Weld sequence optimization: the use of surrogate models for solving sequential combinatorial problems

The solution of combinatorial optimization problems usually involves the consideration of many possible design configurations. This often makes such approaches computationally expensive, especially when dealing with complex finite element models. Here a surrogate model is proposed that can be used to reduce substantially the computational expense of sequential combinatorial finite element problems. The model is illustrated by application to a weld path planning problem

Mutli-objective optimisation of GENIE Earth system models

Overview:•GENIE Project•Multi-objective Optimisation•Surrogate Modelling•Grid Computing Infrastructure•Parameter Estimation for a new Ocean Mixing Scheme•Conclusion

Multiobjective optimization using surrogates

Until recently, optimization was regarded as a discipline of rather theoretical interest, with limited real-life applicability due to the comutational or experimental expense involved. Multiobjective optimization was considered as a utopia even in academic studies due to the multiplication of this expense. This paper discusses the idea of using surrogate models for multiobjective optimization. With recent advances in grid and parallel computing more companies are buying inexpensive computing clusters that work in parallel. This allows, for example, efficient fusion of surrogates and finite element models into a multiobjective optimization cycle. The research preented here demonstrates this idea using several response surface methods on a pre-selected set of test functions. It shows that a careful choice of response surface methods is important when carrying out surrogate assisted multiobjective search

Multi-objective optimization of GENIE earth system models

The tuning of parameters in climate models is essential to provide reliable long-term forecasts of Earth system behaviour. We apply a multi-objective optimization algorithm to the problem of parameter estimation in climate models. This optimization process involves the iterative evaluation of response surface models (RSMs), followed by the execution of multiple Earth system simulations. These computations require an infrastructure that provides high-performance computing for building and searching the RSMs and high-throughput computing for the concurrent evaluation of a large number of models. Grid computing technology is therefore essential to make this algorithm practical for members of the GENIE project

The use of multifidelity approximations in engineering design

Approximation methods are considered as surrogates to an expensive computer simulation during optimization. In particular the use of models of varying fidelity throughout this process is discussed

Multiobjective optimization using kriging for industrial applications

This study demonstrates advances in multiobjective optimization, supporting a robustness study of a simplified jet engine structural model. The ultimate goal is to find the best structural configuration of shell thicknesses along the engine that will be robust to a variety of exgternal loads, will be as light as possible and where fuel consumption will be minimal. These are competitive objectives some of which are stochastic rather than deterministic in nature. The paper demonstrates that a deep level multiobjective search pays off many times the investment in time and money by providing significant design improvemen

An integrated approach to friction surfacing process optimisation

This paper discusses the procedures for data collection, management and optimisation of the friction surfacing process. Experimental set-up and characteristics of measuring equipment are found to match the requirements for accurate and unbiased data signals. The main friction surfacing parameters are identified and the first stage of the optimisation process is achieved by visually assessing the coatings and introducing the substrate speed vs. force map. The optimum values from this first stage forms a region around the middle of a trapezium-shaped area whose borders are found experimentally. Data collected for the second stage were analysed using the least squares method which were applied to find the coefficients of a second order regression model. Advantages of applying artificial intelligence methods to friction surfacing modelling are also described and the higher accuracy achieved using neural networks demonstrated