Modeling
Modeling: Introduction and Learning Outcomes
- This module provides the rationale for why we should model. It includes
- Modeling as a pillar of science
- Modeling to predict safety, understand and optimize in design of products and processes,
- Faster, cheaper, and better options for industry.
- Modules are under construction.
- Prerequisites: …
- Recommended sequence: …
- Learning outcomes:
- Formulate a real-world physical problem in terms of its mathematical equivalent that can be solved with available computational resources
- Describe the typical structure of computational software consisting of the steps of pre-processing, processing, and post-processing
- Implement the mathematical problem in computational software and obtain a solution
- Describe the types of errors and how to reduce them in simulation results, in modeling a physical process
- Validate the model
- Estimate the sensitivity of the results to various model parameters (representing variability, uncertainty)
- Evaluate, using simple optimization, the best product/process conditions
- Approximate duration including quizzes: ** min
Modeling: Rationale-Principles-Steps
- This module contains guiding principles for developing a model. It includes
- How modeling complements experimentation,
- “All models are wrong, but some are useful,”
- Problem formulation and simplification as major steps, and
- Food’s unique challenges
Modeling: Problem Formulation
- This module breaks down problem formulation into
- Goals
- Geometry,
- Governing equations,
- Boundary conditions,
- Properties, and
- Auxiliary parameters
Modeling: Software Implementation
- This module contains basic organization of preprocessing, processing and post-processing in a software (with COMSOL as example). It includes:
- Basic interface for implementing heat and mass transfer governing equations, boundary conditions and properties
- Choice of mesh and solver.
- Display results in post-processing.
- Modeling: Introductory Numerical Methods
- This module contains:
- Finite difference method
- Finite element method (simple 1D that introduces the major steps)
Modeling: Validation and Sensitivity Analysis
- This module contains
- Model validation,
- Debugging, and
- Sensitivity analysis
Modeling: Communication of the Results
- An important part of the modeling process is to communicate the simulation results appropriately in oral or written form
