IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i10p3755-d819652.html
   My bibliography  Save this article

Numerical and Experimental Study on Thermal Comfort of Human Body by Split-Fiber Air Conditioner

Author

Listed:
  • Jie Yang

    (School of Petroleum Engineering, Changzhou University, Changzhou 233016, China)

  • Zhimeng Dong

    (China Electronic Systems Engineering Second Construction Co., Ltd., Wuxi 214000, China)

  • Huihan Yang

    (School of Petroleum Engineering, Changzhou University, Changzhou 233016, China)

  • Yanyan Liu

    (School of Petroleum Engineering, Changzhou University, Changzhou 233016, China)

  • Yunjie Wang

    (School of Petroleum Engineering, Changzhou University, Changzhou 233016, China)

  • Fujiang Chen

    (School of Petroleum Engineering, Changzhou University, Changzhou 233016, China)

  • Haifei Chen

    (School of Petroleum Engineering, Changzhou University, Changzhou 233016, China)

Abstract

The thermal comfort of an enclosed room with air conditioner and air-distribution duct coupling can be studied, and the parameters of a split-fiber air conditioner can be optimized on the basis of studying the thermal comfort of various parts of the human body. In this paper, a room model with a distributed air conditioner was proposed. First, the rationality of the three thermal comfort characterization models of predict mean vote (PMV), predicted percentage of dissatisfied (PPD), and percentage of dissatisfied (PD) were verified through experiments and simulations. Then, the temperature and thermal comfort of various parts of the human body were explored when the air-distribution duct had different openings and different positions of the air outlet. The simulation results showed that compared with other situations, when the split-fiber air conditioner had three rows of holes (5-o’clock, 6-o’clock, 7-o’clock) and the air outlet was located in the middle of the right wall of the human body, the PMV, PPD, and PD of the measuring points around the human body fluctuated less, the indoor temperature field distribution fluctuated less, and there was no wind feeling around the human body, which can better meet the needs of human thermal comfort.

