![](/ii/tfec2022.jpg)
ISSN Online: 2379-1748
7th Thermal and Fluids Engineering Conference (TFEC)
SJR:
0.152
SNIP:
0.14
CiteScore™::
0.5
Indexed in
AN INVERSE IDENTIFICATION OF THE AIR MASS FLOW RATE DISTRIBUTION IN THE AIR CHANNELS OF AN AIR-PCM HEAT EXCHANGER
Аннотация
The paper explores the application of the particle swarm optimization (PSO) method to heat transfer in an air-PCM heat exchanger (PCMHX). The studied PCMHX consisted of PCM- filled aluminum panels with air channels between the panels. The study focused on the air mass flow rate distribution in the air channels of the PCMHX. Ideally, there should be the same air mass flow rates in all air channel of the PCMHX. However,
due to the connection of an air duct (with a relatively small cross section area) to the PCMHX, the distribution of air mass flow rates to the air channels was not uniform. In the first step, the direct heat transfer problem was solved with the use of the simulation model of the PCMHX. The proposed numerical model was based on the energy balance approach and programmed in MATLAB. The total air mass flow rate and its distribution
to the particular air channels was known (20 air channels were considered) with the average mass flow rate of mAVG = 0.078 kg s−1. The complete heat storage cycle of the PCMHX (charging/discharging of heat) was simulated. In the second step, the total air mass flow rate was considered known but its distribution to the
particular air channels was assumed to be unknown. The distribution of the air mass flow rates was sought out with the use of the PSO method from the inverse heat transfer problem where the outlet air temperature evolution in time was known (it was obtained in the first step by the solution of the direct problem). A good accuracy of the inversely identified air mass flow rate distribution was achieved in the study. The relative difference of up to 0.1 % and 1 % between the pre-simulated and optimised scenarios was obtained for parameters Δmmax and σa, respectively.
Ключевые слова:
phase change material, inverse identification, particle swarm optimization, mass flow rate