AI-Enhanced design of excavator engine room cooling system using computational fluid dynamics and artificial neural networks

被引：2

作者：

Kwon, Tae Woo ^{[1
,2
]}

Kim, Hui Geun ^{[3
]}

Lee, Jae Seok ^{[4
]}

Jeong, Chan Hyeok ^{[4
]}

Choi, You Chul ^{[4
]}

Ha, Man Yeong ^{[3
]}

机构：

[1] Rolls Royce, 2 Busandaehak Ro 63Beon Gil, Busan 46241, South Korea

[2] Pusan Natl Univ, Pusan Natl Univ Technol Ctr Thermal Management, 2 Busandaehak Ro 63Beon Gil, Busan 46241, South Korea

[3] Pusan Natl Univ, Sch Mech Engn, 2 Busandaehak Ro 63Beon Gil, Busan 46241, South Korea

[4] Hyundai Xitesolut, 477 Bundangsuseo Ro, Seongnam Si 13553, South Korea

来源：

CASE STUDIES IN THERMAL ENGINEERING | 2024年 / 54卷

基金：

新加坡国家研究基金会;

关键词：

Excavator; Cooling system; Computational fluid dynamics; AI learning; Artificial neural network; Optimization; TUBE HEAT-EXCHANGERS; NUMERICAL-ANALYSIS; PERFORMANCE; FLOW; PREDICTION; OPTIMIZATION;

D O I：

10.1016/j.csite.2023.103959

中图分类号：

O414.1 [热力学];

学科分类号：

摘要：

Excavators mainly perform high -load operations in fixed positions, so the stability of their performance depends solely on their cooling system. In this study, computational fluid dynamics (CFD) analysis was conducted using Fluent 2022 R22 software to analyze the cooling system in the engine room of an excavator. A comprehensive parametric study was performed, considering different cooling fan layouts and operating rates, to establish a database of cooling performance data for the excavator. Artificial neural network (ANN) models were trained on the constructed database and were then applied to design the cooling system and predict the performance. Further, optimal designs that maximized the cooling performance and energy efficiency were selected. This study demonstrates the feasibility of using ANN models to quickly and accurately predict and design the cooling system of an excavator in a cost-effective manner.

引用

页数：13

共 46 条

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