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Modeling and Optimization of IC Engines using Python

7,500.00 4,999.00

Upgrade your Automotive knowhow with the requisite skills in DOE, Engine Modeling and optimization. This course uses data analytics rather than physics based modeling. Exercises are hands-on and implemented in Python. Python training modules are included.

Prerequisites: Mechanical Engineering basics, basic coding skills in any language.

Category:

Engine and vehicle development include different stages of development – Concept development, Mechanical development, Electronics development, System integration, calibration development and validation. Different simulation softwares and packages are available for each of the stages. Modeling is the process by which a real-world problem is translated to a mathematical model, which can be used to gain insights on the real-world problem. This course provides the details of Engine calibration challenges and methodology to solve using python and its packages which helps to reduce the Engine testing and calibration time. This course will bring the concepts to the very basic level and understand the various parameters that affect the Engine optimization. This course doesn’t need any prior knowledge but the knowledge of python scripting is an advantage. This course clears all the fundamental questions.

 

Learning Objectives
By attending this seminar, you will be able to:

  • Understand the challenges of IC Engine development
  • Understand the importance of Applied mathematics for Numerical modeling
  • Apply Different Design of experiments to any system
  • Learn scripting and application of python to any optimization problem
  • Understand the real-world problems and methods to solve

Who Should Attend
Engineering Management, Engineers charged with Modeling, Design, Calibration and Validation, OBD Engineers, Senior Management from Powertrain, Engine and Vehicles departments.

 

Topical Outline

  • Python and packages installation
  • Challenges in IC Engines development
  • Introduction to Mathematical modeling
  • Introduction to Optimization methods
  • Introduction to System identification
  • DoE designs for Engine Testing
    • Lattice Hypercube Sampling Design
    • Central Composite Design
    • Box Behnken Design
    • Sobol Design
    • Plackett Burman Design
  • Hands-on DoE designs application using PyDoE packages
    • IC Engine parameterization
    • Global design application
      • Lattice Hypercube Sampling Design
      • Central Composite Design
      • Box Behnken Design
      • Sobol Design
      • Plackett Burman Design
    • Local design application
      • Lattice Hypercube Sampling Design
      • Central Composite Design
      • Box Behnken Design
      • Sobol Design
      • Plackett Burman Design
  • Accelerating Engine optimization through Surrogate Modeling
    • Engine Modeling
    • Numerical modeling with Numpy
      • Linear polynomial regression
      • Gaussian process regression
  • calG
    • Engine data collection
    • Numerical modeling with calG
    • Emissions optimization for cycle NOx and Cycle Soot
    • Creation of optimized parameter maps

 

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