Computational Modeling and Learning-Based Adaptive Control of Solid-Fuel Ramjets

TL;DR

The paper tackles thrust regulation in solid-fuel ramjets (SFRJs), addressing the challenge that strong nonlinearities and coupled multi-physics hinder conventional model-based control. It develops a CFD model with simplified heat addition to capture thrust response and inlet unstart within a 2D axisymmetric geometry using the $k-\omega$ SST turbulence model, solved by SU2. An online RCAC-based adaptive PI controller is proposed, where the control signal follows $u_k = K_{P,k} z_k + K_{I,k} \gamma_k$ with $z_k = r_k - y_k$ and $\gamma_k = \sum_{i=0}^k z_i$, and the heat input is $w_k = \overline{w} + K_w u_k$, enabling model-free adaptation. Closed-loop simulations demonstrate robust thrust regulation under static and dynamic conditions, with RCAC gains adapting to varying commands and inlet states, highlighting the approach’s potential for reliable SFRJ operation in next-generation missiles and hypersonic flight.

Abstract

Solid-fuel ramjets offer a compact, energy-dense propulsion option for long-range, high-speed flight but pose significant challenges for thrust regulation due to strong nonlinearities, limited actuation authority, and complex multi-physics coupling between fuel regression, combustion, and compressible flow. This paper presents a computational and control framework that combines a computational fluid dynamics model of an SFRJ with a learning-based adaptive control approach. A CFD model incorporating heat addition was developed to characterize thrust response, establish the operational envelope, and identify the onset of inlet unstart. An adaptive proportional-integral controller, updated online using the retrospective cost adaptive control (RCAC) algorithm, was then applied to regulate thrust. Closed-loop simulations demonstrate that the RCAC-based controller achieves accurate thrust regulation under both static and dynamic operating conditions, while remaining robust to variations in commands, hyperparameters, and inlet states. The results highlight the suitability of RCAC for SFRJ control, where accurate reduced-order models are challenging to obtain, and underscore the potential of learning-based adaptive control to enable robust and reliable operation of SFRJs in future air-breathing propulsion applications.

Figures (18)

• Figure 1: Different fidelities to investigate SFRJs where accuracy comes at the cost of resources.
• Figure 2: Simplified SFRJ geometry
• Figure 3: Computational mesh consisting of approximately 60,000 unstructured cells.
• Figure 4: Mach number contour for SFRJ with no heat addition
• Figure 5: Thrust at various Heat Flux Values