In 1995, Vapnik published a book named “The Nature of Statistical Learning Theory”, systematically expounded the concepts of statistical learning theory and SVM method [43]. Six years later, SVMR (support vector machine regression) was applied to conduct optimization control research, extending the research field of the SVM method to the control field [44]. Although it has been proved very useful in solving the problems in many fields [45–48], the feasibility of the SVM method in the field of basic combustion still needs to be explored in details. It is generally accepted that the positive effect of the electric field on the combustion process is mainly applicable to hydrocarbon fuel. However, in the case of non-hydrocarbon fuel, electric field shows no influence, because chemic-ionization process, which is initiated by CH radicals, is not available with the flame of non-hydrocarbon fuel such as hydrogen. Tewari [49] carried out experiments in which high frequency

Fig. 1. Schematic of the experimental setup. 3

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electric fields were applied across flames generated by the action of laser-induced sparks. The results show that for hydrogen/air flames no significant increases in flame propagation rates are reported. However, with the improvement of experimental conditions, some different voice for the effect of electric fields on hydrogen/air flame appears. In 2011, the flame propagation behavior of homogeneous hydrogen-air mixture under application of high-voltage uniform or non-uniform electric field was explored by Yamaguchi [50]. It was observed that a negative polarity non-uniform electric field or a positive polarity uniform electric field would not show any impact on the combustion of homogeneous hydrogen-air mixture. But when a positive polarity non-uniform electric field was applied and the input voltage was higher than 12 kV, the flame propagation was enhanced in the downward direction. Therefore, although the effect of electric fields on hydrocarbon fuel (mainly methane) flame has been studied extensively in the past several decades, it is also necessary to research the field influence on non-hydrocarbon fuel. On the other hand, previous research on electric field assisted combustion mainly focuses on a single fuel. Nevertheless, for a mixture of more than two different fuels, related research is rarely limited. So in order to research the impact of the concentration of each fuel component on the promoting effect of electric field on combustion process, it is also very necessary to study the effect of electric field on multicomponent fuel blends. Thus, in this paper, the flame behavior of HCNG under a 100 Hz low-frequency AC electric field in a constant volume combustion chamber with various hydrogen blend ratios (H) (0–100%) and fuel–air equivalent ratios (Φ) (1.0/0.8/0.6) is further explored, aiming at deepening the understanding of the ion wind effect and making necessary experimental basis for HCNG fueled engines. Meanwhile, the data obtained from experiments is used for SVM prediction of the flame propagation speed and combustion pressure, in the purpose of verifying the feasibility of using the SVM method to predict combustion characteristics.

The inner cavity of the bomb, which is made of 45-grade steel, is a cylinder with a diameter of 140 mm and a length of 180 mm. In order to prevent the discharge phenomenon between the high-voltage electrodes and the chamber’s inner-wall, the cavity was closely embedded with a polytetrafluoroethylene (PTFE) insulated inner lining, which has an inner diameter of 130 mm, an outer diameter of 140 mm, and a length of 180 mm. A pair of ignition electrodes, symmetrically distributed vertically in the center of the bomb, was set up for spark ignition to form a fire core. With a diameter of 2 mm, it is also made of 45-grade steel. Consisting of two identical electrodes with an outer diameter of 60 mm, the stainless steel mesh high-voltage electrodes are mounted horizontally and symmetrically at a distance of 35 mm from the center of the chamber. The high-voltage power supply is connected to the mesh electrodes to form high-voltage electric field with the ignition electrodes as the ground electrodes after ignition. Mounted on both sides of the chamber, a pair of quartz glass windows with a diameter of 190 mm and a thickness of 60 mm are served to provide optical access. Besides, some sealing elements such as silicone rubber gaskets are used to ensure the sealing performance of the bomb. The WCL schlieren device used in the experiment is produced by Jinzhou Hangxing Optoelectronic Equipment Co., Ltd. It consists of a slit light-source-generation system, a main-mirror system, a knife-edgephotograph system and a power-transformer system. The HG-100 K high-speed camera made by the REDLAKE Company is applied to record the flame propagation process, and the frame rate is set to 5000 frames per second. The pressure-measurement system is composed of a pressure sensor, a charge amplifier, and a data acquisition instrument. The pressure sensor is a Kistler 7061B piezoresistive absolute pressure sensor with a measurement range of 0–25 MPa, the largest sampling frequency of 10 MHz and an accuracy of 0.5% relative to actual pressure, which is matched with the Kistler 4618A charge amplifier. The data acquisition instrument is a DL750 oscilloscope developed by Yokogama, which has an error relative to full scale of less than 0.3% and an acquisition frequency of 20 kHz. The low-frequency AC power supply is customized by Xi'an Siwei Electric Co., Ltd, with a type of XIELI HV20kV/10–1000 Hz. The applied voltages on the AC fields mentioned here are all the root mean square (RMS) voltages (U; kV). Before the experiment, the Tektronix P6015A high voltage probe is used to detect the low-frequency AC voltage output signal (proved to be sine waves), and the initial voltage phase is controlled at 0° relative to sine waves. The intake/exhaust system consists of a vacuum pump, a pressure transmitter, H2, CH4 and N2/O2 mixtures (79% N2 and 21% O2

