Developing a LSTM-Enhanced Surrogate Model for Particle-Laden Fluid Systems

Temel Kavramlar

Developing a non-intrusive data-driven reduced order model using LSTM and POD techniques for CFD-DEM simulations.

Özet

This article introduces a novel approach to simulate particle-laden fluid systems using a LSTM-enhanced surrogate model. The content covers the challenges of fluid-particle systems, the development of Reduced Order Models (ROMs), and the application of Proper Orthogonal Decomposition (POD) and Long Short-Term Memory (LSTM) networks. The study evaluates the accuracy and efficiency of the ROM approach through a fluidized bed benchmark problem, highlighting insights on system identification, prediction, and efficiency improvements.
Directory:

Introduction

Challenges in fluid-particle systems.
Importance of Reduced Order Models (ROMs).

Full Order Model

Description of governing equations for fluid-phase flow.
Description of governing equations for solid particles.

Reduced Order Model

Approximation of variables using basis functions.
Implementation of Proper Orthogonal Decomposition (POD) and Long Short-Term Memory (LSTM) network.

Filtering Process

Application of Fast Fourier Transform (FFT) for dimensionality reduction.

Numerical Results

Evaluation of ROM accuracy in system identification and prediction.

Lagrangian Phase

Analysis of particle position dynamics with ROM approach.

Computational Cost

Comparison of CPU time between FOM and ROM simulations.

İstatistikler

"The CPU time associated with the FOM simulation is around 1.8e5 s."
"The computation of reduced coefficients takes approximately 85 s for ε."
"For e_z, e_y, and e_x, the computation times are 113.58 s, 91.12 s, and 101.01 s respectively."

Alıntılar

"We develop a non intrusive data-driven reduced order model using POD and LSTM techniques."
"Our ROM approach shows promising results in system identification and short-time prediction."
"The speed up achieved by our ROM approach is around 1e3 compared to standard CFD-DEM simulations."

Önemli Bilgiler Şuradan Elde Edildi

A LSTM-enhanced surrogate model to simulate the dynamics of particle-laden fluid systems

by Arash Hajish... : arxiv.org 03-22-2024

https://arxiv.org/pdf/2403.14283.pdf

Daha Derin Sorular

How can recent machine learning techniques like transformers enhance the accuracy of Lagrangian phase prediction

Recent machine learning techniques like transformers can enhance the accuracy of Lagrangian phase prediction by leveraging their ability to capture long-range dependencies in sequential data. Transformers, with their self-attention mechanism, can effectively model the complex interactions and relationships between particles in a fluid-particle system. This is crucial for accurately predicting the behavior of individual particles over time, especially in systems where particle motion is influenced by various dynamic factors. By incorporating transformer models into the ROM approach for Lagrangian phase prediction, we can improve the understanding and representation of particle dynamics within fluid systems.

What are potential limitations or drawbacks in applying LSTM networks to fluid-particle systems

Applying LSTM networks to fluid-particle systems may have some limitations or drawbacks that need to be considered:

Limited Long-Term Dependency: LSTM networks are designed to capture short-term dependencies well but may struggle with modeling long-term dependencies effectively. In complex fluid-particle systems where interactions evolve over extended periods, LSTM's limited memory retention might lead to inaccuracies in predictions.

Complexity of System Dynamics: Fluid-particle systems exhibit intricate and nonlinear behaviors that may not be fully captured by traditional LSTM architectures. The complexity of these systems could pose challenges for LSTM networks in capturing all relevant features and patterns accurately.

Training Data Requirements: LSTM networks require large amounts of training data to learn complex patterns effectively. In fluid dynamics simulations where obtaining high-fidelity data is expensive or challenging, acquiring sufficient training samples for robust model performance could be a limitation.

Interpretability: While LSTMs are powerful tools for sequence prediction tasks, they are often criticized for their lack of interpretability compared to other machine learning models like decision trees or linear regression.

How can the findings from this study be applied to real-world industrial applications beyond fluidized bed benchmarks

The findings from this study on using a non-intrusive data-driven reduced order model (ROM) enhanced with Long Short-Term Memory (LSTM) network for simulating particle-laden fluid systems can have significant implications for real-world industrial applications beyond fluidized bed benchmarks:

Process Optimization: The developed ROM approach can be applied in industries such as pharmaceuticals, chemicals, and food processing where understanding and optimizing fluid-solid interactions are critical for process efficiency.

Environmental Impact Assessment: Industries dealing with particulate emissions or waste management could benefit from accurate simulations provided by ROMs to assess environmental impacts and optimize mitigation strategies.

3Predictive Maintenance: Implementing ROMs based on advanced machine learning techniques like transformers can enable predictive maintenance strategies in industrial equipment involving particle-fluid interactions, reducing downtime and enhancing operational efficiency.
4Product Development: Companies involved in designing products that involve complex flow phenomena such as aerosol delivery devices or filtration systems could use ROM approaches derived from this study to streamline product development processes.
These applications demonstrate how advancements in computational modeling using machine learning techniques can drive innovation across various industrial sectors reliant on efficient handling of particle-laden fluidsystems