Statistical modelling of discharge behavior of atmospheric pressure dielectric barrier discharge

Tay, Wee Horng; Kausik, S. S.; Wong, C. S.; Yap, S. L.; Muniandy, S. V.

doi:10.1063/1.4901250

Cited by 7 publications

(4 citation statements)

References 29 publications

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“…Instead, an oscilloscope with large sampling rate or coincidence techniques are used to collect data with mutual correlation (in time, voltage or magnitude) and with significant statistical weight [2,25,30]. Important information about the discharge behaviour can be inferred in that way [10,[33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Advanced electrical current measurements of microdischarges: evidence of sub-critical pulses and ion currents in barrier discharge in air

Synek¹,

Zemánek²,

Kudrle³

et al. 2018

Plasma Sources Sci. Technol.

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Electrical current measurements in corona or barrier microdischarges are a challenge as they require both high temporal resolution and a large dynamic range of the current probe used. In this article, we apply a simple self-assembled current probe and compare it to commercial ones. An analysis in the time and frequency domain is carried out. Moreover, an improved methodology is presented, enabling both temporal resolution in sub-nanosecond times and current sensitivity in the order of tens of microamperes. Combining this methodology with a high-tech oscilloscope and self-developed software, a unique statistical analysis of currents in volume barrier discharge driven in atmospheric-pressure air is made for over 80 consecutive periods of a 15 kHz applied voltage. We reveal the presence of repetitive sub-critical current pulses and conclude that these can be identified with the discharging of surface charge microdomains. Moreover, extremely low, long-lasting microsecond currents were detected which are caused by ion flow, and are analysed in detail. The statistical behaviour presented gives deeper insight into the discharge physics of these usually undetectable current signals.

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Section: Introductionmentioning

confidence: 99%

Advanced electrical current measurements of microdischarges: evidence of sub-critical pulses and ion currents in barrier discharge in air

Synek¹,

Zemánek²,

Kudrle³

et al. 2018

Plasma Sources Sci. Technol.

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“…Here we report our experimental findings from plasma treatment on UHMWPE/MWCNT nanocomposites. The treatment was achieved by modifying the alternating current (AC) glow discharge system into a dielectric barrier discharge (DBD) configuration by using glass as a dielectric material [ 22 ]. The main reason behind the design modification is that DBD offers a non-equilibrium plasma generated at atmospheric pressure, providing an effective tool for surface activation.…”

Section: Introductionmentioning

confidence: 99%

Effect of Argon Plasma Treatment on Tribological Properties of UHMWPE/MWCNT Nanocomposites

et al. 2016

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Ultra-high molecular weight polyethylene (UHMWPE) is widely used in artificial joints in the replacement of knee, hip and shoulder that has been impaired as a result of arthritis or other degenerative joint diseases. The UHMWPE made plastic cup is placed in the joint socket in contact with a metal or ceramic ball affixed to a metal stem. Effective reinforcement of multi-walled carbon nanotubes (MWCNTs) in UHMWPE results in improved mechanical and tribological properties. The hydrophobic nature of the nanocomposites surface results in lesser contact with biological fluids during the physiological interaction. In this project, we investigate the UHMWPE/MWCNTs nanocomposites reinforced with MWCNTs at different concentrations. The samples were treated with cold argon plasma at different exposure times. The water contact angles for 60 min plasma-treated nanocomposites with 0.0, 0.5, 1.0, 1.5, and 2.0 wt % MWCNTs were found to be 55.65˝, 52.51˝, 48.01˝, 43.72˝, and 37.18˝respectively. Increasing the treatment time of nanocomposites has shown transformation from a hydrophobic to a hydrophilic nature due to carboxyl groups being bonded on the surface for treated nanocomposites. Wear analysis was performed under dry, and also under biological lubrication, conditions of all treated samples. The wear factor of untreated pure UHMWPE sample was reduced by 68% and 80%, under dry and lubricated conditions, respectively, as compared to 2 wt % 60 min-treated sample. The kinetic friction co-efficient was also noted under both conditions. The hardness of nanocomposites increased with both MWCNTs loading and plasma treatment time. Similarly, the surface roughness of the nanocomposites was reduced.

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“…Earlier researchers employed conventional artificial neural network (ANN) methods for the complex plasma conversion of syngas and methane. In plasma-centered studies, different ML algorithms are used according to their applicability and drawbacks [38][39][40]. ML algorithms are shortlists based on performance and solutions of process in industry vital for nonlinear and complex systems.…”

Section: Introductionmentioning

confidence: 99%

Advancing Sustainable Decomposition of Biomass Tar Model Compound: Machine Learning, Kinetic Modeling, and Experimental Investigation in a Non-Thermal Plasma Dielectric Barrier Discharge Reactor

Arshad,

Saeed,

Tahir

et al. 2023

Energies

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This study examines the sustainable decomposition reactions of benzene using non-thermal plasma (NTP) in a dielectric barrier discharge (DBD) reactor. The aim is to investigate the factors influencing benzene decomposition process, including input power, concentration, and residence time, through kinetic modeling, reactor performance assessment, and machine learning techniques. To further enhance the understanding and modeling of the decomposition process, the researchers determine the apparent decomposition rate constant, which is incorporated into a kinetic model using a novel theoretical plug flow reactor analogy model. The resulting reactor model is simulated using the ODE45 solver in MATLAB, with advanced machine learning algorithms and performance metrics such as RMSE, MSE, and MAE employed to improve accuracy. The analysis reveals that higher input discharge power and longer residence time result in increased tar analogue compound (TAC) decomposition. The results indicate that higher input discharge power leads to a significant improvement in the TAC decomposition rate, reaching 82.9%. The machine learning model achieved very good agreement with the experiments, showing a decomposition rate of 83.01%. The model flagged potential hotspots at 15% and 25% of the reactor’s length, which is important in terms of engineering design of scaled-up reactors.

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Statistical modelling of discharge behavior of atmospheric pressure dielectric barrier discharge

Cited by 7 publications

References 29 publications

Advanced electrical current measurements of microdischarges: evidence of sub-critical pulses and ion currents in barrier discharge in air

Advanced electrical current measurements of microdischarges: evidence of sub-critical pulses and ion currents in barrier discharge in air

Effect of Argon Plasma Treatment on Tribological Properties of UHMWPE/MWCNT Nanocomposites

Advancing Sustainable Decomposition of Biomass Tar Model Compound: Machine Learning, Kinetic Modeling, and Experimental Investigation in a Non-Thermal Plasma Dielectric Barrier Discharge Reactor

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