Suggested Citation

  • Jie Yang & Zhimeng Dong & Huihan Yang & Yanyan Liu & Yunjie Wang & Fujiang Chen & Haifei Chen, 2022. "Numerical and Experimental Study on Thermal Comfort of Human Body by Split-Fiber Air Conditioner," Energies, MDPI, vol. 15(10), pages 1-24, May.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:10:p:3755-:d:819652
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/10/3755/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/10/3755/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Turhan, Cihan & Simani, Silvio & Gokcen Akkurt, Gulden, 2021. "Development of a personalized thermal comfort driven controller for HVAC systems," Energy, Elsevier, vol. 237(C).
    2. Guo, Yabin & Tan, Zehan & Chen, Huanxin & Li, Guannan & Wang, Jiangyu & Huang, Ronggeng & Liu, Jiangyan & Ahmad, Tanveer, 2018. "Deep learning-based fault diagnosis of variable refrigerant flow air-conditioning system for building energy saving," Applied Energy, Elsevier, vol. 225(C), pages 732-745.
    3. Zhuang, Chaoqun & Wang, Shengwei, 2020. "Risk-based online robust optimal control of air-conditioning systems for buildings requiring strict humidity control considering measurement uncertainties," Applied Energy, Elsevier, vol. 261(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kusnandar & Indra Permana & Weiming Chiang & Fujen Wang & Changyu Liou, 2022. "Energy Consumption Analysis for Coupling Air Conditioners and Cold Storage Showcase Equipment in a Convenience Store," Energies, MDPI, vol. 15(13), pages 1-13, July.
    2. Xinge Du & Guoyao Gao & Feng Gao & Zhihua Zhou, 2023. "A Study on Modifying Campus Buildings to Improve Habitat Comfort—A Case Study of Tianjin University Campus," Sustainability, MDPI, vol. 15(19), pages 1-24, September.
    3. Kai Yang & Tianhao Shi & Tingzhen Ming & Yongjia Wu & Yanhua Chen & Zhongyi Yu & Mohammad Hossein Ahmadi, 2023. "Study of Internal Flow Heat Transfer Characteristics of Ejection-Permeable FADS," Energies, MDPI, vol. 16(11), pages 1-20, May.
    4. Jingyu Cao & Wei Wu & Mingke Hu & Yunfeng Wang, 2023. "Green Building Technologies Targeting Carbon Neutrality," Energies, MDPI, vol. 16(2), pages 1-3, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chen, Zhelun & O’Neill, Zheng & Wen, Jin & Pradhan, Ojas & Yang, Tao & Lu, Xing & Lin, Guanjing & Miyata, Shohei & Lee, Seungjae & Shen, Chou & Chiosa, Roberto & Piscitelli, Marco Savino & Capozzoli, , 2023. "A review of data-driven fault detection and diagnostics for building HVAC systems," Applied Energy, Elsevier, vol. 339(C).
    2. Zhong, Fangliang & Calautit, John Kaiser & Wu, Yupeng, 2022. "Assessment of HVAC system operational fault impacts and multiple faults interactions under climate change," Energy, Elsevier, vol. 258(C).
    3. Rongjiang Ma & Shen Yang & Xianlin Wang & Xi-Cheng Wang & Ming Shan & Nanyang Yu & Xudong Yang, 2020. "Systematic Method for the Energy-Saving Potential Calculation of Air-Conditioning Systems via Data Mining. Part I: Methodology," Energies, MDPI, vol. 14(1), pages 1-15, December.
    4. Sun, Hongchang & Niu, Yanlei & Li, Chengdong & Zhou, Changgeng & Zhai, Wenwen & Chen, Zhe & Wu, Hao & Niu, Lanqiang, 2022. "Energy consumption optimization of building air conditioning system via combining the parallel temporal convolutional neural network and adaptive opposition-learning chimp algorithm," Energy, Elsevier, vol. 259(C).
    5. Shariq, M. Hasan & Hughes, Ben Richard, 2020. "Revolutionising building inspection techniques to meet large-scale energy demands: A review of the state-of-the-art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    6. Liu, Xiangfei & Ren, Mifeng & Yang, Zhile & Yan, Gaowei & Guo, Yuanjun & Cheng, Lan & Wu, Chengke, 2022. "A multi-step predictive deep reinforcement learning algorithm for HVAC control systems in smart buildings," Energy, Elsevier, vol. 259(C).
    7. Ahmad, Tanveer & Chen, Huanxin, 2019. "Deep learning for multi-scale smart energy forecasting," Energy, Elsevier, vol. 175(C), pages 98-112.
    8. Zhuang, Chaoqun & Wang, Shengwei & Shan, Kui, 2020. "A risk-based robust optimal chiller sequencing control strategy for energy-efficient operation considering measurement uncertainties," Applied Energy, Elsevier, vol. 280(C).
    9. Fei Mei & Yong Ren & Qingliang Wu & Chenyu Zhang & Yi Pan & Haoyuan Sha & Jianyong Zheng, 2018. "Online Recognition Method for Voltage Sags Based on a Deep Belief Network," Energies, MDPI, vol. 12(1), pages 1-16, December.
    10. Eom, Yong Hwan & Yoo, Jin Woo & Hong, Sung Bin & Kim, Min Soo, 2019. "Refrigerant charge fault detection method of air source heat pump system using convolutional neural network for energy saving," Energy, Elsevier, vol. 187(C).
    11. Lee, Minjung & Ham, Jeonggyun & Lee, Jeong-Won & Cho, Honghyun, 2023. "Analysis of thermal comfort, energy consumption, and CO2 reduction of indoor space according to the type of local heating under winter rest conditions," Energy, Elsevier, vol. 268(C).
    12. Tien, Paige Wenbin & Wei, Shuangyu & Liu, Tianshu & Calautit, John & Darkwa, Jo & Wood, Christopher, 2021. "A deep learning approach towards the detection and recognition of opening of windows for effective management of building ventilation heat losses and reducing space heating demand," Renewable Energy, Elsevier, vol. 177(C), pages 603-625.
    13. Wang, Jingfan & Tchapmi, Lyne P. & Ravikumar, Arvind P. & McGuire, Mike & Bell, Clay S. & Zimmerle, Daniel & Savarese, Silvio & Brandt, Adam R., 2020. "Machine vision for natural gas methane emissions detection using an infrared camera," Applied Energy, Elsevier, vol. 257(C).
    14. Li, Tingting & Zhou, Yangze & Zhao, Yang & Zhang, Chaobo & Zhang, Xuejun, 2022. "A hierarchical object oriented Bayesian network-based fault diagnosis method for building energy systems," Applied Energy, Elsevier, vol. 306(PB).
    15. Du, Zhimin & Liang, Xinbin & Chen, Siliang & Zhu, Xu & Chen, Kang & Jin, Xinqiao, 2023. "Knowledge-infused deep learning diagnosis model with self-assessment for smart management in HVAC systems," Energy, Elsevier, vol. 263(PD).
    16. Yang, Hongxing & Shi, Wenchao & Chen, Yi & Min, Yunran, 2021. "Research development of indirect evaporative cooling technology: An updated review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    17. Wang, Huilong & Wang, Shengwei, 2021. "A hierarchical optimal control strategy for continuous demand response of building HVAC systems to provide frequency regulation service to smart power grids," Energy, Elsevier, vol. 230(C).
    18. Guo, Yabin & Li, Yuduo & Li, Weilin, 2023. "On-site fault experiment and diagnosis research of the carbon dioxide transcritical heat pump system for energy saving," Energy, Elsevier, vol. 274(C).
    19. Jun Kwon Hwang & Patrick Nzivugira Duhirwe & Geun Young Yun & Sukho Lee & Hyeongjoon Seo & Inhan Kim & Mat Santamouris, 2020. "A Novel Hybrid Deep Neural Network Model to Predict the Refrigerant Charge Amount of Heat Pumps," Sustainability, MDPI, vol. 12(7), pages 1-23, April.
    20. Dong, Zhe & Liu, Miao & Guo, Zhiwu & Huang, Xiaojin & Zhang, Yajun & Zhang, Zuoyi, 2019. "Adaptive state-observer for monitoring flexible nuclear reactors," Energy, Elsevier, vol. 171(C), pages 893-909.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:10:p:3755-:d:819652. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.