Optimization of geometrically nonlinear lattice girders. Part I: considering member strengths

In this study, the entire weight, joint displacements and load-carrying capacity of tubular lattice girders are simultaneously optimized by a multi-objective optimization algorithm, named Non-dominated Sorting Genetic Algorithm II (NSGAII). Thus, the structural responses of tubular lattice girders are obtained by use of arc-length method as a geometrically nonlinear analysis approach and utilized to check their member strengths at each load step according to the provisions of the American Petroleum Institute specification (API RP2A-LRFD 1993). In order to improve the computing capacity of proposed optimization approach, while the optimization algorithm is hybridized with a radial basis neural network approach, an automatic lattice girder generator is included into the design stage. The improved optimization algorithm, called ImpNSGAII, is applied to both a benchmark lattice girder with 17 members and a lattice girder with varying span lengths and loading conditions. Consequently, it is demonstrated: 1) the optimal lattice girder configuration generated has a higher load-carrying capacity ensuring lower weight and joint displacement values; 2) the use of a multi-objective optimization approach increases the correctness degree in evaluation of optimality quality due to the possibility of performing a trade-off analysis for optimal designations; 3) the computing performance of ImpNSGAII is higher than NSGAII’s

Multi-Objective Optimization of Solar Thermal Combisystems

Solar thermal combisystems can significantly reduce primary energy consumption for residential buildings and therefore cut down greenhouse gas emissions; however, the overall performance of such systems depends on their design (i.e., configuration and sizing of their components) and operating conditions. Designing solar thermal combisystems can be improved by using optimization methods. Therefore, this doctoral thesis introduces a multi-objective optimization framework for optimizing the configuration and equipment sizing of solar thermal combisystems. A micro-time variant multiobjective particle swarm optimization (micro-TVMOPSO) algorithm is developed for handling engineering optimization problems, such as the multi-objective optimization of solar combisystems, where evaluating objective functions is time-consuming. The proposed framework uses a generic solar combisystem model coupled with the micro-TVMOPSO algorithm to find a set of optimized combisystem designs. Applied to two case studies, the multi-objective optimization framework was able to find designs reducing the life cycle cost, life cycle energy use, and life cycle exergy destroyed of solar thermal combisystems. The proposed multi-objective optimization framework can therefore be used to get the most out of solar thermal combisystems given specificc economic and environmental conditions