A comprehensive, combined, numerical model is presented, based on genetic algorithm (GA) optimization and heat-transfer finite-element computations. The numerical analyses were carried out to evaluate the final cross-linking degree of a medium-voltage electric cable subjected to industrial peroxide reticulation. The difference between numerically predicted and experimentally determined cross-linking degree along the thickness of the insulator is then minimized, when a variable steam-temperature profile along the pipe length is assumed to explain the unexpected undervulcanization of the cable in the internal layers. To minimize the gap between the experimentally determined degree of curing and numerical predictions, a GA optimization is used for best fit, instead of the steepest-descendent, standard least-squares, which is not applicable in this case because the objective function is not analytically known. The cable was supposedly vulcanized under four different conditions. The degree of cross-linking is experimentally obtained by determining the differential scanning calorimetry (DSC) of the nondecomposed peroxide from the external layer to the core of the cable. The proposed GA approach exploits a specifically crafted, zooming strategy, consisting of the subdivision of the population at each iteration into two subgroups, depending on the individual's grade of fitness (elitist strategy). The integrated, numerical, experimental approach allows optimization of the amount of peroxide in the compound and comparison of the performances of different peroxide mixtures. The degree of cross-linking compliance can be obtained as a function of the temperature gradient measured in the steam pipe. Production conditions can be automatically calculated according to the cable parameters, by increasing the quality reliability and reducing the scraps.
COMBINED NUMERICAL FINITE ELEMENT AND EXPERIMENTAL OPTIMIZATION APPROACH IN THE PRODUCTION PROCESS OF MEDIUM-VOLTAGE, RUBBER-INSULATED ELECTRIC CABLES VULCANIZED WITH STEAM WATER. PART 2: NUMERICAL SIMULATIONS AND INVERSE ANALYSES
MILANI, GABRIELE;
2015-01-01
Abstract
A comprehensive, combined, numerical model is presented, based on genetic algorithm (GA) optimization and heat-transfer finite-element computations. The numerical analyses were carried out to evaluate the final cross-linking degree of a medium-voltage electric cable subjected to industrial peroxide reticulation. The difference between numerically predicted and experimentally determined cross-linking degree along the thickness of the insulator is then minimized, when a variable steam-temperature profile along the pipe length is assumed to explain the unexpected undervulcanization of the cable in the internal layers. To minimize the gap between the experimentally determined degree of curing and numerical predictions, a GA optimization is used for best fit, instead of the steepest-descendent, standard least-squares, which is not applicable in this case because the objective function is not analytically known. The cable was supposedly vulcanized under four different conditions. The degree of cross-linking is experimentally obtained by determining the differential scanning calorimetry (DSC) of the nondecomposed peroxide from the external layer to the core of the cable. The proposed GA approach exploits a specifically crafted, zooming strategy, consisting of the subdivision of the population at each iteration into two subgroups, depending on the individual's grade of fitness (elitist strategy). The integrated, numerical, experimental approach allows optimization of the amount of peroxide in the compound and comparison of the performances of different peroxide mixtures. The degree of cross-linking compliance can be obtained as a function of the temperature gradient measured in the steam pipe. Production conditions can be automatically calculated according to the cable parameters, by increasing the quality reliability and reducing the scraps.File | Dimensione | Formato | |
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2015_RCT2.pdf
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Vulcanization of electric cables-Part II-Numerical simulations and inverse analyses_11311-973606_Milani.pdf
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