3 resultados para MOO
em Cambridge University Engineering Department Publications Database
Resumo:
Electrical double-layer capacitors owe their large capacitance to the formation of a double-layer at the electrode/electrolyte interface of high surface area carbon-based electrode materials. Greater electrical energy storage capacity has been attributed to transition metal oxides/nitrides that undergo fast, reversible redox reactions at the electrode surface (pseudo-capacitive behavior) in addition to forming electrical double-layers. Solution Precursor Plasma Spray (SPPS) has shown promise for depositing porous, high surface area transition metal oxides. This investigation explored the potential of SPPS to fabricate a-MoO 3 coatings with micro-structures suitable for use as super-capacitor electrodes. The effects of number of spray passes, spray distance, solution concentration, flow rate and spray velocity on the chemistry and micro-structure of the a-MoO 3 deposits were examined. DTA/TGA, SEM, XRD, and electrochemical analyses were performed to characterize the coatings. The results demonstrate the importance of post-deposition heating of the deposit by subsequent passes of the plasma on the coating morphology. © ASM International.
Resumo:
Genetic algorithms (GAs) have been used to tackle non-linear multi-objective optimization (MOO) problems successfully, but their success is governed by key parameters which have been shown to be sensitive to the nature of the particular problem, incorporating concerns such as the numbers of objectives and variables, and the size and topology of the search space, making it hard to determine the best settings in advance. This work describes a real-encoded multi-objective optimizing GA (MOGA) that uses self-adaptive mutation and crossover, and which is applied to optimization of an airfoil, for minimization of drag and maximization of lift coefficients. The MOGA is integrated with a Free-Form Deformation tool to manage the section geometry, and XFoil which evaluates each airfoil in terms of its aerodynamic efficiency. The performance is compared with those of the heuristic MOO algorithms, the Multi-Objective Tabu Search (MOTS) and NSGA-II, showing that this GA achieves better convergence.