Comparison of thermal comfort between radiant and convective systems using field test data from the Chinese Thermal Comfort Database

Du, Heng; Lian, Zhiwei; Lai, Dayi; Lin, Defu; Zhai, Yongchao; Cao, Bin; Zhang, Yufeng; Zhou, Xiang; Wang, Zhaojun; Zhang, Xiaojing; Hou, Zhenshan

doi:10.1016/j.buildenv.2021.108685

Cited by 23 publications

(6 citation statements)

References 22 publications

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“…Researchers have taken steps to enhance thermal stability by incorporating optimization algorithms to design or regulate HVAC systems based on a combination of thermal environment parameters [8] employed a genetic algorithm to create an improved thermal environment through a combined HVAC system [9] integrated a genetic algorithm and energy simulation tool to control the HVAC system, aiming to achieve optimal thermal performance while minimizing energy consumption. However, it is important to note that these studies primarily focused on estimating average thermal environment parameters in QC laboratories [10].…”

Section: Literature Reviewmentioning

confidence: 99%

HVAC Design Optimization for Pharmaceutical Facilities with BIM and CFD

Liu,

Huang

2024

Buildings

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Building Information Modeling (BIM) has been widely used in the past decade to enhance the design quality of Heating, Ventilation, and Air Conditioning (HVAC) systems. However, in specialized areas such as pharmaceutical facilities, HVAC design has traditionally relied on Computer-Aided Design (CAD) drawings. This conventional approach does not allow for the simulation of temperature distribution or the verification of system efficiency, which may lead to design failures. To address these challenges in pharmaceutical facilities, this study proposed a BIM-based approach for optimizing HVAC design with Computational Fluid Dynamics (CFD). By employing CFD to simulate the dynamic airflow conditions of pharmaceutical clean rooms, the effectiveness of HVAC systems can be verified. A case study of a clean room HVAC design is presented to demonstrate the workflow. The results of the case study indicated that the pharmaceutical temperature requirements were met within 1 °C during the design optimization simulation, and there was a 95% match in the 72 h temperature mapping test during site validation. The results confirmed that using CFD with BIM not only successfully simulates the design intentions of indoor air quality but also suggests HVAC system optimization for the required clean room design. The findings of this paper contribute to the body of knowledge on overcoming the limitations of the traditional CAD-based HVAC design process and provide valuable insights on optimizing HVAC design with BIM and CFD technologies.

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Section: Literature Reviewmentioning

confidence: 99%

HVAC Design Optimization for Pharmaceutical Facilities with BIM and CFD

Liu,

Huang

2024

Buildings

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“…Based on the literatures listed in Table 1, we find that human‐environment researches generally contain the following three characteristics, which have a practical impact on the implementation of regression analysis. First, the number of variables involved in study is generally large, and variables can be mainly derived into two sources, one person‐related, including physiological parameters and perception voting, 35–37 and the other environment‐related, that is, a series of parameters divided from assessments in thermal environment, acoustic environment, light environment, and air quality 38–40 ; in addition, some variables are highly subjective. Second, the sample size of study is highly valued, based on Cohen's research on sample size analysis, 41 Lan and Lian 42 specifically explored the minimum sample size calculation method in human health, comfort, and productivity researches, thus the sample size in these studies was often larger than the statistically requirement, and samples were even collected as much as possible in some field tests 37,40,43 .…”

Section: Introductionmentioning

confidence: 99%

“…First, the number of variables involved in study is generally large, and variables can be mainly derived into two sources, one person‐related, including physiological parameters and perception voting, 35–37 and the other environment‐related, that is, a series of parameters divided from assessments in thermal environment, acoustic environment, light environment, and air quality 38–40 ; in addition, some variables are highly subjective. Second, the sample size of study is highly valued, based on Cohen's research on sample size analysis, 41 Lan and Lian 42 specifically explored the minimum sample size calculation method in human health, comfort, and productivity researches, thus the sample size in these studies was often larger than the statistically requirement, and samples were even collected as much as possible in some field tests 37,40,43 . Third, regression equations are mostly polynomials composed of several fundamental functions, as some researchers in the field of medicine and sociology raised the question “are complex models necessary?” 44,45 ; similarly, in human‐environment researches, linear and logistic regressions are always used to explain continuity and classification problems, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…Based on the literatures listed in Table 1, we find that human-environment researches generally contain the following three characteristics, which have a practical impact on the implementation of regression analysis. First, the number of variables involved in study is generally large, and variables can be mainly derived into two sources, one person-related, including physiological parameters and perception voting, [35][36][37] and the other environmentrelated, that is, a series of parameters divided from assessments in thermal environment, acoustic environment, light environment, and air quality [38][39][40] ; in addition, some variables are highly subjective.…”

mentioning

confidence: 99%

“…Second, the sample size of study is highly valued, based on Cohen's research on sample size analysis, 41 Lan and Lian 42 specifically explored the minimum sample size calculation method in human health, comfort, and productivity researches, thus the sample size in these studies was often larger than the statistically requirement, and samples were even collected as much as possible in some field tests. 37,40,43 Third, regression equations are mostly polynomials composed of several fundamental functions, as some researchers in the field of medicine and sociology raised the question "are complex models necessary?" 44,45 ; similarly, in human-environment researches, linear and logistic regressions are always used to explain continuity and classification problems, respectively.…”

mentioning

confidence: 99%

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Discussion on regression analysis with small determination coefficient in human‐environment researches

Lian

2022

Indoor Air

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As the main indicator for assessing the explanatory strength of regression model, there is no denying that a bigger value of determination coefficient (R‐squared, R2) is the consistent pursuit of researchers in human‐environment field, but whether to abandon or apply the model with a small value of R2 is an ongoing argument. This paper summarizes three characteristics of human‐environment researches (large number of various variables, large mathematical sample size, and polynomial regression model). Based on the mathematical mechanism of regression analysis, theoretical analysis and case study are combined to point out the misconceptions that are easy to step into and the corresponding suggested methods from three perspectives: selection of determination coefficients, consideration of independent variables, and application of regression models. An extraordinary important point is, if the regression model passes the significance test, even with a small coefficient of determination, it can still quantitatively explain the impact extent of independent variables on dependent variables, but cannot comprehensively and accurately predict the specific value of dependent variable based on existing independent variables; moreover, the larger the sample size, the closer the interpretation of dependent variables in local model to ideal model. It is expected that these cases and lessons could help researchers to better apply regression analysis in human‐environment researches, and that the small value of R2 would not be an excessive restriction affecting the development of scientific research in this field.

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State of Art on Enhanced Energy-Efficient Building Through Various Materials and Construction Techniques

Abinaya,

Murugesan

2023

Lecture Notes in Civil Engineering

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Comparison of thermal comfort between radiant and convective systems using field test data from the Chinese Thermal Comfort Database

Cited by 23 publications

References 22 publications

HVAC Design Optimization for Pharmaceutical Facilities with BIM and CFD

HVAC Design Optimization for Pharmaceutical Facilities with BIM and CFD

Discussion on regression analysis with small determination coefficient in human‐environment researches

State of Art on Enhanced Energy-Efficient Building Through Various Materials and Construction Techniques

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