scholarly journals Machine learning and a computational fluid dynamic approach to estimate phase composition of chemical vapor deposition boron carbide

2021 ◽  
Vol 10 (3) ◽  
pp. 537-550
Author(s):  
Qingfeng Zeng ◽  
Yong Gao ◽  
Kang Guan ◽  
Jiantao Liu ◽  
Zhiqiang Feng

AbstractChemical vapor deposition is an important method for the preparation of boron carbide. Knowledge of the correlation between the phase composition of the deposit and the deposition conditions (temperature, inlet gas composition, total pressure, reactor configuration, and total flow rate) has not been completely determined. In this work, a novel approach to identify the kinetic mechanisms for the deposit composition is presented. Machine leaning (ML) and computational fluid dynamic (CFD) techniques are utilized to identify core factors that influence the deposit composition. It has been shown that ML, combined with CFD, can reduce the prediction error from about 25% to 7%, compared with the ML approach alone. The sensitivity coefficient study shows that BHCl2 and BCl3 produce the most boron atoms, while C2H4 and CH4 are the main sources of carbon atoms. The new approach can accurately predict the deposited boron–carbon ratio and provide a new design solution for other multi-element systems.

2021 ◽  
Author(s):  
Q.F. Zeng ◽  
Yong Gao ◽  
Kang Guan ◽  
Jiantao Liu ◽  
Zhiqiang Feng

Abstract Chemical vapor deposition is an important method for the preparation of boron carbide. Knowledge of the correlation between the phase composition of the deposit and the deposition conditions (temperature, inlet gas composition, total pressure, reactor configuration, and total flow rate) has not been completely determined. In this work, a novel approach to identify the kinetic mechanisms for the deposit composition is presented. Machine leaning (ML) and computational fluid dynamic (CFD) techniques are utilized to identify core factors that influence the deposit composition. It has been shown that ML, combined with CFD, can reduce the prediction error from 25% to 7%, compared with the ML approach alone. The sensitivity coefficient study shows that BHCl2 and BCl3 produce the most boron atoms, while C2H4 and CH4 are the main sources of carbon atoms. The new approach can accurately predict the deposited boron-carbon ratio and provide a new design solution for other multi-element systems.


2020 ◽  
Author(s):  
Q.F. Zeng ◽  
Yong Gao ◽  
Kang Guan ◽  
Jiantao Liu ◽  
Zhiqiang Feng

Abstract Chemical vapor deposition is an important method for the preparation of boron carbide. Knowledge of the correlation between the phase composition of the deposit and the deposition conditions (temperature, inlet gas composition, total pressure, reactor configuration, and total flow rate) has not been completely determined. In this work, a novel approach to identify the kinetic mechanisms for the deposit composition is presented. Machine leaning (ML) and computational fluid dynamic (CFD) techniques are utilized to identify core factors that influence the deposit composition. It has been shown that ML, combined with CFD, can reduce the prediction error from 25% to 7%, compared with the ML approach alone. The sensitivity coefficient study shows that BHCl2 and BCl3 produce the most boron atoms, while C2H4 and CH4 are the main sources of carbon atoms. The new approach can accurately predict the deposited boron-carbon ratio and provide a new design solution for other multi-element systems.


2012 ◽  
Vol 15 (1) ◽  
Author(s):  
Sukarsono Sukarsono ◽  
Liliek Harmianto ◽  
Muhadi Ayub Wasitho ◽  
Sudibyo Sudibyo ◽  
Dhandang Purwadi

