﻿ 水下舱室温控系统一维-三维协同仿真研究
 舰船科学技术  2023, Vol. 45 Issue (1): 175-179    DOI: 10.3404/j.issn.1672-7649.2023.01.032 PDF

1. 深海技术科学太湖实验室，江苏 无锡 214082;
2. 中国船舶科学研究中心深海载人装备国家重点实验室，江苏 无锡 214082

1D-3D co-simulation research of undersea cabin temperature control system
ZHOU Xin-tao1,2, ZHAO Yuan-hui1,2, WU Xian1,2, XU Meng1,2
1. Taihu Laboratory of Deepsea Technological Science, Wuxi 214082, China;
2. State Key Laboratory of Deepsea Manned Vehicles, China Ship Scientific Research Center, Wuxi 214082, China
Abstract: The 1D engineering system simulation can respond system dynamic state characteristic effectively, the 3D CFD number simulation can respond details of the cabin room flows. This article carries out simulation research of undersea cabin temperature control system by 1D-3D co-simulation method. This article draw following conclusions. 1D-3D co-simulation can not only reflect three-dimensional such as uneven distribution of temperature and humidity but also system dynamic characteristics. It has certain advantages. Control system based on comfort index can increase comfort level comparing to that based on temperature. Control system based on comfort index and multisensory can not only improve temperature uniformity but also comfort level.
Key words: co-simulation     numerical simulation     engineering system simulation     temperature control system     comfort level
0 引　言

1 计算方法及验证 1.1 计算流体力学方法

 $\frac{\partial \left(\rho \varPhi \right)}{\partial t}+{\rm{div}}\left(\rho u\varPhi -{\varGamma }_{\Phi ,eff}{\rm{grad}}\varPhi \right)={S}_{\varPhi }。$ (1)

 图 1 小型办公室几何模型 Fig. 1 Small office geometry model

 图 2 房间中央气流速度、空气温度对比 Fig. 2 Air velocity and air temperature in the center of the room
1.2 系统仿真和协同仿真方法

 图 3 求解器交互原理图 Fig. 3 Solver interaction schematic diagram
2 计算模型 2.1 物理模型

 图 4 系统仿真草图 Fig. 4 System simulation sketch
2.2 CFD计算方法及边界条件设置

2.3 网格独立性检验

 图 5 网格划分结果 Fig. 5 Mesh results

3 结果分析 3.1 一维系统仿真与协同仿真结果对比

 图 6 舱室温湿度 Fig. 6 Cabin temperature and relative humidity

 图 7 舱室温度分布 Fig. 7 Cabin temperature distribution
3.2 基于热舒适性控制仿真结果

 图 8 舱室平均PMV值 Fig. 8 Mean cabin PMV
3.3 基于多传感器控制的协同仿真

 图 9 舱室温度分布 Fig. 9 Cabin temperature distribution

 图 10 人员附近监测点PMV值 Fig. 10 PMV of monitoring point near personnel
4 结　语

1）一维-三维协同仿真不仅能够反映舱室温湿度分布不均匀等三维效应，且能够反映系统动态特征，具有一定优势；

2）相较于传统基于温度控制的结果，基于舒适性控制的仿真有利于提高人员热舒适性；

3）相较于传统回风温度控制的结果，基于舒适性及多传感器控制的仿真结果不仅能提高舱室温度均匀性，而且能提高人员舒适性。

 [1] YUAN X, CHEN Q, GLICKSMAN L R, et al. Measurements and computations of room airflow with displacement ventilation[J]. ASHRAE Transaction, 1999, 105(1): 340-352. [2] ZHANG ZHAO, CHEN XI, MAZUMDAR S, et al. Experimental and numerical investigation of airflow and contaminant transport in an airliner cabin mockup[J]. Building and Environment, 2009, 44(1): 85-94. DOI:10.1016/j.buildenv.2008.01.012 [3] 孙贺江, 吴尘. 基于正交试验法的大型客机座舱气流组织优化及热舒适性分析[J]. 天津大学学报(自然科学与工程技术版), 2013, 46(5): 415-422. SUN H J, WU C. Optimization of air distribution with orthogonal test and thermal comfort analysis in commercial aircraft cabin[J]. Journal of Tianjin University(Science and Technology), 2013, 46(5): 415-422. [4] 臧旭, 刘毅巍, 徐向东. 基于AMESim的飞机新型环控系统方案性能仿真与优化[J]. 飞机设计, 2018, 38(4): 56-60. ZANG X, LIU Y W, XU X D. Performance simulation and improvement of new type aircraft environment control system base on AMESim software[J]. Aircrft Design, 2018, 38(4): 56-60. DOI:10.19555/j.cnki.1673-4599.2018.04.013 [5] 杨英, 盘飞. 基于AMESim汽车冷却系统热管理影响因素分析[J]. 机械设计与制造, 2020(5): 293-301. YANY Y, PAN F. Effect factors analysis of thermal management of vehicle cooling system based on AMESim[J]. Machinery Design & Manufacture, 2020(5): 293-301. DOI:10.3969/j.issn.1001-3997.2020.05.071 [6] 付永领, 祁晓野. LMS Imagine. Lab AMESim 系统建模和仿真参考手册[M]. 北京: 北京航空航天大学出版社, 2011. [7] 王福军. 计算流体动力学分析—CFD软件原理与应用[M]. 北京: 清华大学出版社, 2004. [8] 陶文铨. 数值传热学(第二版)[M]. 西安: 西安交通大学出版社, 2004. [9] CHEN Q. Comparison of different k-e models for indoor air flow computations[J]. Numerical Heat Transfer, Part B:Fundamentals:An International Journal of Computation and Methodology, 1995, 28(3): 353-369. [10] GBT 18049-2000中等热环境 PMV和PPD指数的测定及热舒适条件的规定[S]. 国家质量技术监督局, 2000. [11] STEVEN L B, BERND R N, PETROS K. Machine learning for fluid mechanics[J]. Annual Reviews, 2020, 52(1): 477-508. [12] 张伟伟, 寇家庆, 刘溢浪. 智能赋能流体力学展望[J]. 航空学报, 2021, 42(4): 524-689. ZHANG W W, KOU J Q, LIU Y L. Prospect of artificial intelligence empowered fluid mechanics[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42(4): 524-689. [13] 芮庆. 基于本征正交分解与人工智能的快速温度分布预测和控制策略研究[D]. 上海: 上海交通大学, 2019.