Documents disponibles écrits par cet auteur

Accelerated formation of methane hydrate in aluminum foam / Liang Yang in Industrial & engineering chemistry research, Vol. 50 N° 20 (Octobre 2011) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 50 N° 20 (Octobre 2011) . - pp. 11563-11569 Titre : Accelerated formation of methane hydrate in aluminum foam Type de document : texte imprimé Auteurs : Liang Yang, Auteur ; Shuanshi Fan, Auteur ; Yanhong Wang, Auteur Année de publication : 2011 Article en page(s) : pp. 11563-11569 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Foam Résumé : The effects of aluminum foam (AF, average pore size of 1000 μm) on formation and growth kinetic behaviors of methane hydrate with 0.03 wt % sodium dodecyl sulfate (SDS) were investigated in a 300 cm3 stainless steel vessel without stirring under 4.2, 6.0, and 8.3 MPa and 273.15 K. AF is a porous metal medium possessing large rough surface and excellent thermal conductivity. The experimental results demonstrated that porous AF played an acceleration role in the initial formation and further growth of methane hydrate by promoting hydrate nucleation and facilitating the removal of hydration heat. When AF was used, not only was the induction time reduced but the formation and growth were also sped up significantly, compared to conditions without it. In addition, under the above three pressures, the maximum formation rates (Rf,max) were increased by enormous times, 52% and 23%, with the help of AF, respectively. The relatively low increment of Rf,max under high pressure most likely was caused by AF's own limitations (pore size). AF with smaller pore size can be selected for further study. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=24612379 [article] Accelerated formation of methane hydrate in aluminum foam [texte imprimé] / Liang Yang, Auteur ; Shuanshi Fan, Auteur ; Yanhong Wang, Auteur . - 2011 . - pp. 11563-11569. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 50 N° 20 (Octobre 2011) . - pp. 11563-11569 Mots-clés : Foam Résumé : The effects of aluminum foam (AF, average pore size of 1000 μm) on formation and growth kinetic behaviors of methane hydrate with 0.03 wt % sodium dodecyl sulfate (SDS) were investigated in a 300 cm3 stainless steel vessel without stirring under 4.2, 6.0, and 8.3 MPa and 273.15 K. AF is a porous metal medium possessing large rough surface and excellent thermal conductivity. The experimental results demonstrated that porous AF played an acceleration role in the initial formation and further growth of methane hydrate by promoting hydrate nucleation and facilitating the removal of hydration heat. When AF was used, not only was the induction time reduced but the formation and growth were also sped up significantly, compared to conditions without it. In addition, under the above three pressures, the maximum formation rates (Rf,max) were increased by enormous times, 52% and 23%, with the help of AF, respectively. The relatively low increment of Rf,max under high pressure most likely was caused by AF's own limitations (pore size). AF with smaller pore size can be selected for further study. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=24612379

Network modeling of fin-and-tube evaporator performance under dry and wet conditions / Ling-Xiao Zhao in Journal of heat transfer, Vol. 132 N° 7 (Juillet 2010) 

Public ISBD [article]  in Journal of heat transfer > Vol. 132 N° 7 (Juillet 2010) . - pp. [074502-1/4] Titre : Network modeling of fin-and-tube evaporator performance under dry and wet conditions Type de document : texte imprimé Auteurs : Ling-Xiao Zhao, Auteur ; Liang Yang, Auteur ; Chun-Lu Zhang, Auteur Article en page(s) : pp. [074502-1/4] Note générale : Physique Langues : Anglais (eng) Mots-clés : Heat  exchanger  Evaporator  Model  Neural  network Index. décimale : 536 Chaleur. Thermodynamique Résumé : A new neural network modeling approach to the evaporator performance under dry and wet conditions has been developed. Not only the total cooling capacity but also the sensible heat ratio and pressure drops on both air and refrigerant sides are modeled. Since the evaporator performance under dry and wet conditions is, respectively, dominated by the dry-bulb temperature and the web-bulb temperature, two neural networks are used together for capturing the characteristics. Training of a multi-input multi-output neural network is separated into training of multi-input single-output neural networks for improving the modeling flexibility and training efficiency. Compared with a well-developed physics-based model, the standard deviations of trained neural networks under dry and wet conditions are less than 1% and 2%, respectively. Compared with the experimental data, errors fall into ±5%. DEWEY : 536 ISSN : 0022-1481 En ligne : http://asmedl.aip.org/vsearch/servlet/VerityServlet?KEY=JHTRAO&ONLINE=YES&smode= [...] [article] Network modeling of fin-and-tube evaporator performance under dry and wet conditions [texte imprimé] / Ling-Xiao Zhao, Auteur ; Liang Yang, Auteur ; Chun-Lu Zhang, Auteur . - pp. [074502-1/4]. Physique Langues : Anglais (eng) in Journal of heat transfer > Vol. 132 N° 7 (Juillet 2010) . - pp. [074502-1/4] Mots-clés : Heat  exchanger  Evaporator  Model  Neural  network Index. décimale : 536 Chaleur. Thermodynamique Résumé : A new neural network modeling approach to the evaporator performance under dry and wet conditions has been developed. Not only the total cooling capacity but also the sensible heat ratio and pressure drops on both air and refrigerant sides are modeled. Since the evaporator performance under dry and wet conditions is, respectively, dominated by the dry-bulb temperature and the web-bulb temperature, two neural networks are used together for capturing the characteristics. Training of a multi-input multi-output neural network is separated into training of multi-input single-output neural networks for improving the modeling flexibility and training efficiency. Compared with a well-developed physics-based model, the standard deviations of trained neural networks under dry and wet conditions are less than 1% and 2%, respectively. Compared with the experimental data, errors fall into ±5%. DEWEY : 536 ISSN : 0022-1481 En ligne : http://asmedl.aip.org/vsearch/servlet/VerityServlet?KEY=JHTRAO&ONLINE=YES&smode= [...]