Figure
1.
(a) Calculation model and (b) enlarged view of the pore.
| Citation: |
Jun XIONG, Shiyu LU, Xiaoming LIU, Wenjun ZHOU, Xiaoming ZHA, Xuekai PEI. Machine learning for parameters diagnosis of spark discharge by electro-acoustic signal[J]. Plasma Science and Technology, 2024, 26(8): 085403. DOI: 10.1088/2058-6272/ad495e
|
Discharge plasma parameter measurement is a key focus in low-temperature plasma research. Traditional diagnostics often require costly equipment, whereas electro-acoustic signals provide a rich, non-invasive, and less complex source of discharge information. This study harnesses machine learning to decode these signals. It establishes links between electro-acoustic signals and gas discharge parameters, such as power and distance, thus streamlining the prediction process. By building a spark discharge platform to collect electro-acoustic signals and implementing a series of acoustic signal processing techniques, the Mel-Frequency Cepstral Coefficients (MFCCs) of the acoustic signals are extracted to construct the predictors. Three machine learning models (Linear Regression, k-Nearest Neighbors, and Random Forest) are introduced and applied to the predictors to achieve real-time rapid diagnostic measurement of typical spark discharge power and discharge distance. All models display impressive performance in prediction precision and fitting abilities. Among them, the k-Nearest Neighbors model shows the best performance on discharge power prediction with the lowest mean square error (MSE = 0.00571) and the highest R-squared value (R2=0.93877). The experimental results show that the relationship between the electro-acoustic signal and the gas discharge power and distance can be effectively constructed based on the machine learning algorithm, which provides a new idea and basis for the online monitoring and real-time diagnosis of plasma parameters.
Volatile organic compounds (VOCs), including more than 120 kinds of organic gases, are one of the key carriers of PM2.5 and photochemical smog, which induce severe air pollution [1]. VOCs are mainly emitted from industries such as petrochemical industries, vehicle manufacturing, electronic manufacturing, furniture manufacturing and bio-pharming, etc. For waste gas containing VOCs of low concentration, various destruction methods, including discharge plasma treatment [2–10], are used for the removal of VOCs from the gas. During electric discharge in air or at an air/water interface, reactive oxygen species including O, OH and O3 are generated, and these active species can react with organic pollutants to produce CO2, H2O, etc [11–13]. The degradation of pollutants by electric discharge plasma is fast and nonselective. Therefore, various discharge plasma technologies, especially plasma catalysis processes including the in-plasma catalysis (IPC) and post-plasma catalysis (PPC) processes, have been extensively studied for treating contaminated gas [14]. In the IPC process, a catalyst packed in the discharge zone will remarkably enhance plasma chemical reactions via the efficient use of short-lived and long-lived species and the synergy between catalyst and plasma, etc [15]. Tu et al found that there is a combination of surface discharge and microdischarge in the packed bed plasma reactor, and the filamentary microdischarge begins to expand and diffuse to form the surface discharge with the increase in discharge power [16]. Wang et al reported that the packed bed DBD reactor in atmospheric dry air may appear in the form of forward restrike, filamentary microdischarge and surface discharge. Moreover, the streamlined propagation and the generation of reactive species are faster at a higher dielectric constant [17].
The catalyst particles are usually porous. Recently, the microdischarge phenomenon in the pores of catalysts has been paid attention to. Holzer et al reported that reactive species could be generated in catalyst pores since the electric field in the pores of porous catalysts is stronger than that in the air gap [18–20]. Hensel et al [21–24] experimentally studied the generation of microdischarge in porous particles under a high DC voltage (2–10 kV) and found that electric discharge happens in pores of 15 μm diameter, and that the onset voltage of the microdischarge decreased with the increase in pore size. However, it is impossible to experimentally measure the plasma characteristics (e.g. the electron density and electron temperature, the ionization rate, etc) in pores. Zhang et al studied the microdischarge characteristics in pores under a He atmosphere via numerical simulation [25–28] and found that microdischarge can occur in pores of specific sizes. Wang et al investigated the formation of microdischarge and the propagation of plasma flow in pores of different diameters and depths, and found that a larger voltage can make the plasma flow penetrate smaller pores, and the pore size and depth can lead to enhanced discharge in pores by influencing the surface charge of the side wall of porous catalysts [29]. Zhang et al studied the formation of microdischarge in micro- and nanocatalyst pores using a two-dimensional intracellular particle/Monte Carlo collision model and found that microdischarge can form in both microns and nanopores [30]. However, the plasma catalysis process for gaseous pollutant treatment usually happens in air atmosphere, so these simulation studies on microdischarge in pores under He atmosphere [26–28] are inconsistent with the application background, or the simulation parameters are out of the actual electric discharge conditions [30].
