Hypersonic Vehicle Research Articles

Hypersonic vehicles experience intense aerodynamic heating during re-entry and high-speed flight, imposing stringent demands on thermal protection systems (TPS) and insulation materials. Accordingly, this paper reviews the classification and design principles of TPS, along with the structural properties and performance of typical insulation materials. In this context, passive, semi-active, and active TPS offer protection across varying thermal environments, with active systems delivering superior performance in extreme conditions, yet they entail higher complexity and cost. In terms of insulation materials, foam ceramics hinder heat conduction through porous structures, fibrous materials suppress conduction and convection through multi-level pores, whereas aerogels offer ultralow thermal conductivity and lightweight features. The results reveal that while considerable strides have been made in TPS and insulation materials, challenges persist in attaining thermal stability at extreme conditions, integrating multiple functionalities, and accelerating development, highlighting the need for innovative, adaptive TPS solutions for future hypersonic missions.

This study experimentally investigates the effects of nose-tip bluntness on boundary-layer transition over a 7^circ half-angle cone at Mach 6.76, using the Seoul National University Hypersonic Shock Tunnel. Transition characteristics were examined through high-speed schlieren visualization, surface heat flux measurements, and high-frequency surface pressure measurements for varying nose-tip radii (0.1 mm, 1 mm, and 2 mm) and unit Reynolds numbers. Increasing nose-tip bluntness effectively delayed transition onset, as indicated by turbulent intermittency and heat flux distributions. Spectral proper orthogonal decomposition and pressure spectral analyses revealed distinct second-mode instabilities with frequency shifts to lower values as bluntness increased. Additionally, a low-frequency instability around 200 kHz was identified in the configuration with a 2 mm nose-tip radius, suggesting the presence of multiple instability modes. These observations highlight the influence of nose-tip bluntness on hypersonic boundary-layer stability and emphasize the necessity for comprehensive consideration of multiple instability modes in hypersonic vehicle design.

Sideslip maneuverability is a critical future requirement for hypersonic airbreathing vehicles. One of the main challenges is maintaining inlet performance under not only non-uniform inflow (NUF) but also varying inflow angles. This study systematically investigates the aerodynamic performance, sensitivity, and underlying flow mechanisms of four vector adaptive inward-turning inlets (VAIs) under angle of sideslip (AOS) conditions. In contrast to classical design methods, this study utilizes an originally developed methodology that integrates osculating flow theory and multi-objective optimization. A key advantage of this approach is its ability to design inlets for arbitrary NUFs. In this work, this method is applied to generate configurations for several representative NUFs. A high level of flow properties is achieved for all VAIs within AOS −3° to 3° (σex ≥ 0.534, φ ≥ 97.6%, and ηKE ≥ 0.966). A lateral flow mechanism is identified: as AOS increases, the lateral velocity component intensifies, leading to a significantly asymmetric flow structure. This evolution results in increased distortion and a gradual migration of low-kinetic flow in both circumferential and spatial directions, thereby reducing flow capture and energy conversion efficiency, while enhancing compressive capability. The findings provide critical insight into the lateral adaptability of such inlets, which is essential for designing maneuverable hypersonic vehicles.

Research on the performance of an integrated active cooling and propulsion system for hypersonic glide vehicles

Aerodynamic shape optimization of hypersonic vehicle based on improved class-shape-transformation method

Aerothermoelastic problems of hypersonic vehicles and their recent research progress

Hypersonic vehicles experience extreme aerodynamic heating due to intense shock interactions at speeds beyond Mach 5. Effective thermal management is critical to prevent structural damage and ensure mission success. Traditional mitigation techniques such as aerospikes and opposing jets offer partial improvements but introduce tradeoffs, including increased drag or flow instability. This study introduces a lateral jet configuration integrated with aerospikes as an innovative solution for reducing both thermal loads and aerodynamic drag. A comprehensive CFD analysis was conducted using the validated High-Resolution Flow Solver on Unstructured Meshes (HiFUN) under freestream Mach 5.75 and a static temperature of 140 K. Simulations were performed for three spike lengths (50, 75, and 100 mm) and three lateral jet pressures (2, 4, and 6 bar) to evaluate their influence on surface temperature and drag coefficient. A grid independence study was completed to ensure numerical reliability, and results were validated against established literature benchmarks. Findings reveal that lateral jet injection significantly reduces stagnation point temperatures—by up to 80%—and alters shock structures to enhance flow separation and recirculation. The 75 mm spike at 6 bar jet pressure was found to provide the most balanced performance, delivering optimal thermal shielding and minimal drag. Additionally, the use of lateral jets shifted the bow shock upstream, thereby reducing pressure on the nose cone but increasing localized drag on the aerodisk. This work demonstrates the viability of combining passive and active flow control strategies—optimized using HiFUN—for improved thermal management in hypersonic flight. The results can inform future aerospace designs requiring robust heat protection with aerodynamic efficiency.

