Work

Evaluation of machine learning approaches for estimating thermodynamic properties of new generation refrigerant R513A

Year: 2022

Type: article

Source: Sustainable Energy Technologies and Assessments

DOI: 10.1016/j.seta.2022.102973

Authors: I. Pence, R. Yıldırım, M. Siseci Cesmeli, A. Güngör, A. Akyüz

Institutions: Burdur Mehmet Akif Ersoy Üniversitesi, Akdeniz University, American Society of Heating, Refrigerating, and Air-Conditioning Engineers

Language: English

Topic: Refrigeration and Air Conditioning Technologies

Subfield: Mechanical Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: closed

OpenAlex ID: W4311909865

FWCI: 1.226

Cites: 31

Cited by: 17

Related to: 10