In situ particles deposition imaging in centrifugal fields by implemented SPH-DEM-ANN into linear sensor-type wireless electrical resistance tomography (lsWERT)
In situ particles deposition images in centrifugal fields have been reconstructed by combination of smoothed particle hydrodynamics, discrete element method, and artificial neural network (SPH-DEM-ANN) which are implemented into linear sensor-type wireless electrical resistance tomography (lsWERT)....
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022
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| Online Access: | https://repository.ugm.ac.id/283884/1/Prayitno-3_SV.pdf |
| _version_ | 1826050706331664384 |
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| author | Kimura, Kota Prayitno, Yosephus Ardean Kurnianto Kawashima, Daisuke Sejati, Prima Asmara Takei, Masahiro |
| author_facet | Kimura, Kota Prayitno, Yosephus Ardean Kurnianto Kawashima, Daisuke Sejati, Prima Asmara Takei, Masahiro |
| author_sort | Kimura, Kota |
| collection | UGM |
| description | In situ particles deposition images in centrifugal fields have been reconstructed by combination of smoothed particle hydrodynamics, discrete element method, and artificial neural network (SPH-DEM-ANN) which are implemented into linear sensor-type wireless electrical resistance tomography (lsWERT). The implemented SPH-DEM-ANN into lsWERT has a training section and a real imaging section. The training section is composed of four components which are 1) calculation component of phase-particles position by SPH-DEM for bead- and liquid-particles position array XP and XL in two-dimensional centrifugal fields, 2) generation component of conductivity map by conductivity map generator for simulated phase-particles array Dsim consisting of element array Γ, node-particles array bX, and phase-particles conductivity array σ, 3) simulation component of normalized resistance by electrical forward problem for simulated normalized resistance array eRsim consisting of simulated resistance array Rsim and simulated reference resistance arrayR ˇ sim , and 4) training component of model factors by ANN for weight factors ki, j and bias βj in hidden layers. After the 4th training component in the training section, the real imaging section reconstructed the in situ particles deposition images in centrifugal fields based on the
experimental normalized resistance array eRexp under three bead-particles numbers NP = 40, 48, 56, and four rotational speeds ω =175, 205, 235, and 255 rpm. As the results, the implemented SPH-DEM-ANN into lsWERT is able to reconstruct the in situ particles deposition images accurately with four evaluation indicators which are the averaged difference of phase-particles array between Dsim and Dexp: Dδ ¼ 0:915, the averaged deviation of normalized resistance array between eRexp and eRsim : Rδ ¼ 0:153, the averaged deviation of particles maximum height Zmax P by a high-speed camera (HSC): Zδ ¼ 7:18%, and the averaged root mean square error of surface particles deposition quadratic curve: RMSE ¼ 3:465. |
| first_indexed | 2024-03-14T00:08:50Z |
| format | Article |
| id | oai:generic.eprints.org:283884 |
| institution | Universiti Gadjah Mada |
| language | English |
| last_indexed | 2024-03-14T00:08:50Z |
| publishDate | 2022 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oai:generic.eprints.org:2838842023-11-23T06:57:23Z https://repository.ugm.ac.id/283884/ In situ particles deposition imaging in centrifugal fields by implemented SPH-DEM-ANN into linear sensor-type wireless electrical resistance tomography (lsWERT) Kimura, Kota Prayitno, Yosephus Ardean Kurnianto Kawashima, Daisuke Sejati, Prima Asmara Takei, Masahiro Mechanical Engineering In situ particles deposition images in centrifugal fields have been reconstructed by combination of smoothed particle hydrodynamics, discrete element method, and artificial neural network (SPH-DEM-ANN) which are implemented into linear sensor-type wireless electrical resistance tomography (lsWERT). The implemented SPH-DEM-ANN into lsWERT has a training section and a real imaging section. The training section is composed of four components which are 1) calculation component of phase-particles position by SPH-DEM for bead- and liquid-particles position array XP and XL in two-dimensional centrifugal fields, 2) generation component of conductivity map by conductivity map generator for simulated phase-particles array Dsim consisting of element array Γ, node-particles array bX, and phase-particles conductivity array σ, 3) simulation component of normalized resistance by electrical forward problem for simulated normalized resistance array eRsim consisting of simulated resistance array Rsim and simulated reference resistance arrayR ˇ sim , and 4) training component of model factors by ANN for weight factors ki, j and bias βj in hidden layers. After the 4th training component in the training section, the real imaging section reconstructed the in situ particles deposition images in centrifugal fields based on the experimental normalized resistance array eRexp under three bead-particles numbers NP = 40, 48, 56, and four rotational speeds ω =175, 205, 235, and 255 rpm. As the results, the implemented SPH-DEM-ANN into lsWERT is able to reconstruct the in situ particles deposition images accurately with four evaluation indicators which are the averaged difference of phase-particles array between Dsim and Dexp: Dδ ¼ 0:915, the averaged deviation of normalized resistance array between eRexp and eRsim : Rδ ¼ 0:153, the averaged deviation of particles maximum height Zmax P by a high-speed camera (HSC): Zδ ¼ 7:18%, and the averaged root mean square error of surface particles deposition quadratic curve: RMSE ¼ 3:465. Elsevier 2022-01-02 Article PeerReviewed application/pdf en https://repository.ugm.ac.id/283884/1/Prayitno-3_SV.pdf Kimura, Kota and Prayitno, Yosephus Ardean Kurnianto and Kawashima, Daisuke and Sejati, Prima Asmara and Takei, Masahiro (2022) In situ particles deposition imaging in centrifugal fields by implemented SPH-DEM-ANN into linear sensor-type wireless electrical resistance tomography (lsWERT). Powder Technology, 398 (2022). pp. 1-14. ISSN 0032-5910 http://www.elsevier.com/locate/powtec https://doi.org/10.1016/j.powtec.2022.117140 |
| spellingShingle | Mechanical Engineering Kimura, Kota Prayitno, Yosephus Ardean Kurnianto Kawashima, Daisuke Sejati, Prima Asmara Takei, Masahiro In situ particles deposition imaging in centrifugal fields by implemented SPH-DEM-ANN into linear sensor-type wireless electrical resistance tomography (lsWERT) |
| title | In situ particles deposition imaging in centrifugal fields by implemented SPH-DEM-ANN into linear sensor-type wireless electrical resistance tomography (lsWERT) |
| title_full | In situ particles deposition imaging in centrifugal fields by implemented SPH-DEM-ANN into linear sensor-type wireless electrical resistance tomography (lsWERT) |
| title_fullStr | In situ particles deposition imaging in centrifugal fields by implemented SPH-DEM-ANN into linear sensor-type wireless electrical resistance tomography (lsWERT) |
| title_full_unstemmed | In situ particles deposition imaging in centrifugal fields by implemented SPH-DEM-ANN into linear sensor-type wireless electrical resistance tomography (lsWERT) |
| title_short | In situ particles deposition imaging in centrifugal fields by implemented SPH-DEM-ANN into linear sensor-type wireless electrical resistance tomography (lsWERT) |
| title_sort | in situ particles deposition imaging in centrifugal fields by implemented sph dem ann into linear sensor type wireless electrical resistance tomography lswert |
| topic | Mechanical Engineering |
| url | https://repository.ugm.ac.id/283884/1/Prayitno-3_SV.pdf |
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