Towards Data-Physics-Driven Multiscale Approach for Integrated High-Volume Resin Transfer Molding and Component Design | 9am PT Tues, Sep 19

[Grigory Bronevetsky]

Modeling Talks

Towards Data-Physics-Driven Multiscale Approach for Integrated High-Volume Resin Transfer Molding and Component Design Jacob Fish, Columbia University Tuesday, Sep 19 | 9am PT Meet | Youtube Stream Hi all, The presentation will be via Meet and all questions will be addressed there. If you cannot attend live, the event will be recorded and can be found afterward at https://sites.google.com/modelingtalks.org/entry/towards-data-physics-driven-multiscale-approach Abstract: In the first part of my talk, I will present a hybrid data-physics driven reduced-order homogenization (dpROH) approach for efficient analysis of fiber reinforced composites. The dpROH improves the accuracy of the physics-based approaches, but retains its unique characteristics, such as interpretability and extrapolation. In the second part of my talk, I will present a hybrid data-physic driven computational framework for high-volume resin transfer molding (HV-RTM) of fiber reinforced composites. Due relatively high speed of resin flow and significant convective effects, the hybrid data-physics drive approach efficiently solves the nonlinear steady-state Navier-Stokes equations rather than the linear Stokes equations commonly adopted for the simulation of classical resin transfer molding processes. Bio: Dr. Fish is the Carleton Professor and Chair of the Department of Civil Engineering and Engineering Mechanics at Columbia University. He is a Founder and Director of Columbia University initiative for Computational Science and Engineering (iCSE) involving 80 faculty from multiple schools. Dr. Fish is a recipient of the John von Neumann Medal from USACM for "sustained and seminal contributions to the field of multiscale computational science and engineering and for its major impact on industry” and the Grand Prize from the Japan Society for Computational Engineering and Science among numerous other awards. Dr. Fish is a two-term past President of the United States Association for Computational Mechanics (USACM) and currently serves as the Vice-President of the International Association for Computational Mechanics. Dr. Fish is a Founder and Editor-in-Chief of the International Journal of Multiscale Computational Engineering, an Editor of the International Journal for Numerical Methods in Engineering and serves on the editorial board of several journals. More information on previous and future talks: https://sites.google.com/modelingtalks.org/entry/home

[Grigory Bronevetsky]

FYI, this talk has been rescheduled to Thursday, Sep 28. Thanks!

-Greg Bronevetsky

[Grigory Bronevetsky]

Talk just started.

New Stream link:https://www.youtube.com/watch?v=CGFeHHmdxbI

[Grigory Bronevetsky]

Video Recording: https://youtu.be/1SEtVwGYSzA

Slides:

  pptx: https://docs.google.com/presentation/d/1s7eR2fFoCmR4Dfu5QNxlc0aZpyPLibfX/edit?usp=sharing&ouid=110584304018462442605&rtpof=true&sd=true

  pdf: https://drive.google.com/file/d/1eXs51px8xINAdO8x2zL6pD-1rji2_3wk/view?usp=sharing

Summary:

• Focus: Digital Twins for complex physical systems

• Computational challenges:

  • Example: injecting resin into mold to create parts

    • Flow: rate depends on pressure non-linearly

    • Solids

  • Scale mixing

    • High range of reynolds numbers

    • High gradient (changing quickly over space)

  • Multiphysics at multiple scale

    • Different physical processes coupled at microscopic scale

• Data-physics driven multiscape approach for high-pressure resin transfer molding

  • Molding parts of large devices (cars) quickly and reliably

    • We have a mold

    • Inject resin into the mold at high pressure

    • Cure the resin

  • Model: Navier-Stokes fluid flow

    • Single-phase saturated flow model

    • Two-scale composite

    • Non-linear relation between average velocity and pressure gradient

    • Non-linear map from micro-scale finite volumes to macro-scale mold’s behavior

    • Unique solution: given pressure gradient one can uniquely determine average velocity and instantaneous permeability

    • Thus, can train a neural surrogate of these two bi-variate relationships (99.9% accuracy after 5 minutes training)

  • 3-scale model

    • Microscale representative volume: porous medium

    • Mesoscale representative volume: fluid, with history dependence

    • Macroscale

  • Fit a neural surrogate to this 3-scale model with high accuracy (different nets for the scales)

• Reduced Order Homogenization for component analysis

  • We have a solid that has been cured curing manufacturing

  • Transformation field analysis

    • Fine-scale train can be computed from the coarse scale strain

    • Small changes in volume/strain induces a deviation in shape

    • Need to compute the eigen-strains: 

      • independent types of macro deformations and how they affect the micro-state

      • Eigen-strains need to be discretized

  • Inference of eigen-strains

    • Trained a Recurrent neural network (GRU) on high-fidelity model of the micro behavior

      • Several runs of high-fidelity model produces the history of the process, which simplifies the training process

    • Bayesian inference uses neural network model to optimize

    • Produces a high-quality model with good values of free parameters

• Coupled Chemo-Thermo-Mechanical Multiscale model for predicting effects of manufacturing on the product

  • Interactions among:

    • Temperature-induced crystallization

    • Thermal strains

    • Effective material properties

  • Multi-scale approach makes it possible to predict defects in micro-structure (prior work could only predict for macro-structure)

  • Was used to model impacts on car bodies, which enables GM to use lighter materials

• Summary:

  • Data-driven physics multi-scale model can approximate Navier Stokes well

  • Multi-phase and multi-scale modeling is needed to account for all the phenomena with resins

  • Prediction: integrated multi-scale analysis tools for digital twins will become practically used in 5-7 years

• Observations:

  • In using data-driven models, start with simple approaches

    • Few parameters

    • Phenomenological

  • Data-driven approximations work best when solutions are unique

  • When the model is history-dependent the training challenge is much larger

  • Good use-case: model reduction

  • Analytic and data driven models need to be validated using experimental data in similar