Unexpected noise in reservoir stochastic simulation realization may be too high to make the realization useful, especially when there is a lack of hard data. Through discussing the uncertainties, we present two ways t...Unexpected noise in reservoir stochastic simulation realization may be too high to make the realization useful, especially when there is a lack of hard data. Through discussing the uncertainties, we present two ways to control the uncertainty ratio that is brought by the algorithm of stochastic simulation. By reasonably reducing the random value of the stochastic simulation result, the unexpected values introduced by the residual that associates with random series can be controlled. Another way when the data disperse unevenly is to control the stochastic simulation order by grouping the points that need to be simulated to make those points which can be simulated by more neighborhood hard data calculated first. Both methods do not go against the core stochastic simulation algorithm.展开更多
Advanced engineering systems, like aircraft, are defined by tens or even hundreds of design variables. Building an accurate surrogate model for use in such high-dimensional optimization problems is a difficult task ow...Advanced engineering systems, like aircraft, are defined by tens or even hundreds of design variables. Building an accurate surrogate model for use in such high-dimensional optimization problems is a difficult task owing to the curse of dimensionality. This paper presents a new algorithm to reduce the size of a design space to a smaller region of interest allowing a more accurate surrogate model to be generated. The framework requires a set of models of different physical or numerical fidelities. The low-fidelity （LF） model provides physics-based approximation of the high-fidelity （HF） model at a fraction of the computational cost. It is also instrumental in identifying the small region of interest in the design space that encloses the high-fidelity optimum. A surrogate model is then constructed to match the low-fidelity model to the high-fidelity model in the identified region of interest. The optimization process is managed by an update strategy to prevent convergence to false optima. The algorithm is applied on mathematical problems and a two-dimen-sional aerodynamic shape optimization problem in a variable-fidelity context. Results obtained are in excellent agreement with high-fidelity results, even with lower-fidelity flow solvers, while showing up to 39% time savings.展开更多
文摘Unexpected noise in reservoir stochastic simulation realization may be too high to make the realization useful, especially when there is a lack of hard data. Through discussing the uncertainties, we present two ways to control the uncertainty ratio that is brought by the algorithm of stochastic simulation. By reasonably reducing the random value of the stochastic simulation result, the unexpected values introduced by the residual that associates with random series can be controlled. Another way when the data disperse unevenly is to control the stochastic simulation order by grouping the points that need to be simulated to make those points which can be simulated by more neighborhood hard data calculated first. Both methods do not go against the core stochastic simulation algorithm.
文摘Advanced engineering systems, like aircraft, are defined by tens or even hundreds of design variables. Building an accurate surrogate model for use in such high-dimensional optimization problems is a difficult task owing to the curse of dimensionality. This paper presents a new algorithm to reduce the size of a design space to a smaller region of interest allowing a more accurate surrogate model to be generated. The framework requires a set of models of different physical or numerical fidelities. The low-fidelity （LF） model provides physics-based approximation of the high-fidelity （HF） model at a fraction of the computational cost. It is also instrumental in identifying the small region of interest in the design space that encloses the high-fidelity optimum. A surrogate model is then constructed to match the low-fidelity model to the high-fidelity model in the identified region of interest. The optimization process is managed by an update strategy to prevent convergence to false optima. The algorithm is applied on mathematical problems and a two-dimen-sional aerodynamic shape optimization problem in a variable-fidelity context. Results obtained are in excellent agreement with high-fidelity results, even with lower-fidelity flow solvers, while showing up to 39% time savings.
