Artificial Intelligence And Data Driven Optimization Of Internal Combustion Engines

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

This two-volume set of IFIP-AICT 675 and 676 constitutes the refereed proceedings of the 19th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2023, held in León, Spain, during June 14–17, 2023. This event was held in hybrid mode. The 75 regular papers and 17 short papers presented in this two-volume set were carefully reviewed and selected from 185 submissions. The papers cover the following topics: Deep Learning (Reinforcement/Recurrent Gradient Boosting/Adversarial); Agents/Case Based Reasoning/Sentiment Analysis; Biomedical - Image Analysis; CNN - Convolutional Neural Networks YOLO CNN; Cyber Security/Anomaly Detection; Explainable AI/Social Impact of AI; Graph Neural Networks/Constraint Programming; IoT/Fuzzy Modeling/Augmented Reality; LEARNING (Active-AutoEncoders-Federated); Machine Learning; Natural Language; Optimization-Genetic Programming; Robotics; Spiking NN; and Text Mining /Transfer Learning.

Computational Optimization of Internal Combustion Engines presents the state of the art of computational models and optimization methods for internal combustion engine development using multi-dimensional computational fluid dynamics (CFD) tools and genetic algorithms. Strategies to reduce computational cost and mesh dependency are discussed, as well as regression analysis methods. Several case studies are presented in a section devoted to applications, including assessments of: spark-ignition engines, dual-fuel engines, heavy duty and light duty diesel engines. Through regression analysis, optimization results are used to explain complex interactions between engine design parameters, such as nozzle design, injection timing, swirl, exhaust gas recirculation, bore size, and piston bowl shape. Computational Optimization of Internal Combustion Engines demonstrates that the current multi-dimensional CFD tools are mature enough for practical development of internal combustion engines. It is written for researchers and designers in mechanical engineering and the automotive industry.

In the realm of modern engineering, the integration of artificial intelligence (AI) has revolutionized the way engines are designed, operated, and maintained. From automotive powertrains to industrial turbines, AI technologies offer unprecedented opportunities to optimize performance, enhance reliability, and reduce environmental impact. This book, "AI for Engines," delves into the multifaceted applications of AI in the domain of engines, offering insights into how these advanced technologies are reshaping the landscape of engine design, control, and maintenance. Engines, whether they power vehicles, industrial machinery, or renewable energy systems, serve as the heart of countless applications across various sectors. Traditionally, the optimization of engine performance and the prediction of maintenance needs have relied on empirical methods and manual intervention. However, with the advent of AI and machine learning algorithms, engineers now have access to powerful tools capable of analyzing vast amounts of data, detecting patterns, and making intelligent decisions in real-time. This book explores the diverse ways in which AI is transforming the field of engines. It begins by tracing the evolution of AI in engineering and discussing its importance in engine design and maintenance. Subsequent chapters delve into the various types of engines, including internal combustion, electric, and turbine engines, elucidating the unique challenges and opportunities presented by each. Through in-depth discussions and practical examples, readers will gain insights into the fundamental components and functionalities of engines, as well as the key performance metrics used to evaluate their operation. Furthermore, the book explores advanced AI techniques such as machine learning, genetic algorithms, and neural networks, demonstrating how these methods can be leveraged to optimize engine design, control systems, and predictive maintenance strategies. From autonomous vehicle engines to energy-efficient power generation systems, this book covers a wide range of AI applications in engine control, performance optimization, and fault diagnosis. Real-world case studies and examples illustrate the transformative impact of AI on engine technology, highlighting success stories and lessons learned from industry implementations. Ultimately, "AI for Engines" serves as a comprehensive guide for engineers, researchers, and enthusiasts seeking to harness the power of AI to unlock new possibilities in engine innovation, efficiency, and sustainability. Whether you're a seasoned professional or a curious newcomer to the field, this book provides valuable insights into the cutting-edge intersection of artificial intelligence and engine technology.

The variety of future powertrain concepts has drastically increased the development cost for automotive manufactures. Profitable investment requires a significantly leaner and efficient powertrain development process. Traditional methods of test and model based development need to be assisted by big data and data analytics. For this purpose, a valuable tool is available at the right time - artificial intelligence (AI). But what does AI really mean in a narrower sense? What concepts lie behind it? And how are the methods and algorithms transferable to powertrain applications? For the first time, this book aims to bridge the gap between automotive engineering and computer science, by illuminating the complexity of current AI concepts and demystifying it for powertrain applications. By elaborating on work processes, it shows how AI could be implemented and how completely novel methods can help us reshape the future of mobility.

The increasing demands for internal combustion engines with regard to fuel consumption, emissions and driveability lead to more actuators, sensors and complex control functions. A systematic implementation of the electronic control systems requires mathematical models from basic design through simulation to calibration. The book treats physically-based as well as models based experimentally on test benches for gasoline (spark ignition) and diesel (compression ignition) engines and uses them for the design of the different control functions. The main topics are: - Development steps for engine control - Stationary and dynamic experimental modeling - Physical models of intake, combustion, mechanical system, turbocharger, exhaust, cooling, lubrication, drive train - Engine control structures, hardware, software, actuators, sensors, fuel supply, injection system, camshaft - Engine control methods, static and dynamic feedforward and feedback control, calibration and optimization, HiL, RCP, control software development - Control of gasoline engines, control of air/fuel, ignition, knock, idle, coolant, adaptive control functions - Control of diesel engines, combustion models, air flow and exhaust recirculation control, combustion-pressure-based control (HCCI), optimization of feedforward and feedback control, smoke limitation and emission control This book is an introduction to electronic engine management with many practical examples, measurements and research results. It is aimed at advanced students of electrical, mechanical, mechatronic and control engineering and at practicing engineers in the field of combustion engine and automotive engineering.