Work

Optimization of airflow over an evaporator using blade arrangements: A combined CFD, machine learning, and experimental approach

Year: 2025

Type: article

Abstract: This study presents a comprehensive investigation into the optimization of airflow over a household refrigerator's evaporator through the strategic use of guide blades. Unsuitable airflow in conventional designs often leads to inefficient heat exchange, non-uniform temperature distribution, and accelerated frost accumulation. To address these issues, guide blades were introduced near the outlet of return duct. A total of 52 blade configurations were analyzed using two-dimensional computational fluid dynamics (CFD). By using a neural network model, optimal blade geometry can be predicted based on thermal performance of trained CFD results. The optimal configuration, identified through simulation and confirmed experimentally, yielded a 0.62 °C reduction in average surface temperature and a 7 % decrease in total frost mass over time. The results demonstrate that even minor geometric adjustments can significantly enhance heat transfer performance and suppress frost formation, offering a practical, low-cost approach for improving the energy efficiency of domestic refrigeration systems.

Source: International Communications in Heat and Mass Transfer

DOI: 10.1016/j.icheatmasstransfer.2025.109905

Authors: Mohammad Nazemi Babadi, Suhwan Lee, Hoyoon Kim, Yongbum Cho, Eunseop Yeom

Institutions: Pusan National University, LG (South Korea)

Language: English

Topic: Refrigeration and Air Conditioning Technologies

Subfield: Mechanical Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: hybrid

Estimated APC paid: $3,560