In this work, a two-dimensional fluid model was established to study the microdischarge in a pore in air atmosphere. This study is important to understand the air plasma behavior in a catalyst's pores, which is helpful in the design of suitable porous catalysts of special pore size for gaseous pollutant treatment in the electric discharge process.
A two-dimensional fluid model is established by COMSOL software [31]. The main steps include the establishment of the model, the setting of variables, the selection of collision response equation, the setting of boundaries and the division of the network. The model adopts the usual DBD configuration, which is extensively used for gaseous pollutant treatment, as shown in figure 1. From top to bottom, the ceramic with a thickness of 1 mm is connected to the high-voltage electrode, then there is an air gap with a thickness of 2 mm, and then the ceramic with a thickness of 0.5 mm is grounded. A positive pulse voltage is applied (the rise time is 400 ns and the fall time is 400 ns). Since the experiment is basically carried out in air, we choose N2 (79%) and O2 (21%) as the medium in the air gap and set the gas temperature to 300 K. The gas pressure is set to 1 atm. The relative dielectric constant of the selected material is 9. Twenty-six important plasma reactions (table A1) involving e, N2, O2, N+2,N+4,O+2,O+2N2,O+4,O, O2−, O3 and O− are included in the model, and the reaction rates of the first five formulas are calculated from the collision cross-section data by solving the electron Boltzmann equation (BE) with BOLSIG+ [32–34]. The required average electron energy and its transport coefficient (R1–R5) are also calculated by BOLSIG+ [35].
The required parameters are obtained through a set of coupled equations. The continuity equations of electron density and electron energy are given as equations (1) and (2), respectively.
| \frac{\partial n_{\mathrm{e}}}{\partial t}+\nabla \cdot\left(-n_{\mathrm{e}}\left(-\mu_{\mathrm{e}} \cdot \boldsymbol{E}\right)-D_{\mathrm{e}} \cdot \nabla n_{\mathrm{e}}\right)=R_{\mathrm{e}} | (1) |
| \frac{\partial n_{\varepsilon}}{\partial t}+\nabla \cdot\left(-n_{\varepsilon}\left(-\mu_{\varepsilon} \cdot \boldsymbol{E}\right)-D_{\varepsilon} \cdot \nabla n_{\varepsilon}\right)=R_{\varepsilon} | (2) |
Here, n_{\mathrm{e}}, n_{\varepsilon}, \mu_{\mathrm{e}} and \mu_{\varepsilon} are the electron number density and electron energy density, electron mobility and electron energy mobility, respectively. D_{\mathrm{e}} and D_{\varepsilon} are the electron diffusion coefficient and electron energy diffusion coefficient, respectively. E is the electric field vector. Re and R_{\varepsilon} are the source terms for the electron density and electron energy, respectively.
Heavy species follow the continuity equation (3):
| \rho \frac{\partial w_k}{\partial t}+\nabla\left(\rho w_k V_k\right)=R_k | (3) |
Here, w_k, V_k \;\text { and }\; R_k are the mass fraction, velocity and source term of the heavy species (k), respectively. Drift is only suitable for electrons and ions, and neutral species contain only diffusion terms.
In addition, the Poisson equation (4) is used to solve the electric field.
| \nabla^2 V=-\frac{\rho_{\text {charge }}}{\varepsilon_0 \varepsilon_{\mathrm{r}}} | (4) |
Here, V is the electric potential and \rho_{\text {charge }} is the space charge density, which is zero inside the dielectric material. \varepsilon_0 is the vacuum dielectric constant and \varepsilon_\text {r } is the relative dielectric constant.
The boundary conditions of electron flux during surface scattering and absorption of electrons at the cathode and anode are as follows:
| \boldsymbol{\Gamma}_{\mathrm{e}} \cdot \boldsymbol{n}=\frac{1-\gamma_{\mathrm{e}}}{1+\gamma_{\mathrm{e}}}\left[\frac{1}{2} v_{\mathrm{e}, \mathrm{th}} n_{\mathrm{e}}-\left(2 a_{\mathrm{e}}-1\right) n_{\mathrm{e}} \mu_{\mathrm{e}} \boldsymbol{E} \cdot \boldsymbol{n}\right] | (5) |
\boldsymbol{\Gamma}_{\mathrm{e}} and n are the electron density flux and the electrode normal vector, respectively. \gamma_{\mathrm{e}} is the reflection coefficient of electrons on the electrode surface, which is 1 when the direction of the electron flux is towards the electrode, and otherwise it is 0. v_{\mathrm{e}, \mathrm{th}} is the thermodynamic velocity of the electron.
The boundary condition of the conservation equation for electron energy density on the electrode surface is as follows:
| \boldsymbol{\Gamma}_{\varepsilon} \cdot \boldsymbol{n}=\frac{1}{3} v_{\mathrm{e}, \mathrm{th}} \bar{\varepsilon} n_{\mathrm{e}} | (6) |
\boldsymbol{\Gamma}_{\varepsilon} is the electron energy flux vector and \bar{\varepsilon} is the average electron energy.
The initial electron density is set as 1013 m−3. The average electron energy follows Maxwellian distribution and the initial value of the average electron energy is 2 eV. The secondary electron emission coefficient in this model is set as 0.01 and the average energy of secondary electrons is 2 eV [36]. Quadrilateral mesh is mainly used in the air gap bulk region, and the mesh size is about 12 μm. The size of the grid near the positive plate is 1–2 μm. For the grid near and in the pore, a triangular grid with a size of about 0.5–1 μm is adopted. In other areas, coarse grids are used under the condition that they will not affect the reliability of the calculation model. The nonlinear method is constant (Newtons), with a maximum number of iterations of 4 and a tolerance factor of 0.1. The solver uses PARDISO. The minimum out-of-core memory fraction is 0.99, the minimum in-core memory is 512 MB, the total memory usage ratio is 0.8, and the internal memory usage factor is 3. The selected time-stepping method is the backward difference formula. The initial step size adopted by the solver is 10−14 s, the maximum backward difference formula (BDF) order is 2, and the minimum BDF order is 1. The event tolerance is 0.01, and the backward Euler method is used for uniform initialization.
The microdischarge characteristics of a porous catalyst under different pore sizes and voltages are studied by calculating the distribution of plasma density, electron temperature, electric field, ionization rate and electric potential. During the study, the voltage amplitude is set as 9 kV, and the ratio of fixed aperture to depth is 0.51.
Hensel et al reported that the pore size has an important effect on the air microdischarge in pores, and the onset voltage of microdischarge in pores is lower at a larger pore aperture [22, 23]. However, it is difficult to directly observe the microdischarge phenomenon in pores. The ionization rate is studied as a direct basis to judge whether microdischarge occurs in pores. To understand the effect of pore size on onset microdischarge in pores, the ionization rate (figure 2) and electron density (figure A1) in and above the pore for different pore diameters (20–100 μm) at different times are numerically simulated.
Figures 2 and A1 indicate that strong ionization occurs preferentially in the pores of larger size. When the pore diameter is 20 μm, strong ionization mainly appears in the bulk region and near the pore opening, and the ionization in the pore is low (1024 m−3 s−1), so that the electron density in the pore is the lowest in the whole air gap. However, ionization occurs in the pore of 60 μm at 155 ns, and even at 120 ns, strong ionization occurs near the opening of the 80 μm pore and in the 100 μm pore. Moreover, strong ionization (1026 m−3 s−1) occurs in the bottom of the pore of 100 μm diameter, and the electron density in the pore also reaches a maximum value.
The electric field \left(\sqrt{E_x^2+E_y^2}\right) in and above the pore (shown in figure A2) can explain the effect of pore size on the ionization process in pores. When the pore size is 20 μm, the maximum electric field appears on the surface of the dielectric layer near the pore opening, which explains why the maximum ionization occurs in the bulk region but not in the pore. When the pore size ranges from 60 to 100 μm, the electric field in the bulk region is uniform and low (104–105 V m−1), but the electric field intensity in the pore increases to about 106 V m−1, which can lead to an efficient ionization due to the strong air breakdown. The pore size is larger than the Debye length (about 2 μm) by theoretical calculation, which also shows that the discharge can be generated in the pore under this condition.
To further understand the effect of pore size on the plasma characteristics in the air gap, the time-averaged electron density, total ion density and electron temperature are calculated, and their distribution curves along the vertical centerline from the pore bottom to the top dielectric plate (y = 0 mm) are given in figure 3. Figure 3(a) clearly shows that the distribution of electron density in the air gap is relatively uniform when there is no pore at the bottom, and the maximum value of electron density appears at the bottom of the air gap. When there is a small pore of 20 μm on the dielectric, the strong electric field in the pore (see figure 4(a)) causes a large loss of particles on the wall and pushes electrons out of the pore, leading to low electron density in the pore, but the electron density in the bulk region (air gap) is hardly affected by the existence of the pore. However, the electron density in and near the pore begins to rise sharply when the pore diameter exceeds 30 μm, and the electron density in the pore with 70 μm diameter reaches a peak value of 3.7 × 1018 m−3, which is about one order of magnitude higher than in the bulk region.
