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Table 3. Principal performance thermal-hydraulic parameter.

Table 3 Principal performance thermal-hydraulic parameter.

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The first thermodynamic law, for the heat exchanger according to [4] Eq. (1).
The first thermodynamic law, for the heat exchanger according to [4] Eq. (1).
4. Results and discussions
4. Results and discussions
Applying the principles of entransy dissipation and the empirical equations presented above for total cost estimation an elitist genetic algorithm NSGA-II was implemented. Pareto front shown in figure 4 is obtained. For this case, in advantage over single-objective optimization, the multi- objective optimization displays a set of solutions, bean the Pareto front a versatile tool for this applications, although in some cases this is a problem because of necessary decisions making process to select the best solution for the design. Fig. 4. Pareto front for the optimization.
Applying the principles of entransy dissipation and the empirical equations presented above for total cost estimation an elitist genetic algorithm NSGA-II was implemented. Pareto front shown in figure 4 is obtained. For this case, in advantage over single-objective optimization, the multi- objective optimization displays a set of solutions, bean the Pareto front a versatile tool for this applications, although in some cases this is a problem because of necessary decisions making process to select the best solution for the design. Fig. 4. Pareto front for the optimization.
Table 2. Vector decisions for the best solutions. In order to make the best selection is recommended the following criteria, select the equipment with the highest heat transfer rate at the minimum cost. For this case is used the graph shown in Figure 5, where it can be seen that the best solutions are those where the fuel outlet temperature is about 125°C. These heat exchangers are those with a high rate of heat transfer at minimal cost. In Table 2 is showed the decisions vector for the best solutions according to figure 5, in Table 3 a set of parameters of interest of the thermal and hydraulic performance are given.
Table 2. Vector decisions for the best solutions. In order to make the best selection is recommended the following criteria, select the equipment with the highest heat transfer rate at the minimum cost. For this case is used the graph shown in Figure 5, where it can be seen that the best solutions are those where the fuel outlet temperature is about 125°C. These heat exchangers are those with a high rate of heat transfer at minimal cost. In Table 2 is showed the decisions vector for the best solutions according to figure 5, in Table 3 a set of parameters of interest of the thermal and hydraulic performance are given.
Table 4. The main geometrical parameter. 5. Conclusions
Table 4. The main geometrical parameter. 5. Conclusions
Related topics:
Materials ScienceEffectivenessMultiobjective OptimizationHeuristic AlgorithmsMultiple SolutionsShell and Tube Heat ExchangerEnergy SustainabilityOperating Condition
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