2. Experimental setup The entire test bench system is mainly composed of a constant-volume-combustion-chamber system, an optical-schlieren-system, a highspeed-photography-system, a pressure-collection system, a spark-ignition-control system, a high-voltage-power-supply system, and an intake/exhaust system, as shown in Fig. 1. Fig. 2 displays the structure of the constant-volume-combustionchamber system with a pair of mesh high-voltage electrodes installed.

Fig. 2. Structure of the constant volume combustion chamber and mesh high-voltage electrodes. 4

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problem into the below one:

by volume) and various connecting pipes. Among them, the 2XZ-4 vacuum pump is produced by Shanghai Lean Vacuum Pump Company and the type of the pressure transmitter is Rosemount 3051T with an accuracy of 0.15%. The experiment was conducted at ambient temperature and pressure. The constant volume combustion bomb was evacuated first, and the fuel and oxidant were slowly charged into it according to the calculated partial pressures. In order to achieve the homogeneous mixture of combustible gas, a 150 s delay was needed before ignition. Meanwhile, the high-voltage power supply was set to provide electric field. After the experiment, the power supply must be turn off immediately and the combustion products were discharged with the help of vacuum pumps at least 3 times. In addition, each condition was repeated at least 5 times to ensure excellent repeatability, and the relative error of the experiment is within 5%.

l

min

s.t. yi − (w·xi ) − b ⩽ ε + ξi

f (x ) =

s.t. yi − (w·xi ) − b ⩽ ε ,

(7)

f (x ) = w·Φ (x ) + b,

(8)

where Φ(x ) represents the nonlinear function. And the final regression function is shown in Equation (9).

(1)

f (x ) =

∑

(ai − ai∗) K (xi ·x ) + b,

xi ∈ SV

(9)

The optimal process contains the optimization of the penalty factor C, the kernel function width σ (denoted by q) and the insensitive band loss function ε (denoted by g). Grid parameter optimization, particle swarm optimization (PSO) optimization and genetic algorithm (GA) optimization methods are the commonly used optimal algorithms. After that, a further evaluation of the model's relevance and predictive performance is also indispensable. Normally, the multiple correlation coefficient (R) is investigated to evaluate the relevance between independent and dependent variables; parameters such as the mean absolute percent error, the mean square error and the Thiel inequality coefficient are always investigated to evaluate the prediction ability of the model. The principle of the modeling of SVM is illustrated in Fig. 3.

(3)

(w·xi ) + b − yi ⩽ ε .

(ai − ai∗ )(xi ·x ) + b,

are Lagrange multipliers and the solution correwhere ai and sponding to the multiplier other than 0 is the support vector. To solve a nonlinear problem, the key part is to use a nonlinear mapping function (which is called the kernel function) to map sample to higher dimensional linear space. Then the linear regression is able to conduct in the high dimension space. Obviously, the type of the kernel function and its parameters play a decisive role on the performance of the SVM model. After kernel transformation by using the kernel function K(x, y), the decision function becomes to Equation (8):

(2)

1 ∥w∥2 , 2

(6)

ai∗

where b and w are bias terms and weight vector, respectively. Then the optimization problem is formulated as:

min

∑ x i ∈ SV

where f is the decision function. For linear datasets, the decision function f can be written as follows:

f (x ) = w·x + b,

(w·xi ) + b − yi ⩽ ε + ξi∗.

where ≥ 0 , i = 1,2,…,l, and C is the penalty factor. By using the Lagrange function and the optimized Karush-Kuhn-Tucker condition, the final regression estimation function can be calculated as follows:

Based on the VC dimension theory and the SRM principle, the SVM method is mainly used to solve the regression problems. For a given dataset {(xi, yi), i = 1,2,…,l}, where xi ∈ Rn, yi ∈ Rn, and l is the capacity of samples, since the regression problem requires the dependency relation of y on x from the sample data, the insensitive band loss function (ε) is defined. Equation 1 defines the linear insensitive loss function.

|y − f (x )| ⩽ ε , |y − f (x )| > ε

(5)

ξi , ξi∗

3. Support vector method modeling

0 |y − f (x )|ε = ⎧ ⎨ | ⎩ y − f (x )| − ε

1 ∥w∥2 + C∑ (ξi + ξi∗), 2 i=1

(4)

In this case, the regression prediction of the decision function is expected to be very strict. So slack variables ξi and ξi∗ are defined to prevent the outcome from being too strict. Then the optimization

Fig. 3. The principle of the modeling of SVM. 5

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tid = t90 − t10.