As indicated in figure 3(b), the distribution of total ion density in the pore is similar to that of the electron density, and the total ion density in the pore of 70 μm is also the largest (3.5 × 1020 m−3), which is five times higher than that without a pore. The effect of the pore size on the total ion density is different from the effect on the ionization rate, which reaches the largest value in the pore of 30 μm diameter (figure A3). The results can be explained as follows: although the ionization rate in the pore tends to decrease when the pore size is bigger than 30 μm, a large pore size is helpful for accommodating more electrons and ions, which resulted from the ionization of air in the pore, leading to a higher total ion density in the pore of 70 μm.
Figure 3(c) shows that the electron temperature in and near the pore is higher, therefore a higher energy of electrons enhances the ionization process and produces more electrons, which explains the effect of the pore on the distribution of electron density and total ion density. However, we observed that the electron density at the pore bottom decreases to a low value. The result is attributed to the special electric potential distribution in the pore (see figure (4)). Figures 4(a)–(c) show the distribution of time-averaged electric potential and electric field \left(\sqrt{E_x^2+E_y^2}\right) inside and above the pore at three typical pore sizes. Figure 4 indicates that the electric potential at the bottom of the pore is low, and the strong reverse electric field pushes the electrons from the bottom to the middle of the pore and to the bulk region, resulting in a low electron density at the pore bottom [27]. In addition, we can observe that the distribution of electric potential lines in and near the pore is relatively flat and dense when the pore is small (20 μm). As the pore grows larger (60 μm and 100 μm), the electric potential lines begin to bend toward the bottom medium (especially inside the pore) and are sparsely distributed in most areas of the pore, which explains the distribution of the electric field.
Zhang et al reported that the plasma Debye length decreased when the voltage was increased under certain conditions, making it easier to generate electric discharge in smaller pores [30]. Hensel et al also found that the luminous intensity increases with the increase in discharge power when the fixed aperture is 80 μm [24]. These studies show that the applied voltage notably affects the microdischarge in pores. Figure 5 shows the distribution of time-averaged electron density, electron temperature and total ion density along the vertical centerline of the pore at different voltages. As shown in figure 5(a), when the voltage is 2 kV, the electron density in the pore (y = −2.03 mm) and near the pore (y = −1.7 mm) shows two peak values, which are higher than those in the bulk region and near the top dielectric plate, but the electron density is low (1017 m−3), especially at the pore bottom. When the voltage increases to 6 kV, the maximum electron density in the pore (2.1 × 1018 m−3) is noticeably higher than that at the pore opening (1.2 × 1018 m−3) and in the bulk region (2.5 × 1017 m−3). Moreover, the electron density in and near the pore differs from that in the bulk region, and the difference is more apparent when the voltage further rises. Figure 5(b) shows that the electron temperature in the pore, near the pore and near the top dielectric plate is higher at higher applied voltage, which leads to a higher electron collision ionization rate (figure A4) and total ion density (figure 5(c)). Figure 5(c) shows that the total ion density decreases to a low value at the bottom of the pore and near the positive polar plate, and the distribution of total ion density in the whole air gap is relatively uniform (1019 m−3) when the voltage is low (2 kV). The total ion density in the pore and in the bulk region begins to differ by one order of magnitude when the voltage increases (9 kV), indicating that the ionization in the pore is greatly improved at higher voltage. The stronger electric field at higher applied voltage facilitates the production of electrons of higher energy, leading to a more efficient collision of electrons with air to generate more ions. The results also prove that the generation of plasma in the pore increases significantly at higher voltage, therefore leading to different discharge behaviors.
Although it was observed that the electric field in the pore is significantly enhanced at 2 kV, the discharge behavior is still different from that at higher voltage. Figure 6 shows the distribution of time-averaged electric potential and electric field \left(\sqrt{E_x^2+E_y^2}\right) inside and above the pore with different voltage amplitudes and a pore size of 80 μm. It can be seen that the electric field in and near the pore is significantly greater than that in the bulk region at a voltage of 2 kV, but the electric potential lines away from the pore are flat. However, when the voltage exceeds 6 kV, the electric potential lines in the bulk region begin to bend, and they are very dense at the pore bottom (especially at 12 kV). Moreover, the influence of the pore on the electric potential in the air gap is greater at higher applied voltage, and a strong electric field can be seen at the pore bottom, which explains the low electron density at the pore bottom. Holzer et al [18] proposed that the electric field in the pore must be higher than the electric field in the body if short-lived species are produced in the pore. The simulation results agree well with the experiment. The results show that the applied voltage and aperture have important effects on the characteristics of microdischarge in the pore.
