Analysis of two-phase injection heat pump using artificial neural network considering APF and LCCP under various weather conditions

Dongwoo Kim, Kang Sub Song, Junyub Lim, Yong Chan Kim

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

The objective of this study is to optimize the performance of a two-phase injection (TPI) heat pump considering annual performance factor (APF) and life cycle climate performance (LCCP). The performances of non-injection (NI), vapor injection (VI), and TPI heat pumps are measured under various outdoor temperatures. Based on the measured data, artificial neural network models for the NI, VI, and TPI heat pumps are developed to predict the performance indexes during cooling and heating seasons. As a result, the TPI heat pump shows higher heating capacity than the NI and VI heat pumps with a lower compressor discharge temperature in cold weather conditions. Therefore, the application of the TPI has a merit on reducing the size of the heat pump due to its lower back-up heater loss and over-capacity penalty. When the objective function maximizes the APF for system optimization in three climate regions, the TPI heat pump shows a 1.4–2.7% higher APF than the NI heat pump, and a 11.1%–18.1% smaller optimum rated heating capacity.

Original languageEnglish
Pages (from-to)117-127
Number of pages11
JournalEnergy
Volume155
DOIs
Publication statusPublished - 2018 Jul 15

Fingerprint

Life cycle
Pumps
Neural networks
Vapors
Heating
Hot Temperature
Discharge (fluid mechanics)
Compressors
Cooling
Temperature

Keywords

  • Annual performance
  • Artificial neural network
  • LCCP
  • Optimization
  • Two-phase injection

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Pollution
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

Cite this

Analysis of two-phase injection heat pump using artificial neural network considering APF and LCCP under various weather conditions. / Kim, Dongwoo; Song, Kang Sub; Lim, Junyub; Kim, Yong Chan.

In: Energy, Vol. 155, 15.07.2018, p. 117-127.

Research output: Contribution to journalArticle

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