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Building Thermal Environment: Improving Indoor Comfort by Optimizing Ventilation Systems
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Building Thermal Environment: Improving Indoor Comfort by Optimizing Ventilation Systems

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Energy, Physics, Environment, and Systems".

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 1882

Special Issue Editors

Dr. Congcong Wang

E-Mail Website
Guest Editor
School of Environment and Energy Engineering, Beijing University of Civil Engineering and Architecture, Beijing 102616, China
Interests: building environment; building ventilation; CFD; building green energy
Dr. Jiayu Li

E-Mail Website
Guest Editor
Berkeley Education Alliance for Research in Singapore, Singapore 138602, Singapore
Interests: indoor air quality; indoor airflow dynamics; building ventilation; bioaerosols
Special Issues, Collections and Topics in MDPI journals
Dr. Lizhi Jia

E-Mail Website
Guest Editor
School of Mechanical Engineering, Tianjin University of Commerce, Tianjin 300134, China
Interests: indoor environment control; ventilation and purification; energy-saving building designs; optimal control of HVAC; fault detection and diagnosis of HVAC

Special Issue Information

Dear Colleagues,

This Special Issue, titled "Building Thermal Environment: Improving Indoor Comfort by Optimizing Ventilation Systems", offers an insightful exploration of the critical relationship between indoor thermal comfort and the efficient operation of ventilation systems. With a primary focus on elevating the quality of indoor spaces, this collection of articles brings to the forefront the latest strategies, technologies, and innovative approaches dedicated to optimizing ventilation systems.

Encompassing a wide spectrum of topics, this Special Issue delves into cutting-edge HVAC systems, passive ventilation techniques, and advanced sensor-driven control systems. These components collectively work towards creating healthier and more comfortable indoor environments while simultaneously improving energy efficiency.

The integration of natural ventilation strategies alongside mechanical systems is a central theme, illustrating the harmonious balance between sustainable, energy-efficient building designs and occupant well-being.

This Special Issue is an indispensable resource for architects, engineers, and environmental professionals, providing comprehensive insights and practical solutions for enhancing building sustainability, energy efficiency, and overall quality of life for those who occupy these spaces.

Dr. Congcong Wang
Dr. Jiayu Li
Dr. Lizhi Jia
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Buildings is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • indoor environment
  • ventilation
  • airflow distribution
  • HVAC
  • thermal comfort
  • CFD
  • airflow structure
  • energy saving

Published Papers (2 papers)

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Research

16 pages, 7063 KiB  
Article
Analysis of Resistance Characteristics and Research into Resistance Reduction of a Tee Based on Field Synergy
by Yajing Yan, Chongfang Song, Wuxuan Pan, Jie Wang and Yifan Bai
Buildings 2024, 14(5), 1271; https://doi.org/10.3390/buildings14051271 - 01 May 2024
Viewed by 207
Abstract
The resistance loss and energy consumption when fluid flows through a tee in an HVAC system are severe. To improve energy efficiency and reduce carbon emissions, a novel tee with a U-shaped deflector is proposed, supported by experiments and numerical simulations. The resistance [...] Read more.
The resistance loss and energy consumption when fluid flows through a tee in an HVAC system are severe. To improve energy efficiency and reduce carbon emissions, a novel tee with a U-shaped deflector is proposed, supported by experiments and numerical simulations. The resistance reduction mechanism of the U-shaped deflector was analyzed according to the viscous dissipation principle and the field synergy principle. The resistance reduction of the novel tee with different deflector angles and a traditional tee were compared. The results show that the resistance loss of the tee was mainly due to the flow separation and deformation of the fluid in the main branch. The relationship between the local resistance coefficient and the diameter ratio of the main-branch pipe was exponential, and the relationship between the local resistance coefficient and the diameter ratio of the main straight pipe was linear. The total resistance loss reduction rate of the tee with the addition of a 26° deflector was the highest, reaching 72.4%, the volume-weighted average synergy angle increased by 1°, and the viscous dissipation decreased by 21.7%. This study provides a reference for the resistance reduction design of complex local components such as tees in HVAC systems. Full article
(This article belongs to the Special Issue Building Thermal Environment: Improving Indoor Comfort by Optimizing Ventilation Systems)
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13 pages, 1855 KiB  
Article
Preliminary Research on Outdoor Thermal Comfort Evaluation in Severe Cold Regions by Machine Learning
by Tianyu Xi, Ming Wang, Enjia Cao, Jin Li, Yong Wang and Salanke Umar Sa’ad
Buildings 2024, 14(1), 284; https://doi.org/10.3390/buildings14010284 - 20 Jan 2024
Cited by 1 | Viewed by 1302
Abstract
The thermal comfort evaluation of the urban environment arouses widespread concern among scholars, and research in this field is mostly based on thermal comfort evaluation indexes such as PMV, PET, SET, UTCI, etc. These thermal comfort index evaluation models are complex in the [...] Read more.
The thermal comfort evaluation of the urban environment arouses widespread concern among scholars, and research in this field is mostly based on thermal comfort evaluation indexes such as PMV, PET, SET, UTCI, etc. These thermal comfort index evaluation models are complex in the calculation process and poor in operability, which makes it difficult for people who lack a relevant knowledge background to understand, calculate, and apply them. The purpose of this study is to provide a simple, efficient, and easy-to-operate outdoor thermal comfort evaluation model for severe cold areas in China using a machine learning method. In this study, the physical environment parameters are obtained by field measurement, and individual information is obtained by a field questionnaire survey. The applicability of four machine learning models in outdoor thermal comfort evaluation is studied. A total of 320 questionnaires are collected. The results show that the correlation coefficients between predicted values and voting values of the extreme gradient lifting model, gradient lifting model, random forest model, and neural network model are 0.9313, 0.7148, 0.9115, and 0.5325, respectively. Further analysis of the extreme gradient model with the highest correlation coefficient shows that individual factors (such as residence time, distance between hometown and residence, clothing, age, height, and weight) and environmental factors (such as air humidity (RH), wind speed (v), air temperature (Ta), and black bulb temperature (Tg)) have different influences on thermal comfort evaluation. In summary, using a machine learning method to evaluate outdoor thermal comfort is simpler, more direct, and more efficient, and it can make up for the lack of consideration of complex individual factors in the evaluation method of thermal comfort index. The results have reference value and application value for the research of outdoor thermal comfort evaluation in severe cold areas of China. Full article
(This article belongs to the Special Issue Building Thermal Environment: Improving Indoor Comfort by Optimizing Ventilation Systems)
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