In this study, a two-dimensional fluid model is established to examine the discharge behavior in a pore under air dielectric barrier discharge. The pore size notably affects the microdischarge in the pore, and strong ionization and microdischarge occur in the pore with large diameter. When the pore size is less than 20 μm, the ionization in the pore does not increase significantly at 9 kV voltage, but the time-averaged ionization rate in the pore of 30 μm diameter is the largest. However, the time-averaged plasma density in the pore of 70 μm diameter is the largest. The applied voltage also remarkably affects the microdischarge in the pore. When the voltage is greater than 2 kV, the plasma density and ionization rate in the pore of 80 μm diameter begin to change significantly, but the plasma density in the bulk region changes little with the voltage. This study is helpful to understand the air microdischarge mechanism in porous catalysts, and helps in the design of porous catalysts for plasma catalysis processes.
| Number | Reaction | Rate coefficient | ΔE (eV) | Reference |
| R1 | \mathrm{O}_2+\mathrm{e} \rightarrow \mathrm{e}+\mathrm{O}_2 | cross-section | 1.0 | [32] |
| R2 | \mathrm{N}_2+\mathrm{e} \rightarrow \mathrm{e}+\mathrm{N}_2 | cross-section | 1.0 | [32] |
| R3 | \mathrm{O}_2+\mathrm{e} \rightarrow 2 \mathrm{e}+\mathrm{O}_2^{+} | cross-section | 12.06 | [32] |
| R4 | \mathrm{N}_2+\mathrm{e} \rightarrow 2 \mathrm{e}+\mathrm{N}_2^{+} | cross-section | 15.6 | [32] |
| R5 | \mathrm{O}_2+\mathrm{e} \rightarrow \mathrm{O}+\mathrm{O}^{-} | cross-section | 3.6 | [33] |
| R6 | \mathrm{N}_2^{+}+\mathrm{N}_2+\mathrm{M} \rightarrow \mathrm{N}_4^{+}+\mathrm{M} | 5 × 10−41 | [34] | |
| R7 | \mathrm{N}_4^{+}+\mathrm{O}_2 \rightarrow \mathrm{O}_2^{+}+2 \mathrm{N}_2 | 2.5 × 10−16 | [34] | |
| R8 | \mathrm{N}_2^{+}+\mathrm{O}_2 \rightarrow \mathrm{O}_2^{+}+\mathrm{N}_2 | 1.04 × 10−15T−0.5 | [34] | |
| R9 | 2 \mathrm{N}_2+\mathrm{O}_2^{+} \rightarrow \mathrm{O}_2^{+} \mathrm{N}_2+\mathrm{N}_2 | 8.1 × 10−38T−2 | [34] | |
| R10 | \mathrm{O}_2^{+} \mathrm{N}_2+\mathrm{N}_2 \rightarrow \mathrm{O}_2^{+}+2 \mathrm{N}_2 | 14.6 T−5.3exp(−2357/T) | [34] | |
| R11 | \mathrm{O}_2^{+} \mathrm{N}_2+\mathrm{O}_2 \rightarrow \mathrm{O}_4^{+}+\mathrm{N}_2 | 1 × 10−15 | [34] | |
| R12 | \mathrm{O}_2^{+}+\mathrm{O}_2+\mathrm{M} \rightarrow \mathrm{O}_4^{+}+\mathrm{M} | 2.04 × 10−34T−3.2 | [34] | |
| R13 | \mathrm{O}_4^{+}+\mathrm{e} \rightarrow 2 \mathrm{O}_2 | 1.4 × 10−12(300/Te)0.5 | [34] | |
| R14 | \mathrm{O}_2^{+}+\mathrm{e} \rightarrow 2 \mathrm{O} | 2.42 × 10−13(300/Te) | [34] | |
| R15 | 2 \mathrm{O}_2+\mathrm{e} \rightarrow \mathrm{O}_2+\mathrm{O}_2^{-} | 2 × 10−41(300/Te) | [34] | |
| R16 | \mathrm{O}_4^{+}+\mathrm{O}_2^{-} \rightarrow 3 \mathrm{O}_2 | 1 × 10−13 | [34] | |
| R17 | \mathrm{O}_4^{+}+\mathrm{O}_2^{-}+\mathrm{M} \rightarrow 3 \mathrm{O}_2+\mathrm{M} | 2 × 10−37 | [34] | |
| R18 | \mathrm{O}_2^{+}+\mathrm{O}_2^{-}+\mathrm{M} \rightarrow 2 \mathrm{O}_2+\mathrm{M} | 2 × 10−37 | [34] | |
| R19 | \mathrm{O}+\mathrm{O}_2+\mathrm{M} \rightarrow \mathrm{O}_3+\mathrm{M} | 2.5 × 10−46 | [34] | |
| R20 | 2 \mathrm{e}+\mathrm{N}_2^{+} \rightarrow \mathrm{N}_2+\mathrm{e} | 5.651 × 10−27Te−0.8 | [34] | |
| R21 | \mathrm{e}+\mathrm{N}_2^{+}+\mathrm{N}_2 \rightarrow 2 \mathrm{N}_2 | 6.07 × 10−34Te−2.5 | [34] | |
| Note: The unit of two-body reaction rate is m3/s, the unit of three-body reaction rate is m6/s. Te (electron temperature) and T (gas temperature) are in K, and M = O2, N2. | ||||
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This work was partially supported by National Natural Science Foundation of China (No. 52377155) and the State Key Laboratory of Reliability and Intelligence of Electrical Equipment (No. EERI-KF2021001), Hebei University of Technology.