4. Data processing method 4.1 Flame propagation characteristics parameters definition

5 Experimental results and analysis

The flame propagation characteristics parameters with and without applying the electric field were examined first to research the influence of electric fields on the HCNG fuel. It can be seen that a change in the shape of the flame front is induced mainly in the horizontal direction after applying electric field [51]. However, the change in the vertical direction is very limited because of the influence of the ignition electrodes and the PTFE insulating layer. Thus, only the flame change in the horizontal direction is discussed in present paper. As is shown in Fig. 4, the flame radius (rh/mm) is defined as the average of the flame radii (rhi/mm; i = 1, 2, …,6) at different angles to the horizontal direction (0°, ± 15°, ± 165° and 180°), and the calculated equation is presented as follow: 6

rh =

∑ i =1

rhi , 6

5.1 Flame radius Fig. 5 shows the variation of flame radius versus the elapsed time under electric fields with different applied voltages for HCNG blends at various hydrogen blend ratios and fuel–air equivalent ratios. Obviously, hydrogen addition can enhance the flame propagation effectively. There is a strong correlation between the laminar burning speed and the peak concentration of H radicals in the combustion process of methane [55]. At experimental conditions, the sensitivity coefficient of mass combustion flow of hydrogen-air mixture is researched by CHEMKIN. It is seen that the following four elementary reactions have larger sensitivity coefficients:

(1)

where rhi can be directly measured in the flame pictures. The effective flame radius range used for the analysis is 6–25 mm. On one hand, when the effective flame radius is less than 6 mm, the ignition energy has a greater influence on the flame development [52–54]. On the other hand, when the effective radius is more than 25 mm, the flame development will be significantly affected by not only the pressure and temperature change in the chamber but also the structure of the highvoltage electrodes. The flame propagation speed (Sh/m·s−1), defined as the moving velocity of the flames relative to the stationary combustion wall, can be calculated from the flame radius data. The calculation equation is shown below:

Sh =

drh , dt

re − rb . te − tb

H+ O2 ⟺ O+ OH

(R1)

OH + H2 ⟺ H+ H2 O

(R2)

H+ OH + (M) ⟺H2 O+ (M)

(R3)

H+ O2 + (M) ⟺HO2 + (M)

(R4)

Reactions R1 and R2 produce a large number of H, O and OH radicals in the combustion process, which is the dominant chain branching reaction. Reactions R3 and R4 are important chain termination reactions, which mainly consume H, O and OH radicals to produce stable intermediates or products. Therefore, hydrogen addition increases the concentration of H, O and OH radicals in the flame front, resulting in the enhancement of chemical reaction and the increase of laminar burning speed, which is consistent with Hu's analysis result [26]. Fig. 5 also qualitatively shows that the combustion of HCNG fuel is enhanced and the flame development has been advanced by the application of electric field, which turns to be very apparent at low hydrogen blend ratios and lean burn conditions. During the combustion process of hydrocarbon fuels, kinds of neutral molecules and ions are generated in the flame front, such as H2O, CO, H2, H, O, OH, CH3, CH2O, CH, H3O+, CHO+ and C3H3+ etc. The charged particles among which can be greatly influenced by the electric field and move directionally in the flame front, leading to a directional volumetric flow under a negative electric field, which is called the ionic wind effect [56]. As for the 100 Hz low-frequency AC electric field applied in the experiment, both of the positive particles and negative ones will be

(2)

where rh is the flame radius, and t is the elapsed time from spark ignition. Besides, the mean flame propagation speed (Sa/m·s−1) is also adopted to reflect the flame propagation, which is defined as the average value of the flame propagation speed during the duration when the flame radius developed from 6 mm (rb) to 25 mm (re) (the related timing is tb and te, respectively), which is resented in Equation 3.

