<p>Due to strong interactions among various phases and among the phases
and fluid motions, multiphase flows (MPFs) are so complex that lots of efforts
have to be paid to predict its sequential
patterns of phases and motions. The present paper applies the physical
constraints inherent in MPFs and enforces them to a physics-informed neural
network (PINN) model either explicitly or implicitly, depending on the type of
constraints. To predict the unobserved order
parameters (OPs) (which locate the phases) in the future steps, the conditional
neural processes (CNPs) with long short-term memory (LSTM, combined as CNPLSTM)
are applied to quickly infer the dynamics of the phases after encoding only a
few observations. After that, the multiphase consistent and conservative
boundedness mapping (MCBOM) algorithm is implemented the correction the predicted OPs from
CNP-LSTM so that the mass conservation, the summation of the volume fractions of
the phases being unity, the consistency of reduction, and the boundedness of the OPs are
strictly satisfied. Next, the density of the
fluid mixture is computed from the corrected OPs. The observed velocity and
density of the fluid mixture then encode in a physics-informed conditional
neural processes and long short-term memory (PICNP-LSTM) where the constraint
of momentum conservation is included in the loss function. Finally, the unobserved
velocity in future steps is predicted from PICNP-LSTM. The proposed physics-informed neural
processes (PINPs) model (CNP-LSTM-MCBOM-PICNP-LSTM) for MPFs avoids unphysical behaviors
of the OPs, accelerates the convergence, and requires fewer data. The proposed
model successfully predicts several canonical MPF problems, i.e., the horizontal shear
layer (HSL) and dam break (DB) problems, and its performances are validated.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/14515806 |
| Date | 30 April 2021 |
| Creators | Haoyang Zheng (10141679) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Quantifying_implicit_and_explicit_constraints_on_physics-informed_neural_processes/14515806 |
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