Earticle

초록

영어

Thermophysical properties of helium are significant in practical applications. However, the values of properties vary under different circumstances, which may have bad impacts on practical productions and applications. In our study, computational models like Linear Prediction and Artificial Neural Networks (ANNs) are applied to predict the thermophysical properties of the chemical substances. By analyzing 50 data groups using Linear Prediction, General Regression Neural Network (GRNN) and Multilayer Feedforward Neural Network (MLFN) methods, 9 models were successfully established to predict the thermophysical properties of helium, including density, energy, enthalpy, entropy, isochoric heat capacity, isobaric heat capacity, viscosity, thermal conductivity and dielectric constant. Within permissible error range (30% tolerance), our models were proved to be robust and accurate which indicates that ANN models can be used to predict the thermophysical properties of helium.

목차

Abstract
 1. Introduction
 2. Artificial Neural Networks
 3. Selection of Variables
 4. Training Process of Neural Networks
 5. Results and Discussion
 References

키워드

저자

• Dazuo Yang [ Key Laboratory of Marine Bio-resources Restoration and Habitat Reparation in Liaoning Province, D 2College of Life science and Technology, Dalian University of Technology, Dalian 116021, P. R. Chinaalian Ocean University, Dalian 116023, P. R. China, ]
• Hao Li [ College of Chemistry, Sichuan University, Chengdu, Sichuan 610064, P. R. China ]
• Yibing Zhou [ Key Laboratory of Marine Bio-resources Restoration and Habitat Reparation in Liaoning Province, Dalian Ocean University, Dalian 116023, P. R. China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605