dor_id: 26155

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650.#.4.x: Ingenierías

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336.#.#.3: Artículo de Investigación

336.#.#.a: Artículo

351.#.#.6: http://www.revistas.unam.mx/index.php/ingenieria/index

351.#.#.b: Ingeniería, Investigación y Tecnología

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harvesting_group: RevistasUNAM

270.1.#.p: Revistas UNAM. Dirección General de Publicaciones y Fomento Editorial, UNAM en revistas@unam.mx

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850.#.#.a: Universidad Nacional Autónoma de México

856.4.0.u: http://www.revistas.unam.mx/index.php/ingenieria/article/view/45808/41085

100.1.#.a: Romero Méndez, Ricardo; Hidalgo López, Juan Manuel; Durán García, Héctor Martín; Pacheco Vega, Arturo

524.#.#.a: Romero Méndez, Ricardo, et al. (2014). Use of Artificial Neural Networks for Prediction of Convective Heat Transfer in Evaporative Units Determinación de coeficientes del proceso de transferencia de calor en unidades de evaporación utilizando redes neuronales. Ingeniería Investigación y Tecnología; Vol 15, No 1, 2014. Recuperado de https://repositorio.unam.mx/contenidos/26155

245.1.0.a: Use of Artificial Neural Networks for Prediction of Convective Heat Transfer in Evaporative Units Determinación de coeficientes del proceso de transferencia de calor en unidades de evaporación utilizando redes neuronales

502.#.#.c: Universidad Nacional Autónoma de México

561.1.#.a: Facultad de Ingeniería, UNAM

264.#.0.c: 2014

264.#.1.c: 2015-01-14

653.#.#.a: Redes neuronales artificiales, sistemas térmicos, transferencia de calor, procesos de evaporación; redes neuronales artificiales, sistemas térmicos, transferencia de calor, procesos de evaporación

506.1.#.a: La titularidad de los derechos patrimoniales de esta obra pertenece a las instituciones editoras. Su uso se rige por una licencia Creative Commons BY-NC-ND 4.0 Internacional, https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode.es, fecha de asignación de la licencia 2015-01-14, para un uso diferente consultar al responsable jurídico del repositorio por medio del correo electrónico marciaglez@dirfing.unam.mx

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041.#.7.h: spa

520.3.#.a: Convective heat transfer prediction of evaporative processes is more complicated than the heat transfer prediction of single-phase convective processes. This is due to the fact that physical phenomena involved in evaporative processes are very complex and vary with the vapor quality that increases gradually as more fluid is evaporated. power-law correlations used for prediction of evaporative convection have proved little accuracy when used in practical cases. in this investigation, neural-network-based models have been used as a tool for prediction of the thermal performance of evaporative units. for this purpose, experimental data were obtained in a facility that includes a counter-flow concentric pipes heat exchanger with r134a refrigerant flowing inside the circular section and temperature controlled warm water moving through the annular section. This work also included the construction of an inverse rankine refrigeration cycle that was equipped with measurement devices, sensors and a data acquisition system to collect the experimental measurements under different operating conditions. part of the data were used to train several neural-network configurations. The best neural-network model was then used for prediction purposes and the results obtained were compared with experimental data not used for training purposes. The results obtained in this investigation reveal the convenience of using artificial neural networks as accurate predictive tools for determining convective heat transfer rates of evaporative processes. convective heat transfer prediction of evaporative processes is more complicated than the heat transfer prediction of single-phase convective processes. This is due to the fact that physical phenomena involved in evaporative processes are very complex and vary with the vapor quality that increases gradually as more fluid is evaporated. power-law correlations used for prediction of evaporative convection have proved little accuracy when used in practical cases. in this investigation, neural-network-based models have been used as a tool for prediction of the thermal performance of evaporative units. for this purpose, experimental data were obtained in a facility that includes a counter-flow concentric pipes heat exchanger with r134a refrigerant flowing inside the circular section and temperature controlled warm water moving through the annular section. This work also included the construction of an inverse rankine refrigeration cycle that was equipped with measurement devices, sensors and a data acquisition system to collect the experimental measurements under different operating conditions. part of the data were used to train several neural-network configurations. The best neural-network model was then used for prediction purposes and the results obtained were compared with experimental data not used for training purposes. The results obtained in this investigation reveal the convenience of using artificial neural networks as accurate predictive tools for determining convective heat transfer rates of evaporative processes.

