Artificial intelligence-enabled fuel design

Kiran K. Yalamanchi, Andre Nicolle, S. Mani Sarathy

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

1 Scopus citations

Abstract

Fuel composition plays an important role both in efficiency and effectiveness of engines. Combined with the engine variables, fuel can span a wide range of composition space, which makes it demanding to find an optimal composition. Artificial intelligence (AI) algorithms are attracting significant interest for predicting complex phenomenon. In this chapter, a discussion is presented on exploiting the advantages presented by machine learning algorithms for fuel formulation. The present fuel modeling scenario and a holistic approach necessary for fuel optimization is first presented. A wealth of AI algorithms are available to make use of in fuel formulation. These algorithms are discussed in line with their application to fuel formulation and the literature of the explored space in this area is presented. Additionally, a discussion is presented on how AI also helps in assisting the traditional computational fluid dynamic and chemical kinetic analysis for an elaborate study of fuels. Fuel development is just a step in the entire engine innovation cycle, and a perspective of how the AI fits in to this scenario is presented.

Original languageEnglish (US)
Title of host publicationArtificial Intelligence and Data Driven Optimization of Internal Combustion Engines
PublisherElsevier
Pages47-67
Number of pages21
ISBN (Electronic)9780323884570
ISBN (Print)9780323884587
DOIs
StatePublished - Jan 1 2022

Keywords

  • Combustion
  • Fuel design
  • Kinetics
  • Machine learning
  • Neural networks

ASJC Scopus subject areas

  • General Engineering

Fingerprint

Dive into the research topics of 'Artificial intelligence-enabled fuel design'. Together they form a unique fingerprint.

Cite this