An improved inversion algorithm to reconstruct 2D temperature fields of long sparks with high-speed schlieren technique
•Two-dimensional temperature fields of spark channel are firstly presented.•The deviation of the discharge channel axis can cause huge errors of temperature.•An improved inverse Abel transform algorithm is proposed.•High-speed schlieren technique is used to measure the spark channel temperature. The...
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| Veröffentlicht in: | Measurement : journal of the International Measurement Confederation Jg. 180; S. 109620 |
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| Hauptverfasser: | , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
London
Elsevier Ltd
01.08.2021
Elsevier Science Ltd |
| Schlagworte: | |
| ISSN: | 0263-2241, 1873-412X |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | •Two-dimensional temperature fields of spark channel are firstly presented.•The deviation of the discharge channel axis can cause huge errors of temperature.•An improved inverse Abel transform algorithm is proposed.•High-speed schlieren technique is used to measure the spark channel temperature.
The temperature measurement for discharge channels plays an important role in studying the mechanism of long sparks and natural lightning, in which cases the high-speed schlieren technique is usually applied. However, the high-speed schlieren images are noisy, and the discharge channel axis is not discernibly determined from the discrete pixels, which can sometimes cause huge errors using the traditional inversion algorithm. This may be the reason why two-dimensional (2D) temperature fields of long sparks are rarely reported at present. To fill the gap, an improved inversion algorithm is proposed, in which the dichotomy and the high order total variation (TV) regularization methods are introduced. The proposed algorithm is verified to have better performance compared with the traditional ones by numerical tests. At last, 2D temperature fields of discharge channels are reconstructed and discussed. |
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| Bibliographie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 0263-2241 1873-412X |
| DOI: | 10.1016/j.measurement.2021.109620 |