Pushing the Limits of Gas TurbineEfficiency:Next-Generation Flow Prediction, Precision HeatModeling, and Data-Driven Design

Abstract

The field of gas turbine aerodynamics stands at the precipice of a revolutionary transformation, driven by unprecedented advances in computational fluid dynamics (CFD), high-fidelity simulation techniques, and sophisticated turbulence modeling approaches. This comprehensive review examines the cutting-edge numerical methods that are fundamentally reshaping our understanding of complex flow phenomena, heat transfer mechanisms, and performance characteristics within gas turbine engines. The integration of Direct Numerical Simulation (DNS), hybrid Reynolds Averaged Navier Stokes (RANS), and Large Eddy Simulation (LES) methodologies has enabled researchers to capture previously inaccessible flow physics with remarkable precision, revealing intricate details of boundary layer transitions, secondary flow structures, and unsteady aerodynamic interactions. Contemporary developments in turbulence modeling have transcended traditional approaches, incorporating machine learning algorithms, physicsinformed neural networks, and quantum-inspired computational frameworks that promise to unlock new frontiers in predictive accuracy. Advanced aerothermodynamic analyses now seamlessly couple fluid dynamics with heattransfer, combustion chemistry, and structural mechanics, providing holistic insights into engine performance optimization. The emergence of scalebridging techniques enables simultaneous resolution of phenomena spanning multiple temporal and spatial scales, from molecular-level interactions to full-engine simulations.
This review synthesizes recent breakthroughs in high-order numerical
schemes, adaptive mesh refinement strategies, and massively parallel computing architectures that have democratized access to previously computationally prohibitive simulations. The discussion encompasses revolutionary applications in film cooling optimization, combustor-turbine interactions, and next-generation engine architectures including hydrogen-fueled and hybrid-electric propulsion systems. Furthermore, the integration of artificial intelligence and digital twin technologies is establishing new paradigms for online performance monitoring, part maintenance, and autonomous design improvements.
The implications of these advances extend far beyond academic research,
directly influencing industrial design practices, certification procedures, and
environmental sustainability initiatives. As the aviation industry pursues
ambitious decarbonization goals, these revolutionary numerical methods provide the computational foundation for developing ultra-efficient, low-emission propulsion systems. This comprehensive analysis demonstrates how the convergence of advanced mathematics, computer science, and engineering physics is catalyzing a new era in gas turbine technology, promising unprecedented levels of performance, efficiency, and environmental compatibility.