Application of linear sliding discharges for flow control: study of the energy coupling mechanisms

Castera, Philippe; Elias, Paul-Quentin; Laux, Christophe O.

doi:10.2514/6.2014-2117

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…In reference [34], the theory of resonant broadening was based on the book of Griem published in 1964 [54], and took into account the contributions of three states of the perturbing atom radiatively linked to the radiating atom: (i) g → u, (ii) g → l, and (iii) l →u, where g is the ground state. Later, Ali and Griem [62,63] published improvements and corrections that were taken into account in other publications, such as references [55,59,64,65]. The correction of Ali and Griem leads to a resonant broadening width 1.6 times higher than with the non-corrected formula used in Laux et al [34].…”

Section: Perturbermentioning

confidence: 99%

Fully ionized nanosecond discharges in air: the thermal spark

Minesi

Stepanyan

Mariotto

et al. 2020

Plasma Sources Sci. Technol.

106

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The formation and decay of the thermal spark generated by a single nanosecond highvoltage pulse between pin electrodes are characterized in this study. The influence of air pressure in the range 50 -1000 mbar is investigated at 300 K. By performing short-gate imaging and Optical Emission Spectroscopy (OES), we find that the thermal sparks exhibit an intense emission from excited electronic states of N + , in contrast with non-thermal sparks for which the emission is dominated by electronic transitions of N2. Spark thermalization consists of the following steps: (i) partial ionization of the plasma channel accompanied by N2 emission, (ii) creation of a fully ionized filament at the cathode characterized by N + emission, (iii) formation of a fully ionized filament at the anode, (iv) propagation of these filaments toward the middle of the interelectrode gap, and (v) merging of the filaments. The formation of the filaments, steps (ii) and (iii), occurs at subnanosecond timescales. The propagation speed of the filaments is on the order of 10 4 m/s during step (iv). For the 1-bar condition, the electron number densities are measured from the Stark broadening of N + and Hα lines, with spatial and temporal resolution. The electron temperature is also determined, from the relative emission intensity of N + excited states, attaining a peak value of 48,000 K. In the post-discharge, the electron number density decays from 4×10 19 to 2×10 18 cm -3 in 100 ns. We show that this decay curve can be interpreted as the isentropic expansion of a plasma in chemical equilibrium. Comparisons with previous experiments from the literature support this conclusion. Expressions for the Van der Waals and resonant broadenings of H, Hβ, and several lines of O, O + , N and, N + are derived in the appendix.

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Section: Perturbermentioning

confidence: 99%

Fully ionized nanosecond discharges in air: the thermal spark

Minesi

Stepanyan

Mariotto

et al. 2020

Plasma Sources Sci. Technol.

106

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“…Evaporation is generally measured using two methods: (i) direct methods such as Class A panevaporimeter and (ii) indirect methods include empirical equations (Ghorbani, Deo, Yaseen, Kashani, & Mohammadi, 2017). Doorenbos and Pruitt (1977) said that the Class A pan-evaporimeter performance was affected by support vector machine (SVM), evolutionary computing, data mining and complementary wavelet-AI models (Adnan, Malik, Kumar, Parmar, & Kisi, 2019;Guven & Kisi, 2013;Kisi & Heddam, 2019;Qasem et al, 2019;Qutbudin et al, 2019;Rezaie-Balf, Kisi, & Chua, 2019;Sebbar, Heddam, & Djemili, 2019;Yaseen, El-shafie, Jaafar, Afan, & Sayl, 2015). Tabari, Talaee, and Abghari (2012) predicted daily pan-evaporation using CANFIS and MLPNN techniques in Iran.…”

Section: Introductionmentioning

confidence: 99%

Modeling monthly pan evaporation process over the Indian central Himalayas: application of multiple learning artificial intelligence model

Malik

Kumar

Kim

et al. 2020

Engineering Applications of Computational Fluid Mechanics

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The potential of several predictive models including multiple model-artificial neural network (MM-ANN), multivariate adaptive regression spline (MARS), support vector machine (SVM), multi-gene genetic programming (MGGP), and 'M5Tree' were assessed to simulate the pan evaporation in monthly scale (EP m ) at two stations (e.g. Ranichauri and Pantnagar) in India. Monthly climatological information were used for simulating the pan evaporation. The utmost effective input-variables for the MM-ANN, MGGP, MARS, SVM, and M5Tree were determined using the Gamma test (GT). The predictive models were compared to each other using several statistical criteria (e.g. mean absolute percentage error (MAPE), Willmott's Index of agreement (WI), root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), and Legate and McCabe's Index (LM)) and visual inspection. The results showed that the MM-ANN-1 and MGGP-1 models (NSE, WI, LM, RMSE, MAPE are 0.954, 0.988, 0.801, 0.536 mm/month, 9.988% at Pantnagar station, and 0.911, 0.975, 0.724, and 0.364 mm/month, 12.297% at Ranichauri station, respectively) with input variables equal to six were more successful than the other techniques during testing period to simulate the monthly pan evaporation at both Ranichauri and Pantnagar stations. Thus, the results of proposed MM-ANN-1 and MGGP-1 models will help to the local stakeholders in terms of water resources management. ARTICLE HISTORY

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