| Full text | |
| Author(s): |
Ramirez-Quintero, Deyber Alexander
;
Silva, Fabricio Leonardo
;
Miranda, Matheus H. R.
;
Silva, Ludmila C. A.
;
Eckert, Jony J.
Total Authors: 5
|
| Document type: | Journal article |
| Source: | APPLIED THERMAL ENGINEERING; v. 269, p. 16-pg., 2025-06-15. |
| Abstract | |
A large amount of food is transported globally in refrigerated trucks. To support the transition to clean energy and improve energy performance, enhancing refrigeration unit efficiency in trailers is necessary. This research developed a Simulink/MatlabTMmodel to calculate thermal loads and the dynamic response of a vapor-compression refrigeration system, integrating key structural factors of the refrigerated trailer with system components. The model considers trailer construction, heat transfer through walls, pre-cooling, product properties, and air infiltration during door openings. It analyzes component costs and evaluates insulation, compressor, and refrigerant effects on temperature stability, energy consumption, and performance. The model was validated against literature data for thermal load estimation over a one-day journey. Costs of four compressors-including one for mobile applications, three refrigerants, and three insulation materials were assessed in S & atilde;o Paulo, Brazil, to identify the optimal configuration by evaluating operational cost, runtime and energy efficiency. The results show that, while aerogel provides the best insulation, its high cost makes polyurethane amore cost-effective option. Among refrigerants, R22 offers the best cost-benefit ratio. The 4T2YK compressor achieves the best balance between cost, energy consumption, and operating time. This configuration provides the optimal combination of energy efficiency, cost-effectiveness, and environmental impact, with a total cost of USD$9,336.77, compressor energy consumption of 2.8 kW, and a reduction in operating time by up to 7 times. These findings contribute to optimizing refrigerated transport systems, reducing costs and environmental impact while improving performance. (AU) | |
| FAPESP's process: | 21/14026-8 - Optimization of energy storage and the power management using artificial neural networks |
| Grantee: | Matheus Henrique Rodrigues Miranda |
| Support Opportunities: | Scholarships in Brazil - Doctorate |