| [1] |
Laroussi M et al 2022 IEEE Trans. Radiat. Plasma Med. Sci. 6 127 doi: 10.1109/TRPMS.2021.3135118
|
| [2] |
Lu X et al 2016 Phys. Rep. 630 1 doi: 10.1016/j.physrep.2016.03.003
|
| [3] |
Graves D B et al 2012 J. Phys. D: Appl. Phys. 45 263001 doi: 10.1088/0022-3727/45/26/263001
|
| [4] |
von Woedtke T et al 2013 Phys. Rep. 530 291 doi: 10.1016/j.physrep.2013.05.005
|
| [5] |
Lu X et al 2014 Phys. Rep. 540 123 doi: 10.1016/j.physrep.2014.02.006
|
| [6] |
Snoeckx R et al 2021 Combust. Flame 225 1 doi: 10.1016/j.combustflame.2020.10.028
|
| [7] |
Starikovskiy A et al 2013 Progr. Energy Combust. Sci. 39 61 doi: 10.1016/j.pecs.2012.05.003
|
| [8] |
Matveev I B et al 2010 IEEE Trans. Plasma Sci. 38 3257 doi: 10.1109/TPS.2010.2091153
|
| [9] |
Bogaerts A et al 2022 Plasma Sources Sci. Technol. 31 053002 doi: 10.1088/1361-6595/ac5f8e
|
| [10] |
Corke T C et al 2010 Annu. Rev. Fluid Mechan. 42 505 doi: 10.1146/annurev-fluid-121108-145550
|
| [11] |
Penkov O V et al 2015 J. Coat. Technol. Res. 12 225 doi: 10.1007/s11998-014-9638-z
|
| [12] |
Dingemans G et al 2012 J. Electrochem. Soc. 159 H277 doi: 10.1149/2.067203jes
|
| [13] |
Grosse-Kreul S et al 2015 Plasma Sources Sci. Technol. 24 044008 doi: 10.1088/0963-0252/24/4/044008
|
| [14] |
Pei X et al 2013 Plasma Sources Sci. Technol. 22 025023 doi: 10.1088/0963-0252/22/2/025023
|
| [15] |
Lo A et al 2012 Appl. Phys. B: Lasers Opt. 107 229 doi: 10.1007/s00340-012-4874-3
|
| [16] |
Wu S et al 2022 Plasma Processes Polym 19 e2100164 doi: 10.1002/ppap.202100164
|
| [17] |
Xiong Q et al 2009 J. Appl. Phys. 106 083302 doi: 10.1063/1.3239512
|
| [18] |
Boinet M et al 2004 NDT E Int. 37 213 doi: 10.1016/j.ndteint.2003.09.011
|
| [19] |
Law V J et al 2011 Plasma Sources Sci. Technol. 20 035024 doi: 10.1088/0963-0252/20/3/035024
|
| [20] |
O’Connor N et al 2011 J. Appl. Phys. 110 013308 doi: 10.1063/1.3587225
|
| [21] |
Ghosh P et al 2024 J. Phys. D: Appl. Phys. 57 014001 doi: 10.1088/1361-6463/acfdb6
|
| [22] |
Lin L et al 2023 J. Phys. D: Appl. Phys. 57 015203 doi: 10.1088/1361-6463/acfcc7
|
| [23] |
Zhong L et al 2023 J. Phys. D: Appl. Phys. 56 074006 doi: 10.1088/1361-6463/acb604
|
| [24] |
Sibanyoni H et al 2020 Proc. 21st Int. Symp. High Volt. Eng. 599 1430
|
| [25] |
Shojaei K and Mangolini L 2021 J. Phys. D: Appl. Phys. 54 265202 doi: 10.1088/1361-6463/abf61e
|
| [26] |
Gidon D et al 2019 IEEE Trans. Radiat. Plasma Med. Sci. 3 597 doi: 10.1109/TRPMS.2019.2910220
|
| [27] |
Chang J et al 2023 IEEE Trans. Plasma Sci. 51 311 doi: 10.1109/TPS.2022.3185029
|
| [28] |
Zhang W et al 2022 J. Phys. D: Appl. Phys. 55 26LT01 doi: 10.1088/1361-6463/ac5d04
|
| [29] |
