Flow around cylinders is an important phenomenon in many different engineering fields. In this paper, the fast prediction of the pressure fields of parallel twin cylinders is implemented based on a data-driven algorit...
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Flow around cylinders is an important phenomenon in many different engineering fields. In this paper, the fast prediction of the pressure fields of parallel twin cylinders is implemented based on a data-driven algorithm. Firstly, the pressure fields of parallel twin cylinders with a low Reynolds number are obtained through the Computational Fluid Dynamics (CFD) method. The pressure fields at different time steps are collected to form a snapshot matrix. The Proper Orthogonal Decomposition (pod) algorithm is then applied to obtain the pod basis vectors of the snapshot matrix, enabling the reconstruing the pressure fields. Subsequently, two reduced-order models (ROM) called the pod-RBFNN and pod-BPNN surrogate models are proposed in this paper. The podRBFNN surrogate model uses the Radial Basis Function Neural Network (RBFNN) to train the pod mode coefficients obtained from the pod algorithm, while the pod-BPNN surrogate model uses the Backpropagation Neural Network (BPNN) for the same purpose. Linearly combining the pod mode coefficients predicted by the pod-RBFNN or pod-BPNN surrogate models with the pod basis vectors obtained from the pod algorithm enables fast and efficient prediction of pressure fields for non-sample points. Finally, comparisons are made between the predicted pressure fields obtained from these two surrogate models and the actual values obtained through CFD simulations. It is found that both the pod-RBFNN and pod-BPNN surrogate models proposed in this paper not only significantly improve efficiency but also maintain a high level of accuracy. However, the training time of the pod-RBFNN surrogate model is significantly shorter than that of the pod-BPNN surrogate model. Additionally, the pod-RBFNN surrogate model exhibits smaller Root Mean Square Errors (RMSE) and Mean Absolute Error (MAE). For the data-driven model of parallel twin cylinders described in this paper, the pod-RBFNN surrogate model is more suitable to predict the pressure fields. The r
Historically, Computational Fluid Dynamics (CFD) has been widely used to verify the flow dynamics in rod bundle channels. Nevertheless, the iterative calculation and time consumption make it impractical for the applic...
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Historically, Computational Fluid Dynamics (CFD) has been widely used to verify the flow dynamics in rod bundle channels. Nevertheless, the iterative calculation and time consumption make it impractical for the application of digital twin that require efficiency. In order to tackle this difficulty, we suggest utilizing a datadriven order reduced model (ROM) to rapidly predict flow fields of rod bundle channels. Previous research on Reduced Order Models (ROM) for rod bundle channel has primarily focused on the single-phase state, with little emphasis on the two-phase state. Thus, the study focuses on the ROM of rod bundle channel in a two-phase state. First, a Computational Fluid Dynamics (CFD) data set is processed using the Proper Orthogonal Decomposition (pod) algorithm to identify important modes. Then, a Back-Propagation Neural Network (BPNN) model is trained as the agent model, due to its strong ability to fit non-linear relationship between input and output. The structure of BPNN is optimized. Ultimately, the ROM model is utilized to predict the flow field of a rod bundle channel under new boundary conditions. The comparison with CFD calculation confirms the effectiveness of the Reduced Order Model (ROM), showcasing its high precision in predicting the temperature, velocity, and void fraction fields within the rod bundle channel. In addition, the ROM model reaches a computation speed that is roughly 10<^>4 times quicker than standard CFD simulations. Nonetheless, it is noted that the model's predictive accuracy diminishes under conditions of slight subcooled boiling, due to the lack of samples under the condition. An improvement strategy: Subdivide the sampling space region according to various change stages, implement partition sampling.
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