Simulation Technique for Optimizing AC System Sizing & Cabin Cooling Performance for Customer Comfort

段落 汽车工业 班级(哲学) 尺寸 计算机科学 汽车工程 热的 模拟 工程类 航空航天工程 化学 物理 万维网 气象学 人工智能 有机化学
作者
Shekhar Bhangale,Shriganesh Umbarkar,Mukesh Kumar,Amit Saha,Sudesh Gakhar,Majid Khan
出处
期刊:SAE technical paper series
标识
DOI:10.4271/2023-28-0011
摘要

<div class="section abstract"><div class="htmlview paragraph">The automotive sector is evolving both globally and as well as in India. The Indian customer’s expectations from an automobile are also evolving at fast pace. This is resulting in a continuous shrinkage of the time available for vehicle development. To meet customers’ expectation of superior cabin thermal comfort it is important to predict cabin cooling performance at early stage. This can be achieved through thermal simulation.</div><div class="htmlview paragraph">Existing studies of cabin thermal simulation explained the method of co-simulation. Wherein, Input for the cabin was used a grill air temperature which was obtained from the physical test. It showed good correlation for the cabin inside air temperature with actual test.</div><div class="htmlview paragraph">However, cabin cooling performance does not only depend on cabin structure &amp; layout but also, affected by AC system &amp; its component level performance. AC systems and components were not considered in previous studies. As a result, replacing or modifying the AC system components does not allow us to estimate the cabin inside air temperature.</div><div class="htmlview paragraph">Therefore, it is important to link AC system with existing co-simulation methods.</div><div class="htmlview paragraph">This paper explains that, how the AC system is connected to existing co-simulation model and carryout the real-time cabin cooling simulation.</div><div class="htmlview paragraph">Revised co-simulation approach will help in the development of an effective AC system and predict cabin inside air temperature while considering the impact of AC system components on cabin cooling.</div></div>

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
yao完成签到,获得积分10
1秒前
2秒前
3秒前
共享精神应助aidiresi采纳,获得10
5秒前
ztp完成签到,获得积分10
5秒前
6秒前
cdercder应助zhang采纳,获得10
7秒前
科研小狗完成签到,获得积分10
8秒前
111发布了新的文献求助10
8秒前
达不刘完成签到,获得积分10
8秒前
无极微光应助杨武天一采纳,获得20
11秒前
赘婿应助lilian采纳,获得10
13秒前
14秒前
15秒前
浮游应助科研通管家采纳,获得10
15秒前
科目三应助科研通管家采纳,获得10
15秒前
15秒前
思源应助科研通管家采纳,获得10
15秒前
15秒前
15秒前
15秒前
15秒前
大模型应助科研通管家采纳,获得10
15秒前
浮游应助科研通管家采纳,获得10
16秒前
16秒前
111完成签到,获得积分10
17秒前
闲客完成签到,获得积分10
17秒前
科研通AI6.3应助Jiang采纳,获得10
18秒前
liny发布了新的文献求助10
19秒前
23秒前
我是谁完成签到,获得积分10
23秒前
23秒前
27秒前
lilian发布了新的文献求助10
27秒前
28秒前
28秒前
xiebailu发布了新的文献求助10
30秒前
小六要瘦发布了新的文献求助10
31秒前
abiding发布了新的文献求助30
34秒前
高分求助中
Adhesion Science: Principles & Practice 1234
Signals, Systems, and Signal Processing 610
Petrology and Plate Tectonics,2025 450
Burger's Medicinal Chemistry and Drug Discovery 400
New directions for experimental lessons in science teaching: Myth, Mystery, Necessity? by Emily K. da Silva Cunha Souto (Author), Flávia Lins Silva (Author) 333
Scientific experimentation in the classroom: Comparison between genetic-Socratic-exemplary teaching and workshop teaching by Ingrid Hofer (Author) 333
Programming for Chemical Engineers Using C, C++, and MATLAB 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6724277
求助须知:如何正确求助?哪些是违规求助? 8459953
关于积分的说明 18060189
捐赠科研通 5978308
什么是DOI,文献DOI怎么找? 2997315
邀请新用户注册赠送积分活动 1973595
关于科研通互助平台的介绍 1928418