A new correlation for predicting the thermal conductivity of liquid refrigerants

Chen, Zhixiong; Akbari, Mohammadreza; Forouharmanesh, Forouzan; Keshani, Mojtaba; Akbari, Mohammad; Afrand, Masoud; Karimipour, Arash

doi:10.1007/s10973-019-09238-w

Cited by 9 publications

(4 citation statements)

References 22 publications

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“…(3) Write the control test program into the controller; (4) Close the main valve, open the air compressor, set the output gas pressure of the gas storage tank to 0.6mpa, which is the rated input pressure; (5) Slowly open the main valve, adjust the intake valve to the preset position, roots power machine operation; (6) Adjust the main valve to keep the rotating speed of roots power machine at 600 r/min; (7) After the stable operation of the equipment, manually fine-tune the main valve to simulate the state of interference and record data at the same time 38 .…”

Section: Simulation Analysismentioning

confidence: 99%

“…Ibrahim Muhammad studied stretchable rotating discs with heat transfer functions and carried out numerical analysis of their fluids 5,6 . Zhixiong Chen et al tested 27 refrigerants and studied a thermal conductivity model with better accuracy 7 . Subsequently, nano-particle fluids such as copper oxide or alumina were added into the heat transfer system, and thermodynamics laws and exergy were analyzed.…”

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confidence: 99%

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Research on Control Method of Waste Heat Utilization System Based on Multi-parameter Coupling

Xiao

Zhang

et al. 2022

Sci Rep

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The recovery of low-quality waste heat is a major problem in energy utilization. In order to solve this problem and improve energy utilization, the research group designed a low-quality waste heat power generation device with Roots power machine as the core. However, the device has poor ability to adjust the rotation speed and it is difficult to generate electricity stably. The fundamental reason is that the system has many variables and strong coupling. According to the actual working conditions, the power of the device is 10 kW, and the fluctuation range should be within ± 7%. On the one hand, it can be improved by hardware, on the other hand, the design of software is also very critical. At present, through the investigation of domestic and foreign researches on the control system, it is found that the stability of the system is gradually improved, but the problem of strong coupling between variables has not been effectively solved. Therefore, the research group modeled the variables in the system and obtained a coupled model. Based on the couple model, the research group introduced nonlinear multi-model adaptive closed-loop decoupling control and designed a control system. The simulation results show that the maximum overshoot of the control system is 3.9%, the adjustment time is also reduced, and it is stable in low quality waste heat recovery device. Experimental results show that under the control of the system, the rotational speed of roots motor can keep stable, the maximum deviation is not more than 21.4 r/min, and the fluctuation range is within ± 7%, which meets the requirements of the index. This has laid the foundation for the follow-up research of grid-connected power generation.

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Section: Simulation Analysismentioning

confidence: 99%

mentioning

confidence: 99%

Research on Control Method of Waste Heat Utilization System Based on Multi-parameter Coupling

Xiao

Zhang

et al. 2022

Sci Rep

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“…Cuando se carece de datos experimentales, esta importante propiedad debe ser estimada utilizando modelos empíricos, teóricos o semi-teóricos (Bonyadi y Rostami, 2017) y los basados en redes neuronales e inteligencia artificial (Hezave et al, 2012). Ejemplo de estos modelos son los desarrollados por Hopp y Gross (2019), Cardona et al (2019), Tomassetti et al (2020), Chen et al (2020). No obstante, estos modelos han sido utilizados para predecir sustancias orgánicas y no se han extendido a líquidos iónicos.…”

Section: Introductionunclassified

Modelo simple y generalizado para estimar la conductividad térmica de líquidos iónicos

Cardona-Palacio

2021

Rev. Cient.

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En este trabajo se generaliza un modelo empírico para predecir la conductividad térmica de líquidos iónicos. Inicialmente se realiza una recopilación de datos experimentales con diferentes tipos de aniones y cationes. En total, se evalúan 38 líquidos iónicos (28 sustancias para correlación y 10 sustancias para predicción) que corresponden a 265 datos experimentales. Los rangos de temperatura varían entre 273 K y 390 K a presión atmosférica (1 atm). Los parámetros del modelo son generalizados, utilizando 215 datos experimentales y las desviaciones promedio son de 4.72 %. Posteriormente, se evalúan las capacidades predictivas en 50 datos experimentales con desviaciones promedio de 3.48 %. El modelo generalizado es extendido a mezclas binarias utilizando una regla de mezcla simple con un parámetro de interacción y la desviación absoluta es de 0.77 %. Finalmente, el modelo es comparado con otros en la literatura y los resultados estadísticos muestran que proporciona resultados aceptables.

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“…A methodology that can predict thermal conductivity at the molecular level is a good choice for guiding the molecular design of liquids. Currently, some theoretical, semitheoretical, or empirical correlations, − including models based on the quantitative structure–property relationship (QSPR) method, , the group-contribution (GC) method, − and the artificial neural network (ANN) method, − have been introduced to predict the thermal conductivities of liquids (mainly organic liquids and ILs). Among them, the GC model, in which molecular thermal conductivity is regarded to be the sum of all group contributions, not only considers molecular structure but also does not require extra information, such as critical properties and boiling temperatures, to function.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Thermal Conductivity for Guiding Molecular Design of Liquids

Liu

Wang

Lan

et al. 2020

ACS Sustainable Chem. Eng.

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There is a need for molecular-structure-based predictive models that guide the molecular design of materials with desired properties. Herein, we developed a general model based on group-contribution (GC) theory and vibrational theory that predicts the thermal conductivity of different types of liquids which are used as working media in energy conversion and environmental protection, including three types of organic molecular liquids, ionic liquids, and their mixtures. We also derive the pressure dependencies of the thermal conductivities of these liquids for the first time. The GC model is extended to determining the thermal conductivities of mixtures by developing a group division method and mixing rules that operate without knowing the thermal conductivity of each component. The excellent performance of the presented model is verified by comparing the predicted thermal conductivities with experimental data and those from other models. On the basis of the developed model, group sequences are established according to their contributions to thermal conductivity, and the sensitivity of thermal conductivity to temperature and pressure is analyzed to guide the molecular design of liquids.

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A new correlation for predicting the thermal conductivity of liquid refrigerants

Cited by 9 publications

References 22 publications

Research on Control Method of Waste Heat Utilization System Based on Multi-parameter Coupling

Research on Control Method of Waste Heat Utilization System Based on Multi-parameter Coupling

Modelo simple y generalizado para estimar la conductividad térmica de líquidos iónicos

Prediction of Thermal Conductivity for Guiding Molecular Design of Liquids

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