Using Long Short Term Memory to Obtain Particle Attributes during the Encapsulation Process of High Temperature Phase Change Materials

Project Title: Using Long Short Term Memory to Obtain Particle Attributes during the Encapsulation Process of High Temperature Phase Change Materials
Program:
Northeast (308)

Project Image: https://support.access-ci.org/system/files/webform/project/218/LSTM%20Diagram_MEPCM.png
Tags:
deep-learning (303), machine-learning (272), matlab (2), openfoam (327)

Status: Complete
Project Leader
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Project Leader:
Jingru Benner

Email: jingru.benner@wne.edu
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Project Personnel
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Mentor(s):
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Student-facilitator(s):
Rebecca Shannon (1524)

Mentee(s):
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Project Information
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Project Description:
The main objective of this project is to apply Long Short Term Memory (LSTM), a recurrent neural network (RNN) algorithm to analyze an inverse heat conduction problem with phase change. The inverse heat conduction problem is associated with the manufacturing process of micro-encapsulated phase change particles (MEPCM) for high temperature applications. These particles are composed of a metallic or alloy core, and a glassed based shell. The final dimension of the particles in particular the core to shell ratio is of great importance to the performance of the particles when used for thermal energy storage applications. We are interested in obtaining the size of the particles during a process called Flow Mold Casting (FMC). During the FMC process, the concentric cylindrical microwire with a metal core and glass shell is melted by high power pulsating laser to form encapsulated particles. As opposed to a standard heat conduction problem with well-defined boundary conditions and initial condition, from which we can solve for the temperature distribution, the surface condition of the particle, the dimension of the particles, and the melting state of the particles during this process is challenging to obtain, therefore, making the mathematical problem ill-posed. To compensate the lack of information, the temperature change of the particle over certain time interval may be measured using a high speed thermal camera, which is called the cooling curve. We attempted to obtain the dimension of the MEPCMs based on the cooling curve with the help of LSTM model. In a previous study supported by MGHPCC and NorthEast Cyberteam, a numerical model that represents this manufacturing process was developed and verified in OpenFOAM. The numerical model can calculate the cooling curve of MEPCM particles for a wide range of material properties, diameter, core to shell ratios and surface conditions. The output of the numerical model will be used to generate training data for the machine learning model. Since the process is time dependent and cooling curve reflects the temperature change with time, we use LSMT, a recurrent neural network model, to identify the diameter of the core and the core to shell ratio. The testing data will be provided by the numerical model in OpenFOAM. 



Project Information Subsection
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Project Deliverables:
The main project deliverables include: 
1)	Obtain training and testing data in OpenFOAM
2)	Build the LSTM model frame in MATLAB
3)	Validate the LSTM model for a wide range of thermophyical properties and boundary conditions

Project Deliverables:
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Student Research Computing Facilitator Profile:
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Mentee Research Computing Profile:
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Student Facilitator Programming Skill Level: Some hands-on experience
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Project Institution: Western New England University 
Project Address:
1215 Wilbraham Rd
Springfield, Massachusetts. 01119

Anchor Institution: NE-MGHPCC
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Start as soon as possible.: Yes
Project Urgency: Already behind3Start date is flexible
Expected Project Duration (in months): 6 months
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Project Milestones:
- Milestone Title: Training and Testing Data Generation
  Milestone Description: Obtain training and testing data data sets in OpenFOAM
  Completion Date Goal: 2021-11-30
- Milestone Title: LSTM Model Framework Development
  Completion Date Goal: 2022-01-19
- Milestone Title: LSTM Model Validation
  Completion Date Goal: 2022-02-18
- Milestone Title: LSTM model based process prediction
  Milestone Description: Use LSTM model to predict key MEPCM particle attribute during FMC manufacturing
  Completion Date Goal: 2022-03-18
- Milestone Title: Github contribution
  Milestone Description: Upload well documented non-proprietary model framework (openFOAM and Matlab), and Migrate part of Matlab code to openSource platform Python.

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Planned Portal Contributions (if any):
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Planned Publications (if any):
one conference paper in the related field

What will the student learn?:
What will the student learn:
1)Post processing data from OpenFoam, 
2)Basics of Machine Learning, in particular RNN models

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HPC resources needed to complete this project?:
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Notes:
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Final Report
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Lessons Learned:
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Overall results:
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