Top-down model for dynamic simulation of cold-storage plants

被引:13
|
作者
Hasse, H
Becker, M
Grossmann, K
Maurer, G
机构
[1] UNIV KAISERSLAUTERN,LEHRSTUHL TECH THERMODYNAM,D-67653 KAISERSLAUTERN,GERMANY
[2] UNIV KAISERSLAUTERN,LEHRSTUHL AUTOMAT TECH,D-67653 KAISERSLAUTERN,GERMANY
[3] UNIV KAISERSLAUTERN,ZENTRUM MIKROELEKT,D-67653 KAISERSLAUTERN,GERMANY
关键词
thermodynamics; refrigeration plant; dynamic model; automation; control; simulation; experimental data;
D O I
10.1016/0140-7007(95)00077-1
中图分类号
O414.1 [热力学];
学科分类号
摘要
A simulation model for dynamic processes in cold-storage plants is presented. It consists of interacting subsystems like the cold-store air, goods, room structures, cold-store walls, evaporator, refrigerator and environment. The mathematical description is based on mass and energy balances, coupled with simple assumptions on heat and mass transfer. Freezing of goods and frosting of the evaporator is accounted for, as well as the possibility of defrosting. A concise description of the overall dynamic behaviour is achieved by using a modular structure together with top-down modelling. An advantage of this model is its limited number of parameters, most of which can be determined from readily available information, e.g. from data sheets. Using these parameters, the transient behaviour of the most important variables in cold-store operation can be predicted. Such predictions are compared to experimental data taken in the present work in a medium-size cold store. The transient behaviour of the temperature and relative humidity in that cold store is reported for a cooling period with two different controllers and for a defrosting period. For all studied operating conditions the results of the simulation show favourable agreement with the experimental data. This shows that the simulation model is suited for studies of improved automation and control concepts for for example, at preventing losses in the quality of the goods or for energy savings.
引用
收藏
页码:10 / 18
页数:9
相关论文
共 50 条
  • [41] Top-down influences in the interactive alignment model: The power of the situation model
    Warren, T
    Rayner, K
    BEHAVIORAL AND BRAIN SCIENCES, 2004, 27 (02) : 211 - +
  • [42] Numerical Simulation on Top-down Construction Method of Subway Station in Hefei
    Ding, Kewei
    Guo, Naiyang
    PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON LOGISTICS, ENGINEERING, MANAGEMENT AND COMPUTER SCIENCE (LEMCS 2015), 2015, 117 : 1731 - 1734
  • [43] Simulation of top-down crack propagation in asphalt pavements附视频
    Hui LUOHongping ZHUYu MIAOChuanyao CHEN School of Civil Engineering and MechanicsHuazhong University of Science and TechnologyWuhan China Hubei Key Laboratory of Control StructureHuazhong University of Science and TechnologyWuhan China
    Journal of Zhejiang University-Science A(Applied Physics & Engineering), 2010, (03) : 223 - 230
  • [44] The importance of recurrent top-down synaptic connections for the anticipation of dynamic emotions
    Mermillod, Martial
    Bourrier, Yannick
    David, Erwan
    Kauffmann, Louise
    Chauvin, Alan
    Guyader, Nathalie
    Dutheil, Frederic
    Peyrin, Carole
    NEURAL NETWORKS, 2019, 109 : 19 - 30
  • [45] Numerical Simulation of Subway Station Constructed Using Top-down Method
    Wang, Tiecheng
    Du, Xinghua
    Zhao, Hailong
    TRENDS IN CIVIL ENGINEERING, PTS 1-4, 2012, 446-449 : 3757 - +
  • [46] Top-down behavioral-level simulation of large analog circuits
    Deutsch, J
    COMPUTER DESIGN, 1996, 35 (06): : 91 - 93
  • [47] Top-down based saliency model in traffic driving environment
    Deng, Tao
    Chen, Andong
    Gao, Min
    Yan, Hongmei
    2014 IEEE 17TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), 2014, : 75 - 80
  • [48] A Top-down Approach to S-UTD-CH Model
    Tabakcioglu, Mehmet B.
    APPLIED COMPUTATIONAL ELECTROMAGNETICS SOCIETY JOURNAL, 2017, 32 (07): : 586 - 592
  • [49] A Top-Down Binary Hierarchical Topic Model for Biomedical Literature
    Lin, Xiaoguang
    Liu, Mingxuan
    Zhang, Ju
    IEEE ACCESS, 2020, 8 : 59870 - 59882
  • [50] Incremental learning of Gaussian mixture model: A top-down algorithm
    Chen, C. (thunder@mail.nwpu.edu.cn), 1600, Binary Information Press, P.O. Box 162, Bethel, CT 06801-0162, United States (09):