Work

Co-Optimized Flow Matching and Thrust Retention Control for an Adaptive Cycle Engine in Turbine-Based Combined Cycle Mode Transition

Year: 2026

Type: article

Abstract: This paper presents a comprehensive study on the control law design for the turbine-to-ramjet mode transition in an adaptive-cycle turbine-based combined cycle (TBCC) engine, aiming to mitigate the persistent “thrust gap” challenge. An integrated conceptual configuration of a hypersonic vehicle with a parallel-duct TBCC system, which replaces the conventional turbofan with a three-bypass adaptive cycle engine (ACE), is proposed. High-fidelity performance models for both the ACE and the scramjet are developed, with a Kriging surrogate model employed to accelerate the computationally intensive ACE simulations during the transition. A co-optimization framework is established, defining a comprehensive performance index that balances thrust tracking accuracy and control smoothness under rigorous intake-engine flow matching constraints. Using sequential quadratic programming (SQP), the control schedules for the ACE’s variable geometries are optimized. Comparative analyses reveal that the ACE, with its flexible bypass management and multiple adjustable mechanisms, can actively adapt its airflow demand to match the restricted intake supply. Consequently, the optimized ACE-based TBCC reduces total airflow fluctuation during the Mach 3–3.5 transition from 106% (conventional turbofan baseline) to 42.5%, while maintaining required thrust. This work quantitatively demonstrates the superior flow-handling capability of adaptive cycle technology, providing a viable and effective solution for ensuring stable and efficient mode transition in future hypersonic TBCC propulsion systems.

Source: Energies

DOI: 10.3390/en19040993

Authors: Yu Fu, Wenyan Song, Qiuyin Wang

Institution: Northwestern Polytechnical University

Language: English

Topic: Computational Fluid Dynamics and Aerodynamics

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,137