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This paper proposes a method for simultaneous evaluation of the assembly process complexity together with the performance of the future product. It allows for product design optimization, considering different aspects of the future design at the early stage of the development process. The proposed method, embodied in a fully automated framework, substitutes the traditional sequential development process with a more efficient and rapid combined procedure, which addresses multiple design aspects simultaneously. Design for assembly (DFA) rules, used as quantitative metrics of the ease-of-assembly of the whole product and individual assembly operations, are automatically evaluated together with performance metrics, estimated based on finite element (FE) simulations. The direct solution to this optimization problem might be inefficient or impossible since it requires the recurrent evaluation of computationally expensive discrete and continuous functions with unknown behavior that represent the optimization objectives and constraints. For that reason, the proposed framework employs regression models based on the Gaussian process and artificial neural networks, thus achieving the optimal design of a product as a result of metamodel-based design optimization (MBDO). The suggested approach is demonstrated in the optimization of a gearbox assembly, considering its mechanical performance and assembly process. Comparing the results of the metamodel-based and direct design optimization shows that MBDO allows finding a better solution using a three times smaller computational budget. In addition, analysis of the results obtained using stationary sampling data sets of different sizes highlighted the limitations of the employed sampling procedure. [ABSTRACT FROM AUTHOR]

Pedestrian bridges are an important part of the urban infrastructure that ensures the safety and comfort of pedestrians. They have a number of distinctive features compared to road bridges. Also, the pedestrian load itself has a significant dynamic component, which can lead to the occurrence of resonant phenomena. Composite reinforced concrete bridges are widely used among the road bridges. This is due to the possibility of including the roadway structure in the act, which increases the load-bearing capacity and reliability of the structure. The same advantages are typical for pedestrian aboveground overpasses. However, pedestrian bridges have a number of features that affect the operation of the composite reinforced concrete structure. It is well-known that the difference between bending structures in civil construction and bending structures in bridge and road construction is the ratio of the rigidness of the concrete and steel parts. The load on pedestrian aboveground overpasses is similar to the temporary load in civil buildings, adjusted for a large dynamic component. But at the same time, the spans of pedestrian aboveground overpasses are similar to the spans of road bridges. In this article, the prospects for the development of composite reinforced concrete structures of pedestrian overpasses are reviewed.

The stochastic direct simulation method is a numerical scheme for approximating the solutions of ordinary differential equations by path simulations of certain associated Markov jump processes. Its particular features make it suitable especially when applied to ODE systems originating from the spatial discretization of PDEs. The present paper provides further improvements to this basic method, which are based on the predictor–corrector principle. They are made possible by the fact that in its context a full path of the jump process is computed. With this full set of data one can perform either Picard iterations, Runge–Kutta steps, or a combination, with the goal of increasing the order of convergence. The improved method is applied to standard test problems such as a reaction–diffusion equation modeling a combustion process in 1D and 2D as well as to the radiation–diffusion equations, a system of two partial differential equations in two space dimensions which is very demanding from the computational point of view. Further optimization aspects which are also discussed in this paper are related to the efficient implementation of sampling algorithms based on Huffman trees. [ABSTRACT FROM AUTHOR]