Cooling and power generation performance evaluation of thermophotovoltaic system for hypersonic vehicle engines

A sliding mode predictive control (SMPC) scheme integrated with an extreme learning machine (ELM) disturbance observer is proposed for the trajectory tracking of a flexible air-breathing hypersonic vehicle (FAHV). To streamline the controller design, the longitudinal model is decoupled into a velocity subsystem and an altitude subsystem. For the velocity subsystem, a proportional-integral sliding mode surface is designed, and the control law is derived by minimizing a cost function that weights the predicted sliding mode surface and the control input. For the altitude subsystem, a backstepping control framework is adopted, with the SMPC strategy embedded in each step. Multi-source disturbances are modeled as composite additive disturbances, and an ELM-based neural network observer is constructed for their real-time estimation and compensation, thereby enhancing system robustness. The semi-globally uniformly ultimately bounded (SGUUB) stability of the closed-loop system is rigorously proven using Lyapunov stability theory. Simulation results demonstrate the comprehensive superiority of the proposed method: it achieves reductions in Root Mean Square Error (RMSE) of 99.60% and 99.22% for velocity and altitude tracking, respectively, compared to Prescribed Performance Control with Backstepping Control (PPCBSC), and reductions of 98.48% and 97.12% relative to Terminal Sliding Mode Control (TSMC). Under parameter uncertainties, the developed ELM observer outperforms RBF-based observer and Extended State Observer (ESO) by significantly reducing tracking errors. These findings validate the high precision and strong robustness of the proposed approach.

Adjoint-based optimization methods, that were previously in the realm of computational fluid dynamics (CFD) research, are now available in commercial software. This work explores the use of adjoint-based optimization to maximize mixing and combustion efficiencies for a supersonic combustor. To this end, a two-dimensional combustor was considered with parallel hydrogen injection. Simulations were carried out based on the steady Reynolds-Averaged Navier–Stokes equations and optimization was performed using a simplified passive scalar field instead of the full reactive flow problem. The optimization of a triangle-shaped mixing element is considered in addition to a case allowing the entire bottom of the combustor to deform. The relatively small mixing element could not boost efficiency significantly. By comparison, the optimization of the combustor wall resulted in both mixing and combustion efficiency gains accompanied by total pressure loss penalty. The optimization achieved higher efficiency compared to the baseline by extending the total volume of the reaction zone. The presented proof-of-concept results are relevant for the design of hypersonic vehicle propulsion systems, such as scramjets.

This paper focuses on the issue of unmodeled dynamics and large-range parametric uncertainties in air-breathing hypersonic vehicles (AHV), proposing an adaptive dynamic surface control method based on radial basis function (RBF) neural networks. First, the hypersonic longitudinal model is transformed into a strict-feedback control system with model uncertainties. Then, based on backstepping control theory, adaptive dynamic surface controllers incorporating RBF neural networks are designed separately for the velocity and altitude channels. The proposed controller achieves three key functions: (1) preventing “differential explosion” through low-pass filter design; (2) approximating uncertain model components and unmodeled dynamics using RBF neural networks; (3) enabling real-time adjustment of controller parameters via adaptive methods to accomplish online estimation and compensation of system uncertainties. Finally, stability analysis proves that all closed-loop system signals are semi-globally uniformly bounded (SGUB), with tracking errors converging to an arbitrarily small residual set. The simulation results indicate that the proposed control method reduces steady-state error by approximately 20% compared to traditional controllers.

