With the advent of the“fourth industrial revolution”,the digital trend has become a hot issue in the field of architecture.Colleges and universities are an important part of Chinese society,and the development of di...With the advent of the“fourth industrial revolution”,the digital trend has become a hot issue in the field of architecture.Colleges and universities are an important part of Chinese society,and the development of digital technology has had a profound impact on college life.In order to explore the new campus development path in line with the development trend of the digital society,the old library of North China University of Technology is taken as the research object.The new teaching mode and learning mode under the digital trend are discussed,and the learning space renewal strategy in line with the new era background is explored,with a view to contributing practical experience in space renewal of colleges and universities.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With the advent of the“fourth industrial revolution”,the digital trend has become a hot issue in the field of architecture.Colleges and universities are an important part of Chinese society,and the development of digital technology has had a profound impact on college life.In order to explore the new campus development path in line with the development trend of the digital society,the old library of North China University of Technology is taken as the research object.The new teaching mode and learning mode under the digital trend are discussed,and the learning space renewal strategy in line with the new era background is explored,with a view to contributing practical experience in space renewal of colleges and universities.
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.
