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Observation Based Modeling of Liquid Slosh in Drop Tests

Ekblad, Victor LU and Roos, Axl LU (2022) In Master's Theses in Mathematical Sciences FMAM05 20221
Mathematics (Faculty of Engineering)
Abstract
Tetra Pak® is a world leading food packaging company with a large variety of paperboard beverage packages. In the development of paperboard packages, an essential component is utilizing simulation models long before physical prototypes are manufactured. This speeds up the development process and allows quick evaluation of new designs. For this to be an effective approach, confidence in the virtual models is a key aspect. In the context of paperboard beverage packages, liquid product sloshing in drop tests is currently not adequately modeled in existing simulations. In this thesis, a framework for gathering data related to liquid product motion in drop tests, in a controlled and repeatable manner, is built, using image analysis and computer... (More)
Tetra Pak® is a world leading food packaging company with a large variety of paperboard beverage packages. In the development of paperboard packages, an essential component is utilizing simulation models long before physical prototypes are manufactured. This speeds up the development process and allows quick evaluation of new designs. For this to be an effective approach, confidence in the virtual models is a key aspect. In the context of paperboard beverage packages, liquid product sloshing in drop tests is currently not adequately modeled in existing simulations. In this thesis, a framework for gathering data related to liquid product motion in drop tests, in a controlled and repeatable manner, is built, using image analysis and computer vision algorithms. Both the quantitative and observational results are successfully used to improve and verify new liquid sloshing simulation models at Tetra Pak®. In parallel, the collected data is utilized to create approximate data driven models using neural networks. Three different model architectures are implemented and evaluated, U-Net, convolutional LSTM, and a graph convolutional LSTM. The resulting U-Net data driven model achieved the best performance and is shown to sufficiently approximate liquid behavior in drop tests in the setting of dropped transparent bottles. The U-Net model is found to be an adequate complement to the physics based simulations, offering faster run times but with reduced accuracy. (Less)
Popular Abstract
Fluid dynamics is a notoriously difficult and unintuitive branch of physics. Computer simulations are often utilized to solve these types of problems, but to trust these models we first have to observe what happens in the real world. This work aims to build a solid data gathering framework to capture how different liquids behave when dropped from different heights in a controlled setting. The data is then used to create completely data driven models with neural networks, which can run way faster than the physics based models.
Please use this url to cite or link to this publication:
author
Ekblad, Victor LU and Roos, Axl LU
supervisor
organization
course
FMAM05 20221
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Sloshing, Fluid Dynamics, Image Segmentation, 3D reconstruction, Machine Learning, U-Net, CNN, GCN, LSTM
publication/series
Master's Theses in Mathematical Sciences
report number
2022:E27
ISSN
1404-6342
other publication id
LUTFMA-3476-2022
language
English
id
9086883
date added to LUP
2022-06-13 12:26:22
date last changed
2022-06-13 12:26:22
@misc{9086883,
  abstract     = {{Tetra Pak® is a world leading food packaging company with a large variety of paperboard beverage packages. In the development of paperboard packages, an essential component is utilizing simulation models long before physical prototypes are manufactured. This speeds up the development process and allows quick evaluation of new designs. For this to be an effective approach, confidence in the virtual models is a key aspect. In the context of paperboard beverage packages, liquid product sloshing in drop tests is currently not adequately modeled in existing simulations. In this thesis, a framework for gathering data related to liquid product motion in drop tests, in a controlled and repeatable manner, is built, using image analysis and computer vision algorithms. Both the quantitative and observational results are successfully used to improve and verify new liquid sloshing simulation models at Tetra Pak®. In parallel, the collected data is utilized to create approximate data driven models using neural networks. Three different model architectures are implemented and evaluated, U-Net, convolutional LSTM, and a graph convolutional LSTM. The resulting U-Net data driven model achieved the best performance and is shown to sufficiently approximate liquid behavior in drop tests in the setting of dropped transparent bottles. The U-Net model is found to be an adequate complement to the physics based simulations, offering faster run times but with reduced accuracy.}},
  author       = {{Ekblad, Victor and Roos, Axl}},
  issn         = {{1404-6342}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Master's Theses in Mathematical Sciences}},
  title        = {{Observation Based Modeling of Liquid Slosh in Drop Tests}},
  year         = {{2022}},
}