Sa =

(3)

4.2 Combustion characteristics parameters definition The combustion characteristics parameters such as the peak pressure (pmax/kPa), its corresponding time (tp/ms), and the pressure rise rate (dp/dt/MPa·s−1) can be obtained by the acquired pressure curve conveniently. The mass combustion rate can be calculated by the Elbe’s Equation:

Mf (f ) =

p(t )− pi , pmax − pi

(4)

where p(t) is the instantaneous pressure, pi is the initial pressure of the premixed mixture and pmax is the peak pressure of the combustion process. So the initial combustion duration (tid/ms) is defined as the time duration from the ignition timing (t0/ms) to the moment when the mass combustion rate reaches to 10% (t10/ms), that is,

tid = t10 − t0.

(6)

(5)

And the main combustion duration (tmd/ms) is defined as the time duration from the moment when the mass combustion rate reaches to 10% to the moment when reaches to 90% (t90/ms), that is,

Fig. 4. Schematic diagram of the redefined flame radius. 6

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Fig. 5. Flame radius versus time (A. Φ = 1.0; B. Φ = 0.8; C. Φ = 0.6).

affected by the electric field force, which will contribute to two kinds of ionic wind effects with opposite directions, which is called the bi-ionic wind effect [57,58]. It is just the reason why the flame propagation was promoted in the experiment. To study the influence of electric field on the flame propagation quantitatively, Fig. 6 illustrates the variation of flame propagation speed versus the flame radius under electric fields with different applied voltages for HCNG blends at various hydrogen blend ratios and fuel–air equivalent ratios. Meanwhile, the related mean flame propagation speed and relative change rate (ΔSa/%) are also shown in Table 1. It is seen that the flame propagation speed of HCNG has been improved and the flame propagation has been promoted with the application of the electric field in varying degrees, further indicating that the electric field plays a positive role on the propagation of flame. For example, at a hydrogen blend ratio of 20% and an equivalent ratio of 0.8, Sa is 3.07 m/s with the application of the electric field, which is 10.04% more than that without the electric field (2.79 m/s). On the other hand, the increase rate of the flame propagation speed with the electric field decreases as the hydrogen blend ratio increases. Hence, there is a certain limit exists on which decrease stop and further increase in hydrogen blend ratio not shown any effect. For example, at the equivalent ratio of 0.8 and hydrogen blend ratio of 0%, 20%, 40%, 60%, 80% and 100%, respectively, with the application of the electric field, Sa decline by 14.61%, 10.04%, 6.16%, 3.30%, 0.25% and 0.23%, respectively, compared to those without the electric field. Since the maximum relative error is 5% in the experiment, the limit value of

hydrogen blend ratio at the equivalent ratio of 0.8 is 60%. Compared with pure CNG fuel, HCNG fuel has a lower carbon-hydrogen ratio, and the concentrations of the species in flame front changed as well. Wang [30–32] analyzed the mole fractions of different particles in a hydrogen enriched compressed natural gas flame. Results show that the mole fractions of carbon-based particles decrease while the mole fraction of OH and O increase as hydrogen was blended. Therefore, the above results indicates that the effect of electric field on hydrocarbon ion is much greater than that on hydrogen ion. Besides, the rapid increasing flame propagation speed leads to the decline of residence time of flame in the field at higher hydrogen blend ratios, which will also reduce the influence of electric field on flame propagation. Noted that the Sh-rh curves almost show no change for pure hydrogen with an electric field applied, it makes it clear that the electric field even almost has no effect on the OH and O species. Besides, it is found from Fig. 6 that the contribution of the electric field to the flame propagation process increases as the equivalent ratio decreases. For example, at the hydrogen blend ratio of 20% and the equivalent ratio of 1.0, 0.8 and 0.6, respectively, with the application of the electric field, Sa is increased by 3.78%, 10.04% and 16.35%, respectively, compared to those without the electric field. Meanwhile, the limit value of hydrogen blend ratio increases with the excess air ratio, illustrating that the low-frequency AC electric field can improve the combustion in lean burn condition effectively. In fact, at a low equivalent ratio, the flame propagation speed is also relatively low, which will effectively improve the total duration of the electric field 7

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Fig. 6. Flame propagation speed versus radius (A. Φ = 1.0; B. Φ = 0.8; C. Φ = 0.6).

acting on the flame front. Therefore, the effect of the electric field on flame propagation and combustion process is also improved. Table 1 shows that the limited values of hydrogen blend ratio are 0%, 40% and 60% for the equivalent ratio of 1.0, 0.8 and 0.6, respectively.