773.1.#.t: Ingeniería Investigación y Tecnología; Vol 15, No 1 (2014)

773.1.#.o: http://www.revistas.unam.mx/index.php/ingenieria/index

046.#.#.j: 2021-08-03 00:00:00.000000

022.#.#.a: ISSN impreso: 1405-7743

310.#.#.a: Trimestral

264.#.1.b: Facultad de Ingeniería, UNAM

758.#.#.1: http://www.revistas.unam.mx/index.php/ingenieria/index

handle: 17ee9c6a97c33488

harvesting_date: 2019-02-06 00:00:00.0

856.#.0.q: application/pdf

245.1.0.b: Use of Artificial Neural Networks for Prediction of Convective Heat Transfer in Evaporative Units / Determinación de coeficientes del proceso de transferencia de calor en unidades de evaporación utilizando redes neuronales

last_modified: 2021-08-12 16:00:00

license_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode.es

license_type: by-nc-nd

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Artículo

Use of Artificial Neural Networks for Prediction of Convective Heat Transfer in Evaporative Units Determinación de coeficientes del proceso de transferencia de calor en unidades de evaporación utilizando redes neuronales

Romero Méndez, Ricardo; Hidalgo López, Juan Manuel; Durán García, Héctor Martín; Pacheco Vega, Arturo

Facultad de Ingeniería, UNAM, publicado en Ingeniería, Investigación y Tecnología, y cosechado de Revistas UNAM

Licencia de uso

Procedencia del contenido

Cita

Romero Méndez, Ricardo, et al. (2014). Use of Artificial Neural Networks for Prediction of Convective Heat Transfer in Evaporative Units Determinación de coeficientes del proceso de transferencia de calor en unidades de evaporación utilizando redes neuronales. Ingeniería Investigación y Tecnología; Vol 15, No 1, 2014. Recuperado de https://repositorio.unam.mx/contenidos/26155

Descripción del recurso

Autor(es)
Romero Méndez, Ricardo; Hidalgo López, Juan Manuel; Durán García, Héctor Martín; Pacheco Vega, Arturo
Tipo
Artículo de Investigación
Área del conocimiento
Ingenierías
Título
Use of Artificial Neural Networks for Prediction of Convective Heat Transfer in Evaporative Units Determinación de coeficientes del proceso de transferencia de calor en unidades de evaporación utilizando redes neuronales
Fecha
2015-01-14
Resumen
Convective heat transfer prediction of evaporative processes is more complicated than the heat transfer prediction of single-phase convective processes. This is due to the fact that physical phenomena involved in evaporative processes are very complex and vary with the vapor quality that increases gradually as more fluid is evaporated. power-law correlations used for prediction of evaporative convection have proved little accuracy when used in practical cases. in this investigation, neural-network-based models have been used as a tool for prediction of the thermal performance of evaporative units. for this purpose, experimental data were obtained in a facility that includes a counter-flow concentric pipes heat exchanger with r134a refrigerant flowing inside the circular section and temperature controlled warm water moving through the annular section. This work also included the construction of an inverse rankine refrigeration cycle that was equipped with measurement devices, sensors and a data acquisition system to collect the experimental measurements under different operating conditions. part of the data were used to train several neural-network configurations. The best neural-network model was then used for prediction purposes and the results obtained were compared with experimental data not used for training purposes. The results obtained in this investigation reveal the convenience of using artificial neural networks as accurate predictive tools for determining convective heat transfer rates of evaporative processes. convective heat transfer prediction of evaporative processes is more complicated than the heat transfer prediction of single-phase convective processes. This is due to the fact that physical phenomena involved in evaporative processes are very complex and vary with the vapor quality that increases gradually as more fluid is evaporated. power-law correlations used for prediction of evaporative convection have proved little accuracy when used in practical cases. in this investigation, neural-network-based models have been used as a tool for prediction of the thermal performance of evaporative units. for this purpose, experimental data were obtained in a facility that includes a counter-flow concentric pipes heat exchanger with r134a refrigerant flowing inside the circular section and temperature controlled warm water moving through the annular section. This work also included the construction of an inverse rankine refrigeration cycle that was equipped with measurement devices, sensors and a data acquisition system to collect the experimental measurements under different operating conditions. part of the data were used to train several neural-network configurations. The best neural-network model was then used for prediction purposes and the results obtained were compared with experimental data not used for training purposes. The results obtained in this investigation reveal the convenience of using artificial neural networks as accurate predictive tools for determining convective heat transfer rates of evaporative processes.
Tema
Redes neuronales artificiales, sistemas térmicos, transferencia de calor, procesos de evaporación; redes neuronales artificiales, sistemas térmicos, transferencia de calor, procesos de evaporación
Idioma
spa
ISSN
ISSN impreso: 1405-7743

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