VARIASI KECEPATAN ALIR GAS PADA PROSES PELAPISAN KERNEL UO2 DENGAN COMPUTATIONALFLUID DYNAMIC (CFD). Pelapisan kernel UO2, merupakan salah satu tahap dalam pembuatan bahan bakarnuklir yang sangat menentukan terhadap hasil akhir bahan bakar reaktor suhu tinggi. Kernel hasil prosessintering, yang merupakan partikel bulat UO2 diameter sekitar 0,8 mm, dikenakan proses pelapisan pirokarbondan silika karbida secara chemical vapor deposition (CVD). Aspek utama yang ditinjau dalam fluidisasi adalahmekanika fluida yang menggambarkan apa yang terjadi dalam proses fluidisasi. Kemampuan untukmemprediksi awal terjadinya fluidisasi sangat penting di dalam proses fluidisasi. Hal ini dilakukan untukmemperoleh hasil operasi yang bagus, life time tinggi, penentuan kecepatan minimum fluidisasi dan kecepatanmaksimum fluidisasi. Cairan atau gas apabila dilewatkan dari bawah ke atas pada partikel padat padakecepatan rendah, maka partikel tidak bergerak dan apabila kecepatan ditambah, pada titik tertentu partikelmulai bergerak. Kecepatan alir ini disebut sebagai kecepatan minimum fluidisasi. Dalam fluidisasi apabilakecepatan fluida yang melewati partikel dinaikkan maka perbedaan tekanan di sepanjang reaktor akanmeningkat pula. Partikel-partikel ini akan bergerak-gerak dan mempunyai perilaku sebagai fluida. Keadaanseperti ini dikenal sebagai partikel terfluidisasi (fluidized bed). Reaksi kimia yang terjadi dalam fluidisasi jugaberpengaruh terhadap kondisi proses dan terjadi perpindahan massa selama fluidisasi. Dalam penelitian initelah dilakukan modeling proses pelapisan pirokarbon dan silika karbida dengan Computational Fluid Dynamic(CFD) Fluent 6.3. Pelaksanaan penelitian, pertama-tama digambar reaktor dengan program Gambit 2.2.30 dandijalankan dengan program Fluent 6.3. Proses fluidisasi dihitung dengan model multiphase Eulerian dengan gassebagai fase primer dan kernel sebagai fase sekunder. Model dipilih untuk proses unsteady dan aliran laminar.Teori Syamlal-Obrien digunakan untuk perhitungan interaksi antar fase. Dari perhitungan Fluent 6.3, ternyatakecepatan alir gas masuk 8 m/dt masih ada kernel yang jatuh ke bawah, sehingga ini sesuai denganperhitungan menggunakan persamaan kecepatan minimum fluidisasi yang terhitung = 8,6 m/dt. Pada percobaanmenggunakan reaktor gelas juga diperoleh data pada kecepatan 9,49 m/dt sudah terjadi fluidisasi yang baikdibandingkan dengan fluidisasi pada kecepatan 7,11 m/dt masih terlihat ada kernel yang jatuh ke penampung.Data perhitungan nantinya bisa digunakan untuk operasi reaktor fluidisasi alat pelapisan kernel bahan bakar diPTAPB BATAN Yogyakarta.Kata kunci : kernel, reaktor suhu tinggi, fluidisasi, pirokarbon


2019 ◽  
Vol 10 (20) ◽  
pp. 6253-6259 ◽  
Author(s):  
Daniel Stadler ◽  
David N. Mueller ◽  
Thomas Brede ◽  
Tomáš Duchoň ◽  
Thomas Fischer ◽  
...  

2006 ◽  
Vol 527-529 ◽  
pp. 21-26 ◽  
Author(s):  
A.Y. Polyakov ◽  
Mark A. Fanton ◽  
Marek Skowronski ◽  
Hun Jae Chung ◽  
Saurav Nigam ◽  
...  

A novel approach to the high growth rate Chemical Vapor Deposition of SiC is described. The Halide Chemical Vapor Deposition (HCVD) method uses SiCl4, C3H8 (or CH4), and hydrogen as reactants. The use of halogenated Si source and of separate injection of Si and C precursors allows for preheating of source gases without causing premature chemical reactions. The stoichiometry of HCVD crystals can be controlled by changing the C/Si flow ratio and can be kept constant throughout growth, in contrast to the Physical Vapor Transport technique. HCVD was demonstrated to deposit high crystalline quality, very high purity 4H- and 6H-SiC crystals with growth rates comparable to other bulk SiC growth techniques. The densities of deep electron and hole traps are determined by growth temperature and C/Si ratio and can be as low as that found in standard silane-based CVD epitaxy. At high C/Si flow ratio, the resistivity of HCVD crystals exceeds 105 _cm. These characteristics make HCVD an attractive method to grow SiC for applications in high-frequency and/or high voltage devices.


2003 ◽  
Vol 190 (3) ◽  
pp. 360-372 ◽  
Author(s):  
Ö. D. Eroğlu ◽  
N. A. Sezgi ◽  
H. ö. Özbelge ◽  
H. H. Durmazuçar

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