HCNG blends at various hydrogen blend ratios and fuel–air equivalent ratios. The figure indicates that the pressure increases sharply after the short initial combustion stage (about 20 ms) for all cases. With the application of electric field, the peak pressure has little change when compared to those without the electric field, up to only 3.92% (at a hydrogen blend ratio of 0% and an equivalence ratio of 0.6), which is shown in Table 2. It means that the 100 Hz AC electric field has no effect on the maximum pressure. However, the timing of the peak pressure is shortened apparently with the application of the electric

5.2 Combustion characteristics Fig. 7 represents the variation of combustion pressure versus the elapsed time under electric fields with different applied voltages for Table 1 Mean flame propagation speed for various fuels. Hydrogen blend ratio

HCNG-0% HCNG-20% HCNG-40% HCNG-60% HCNG-80% HCNG-100%

Applied voltage

0 kV 5 kV 0 kV 5 kV 0 kV 5 kV 0 kV 5 kV 0 kV 5 kV 0 kV 5 kV

Sa/m·s−1

ΔSa/%

Φ = 1.0

Φ = 0.8

Φ = 0.6

Φ = 1.0

Φ = 0.8

Φ = 0.6

4.28 4.50 5.29 5.49 6.22 6.36 8.41 8.58 11.97 12.10 19.88 19.88

2.19 2.51 2.79 3.07 3.57 3.79 5.15 5.32 7.85 7.87 12.78 12.81

0.68 0.84 1.04 1.21 1.36 1.47 2.14 2.25 3.61 3.64 7.05 7.07

0.00 5.14 0.00 3.78 0.00 2.25 0.00 2.02 0.00 1.09 0.00 0.00

0.00 14.61 0.00 10.04 0.00 6.16 0.00 3.30 0.00 0.25 0.00 0.23

0.00 23.53 0.00 16.35 0.00 8.09 0.00 5.14 0.00 0.83 0.00 0.28

8

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Fig. 7. Combustion pressure versus elapse time (A. Φ = 1.0; B. Φ = 0.8; C. Φ = 0.6).

field, meaning that the whole combustion process is accelerated evidently. For example, at a hydrogen blend ratio of 20% and an equivalent ratio of 0.8, tp is 62.1 ms with the application of the electric field, which is 7.04% earlier than that without the electric field (66.8 ms). Similar to that of the mean flame propagation speed, the increase rate of tp under the electric field decreases with the increase of hydrogen blend ratio. There also exists a maximum value of hydrogen blend ratio that electric field makes no difference to tp. For example, at the equivalent ratio of 0.8 and the hydrogen blend ratios of 0%, 20%,

40%, 60%, 80% and 100%, respectively, with the application of the electric field, tp declines by 10.48%, 7.04%, 4.85%, 3.65%, 2.69% and 2.01%, respectively, compared to those without the electric field. Additionally, it is noted that the acceleration effect of electric field to the combustion process increases as equivalent ratio decreases. For example, at the hydrogen blend ratio of 20% and the equivalent ratios of 1.0, 0.8 and 0.6, respectively, with the application of the electric field, tp is increased by 5.19%, 7.04% and 12.15%, respectively, compared to those without the electric field. Likewise, the limit value

