Overview

Aerospace propulsion is entering a transformative era in which engines are no longer viewed merely as mechanical powerplants but as rich, data-generating ecosystems capable of revealing their own health, efficiency, and operational potential. Advances in sensing, telemetry, and digital connectivity have opened a window into engine behavior with unprecedented clarity, yet the sheer volume and complexity of this information demand analytical tools that exceed traditional boundaries. Artificial intelligence is emerging as the key to unlocking this capability, enabling propulsion systems to transition from reactive maintenance and rigid operating rules to predictive awareness, adaptive control, and mission-aware optimization. This book, AI in Aerospace Propulsion: Predictive Modeling and System Optimization, explores that transformation in depth, demonstrating how modern data and AI techniques reshape the way engineers comprehend engine signals, forecast degradation, and make real-time decisions about thrust, fuel, and safety. Structured to bridge foundational propulsion knowledge with state-of-the-art analytical methodologies, the chapters guide readers from core engine physics and health monitoring practices to advanced digital twins, prognostics, anomaly detection, and intelligent optimization strategies. The text emphasizes not only models but also the human, operational, and regulatory contexts in which AI must reliably operate. Whether the goal is predicting remaining useful life, minimizing fuel burn, orchestrating fleet-wide maintenance, or ensuring certification-ready AI deployments, this book provides a cohesive, end-to-end framework. It is written for aerospace engineers, data scientists, system architects, and decision-makers seeking to harness AI for safer, cleaner, and more efficient propulsion systems, and for students aspiring to shape the next generation of intelligent aerospace technology.

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