Design of drain tube with cone to prevent frost accumulation of drainage hole in household refrigerators using artificial neural network
The drain tube in household refrigerators serves to discharge defrost water and equalize internal and external pressure. However, its open-ended design permits humid air ingress, accelerating frost buildup and reducing system efficiency. To address this, a simplified cone-shaped drain tube was propo...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-10-01
|
| Series: | Case Studies in Thermal Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214157X25010615 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849236217182289920 |
|---|---|
| author | Suhwan Lee Dongkuk Kang Jinxing Fan Sunghee Kang Dong Kim Eunseop Yeom |
| author_facet | Suhwan Lee Dongkuk Kang Jinxing Fan Sunghee Kang Dong Kim Eunseop Yeom |
| author_sort | Suhwan Lee |
| collection | DOAJ |
| description | The drain tube in household refrigerators serves to discharge defrost water and equalize internal and external pressure. However, its open-ended design permits humid air ingress, accelerating frost buildup and reducing system efficiency. To address this, a simplified cone-shaped drain tube was proposed with a focus on manufacturability. Experiments and computational fluid dynamics (CFD) simulations were conducted to investigate frost formation patterns and the effects of absolute humidity. To reduce the cost of evaluating design variations, an artificial neural network (ANN) was developed as a surrogate model trained on CFD data. The ANN accurately predicted performance based on geometric parameters, capturing nonlinear interactions among pressure release, airflow, and frost accumulation. The optimal cone-type design, predicted with a 5.12 % error margin, was fabricated via 3D printing and validated experimentally through pressure and flow rate measurements. When applied in a commercial refrigerator, the cone-type tube delayed frost blockage by more than twice and reduced negative pressure peaks caused by door opening by approximately 85 % compared to conventional designs. These results highlight the effectiveness of the AI-assisted framework for data-efficient thermal optimization and demonstrate the potential of intelligent, learning-based approaches in thermal system design. |
| format | Article |
| id | doaj-art-c0aac6b8980c4f0e9af6035660d0d56f |
| institution | Kabale University |
| issn | 2214-157X |
| language | English |
| publishDate | 2025-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Thermal Engineering |
| spelling | doaj-art-c0aac6b8980c4f0e9af6035660d0d56f2025-08-20T04:02:26ZengElsevierCase Studies in Thermal Engineering2214-157X2025-10-017410680110.1016/j.csite.2025.106801Design of drain tube with cone to prevent frost accumulation of drainage hole in household refrigerators using artificial neural networkSuhwan Lee0Dongkuk Kang1Jinxing Fan2Sunghee Kang3Dong Kim4Eunseop Yeom5School of Mechanical Engineering, Pusan National University, Busan, South KoreaSchool of Mechanical Engineering, Pusan National University, Busan, South KoreaSchool of Mechanical Engineering, Pusan National University, Busan, South KoreaRefrigerator Research/Engineering Division, LG Electronics, Changwon, South KoreaSchool of Mechanical Engineering, University of Ulsan, South KoreaSchool of Mechanical Engineering, Pusan National University, Busan, South Korea; Corresponding author.The drain tube in household refrigerators serves to discharge defrost water and equalize internal and external pressure. However, its open-ended design permits humid air ingress, accelerating frost buildup and reducing system efficiency. To address this, a simplified cone-shaped drain tube was proposed with a focus on manufacturability. Experiments and computational fluid dynamics (CFD) simulations were conducted to investigate frost formation patterns and the effects of absolute humidity. To reduce the cost of evaluating design variations, an artificial neural network (ANN) was developed as a surrogate model trained on CFD data. The ANN accurately predicted performance based on geometric parameters, capturing nonlinear interactions among pressure release, airflow, and frost accumulation. The optimal cone-type design, predicted with a 5.12 % error margin, was fabricated via 3D printing and validated experimentally through pressure and flow rate measurements. When applied in a commercial refrigerator, the cone-type tube delayed frost blockage by more than twice and reduced negative pressure peaks caused by door opening by approximately 85 % compared to conventional designs. These results highlight the effectiveness of the AI-assisted framework for data-efficient thermal optimization and demonstrate the potential of intelligent, learning-based approaches in thermal system design.http://www.sciencedirect.com/science/article/pii/S2214157X25010615Household refrigeratorsDrain tubeAccumulation of frostIn situ measurementsComputational Fluid Dynamics (CFD) |
| spellingShingle | Suhwan Lee Dongkuk Kang Jinxing Fan Sunghee Kang Dong Kim Eunseop Yeom Design of drain tube with cone to prevent frost accumulation of drainage hole in household refrigerators using artificial neural network Case Studies in Thermal Engineering Household refrigerators Drain tube Accumulation of frost In situ measurements Computational Fluid Dynamics (CFD) |
| title | Design of drain tube with cone to prevent frost accumulation of drainage hole in household refrigerators using artificial neural network |
| title_full | Design of drain tube with cone to prevent frost accumulation of drainage hole in household refrigerators using artificial neural network |
| title_fullStr | Design of drain tube with cone to prevent frost accumulation of drainage hole in household refrigerators using artificial neural network |
| title_full_unstemmed | Design of drain tube with cone to prevent frost accumulation of drainage hole in household refrigerators using artificial neural network |
| title_short | Design of drain tube with cone to prevent frost accumulation of drainage hole in household refrigerators using artificial neural network |
| title_sort | design of drain tube with cone to prevent frost accumulation of drainage hole in household refrigerators using artificial neural network |
| topic | Household refrigerators Drain tube Accumulation of frost In situ measurements Computational Fluid Dynamics (CFD) |
| url | http://www.sciencedirect.com/science/article/pii/S2214157X25010615 |
| work_keys_str_mv | AT suhwanlee designofdraintubewithconetopreventfrostaccumulationofdrainageholeinhouseholdrefrigeratorsusingartificialneuralnetwork AT dongkukkang designofdraintubewithconetopreventfrostaccumulationofdrainageholeinhouseholdrefrigeratorsusingartificialneuralnetwork AT jinxingfan designofdraintubewithconetopreventfrostaccumulationofdrainageholeinhouseholdrefrigeratorsusingartificialneuralnetwork AT sungheekang designofdraintubewithconetopreventfrostaccumulationofdrainageholeinhouseholdrefrigeratorsusingartificialneuralnetwork AT dongkim designofdraintubewithconetopreventfrostaccumulationofdrainageholeinhouseholdrefrigeratorsusingartificialneuralnetwork AT eunseopyeom designofdraintubewithconetopreventfrostaccumulationofdrainageholeinhouseholdrefrigeratorsusingartificialneuralnetwork |