Abstract
Optimization of heat transfer systems (HTSs) benefits energy efficiency. However, current optimization studies mainly focus on the improvement of system design, component design, and local process intensification separately, which may miss the optimal results and lack reliability. This work proposes a synergetic optimization method integrating levels of the local process, component to system, which could guarantee the reliability of results. The system-level optimization employs the heat current method and hydraulic analysis, the component level optimization adopts heuristic optimization algorithm, and the process level optimization applies the field synergy principle. The introduction of numerical simulation and iteration provides the self-consistency and credibility of results. Optimization results of a multi-loop heat transfer system present that the proposed method can save 16.3% pumping power consumption comparing to results only considering system and process level optimization. Moreover, the optimal parameters of component originate from the trade-off relation between two competing mechanisms of performance enhancement, i.e., the mass flow rate increase and shape variation. Finally, the proposed method is not limited to heat transfer systems but also applicable to other thermal systems.
Highlights
Thermal energy is still of great importance in modern society
2, characteristic parameters of the pipeline geometry parameters of heat exchangers 1 and 2, characteristic parameters of the pipeline network, network, VSPsthe remain in the optimization computation
The characteristic parameters and VSPsand remain samethe in same the optimization computation
Summary
Thermal energy is still of great importance in modern society. A heat transfer system (HTS) is widely used to transport thermal energy, its performance improvement benefits the energy efficiency improvement, and efficient and reliable optimization methods and strategies for HTS are necessary [1]. Heat transfer areas of components are given in advance Decision variables of this optimization problem can be attributed to three levels: (1) operation parameters, i.e., operating frequencies of three VSPs, ωi (i = 1, 2, 3), (2) geometry design parameter, i.e., the semi-minor axis rb of the elliptical tube under a constant semi-major axis ra , and (3) local heat transfer2020, process parameters, i.e., the flow and temperature fields in the elliptical tubes, U(x,y,z) and
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