Documents disponibles écrits par cet auteur

A correlation-free on-line optimal control method of heat rejection pressures in CO2 transcritical systems / Wei-Jiang Zhang in International journal of refrigeration, Vol. 34 N° 4 (Juin 2011) 

Public ISBD [article]  in International journal of refrigeration > Vol. 34 N° 4 (Juin 2011) . - pp. 844-850 Titre : A correlation-free on-line optimal control method of heat rejection pressures in CO2 transcritical systems Titre original : Méthode sans corrélation de régulation optimale en ligne des pressions de refoulement dans les systèmes au CO2 transcritiques Type de document : texte imprimé Auteurs : Wei-Jiang Zhang, Auteur ; Chun-Lu Zhang, Auteur Année de publication : 2011 Article en page(s) : pp. 844-850 Note générale : Génie Mécanique Langues : Anglais (eng) Mots-clés : Cycle  Carbon  dioxide  Transcritical  High  pressure  Optimization  Control  Commissioning  Model Index. décimale : 621.5 Energie pneumatique. Machinerie et outils. Réfrigération Résumé : This paper proposes a novel correlation-free on-line optimal control method for CO2 transcritical refrigeration systems. It uses the on-line correction formula to track the optimal pressure set point. As a critical advantage against the existing empirical correlations of the heat rejection pressure, it is independent of the cycle, the system specifications, and the operating conditions. Dynamic numerical simulation demonstrates how to apply the new method to a basic CO2 transcritical refrigeration system. The results show that the proposed method can well track the optimal pressures and is robust to resist the sampling noise. DEWEY : 621.5 ISSN : 0140-7007 En ligne : http://www.sciencedirect.com/science/article/pii/S0140700711000272 [article] A correlation-free on-line optimal control method of heat rejection pressures in CO2 transcritical systems = Méthode sans corrélation de régulation optimale en ligne des pressions de refoulement dans les systèmes au CO2 transcritiques [texte imprimé] / Wei-Jiang Zhang, Auteur ; Chun-Lu Zhang, Auteur . - 2011 . - pp. 844-850. Génie Mécanique Langues : Anglais (eng) in International journal of refrigeration > Vol. 34 N° 4 (Juin 2011) . - pp. 844-850 Mots-clés : Cycle  Carbon  dioxide  Transcritical  High  pressure  Optimization  Control  Commissioning  Model Index. décimale : 621.5 Energie pneumatique. Machinerie et outils. Réfrigération Résumé : This paper proposes a novel correlation-free on-line optimal control method for CO2 transcritical refrigeration systems. It uses the on-line correction formula to track the optimal pressure set point. As a critical advantage against the existing empirical correlations of the heat rejection pressure, it is independent of the cycle, the system specifications, and the operating conditions. Dynamic numerical simulation demonstrates how to apply the new method to a basic CO2 transcritical refrigeration system. The results show that the proposed method can well track the optimal pressures and is robust to resist the sampling noise. DEWEY : 621.5 ISSN : 0140-7007 En ligne : http://www.sciencedirect.com/science/article/pii/S0140700711000272

Fin-and-tube condenser performance evaluation using neural networks / Ling-Xiao Zhao in International journal of refrigeration, Vol. 33 N° 3 (Mai 2010) 

Public ISBD [article]  in International journal of refrigeration > Vol. 33 N° 3 (Mai 2010) . - pp. 625-634 Titre : Fin-and-tube condenser performance evaluation using neural networks Titre original : Evaluation de la performance d'un condenseur à tubes ailetés à l'aide de réseaux neuronaux Type de document : texte imprimé Auteurs : Ling-Xiao Zhao, Auteur ; Chun-Lu Zhang, Auteur Année de publication : 2010 Article en page(s) : pp. 625-634 Note générale : Génie Mécanique Langues : Anglais (eng) Mots-clés : Heat  exchanger  Air-cooled  condenser  Simulation  Performance  Neural  network Index. décimale : 621.5 Energie pneumatique. Machinerie et outils. Réfrigération Résumé : The paper presents neural network approach to performance evaluation of the fin-and-tube air-cooled condensers which are widely used in air-conditioning and refrigeration systems. Inputs of the neural network include refrigerant and air-flow rates, refrigerant inlet temperature and saturated temperature, and entering air dry-bulb temperature. Outputs of the neural network consist of the heating capacity and the pressure drops on both refrigerant and air sides. The multi-input multi-output (MIMO) neural network is separated into multi-input single-output (MISO) neural networks for training. Afterwards, the trained MISO neural networks are combined into a MIMO neural network, which indicates that the number of training data sets is determined by the biggest MISO neural network not the whole MIMO network. Compared with a validated first-principle model, the standard deviations of neural network models are less than 1.9%, and all errors fall into ±5%. DEWEY : 621.5 ISSN : 0140-7007 En ligne : http://www.sciencedirect.com/science/article/pii/S0140700709002722 [article] Fin-and-tube condenser performance evaluation using neural networks = Evaluation de la performance d'un condenseur à tubes ailetés à l'aide de réseaux neuronaux [texte imprimé] / Ling-Xiao Zhao, Auteur ; Chun-Lu Zhang, Auteur . - 2010 . - pp. 625-634. Génie Mécanique Langues : Anglais (eng) in International journal of refrigeration > Vol. 33 N° 3 (Mai 2010) . - pp. 625-634 Mots-clés : Heat  exchanger  Air-cooled  condenser  Simulation  Performance  Neural  network Index. décimale : 621.5 Energie pneumatique. Machinerie et outils. Réfrigération Résumé : The paper presents neural network approach to performance evaluation of the fin-and-tube air-cooled condensers which are widely used in air-conditioning and refrigeration systems. Inputs of the neural network include refrigerant and air-flow rates, refrigerant inlet temperature and saturated temperature, and entering air dry-bulb temperature. Outputs of the neural network consist of the heating capacity and the pressure drops on both refrigerant and air sides. The multi-input multi-output (MIMO) neural network is separated into multi-input single-output (MISO) neural networks for training. Afterwards, the trained MISO neural networks are combined into a MIMO neural network, which indicates that the number of training data sets is determined by the biggest MISO neural network not the whole MIMO network. Compared with a validated first-principle model, the standard deviations of neural network models are less than 1.9%, and all errors fall into ±5%. DEWEY : 621.5 ISSN : 0140-7007 En ligne : http://www.sciencedirect.com/science/article/pii/S0140700709002722