Table 2 pmax and tp for various fuels. Hydrogen blend ratio

HCNG-0% HCNG-20% HCNG-40% HCNG-60% HCNG-80% HCNG-100%

Applied voltage

0 kV 5 kV 0 kV 5 kV 0 kV 5 kV 0 kV 5 kV 0 kV 5 kV 0 kV 5 kV

pmax/kPa

tp/ms

Φ = 1.0

Φ = 0.8

Φ = 0.6

Φ = 1.0

Φ = 0.8

Φ = 0.6

676 686.1 678.5 687 679.9 682.2 682.1 683 690.5 691 695.3 696.7

581.6 599.2 605.9 617.3 617.4 619.4 624.6 627.4 627.5 628.5 634.4 636.1

393.2 408.6 472.1 483.3 499.3 503 516.1 516.3 524 526 533.6 534

47.6 44.1 40.5 38.4 32.7 31.5 23.3 22.6 17.1 16.7 13.1 12.9

84 75.2 66.8 62.1 45.4 43.2 32.9 31.7 22.3 21.7 14.9 14.6

287 240.5 171.2 150.4 125.5 115.2 76.4 72 37.2 36.1 21.7 21.2

9

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and advance magnitude of curve decrease as the hydrogen blend ratio increases and increase as the equivalent ratio decreases. In order to further investigate the influence of electric fields on the combustion properties of various HCNG blends, the initial combustion duration and main combustion duration are also analyzed. Table 3 shows the value of tid and tmd with and without the electric field for various HCNG blends and fuel–air equivalent ratios. It can be noted that tid has been shortened with the application of electric field, indicating that the electric field can effectively promote the combustion speed of HCNG fuel in the initial stage. For example, at a hydrogen blend ratio of 20% and an equivalent ratio of 0.8, tid is 25.37 ms with the application of electric field, which is 13.46% less than that without the electric field (29.31 ms). Meanwhile, the shortened rate of tid under the electric field decreases with the increase of hydrogen blend ratio. For example, at the equivalent ratio of 0.8 and hydrogen blend ratios of 0%, 20%, 40%, 60%, 80% and 100%, respectively, with the application of the electric field, tid declines by 16.10%, 13.46%, 8.61%, 5.03%, 4.12% and 2.62%, respectively, compared to those without the electric field. It also can be found that the effect of electric field on the decrease of tid increases as equivalent ratio decreases. For example, at the hydrogen blend ratio of 20% and the equivalent ratios of 1.0, 0.8 and 0.6, respectively, with the application of the electric field, tid is increased by 8.91%, 13.46% and 17.41%, respectively, compared to those without the electric field. Table 3 also illustrates that the main combustion duration is shortened with the application of electric field, meaning that the electric field can also enhance the combustion of HCNG fuel in the main duration. For example, at a hydrogen blend ratio of 20% and an

increases with the excess air ratio. From the table, it can be noted that the limited values of hydrogen blend ratio are 20%, 20% and 60% for the equivalent ratios of 1.0, 0.8 and 0.6, respectively. Besides, for pure hydrogen at the equivalent ratio of 1.0, a rapid decrease of pressure can be observed in Fig. 7. The combustion product of hydrogen is water, which is first in the form of water vapor. When the combustion pressure reaches its peak value, the combustion is already complete and the chamber is filled with water vapor. After combustion, water vapor condenses after contacting the inner wall of the chamber and the quartz glasses, resulting in the decrease of gas volume and the sharp decrease of pressure in the chamber. With the decrease of water vapor, its condensation and evaporation gradually reach equilibrium. Then the volume content of water vapor in the chamber changes slightly, and the pressure declines slowly. No doubt that the sharp decrease of pressure is greatly influenced by the amount of water vapor in the combustion product. So the higher the hydrogen blend ratio is and the greater the equivalence ratio is, the significant this phenomenon will be. Fig. 8 demonstrates the variation of pressure rise rate versus the elapsed time under electric fields with different applied voltages for various HCNG blends and fuel–air equivalent ratios. With the application of electric field, the pressure rise rate curves have been promoted obviously when compared to those without the electric field, and the peak value of the curve is also increased. For example, at a hydrogen blend ratio of 20% and an equivalent ratio of 0.8, dp/dt is 10.07 MPa/s with the application of the electric field, which is 17.43% less than that without the electric field (8.57 MPa/s). Meanwhile, both peak value

Fig. 8. Pressure rise rate versus elapse time (A. Φ = 1.0; B. Φ = 0.8; C. Φ = 0.6). 10

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Table 3 tid and tmd for various HCNG blends. Hydrogen blend ratio

HCNG-0% HCNG-20% HCNG-40% HCNG-60% HCNG-80% HCNG-100%

Applied voltage

0 kV 5 kV 0 kV 5 kV 0 kV 5 kV 0 kV 5 kV 0 kV 5 kV 0 kV 5 kV

tid/ms

tmd/ms

Φ = 1.0

Φ = 0.8

Φ = 0.6

Φ = 1.0

Φ = 0.8

Φ = 0.6

21.16 18.93 19.07 17.37 17.07 15.89 13.82 13.38 12.21 11.65 11.34 10.17

32.73 27.46 29.31 25.37 22.31 20.39 17.88 16.98 14.07 13.49 11.46 11.16

87.66 64.44 62.74 51.82 53.53 47.88 37 33.98 21.3 19.97 14.28 13.82

16.76 15.51 13.36 12.69 9.22 9.02 6.18 6.07 3.47 3.46 2.03 2.03

39.63 33.43 27.63 24.67 16.32 15.26 9.26 8.93 5.46 5.35 2.53 2.51

128.72 102.86 80.45 68.39 56.21 50.07 29.07 27.33 11.06 10.81 5.065 5.055

range of independent variables on the accuracy of regression. Therefore, the range of each independent variable is unified [0,1], which is calculated as the function listed below:

