The performance of the above neural network structures is investigated, and the diagnostic capabilities of the measuring instruments are examined. Next, in order to improve the generalization capabilities, which are critical for the specific diagnostic problem, a new multinet architecture is developed, based on the idea of 'majority rule' decision. Compared with the classical architectures, this new multinet architecture is characterized by higher generalization capabilities and robustness. A first approach to the design of the appropriate multinet architecture and the selection of the appropriate measuring instruments, in order to provide the basis of a high-performance automated diagnostic system, is proposed. The conclusions derived are of general interest and applicability.
Refrigerant flows helically in a single plane where the axis of the spiral is horizontal.
The focus of this paper is to predict the heat transfer and pressure drop of the refrigerant. An equation set of continuity, momentum, energy, and refrigerant's equation of state are presented. Numerical results of the spatial variation of enthalpy, density, velocity, pressure, and temperature are presented, and comparisons are made to show the effect of the curved-tube on the solution. In addition a second law analysis of the flow is made in order to quantify and compare individual losses. It is found that the heat transfer performance at high quality is improved by the helical flow by five percent.