Yakubovich V A et al 2021 Vestnik St. Petersb. Univ. Math. 54 384 doi: 10.1134/S106345412104021X
|
| [30] |
Tsai Y et al 2018 IFAC-PapersOnLine 51 13 doi: 10.1016/j.ifacol.2018.09.237
|
| [31] |
Huang G et al 2006 Neurocomputing 70 489 doi: 10.1016/j.neucom.2005.12.126
|
| [32] |
Noi P T and Kappas M 2018 Sensors 18 18 doi: 10.3390/s18010018
|
| [33] |
Belgiu M et al 2016 ISPRS J. Photogramm. Remote Sens. 114 24 doi: 10.1016/j.isprsjprs.2016.01.011
|
| [34] |
Breiman L et al 2001 Mach. Learn. 45 5 doi: 10.1023/A:1010933404324
|
| [35] |
McFee B et al 2015 librosa: Audio and Music Signal Analysis in Python Proc. 14th Python Sci. Conf. 1 18 doi: 10.25080/majora-7b98e3ed-003
|
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| [4] | LIU (刘泽), Guogang YU (余国刚), Anping HE (何安平), Ling WANG (王玲). Simulation of thermal stress in Er2O3 and Al2O3 tritium penetration barriers by finite-element analysis[J]. Plasma Science and Technology, 2017, 19(9): 95602-095602. DOI: 10.1088/2058-6272/aa719d |
| [5] | LI Xiang (栗翔), XU Yongjian (许永建), YU Ling (于玲), CHEN Yu (陈宇), HU Chundong (胡纯栋), TAO Ling (陶玲). Analysis of Power Distribution on Beamline Components at Different Neutralization Efficiencies on NBI Test Stand[J]. Plasma Science and Technology, 2016, 18(12): 1215-1219. DOI: 10.1088/1009-0630/18/12/12 |
| [6] | WANG Lei(王磊), HUANG Yiyun(黄懿赟), ZHAO Yanping(赵燕平), ZHANG Jian(张健), YANG Lei(杨磊), GUO Wenjun(郭文军). Structure Design and Analysis of High-Voltage Power Supply for ECRH[J]. Plasma Science and Technology, 2014, 16(11): 1079-1082. DOI: 10.1088/1009-0630/16/11/15 |
| [7] | LI Sen(李森), SONG Zhiquan(宋执权), ZHANG Ming(张明), XU Liuwei(许留伟), LI Jinchao(李金超), FU Peng(傅鹏), WANG Min(王敏), DONG Lin(董琳). Finite Element Method Applied to Fuse Protection Design[J]. Plasma Science and Technology, 2014, 16(3): 294-299. DOI: 10.1088/1009-0630/16/3/22 |
| [8] | ZHANG Zuchao (张祖超), JI Zhenshan (季振山), XIAO Bingjia (肖炳甲), WANG Yong (王勇). Upgrade of the Distributed Time-Keeping and Synchronization System for EAST[J]. Plasma Science and Technology, 2013, 15(10): 1076-1080. DOI: 10.1088/1009-0630/15/10/21 |
| [9] | CHENG Xian (程显), DUAN Xiongying (段雄英), LIAO Minfu (廖敏夫), et al.. The Voltage Distribution Characteristics of a Hybrid Circuit Breaker During High Current Interruption[J]. Plasma Science and Technology, 2013, 15(8): 800-806. DOI: 10.1088/1009-0630/15/8/16 |
| [10] | SHI Xingjian (侍行剑), HU Yemin (胡业民), GAO Zhe (高喆). Optimization of Lower Hybrid Current Drive Efficiency for EAST Plasma with Non-Circular Cross Section and Finite Aspect-Ratio[J]. Plasma Science and Technology, 2012, 14(3): 215-221. DOI: 10.1088/1009-0630/14/3/06 |
Figures(10) / Tables(1)
Supported by: Beijing Renhe Information Technology Co., Ltd. 