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= [...]

Steady-state hybrid modeling of economized screw water chillers using polynomial neural network compressor model / Ling-Xiao Zhao in International journal of refrigeration, Vol. 33 N° 4 (Juin 2010) 

Public ISBD [article]  in International journal of refrigeration > Vol. 33 N° 4 (Juin 2010) . - pp. 729-738 Titre : Steady-state hybrid modeling of economized screw water chillers using polynomial neural network compressor model Titre original : Modélisation hybride en régime permanent des refroidisseurs d’eau à vis munis d’économiseur, à l’aide d’un modèle polynomial à réseau neuronal Type de document : texte imprimé Auteurs : Ling-Xiao Zhao, Auteur ; Liang-Liang Shao, Auteur ; Chun-Lu Zhang, Auteur Année de publication : 2010 Article en page(s) : pp. 729-738 Note générale : Génie Mécanique Langues : Anglais (eng) Mots-clés : Liquid  chiller  Screw  compressor  Injection  Modelling  Performance  Neural  network Index. décimale : 621.5 Energie pneumatique. Machinerie et outils. Réfrigération Résumé : This paper presents steady-state hybrid modeling and analysis of water chillers with economized screw compressors using intermediate gas injection. The hybrid chiller model consists of the polynomial neural network compressor model and other component models grounded on the first principles. The polynomial neural network compressor model works for all full-load and part-load conditions, both economized and non-economized modes. Good agreement between the predicted and measured performance of two compressors from the manufacturer is reached ranging from full-load to unload conditions. The hybrid chiller model predictions on the performance of two chillers agree with the test data within ±5% errors. Based on the validated model, further investigation on the optimal switch points between the economized and non-economized mode is conducted, which can be used to precisely improve the part-load performance of chillers. A simple linear correlation for switching the economized and non-economized mode is proposed as well. DEWEY : 621.5 ISSN : 0140-7007 En ligne : http://www.sciencedirect.com/science/article/pii/S0140700710000095 [article] Steady-state hybrid modeling of economized screw water chillers using polynomial neural network compressor model = Modélisation hybride en régime permanent des refroidisseurs d’eau à vis munis d’économiseur, à l’aide d’un modèle polynomial à réseau neuronal [texte imprimé] / Ling-Xiao Zhao, Auteur ; Liang-Liang Shao, Auteur ; Chun-Lu Zhang, Auteur . - 2010 . - pp. 729-738. Génie Mécanique Langues : Anglais (eng) in International journal of refrigeration > Vol. 33 N° 4 (Juin 2010) . - pp. 729-738 Mots-clés : Liquid  chiller  Screw  compressor  Injection  Modelling  Performance  Neural  network Index. décimale : 621.5 Energie pneumatique. Machinerie et outils. Réfrigération Résumé : This paper presents steady-state hybrid modeling and analysis of water chillers with economized screw compressors using intermediate gas injection. The hybrid chiller model consists of the polynomial neural network compressor model and other component models grounded on the first principles. The polynomial neural network compressor model works for all full-load and part-load conditions, both economized and non-economized modes. Good agreement between the predicted and measured performance of two compressors from the manufacturer is reached ranging from full-load to unload conditions. The hybrid chiller model predictions on the performance of two chillers agree with the test data within ±5% errors. Based on the validated model, further investigation on the optimal switch points between the economized and non-economized mode is conducted, which can be used to precisely improve the part-load performance of chillers. A simple linear correlation for switching the economized and non-economized mode is proposed as well. DEWEY : 621.5 ISSN : 0140-7007 En ligne : http://www.sciencedirect.com/science/article/pii/S0140700710000095