Artificial Intelligence and Data Driven Optimization of Internal Combustion Engines
eBook - ePub

Artificial Intelligence and Data Driven Optimization of Internal Combustion Engines

  1. 260 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Artificial Intelligence and Data Driven Optimization of Internal Combustion Engines

About this book

Artificial Intelligence and Data Driven Optimization of Internal Combustion Engines summarizes recent developments in Artificial Intelligence (AI)/Machine Learning (ML) and data driven optimization and calibration techniques for internal combustion engines. The book covers AI/ML and data driven methods to optimize fuel formulations and engine combustion systems, predict cycle to cycle variations, and optimize after-treatment systems and experimental engine calibration. It contains all the details of the latest optimization techniques along with their application to ICE, making it ideal for automotive engineers, mechanical engineers, OEMs and R&D centers involved in engine design. - Provides AI/ML and data driven optimization techniques in combination with Computational Fluid Dynamics (CFD) to optimize engine combustion systems - Features a comprehensive overview of how AI/ML techniques are used in conjunction with simulations and experiments - Discusses data driven optimization techniques for fuel formulations and vehicle control calibration

Frequently asked questions

Yes, you can cancel anytime from the Subscription tab in your account settings on the Perlego website. Your subscription will stay active until the end of your current billing period. Learn how to cancel your subscription.
No, books cannot be downloaded as external files, such as PDFs, for use outside of Perlego. However, you can download books within the Perlego app for offline reading on mobile or tablet. Learn more here.
Perlego offers two plans: Essential and Complete
  • Essential is ideal for learners and professionals who enjoy exploring a wide range of subjects. Access the Essential Library with 800,000+ trusted titles and best-sellers across business, personal growth, and the humanities. Includes unlimited reading time and Standard Read Aloud voice.
  • Complete: Perfect for advanced learners and researchers needing full, unrestricted access. Unlock 1.4M+ books across hundreds of subjects, including academic and specialized titles. The Complete Plan also includes advanced features like Premium Read Aloud and Research Assistant.
Both plans are available with monthly, semester, or annual billing cycles.
We are an online textbook subscription service, where you can get access to an entire online library for less than the price of a single book per month. With over 1 million books across 1000+ topics, weā€™ve got you covered! Learn more here.
Look out for the read-aloud symbol on your next book to see if you can listen to it. The read-aloud tool reads text aloud for you, highlighting the text as it is being read. You can pause it, speed it up and slow it down. Learn more here.
Yes! You can use the Perlego app on both iOS or Android devices to read anytime, anywhere ā€” even offline. Perfect for commutes or when youā€™re on the go.
Please note we cannot support devices running on iOS 13 and Android 7 or earlier. Learn more about using the app.
Yes, you can access Artificial Intelligence and Data Driven Optimization of Internal Combustion Engines by Jihad Badra,Pinaki Pal,Yuanjiang Pei,Sibendu Som in PDF and/or ePUB format, as well as other popular books in Tecnologia e ingegneria & Trasporti e ingegneria automobilistica. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover
  2. Front Matter
  3. Table of Contents
  4. Copyright
  5. Contributors
  6. Foreword
  7. Preface
  8. List of Illustrations
  9. List of Tables
  10. Chapter 1 : Introduction
  11. Chapter 2 : Optimization of fuel formulation using adaptive learning and artificial intelligence
  12. Chapter 3 : Artificial intelligenceā€“enabled fuel design
  13. Chapter 4 : Engine optimization using computational fluid dynamics and genetic algorithms
  14. Chapter 5 : Computational fluid dynamicsā€“guided engine combustion system design optimization using design of experiments
  15. Chapter 6 : A machine learning-genetic algorithm approach for rapid optimization of internal combustion engines
  16. Chapter 7 : Machine learningā€“driven sequential optimization using dynamic exploration and exploitation
  17. Chapter 8 : Artificial-intelligence-based prediction and control of combustion instabilities in spark-ignition engines
  18. Chapter 9 : Using deep learning to diagnose preignition in turbocharged spark-ignited engines
  19. Index
  20. A