| Number | Reaction | Rate coefficient | ΔE (eV) | Reference |
| R1 | \mathrm{O}_2+\mathrm{e} \rightarrow \mathrm{e}+\mathrm{O}_2 | cross-section | 1.0 | [32] |
| R2 | \mathrm{N}_2+\mathrm{e} \rightarrow \mathrm{e}+\mathrm{N}_2 | cross-section | 1.0 | [32] |
| R3 | \mathrm{O}_2+\mathrm{e} \rightarrow 2 \mathrm{e}+\mathrm{O}_2^{+} | cross-section | 12.06 | [32] |
| R4 | \mathrm{N}_2+\mathrm{e} \rightarrow 2 \mathrm{e}+\mathrm{N}_2^{+} | cross-section | 15.6 | [32] |
| R5 | \mathrm{O}_2+\mathrm{e} \rightarrow \mathrm{O}+\mathrm{O}^{-} | cross-section | 3.6 | [33] |
| R6 | \mathrm{N}_2^{+}+\mathrm{N}_2+\mathrm{M} \rightarrow \mathrm{N}_4^{+}+\mathrm{M} | 5 × 10−41 | [34] | |
| R7 | \mathrm{N}_4^{+}+\mathrm{O}_2 \rightarrow \mathrm{O}_2^{+}+2 \mathrm{N}_2 | 2.5 × 10−16 | [34] | |
| R8 | \mathrm{N}_2^{+}+\mathrm{O}_2 \rightarrow \mathrm{O}_2^{+}+\mathrm{N}_2 | 1.04 × 10−15T−0.5 | [34] | |
| R9 | 2 \mathrm{N}_2+\mathrm{O}_2^{+} \rightarrow \mathrm{O}_2^{+} \mathrm{N}_2+\mathrm{N}_2 | 8.1 × 10−38T−2 | [34] | |
| R10 | \mathrm{O}_2^{+} \mathrm{N}_2+\mathrm{N}_2 \rightarrow \mathrm{O}_2^{+}+2 \mathrm{N}_2 | 14.6 T−5.3exp(−2357/T) | [34] | |
| R11 | \mathrm{O}_2^{+} \mathrm{N}_2+\mathrm{O}_2 \rightarrow \mathrm{O}_4^{+}+\mathrm{N}_2 | 1 × 10−15 | [34] | |
| R12 | \mathrm{O}_2^{+}+\mathrm{O}_2+\mathrm{M} \rightarrow \mathrm{O}_4^{+}+\mathrm{M} | 2.04 × 10−34T−3.2 | [34] | |
| R13 | \mathrm{O}_4^{+}+\mathrm{e} \rightarrow 2 \mathrm{O}_2 | 1.4 × 10−12(300/Te)0.5 | [34] | |
| R14 | \mathrm{O}_2^{+}+\mathrm{e} \rightarrow 2 \mathrm{O} | 2.42 × 10−13(300/Te) | [34] | |
| R15 | 2 \mathrm{O}_2+\mathrm{e} \rightarrow \mathrm{O}_2+\mathrm{O}_2^{-} | 2 × 10−41(300/Te) | [34] | |
| R16 | \mathrm{O}_4^{+}+\mathrm{O}_2^{-} \rightarrow 3 \mathrm{O}_2 | 1 × 10−13 | [34] | |
| R17 | \mathrm{O}_4^{+}+\mathrm{O}_2^{-}+\mathrm{M} \rightarrow 3 \mathrm{O}_2+\mathrm{M} | 2 × 10−37 | [34] | |
| R18 | \mathrm{O}_2^{+}+\mathrm{O}_2^{-}+\mathrm{M} \rightarrow 2 \mathrm{O}_2+\mathrm{M} | 2 × 10−37 | [34] | |
| R19 | \mathrm{O}+\mathrm{O}_2+\mathrm{M} \rightarrow \mathrm{O}_3+\mathrm{M} | 2.5 × 10−46 | [34] | |
| R20 | 2 \mathrm{e}+\mathrm{N}_2^{+} \rightarrow \mathrm{N}_2+\mathrm{e} | 5.651 × 10−27Te−0.8 | [34] | |
| R21 | \mathrm{e}+\mathrm{N}_2^{+}+\mathrm{N}_2 \rightarrow 2 \mathrm{N}_2 | 6.07 × 10−34Te−2.5 | [34] | |
| Note: The unit of two-body reaction rate is m3/s, the unit of three-body reaction rate is m6/s. Te (electron temperature) and T (gas temperature) are in K, and M = O2, N2. | ||||
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