Investigation of Thermal Performance for Atria: a Method Overview
Abstract
The importance of low energy design in large buildings has encouraged researchers to implement different methods for predicting a building’s thermal performance. Atria, as energy efficient features, have been implemented to improve the indoor thermal environment in large modern buildings. Though widely implemented, the thorough study of atrium performance is restricted due to its large size, complex thermodynamic behavior and the inaccuracies and limitations of available prediction tools. This study reviews the most common research tools implemented in previous researches on atria thermal performance, to explore the advantages and limitation of different methods for future studies. The methods reviewed are analytical, experimental, computer modelling and a combination of any or all of these methods. The findings showed that CFD (computational fluid dynamic) models are the most popular tools of recent due to their higher accuracy, capabilities and user-friendly modification. Although the experimental methods were reliable for predicting atria thermal and ventilation performance, they have mostly been used to provide data for validation of CFD models. Furthermore, coupling CFD with other experimental models could increase the reliability and accuracy of the models and provide a more comprehensive analysis.
Keywords
chemistry
microscopy
Medicine
Technology (General)
Chemical technology
Engineering (General). Civil engineering (General)
Technology
Electrical engineering. Electronics. Nuclear engineering
Descriptive and experimental mechanics
Science (General)
renewable energy sources
environmental sciences
architecture
building construction
energy conservation
Access
Citation
ID:
42987
References
Blockchain Verification
Account:
NFT Contract Address:
0x95644003c57E6F55A65596E3D9Eac6813e3566dA
Article ID:
42987
Unique Identifier:
ee95616e1d6094ac56e88834fdb40343
Network:
Scimatic Chain (ID: 481)
Loading...
Blockchain Readiness Checklist
Authors
Abstract
Journal Name
Year
Title
Creates 1,000,000 NFT tokens for this article
Token Features:
- ERC-1155 Standard NFT
- 1 Million Supply per Article
- Transferable via MetaMask
- Permanent Blockchain Record
Scan with Saymatik Web3.0 Wallet