equivalent ratio of 0.8, tmd is 24.67 ms with the application of the electric field, which is 10.71% less than that without the electric field (27.63 ms). The shortened rate of tmd under the electric field also decreases with the increase of hydrogen blend ratio. For example, at the equivalent ratio of 0.8 and the hydrogen blend ratios of 0%, 20%, 40%, 60%, 80% and 100%, respectively, with the application of a 5 kV AC electric field, tmd declines by 15.64%, 10.71%, 6.50%, 3.62%, 2.02% and 0.79%, respectively, compared to those without the electric field. Besides, the effect of the electric field on the decrease of tmd increases as the equivalent ratio decreases. For example, at the hydrogen blend ratio of 20% and the equivalent ratios of 1.0, 0.8 and 0.6, respectively, with the application of the electric field, tmd is increased by 5.02%, 10.71% and 14.99%, respectively, compared to those without the electric field. For both the initial combustion duration and the main combustion duration, it also follows such a rule that the electric field has a great impact at lean burn conditions. As we all know, the ionic wind effect mainly works by the electric field force. Therefore, it is a cumulative effect and no doubt, it is proportional to the actuation duration. At leaner conditions, the flame propagation is much slower than that at richer conditions, so the ionic wind effect contributes more to the promotion of the combustion progress.

x=

x − x min x max − x min

(10)

The radial basis function was selected as the kernel function for the model training. Three different parameters were set before training, including the penalty factor, the kernel function width and the insensitive band loss function. Grid parameter optimization, particle swarm optimization and genetic algorithm optimization methods were adopted as the optimization algorithm, respectively. The optimal process is mainly for searching the optimal value of parameters C, g and q (bestC, bestg and bestq). For the training group, the optimal parameters obtained by three methods for each objective function are shown in Table 5, respectively. It shows that the parameters C, g and q obtained by three methods are quite different. 6.2 Predicted results of SVM model Fig. 10 shows the predicted results obtained with various optimization methods for the mean flame propagation speed and the maximum pressure. It clearly presents that the predicted values of Sa or pmax is very close to the original experimental data. The multiple correlation coefficient is investigated to evaluate the prediction performance of the optimal SVM model, that is, the association degree between the independent and dependent variables. For the independent variable y and the dependent variable of x (x = [x1, x2,…,xk]), the regression function

6 Regression results and analysis 6.1 Establishment of SVM model Normally, an intelligent algorithm model consists of three layers: input, output, and hidden layers. The first two require the data group from the external sources such as experiment to run the simulation of the system. Based on the experimental results, the mathematical relationship between electric field parameters along with initial experimental parameters and flame propagation or combustion property characteristics was established by using the SVM regression method. Three different parameters were chosen as the independent variables, including the hydrogen blend ratio, the fuel–air equivalence ratio and the RMS voltage of the electric field applied; the mean flame propagation speed and the peak combustion pressure were chosen as the dependent variables. Based on the concept of the SVM method, the mathematical structure of the model is represented in Fig. 9. The regression analysis software used in the paper is the LibSVM program written by Professor Lin Zhiren from Taiwan University. In order to explore the application of SVM model for electric field assisted combustion field, the experimental data of HCNG fuels under different hydrogen blend ratios were set up to be the training data. Among them, 30 sets of valid data were set up for the regression training, and another 14 sets of forecast data were set up for testing. Table 4 shows the range of the experimental conditions. All of the variables were normalized before training to avoid the influence of the

Fig. 9. Mathematical structure of SVM model. 11

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error (MAPE/%) and the Hill inequality coefficient (Theil IC) are chosen to make the comparison, as is shown in Table 6. The three evaluation indicators are calculated by the equations listed below, respectively, where  yi is the estimated value of the model for the i-th predicted sample, yi is the corresponding true value (i = 1, 2, …, n), and n is the number of the data.

Table 4 Range of operating conditions of the engine. Item

Range

Hydrogen blend ratio Fuel-air equivalent ratio RMS voltage of electric field/kV

0, 20%, 40%, 60%, 80% and 100% 0.6, 0.7, 0.8, 0.9, 1.0, 1.1 and 1.2 0, 5

MAPE = Table 5 Optimal parameters. Object Functions

Optimization Algorithm

bestC

bestg

bestq

Sa

Grid PSO GA Grid PSO GA

18 403 1508 194 16 1986

0.500 0.590 0.994 0.500 0.641 0.121

0.00198 0.00315 0.00223 0.00292 0.00369 0.00155

pmax

Theil IC =

y − y) ∑ (y − y )( y − y )2 ∑ (y − y )2 ∑ (

n

∑ i=1

y^i − yi × 100 yi 1 n

1 n

n

(12)

n ∑i = 1 (y^i − yi )2

∑i = 1 y^i

2

+

1 n

n

∑i = 1 yi 2

(13)

