Field Demonstrated Extended Graetzian Viscous Dissipative Thermo-Photonic Energy Conversion with a Blended MgO/PVDF/PMMA Coated Glass-Pdms Micro-Pillar Heat Exchanger
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER（2023）
Hong Kong Univ Sci & Technol | City Univ Hong Kong | Hong Kong Polytech Univ
Abstract
Chilled water harvesting is a fundamental application of passive radiative cooling and promotes energy conservation for space cooling in buildings potentially, which relies on well-designed radiative cooling materials and heat transfer interface. This paper reports a scenario leading to viscous dissipative thermo-photonic energy conversion, which takes place in low Peclet number regime of an order of magnitude of 100, where heat transfer is non-Graetzian. Compared to benchmarked glass-polydimethylsiloxane radiative cooler and barium sulphate coating, a newly developed trinary micro-porous 32/4/4 magnesium-oxide/poly(vinylidene-fluoride)/poly (methyl-methacrylate) radiative cooling blend, featuring high atmospheric window emissivity and solar reflectivity, both exceeding 97%, demonstrated a superior cooling performance with additional temperature reduction of 1.6 & DEG;C at daytime. Meanwhile, it chilled water at a flow rate of 6.3 & mu;L/s by 1.3 & DEG;C upon coating on a glasspolydimethylsiloxane micro-pillar heat exchanger. Quantitative evaluation on the chilled water capacity was carried out at nighttime when the system ran pseudo-steadily. Cooling power measurement on a radiative cooler of same materials recorded a cooling power of 134 W/m2 which is close to the ideal limit. And measured water temperature reduction and cooling efficiency were 2.5 & DEG;C and 6.3% respectively. They were significantly lower than the saturation limit. Degraded thermal and energy conversion performances, attributive to extended Graetzian viscous dissipation, were discussed theoretically.
MoreTranslated text
Key words
Radiative cooling,Energy conversion,Extended Graetzian heat transfer,Low Peclet number flow,Micro -fabrication
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Related Papers
Exploring Key Factors of Radiative Cooling Performance of N-Octadecane@sio2 MEPCMs
International Journal of Heat and Mass Transfer 2024
被引用0
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper