PCNN：一种多相流物理约束神经网络
PCNN: A physics-constrained neural network for multiphase flows
摘要：
本文提出了一种物理约束的神经网络（PCNN）模型，用于预测多相流的序列模式和运动，该模型包含了各流体相之间的强相互作用。为了预测的序参量，在未来的时间定位各个阶段，神经网络（NN）被应用到快速推断的动态相位编码的意见。接下来实施多相一致和保守有界映射算法（MCBOM）以校正预测的序参量。这使得预测的序参量严格满足质量守恒、各相体积分数之和为1、约化的一致性以及序参量的有界性。然后，根据校正后的序参量更新流体混合物的密度。最后，在未来时间的速度预测的另一个神经网络具有相同的网络结构，但动量守恒包含在损失函数，以缩小参数空间。所提出的MPF的PCNN顺序执行（NN）-（MCBOM）-（NN），这避免了序参量的非物理行为，加快了收敛速度，并需要更少的数据进行预测。数值实验表明，所提出的PCNN能够有效地预测MPF。
英文摘要：
The present study develops a physics-constrained neural network (PCNN) to predict sequential patterns and motions of multiphase flows (MPFs), which includes strong interactions among various fluid phases. To predict the order parameters, which locate individual phases in the future time, a neural network (NN) is applied to quickly infer the dynamics of the phases by encoding observations. The multiphase consistent and conservative boundedness mapping algorithm (MCBOM) is next implemented to correct the predicted order parameters. This enforces the predicted order parameters to strictly satisfy the mass conservation, the summation of the volume fractions of the phases to be unity, the consistency of reduction, and the boundedness of the order parameters. Then, the density of the fluid mixture is updated from the corrected order parameters. Finally, the velocity in the future time is predicted by another NN with the same network structure, but the conservation of momentum is included in the loss function to shrink the parameter space. The proposed PCNN for MPFs sequentially performs (NN)-(MCBOM)-(NN), which avoids nonphysical behaviors of the order parameters, accelerates the convergence, and requires fewer data to make predictions. Numerical experiments demonstrate that the proposed PCNN is capable of predicting MPFs effectively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
