Abstract (EN):
In this work it was aimed to develop and optimize an artificial neural network (ANN) which
accurately simulates ejector cooling cycle performance according to the operational
conditions. It would also allow for the control of a spindle, located in the primary nozzle, in a
way that ejector performance is maximized. First, it was aimed to optimize an ANN capable
to accurately simulate the performance parameters of a refrigeration cycle and the ejector
performance itself. Input variables were the operational conditions of the cycle. In this first
stage, a data set with some limitations in terms of input variables domain representativeness
was used including those cases that resulted in ejector failure. With this first data set it was
possible to evaluate the effect of those limitations in the artificial neural network
optimization. In the second stage, a new data set was presented to the selected ANN in order
to assess the influence of each input parameters on the cycle performance and also on
optimal spindle control. The condenser temperature was found to be the most important
parameter affecting the ejector performance and so the control of the spindle position brings
great advantages for optimizing the cycle performance in order of the operative conditions.
Language:
English
Type (Professor's evaluation):
Scientific
Contact:
cantonio@fe.up.pt
Notes:
Livro de resumos: pp.559-560;
Edição em CD: Artigo Nº A323756, 10 páginas
No. of pages:
10
License type: