The implicit partition algorithm used to solve fluid–structure coupling problems has high accuracy,but it requires a long computation time.In this paper,a semi-implicit fluid–structure coupling algorithm based on mo...The implicit partition algorithm used to solve fluid–structure coupling problems has high accuracy,but it requires a long computation time.In this paper,a semi-implicit fluid–structure coupling algorithm based on modal force prediction-correction is proposed to improve the computational efficiency.In the pre-processing stage,the fluid domain is assumed to be a pseudo-elastic solid and merged with the solid domain to form a holistic system,and the normalized modal information of the holistic system is calculated and stored.During the sub-step cycle,the modal superposition method is used to obtain the response of the holistic system with the predicted modal force as the load,so that the deformation of the structure and the updating of the fluid mesh can be achieved simultaneously.After solving the Reynolds-averaged Navier-Stokes equations in the fluid domain,the predicted modal force is corrected and a new sub-step cycle is started until the converged result is obtained.In this method,the computation of the fluid equations and the updating of the dynamic mesh are done implicitly,while the deformation of the structure is done explicitly.Two numerical cases,vortex induced oscillation of an elastic beam and fluid–structure interaction of a final stage blade,are used to verify the efficiency and accuracy of the proposed algorithm.The results show that the proposed method achieves the same accuracy as the implicit method while the computational time is reduced.In the case of the vortex-induced oscillation problem,the computational time can be reduced to 18.6%.In the case of the final stage blade vibration,the computational time can be reduced to 53.8%.展开更多
In this paper,we propose a learning algorithm termed linear multistep adaptive moment(LMAdam) to enhance the adaptive moment(Adam) algorithm for machine learning.Considering Adam as a single-step discretization of its...In this paper,we propose a learning algorithm termed linear multistep adaptive moment(LMAdam) to enhance the adaptive moment(Adam) algorithm for machine learning.Considering Adam as a single-step discretization of its continuous counterpart,we develop the LMAdam algorithm based on a linear multistep discretization scheme.We design a feedforward neural network for learning the coefficients of the multistep terms with ensured consistency and select the coefficients to ensure zero stability of the multistep terms.We experimentally demonstrate the superiority of the LMAdam via extensive experimentation on benchmark datasets for training various deep neural networks in three applications.展开更多
基金support of the National Natural Science Foundation of China(No.51675406)the Basic Research Project Group,China(No.514010106-205)。
文摘The implicit partition algorithm used to solve fluid–structure coupling problems has high accuracy,but it requires a long computation time.In this paper,a semi-implicit fluid–structure coupling algorithm based on modal force prediction-correction is proposed to improve the computational efficiency.In the pre-processing stage,the fluid domain is assumed to be a pseudo-elastic solid and merged with the solid domain to form a holistic system,and the normalized modal information of the holistic system is calculated and stored.During the sub-step cycle,the modal superposition method is used to obtain the response of the holistic system with the predicted modal force as the load,so that the deformation of the structure and the updating of the fluid mesh can be achieved simultaneously.After solving the Reynolds-averaged Navier-Stokes equations in the fluid domain,the predicted modal force is corrected and a new sub-step cycle is started until the converged result is obtained.In this method,the computation of the fluid equations and the updating of the dynamic mesh are done implicitly,while the deformation of the structure is done explicitly.Two numerical cases,vortex induced oscillation of an elastic beam and fluid–structure interaction of a final stage blade,are used to verify the efficiency and accuracy of the proposed algorithm.The results show that the proposed method achieves the same accuracy as the implicit method while the computational time is reduced.In the case of the vortex-induced oscillation problem,the computational time can be reduced to 18.6%.In the case of the final stage blade vibration,the computational time can be reduced to 53.8%.
基金supported in part by the National Natural Science Foundation of China(62506148 and 62476115)the Fundamental Research Funds for the Central Universities(lzujbky-2025-pd05 and lzujbky-2025-ytB01)+2 种基金the Research Grants Council of the Hong Kong Special Administrative Region of China(AoE/E-407/24-N and C1013-24G)the Postdoctoral Fellowship Program(Grade C) of China Postdoctoral Science Foundation(GZC20251039)the Supercomputing Center of Lanzhou University。
文摘In this paper,we propose a learning algorithm termed linear multistep adaptive moment(LMAdam) to enhance the adaptive moment(Adam) algorithm for machine learning.Considering Adam as a single-step discretization of its continuous counterpart,we develop the LMAdam algorithm based on a linear multistep discretization scheme.We design a feedforward neural network for learning the coefficients of the multistep terms with ensured consistency and select the coefficients to ensure zero stability of the multistep terms.We experimentally demonstrate the superiority of the LMAdam via extensive experimentation on benchmark datasets for training various deep neural networks in three applications.