Normally, if the MAPE value predicted by the model is less than 10%, the accuracy of the model is viewed as meeting the requirements. The optimal SVM model has a maximum MAPE of only 4.35% for pmax, illustrating that prediction performance of the model for the maximum pressure is accurate enough. However, the maximum MAPE for Sa is relatively higher, even more than 15%, meaning that the predicted performance of the SVM model is relatively limited. The value of the Theil IC is between 0 and 1, and the smaller the value, the higher the prediction accuracy of the model. From the table, it is found that the maximum Theil IC value for Sa and pmax is less than 0.06, respectively, which is far more less than 1, meaning that the optimal model can be used to predict the mean flame propagation speed and the maximum pressure effectively. Fig. 11 shows the relatively error of the test group. It shows that for most samples the predicted accuracy of pmax can be controlled within 10%, while this number increases to 30% for Sa, indicating that the predicted performance for pmax is much better than Sa. Besides, the predicted performances of the optimal SVM models obtained by various optimization methods are different for a constant object function, while the difference is quite limited overall. For example, for the mean flame propagation speed, the correlation

y = a0 + a1 x1 + …+ak xk , where a is the regression can be described as  coefficient vector. Then the multiple correlation coefficient is defined by the equation as follow: R=

1 n

(0 ⩽ y ⩽ 1). (11)

Generally, two variables are considered to be highly correlated when R is greater than 0.7. As is seen from Table 6, the value of the complex correlation coefficient of the training group is larger than 0.97, indicating that the optimal models determined by different combinations of the optimal parameters fit very well. Meanwhile, in order to determine the forecasting ability of the SVM model, the prediction and the experimental results of the test group were compared carefully. In this work, the average absolute percentage

Fig. 10. Predicted results obtained with various optimization methods (A. Sa; B. pmax). 12

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ratio, meaning that electric field can be applied to improve the combustion of lean burn conditions effectively. In fact, at a low equivalent ratio, the flame propagation speed is also relatively low, which will effectively improve the total duration of the electric field acting on the flame front. Therefore, the effect of the electric field on flame propagation and combustion process is also improved. As the fuel–air equivalence ratio decreases, the electric field has greater influence on the flame propagation and combustion of HCNG fuel. The reason is that the combustion speed declines greatly for lean mixture, so the flame front has a longer duration to stay in the electric field, which makes the cumulative effect of the electric field increases apparently. (4) The value of the complex correlation coefficient is larger than 0.97, meaning that the optimal models determined by the bestCs and bestgs fit very well for both the mean flame propagation speed and the maximum pressure. The prediction performance of the optimal model obtained by the SVM method for the maximum pressure is very accurate, while the prediction performance of the mean flame propagation speed is relatively limited.

Table 6 Predicted results of test group. Object Functions

Optimization Algorithm

R

MAPE/%

Theil IC

Sa

Grid PSO GA Grid PSO GA

0.9904 0.9721 0.9869 0.9736 0.9852 0.9719

14.30 15.62 10.91 3.87 3.65 4.35

0.0409 0.0596 0.0421 0.0216 0.0191 0.0233

pmax

Although there are no practical examples of applying electric field to promote combustion process, the results obtained in this study are still of great significance for expanding the electric field assisted combustion theory. Meanwhile, the successful prediction of combustion parameters by SVM method proves the feasibility of the application of artificial intelligence method in the basic combustion field, and also provides an effective research method for the basic combustion theory research. Fig. 11. Relatively errors of predicted and experimental results (A. Sa; B. pmax).

Acknowledgments

coefficient corresponding to the Grid method is the largest (0.9904), while those corresponding to the PSO and GA are relatively less (0.9721 and 0.9869, respectively).

This research is supported by the National Natural Science Foundation of China (Grant No. 51276095) and the National Key Research and Development Project of China (Grant No. 2016YFD0700800).

7. Conclusions

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(1) The electric field can improve the flame propagation and enhance the combustion of HCNG blends with a low hydrogen blend ratio, which is mainly because of the interaction between the charged particles and the electric field. A low frequency AC electric field will influence both positive and negative ions, forming the bi-ionic wind effect. The mean flame propagation speed is increased and the initial/main duration is shortened apparently, and the maximum pressure is increased slightly. At a hydrogen blend ratio of 20% and an equivalent ratio of 0.8, the mean flame propagation speed increases by 10.06%, and the initial duration and the main duration are shortened by 13.46% and 10.72%, respectively. (2) With the increase of hydrogen blend ratio, the promotion effect of electric field on the flame propagation and combustion of HCNG blends decreases gradually. There exists a maximum value of hydrogen blend ratio that electric field makes no difference to the combustion progress. The reason is probably that electric field almost has no effect on the pure hydrogen fuel. It also indicates that the influence particles in the ionic wind effect is mainly carbonbased particles but not the OH and O particles. (3) The limit value of hydrogen blend ratio increases with the excess air 13

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