Multiphase Flow Simulation with Local Diffusivity Correction
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Solution Overview
Problem
Conventional methods for simulating multiphase fluid flows suffer from unphysical phase separations, such as dense bubbles and rarified droplets, leading to inaccurate predictions of drag and lift coefficients, and face challenges with excessive numerical diffusion and high-order derivative terms.
Innovation Solution
A data processing system identifies voxels with incorrect phase separations in a digital representation of a simulation space and alters the local diffusivity parameter to correct these separations, using the Allen-Cahn equation to determine the order parameter and adjust mobility and interface thickness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods (Cahn-Hilliard equation or pseudo-potential model) are used to correct phase separations, then phase separation accuracy is improved, but numerical diffusion of small droplets increases and computational complexity increases
Solution Approach 1:
The patent changes the parameter being corrected from the order parameter itself to the diffusivity parameter. By identifying voxels with incorrect phase separations and locally adjusting their diffusivity values, the method corrects phase separation errors without requiring complex higher-order derivative calculations or solving additional phase-field equations, thus reducing computational complexity while maintaining accuracy
Solution Approach 2:
The patent applies local quality by adjusting the diffusivity parameter only in specific voxels where incorrect phase separations are detected, rather than applying global corrections. This localized approach reduces the overall computational burden while maintaining high accuracy in critical regions
2Measurement precision
If conventional methods (Cahn-Hilliard equation) are used to correct phase separations, then phase separation accuracy is improved, but numerical diffusion of small droplets becomes excessive
Solution Approach 1:
The patent changes from correcting the order parameter to correcting the diffusivity parameter. This parameter substitution allows for more precise control of phase separation corrections while minimizing excessive numerical diffusion that plagues conventional methods, particularly preserving small droplets that would otherwise be lost to numerical diffusion
Solution Approach 2:
The patent uses a simplified correction approach that copies the essential functionality of complex phase-field methods without replicating their computational overhead. By using a simpler diffusivity adjustment mechanism rather than full Cahn-Hilliard solving, it achieves comparable phase separation accuracy with significantly reduced numerical diffusion
3Measurement precision
If conventional methods (pseudo-potential model) are used to correct phase separations, then phase separation accuracy is improved, but handling of high density ratio between phases becomes difficult
Solution Approach 1:
The patent changes the correction mechanism from force-based (pseudo-potential) to diffusivity-based. This parameter transformation simplifies the handling of high density ratio cases by avoiding complex force calculations and higher-order derivative terms, making the method more robust for multiphase flows with large density differences
4Productivity
If Lattice Boltzmann models are used to simulate multiphase fluid flows, then computational efficiency is improved, but unphysical phase separations (dense bubbles and rarified droplets) occur leading to inaccurate results
Solution Approach 1:
The patent implements a self-correcting mechanism where the simulation automatically identifies and corrects its own errors during the computational process. By monitoring the order parameter values and locally adjusting diffusivity in voxels with incorrect phase separations, the LBM model self-corrects unphysical dense bubbles and rarified droplets, maintaining both computational efficiency and simulation reliability
Solution Approach 2:
The patent introduces feedback by continuously monitoring the order parameter during simulation and using this information to guide corrective actions. The identified voxels with incorrect phase separations trigger local diffusivity adjustments, creating a feedback loop that maintains physical accuracy while preserving the computational efficiency of LBM
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach improves the accuracy of multiphase fluid flow simulations by reducing computational complexity and resource usage while maintaining physical small droplets and bubbles, preserving mass conservation and enhancing processing efficiency.
Implementation Method 1
The separate phases in the multiphase fluid flow can be represented by an order parameter, where a specified value (e.g., 0) of the order parameter represents a first phase, and a different specified value (e.g., 1) of the order parameter represents a second phase
Implementation Method 2
altering a local diffusivity parameter of the identified one or more voxels to correct the incorrect phase separation
Data Source
AI summary
Systems and methods for digitally simulating a multiphase fluid flow in a three-dimensional computer-aided design (CAD) model of a simulation space include receiving a digital representation of a simulation space, the digital representation including a three-dimensional CAD model of the simulation space including a mesh represented as a plurality of voxels; and digitally simulating a multiphase fluid flow in the digital representation of the simulation space. While simulating the multiphase fluid flow, one or more voxels in the digital representation with an incorrect phase separation are identified; and a local diffusivity parameter of the identified one or more voxels are altered to correct the incorrect phase separation.