For asymmetric angle of attack (AOA) constraints and short response time, in this paper, an adaptive fast robust method is proposed to control altitude and velocity of the hypersonic flight vehicle (HFV) with parameter perturbations and external disturbances. First, considering the backstepping method, an affine nonlinear model is established. Then, to identify the uncertainties, back-propagation neural networks and their adaptive laws are introduced. To avoid drastic actions of the HFV, an instruction filter is used and its effects are compensated using adaptive high-gain components. The tracking of flight velocity and AOA is achieved fast based on prescribed performance control. Furthermore, the closed-loop system stability and constrained AOA are guaranteed via AOA instruction saturation, asymmetric performance functions and the full-state time-varying barrier Lyapunov function. Finally, the simulation shows that the methodology can guarantee precise tracking of HFV’s altitude and velocity under asymmetric AOA constraints and uncertainties.

Trajectory prediction algorithm based on maneuver analysis for hypersonic glide vehicles in a deep neural network

Abstract To address the extreme aerodynamic heating challenges encountered by the leading edges of hypersonic vehicles, this study develops an aerogel-based thermal insulation material with engineering applicability. It proposes three deep neural operator models, Fourier Neural Operator, DeepONet, and Transformer, for rapid prediction of the temperature field. These models establish an end-to-end mapping from multiple design parameters to the spatial temperature distribution. A global sensitivity analysis involving coupled design parameters is conducted to investigate the influence of different variables on thermal insulation performance. Results demonstrate that all three neural operator models achieve a maximum temperature prediction error of less than 5%, with prediction times reduced to the millisecond level, representing a 4-order-of-magnitude acceleration compared to conventional computational fluid dynamics methods. Furthermore, the Fourier Neural Operator model is employed as a surrogate to explore the impact of multi-parameter design on thermal insulation performance. Sensitivity analysis indicates that thermal load and thermophysical properties (heat conduction phase and radiative attenuation) dominate the system response, contributing 87%-91% of the total variance. The proposed neural operator framework offers a flexible and efficient alternative for predicting temperature fields in aerogel-based insulation systems, overcoming the limitations of traditional computational fluid dynamics methods in handling high-dimensional input spaces and providing valuable guidance for designing and optimizing advanced thermal insulation materials.

Data-driven surrogate models have become increasingly important in aerospace engineering for the rapid prediction of aerodynamic characteristics. However, when modelling aerodynamic data with varying flight conditions and complex shape parameters, traditional surrogates – such as kriging and fully connected neural network (FCNN) – face major challenges, including high dimensionality, large variable disparities, and limited data availability. Specifically, kriging models suffer from inefficient training processes, while FCNN models struggle with diminished prediction accuracy when confronted with diverse input sets. To address these challenges, this paper introduces two improved surrogate models by incorporating variable sensitivity into the kriging and FCNN models. They employ the analysis of variance to identify the global sensitivity of input variables and utilise K-means clustering to group variables based on their sensitivities. For the kriging model, auxiliary parameters corresponding to the number of clusters are introduced to replace hyperparameters, accelerating model training while maintaining high accuracy. For the FCNN model, input variables are grouped based on their sensitivities, with specialised expert networks handling each group, and a gating network combining their outputs to improve prediction accuracy. The effectiveness of these methods is demonstrated through numerical function examples and two aerodynamic data modelling scenarios: the FDL-5A hypersonic vehicle and the Saenger aerospace plane carrier wing. Results indicate that the proposed approaches significantly enhance the kriging model’s training efficiency, achieving a 98% reduction in hyperparameter tuning time compared to conventional method, with minimal sacrifice in accuracy. Simultaneously, the modifications to the FCNN model not only improve its prediction accuracy but also increase its overall practical utility in engineering applications.

This study investigates the influence of wall surface temperature on the flow characteristics of the shock train in an isolator of scramjet engine. The numerical simulations were performed using a Reynolds-Averaged Navier-Stokes framework with the k-ω shear stress transport turbulence model, implemented through a pressure-based steady-state implicit solver. Performance study was conducted by systematically varying wall thermal conditions (21 discrete configurations spanning 120–1100 K) and back pressure ratios (2, 3, 4, 5, 5.1, and 5.2), while maintaining constant inlet conditions: Mach number 2, total pressure 0.37 MPa, and static pressure 47,314 Pa. The effect of wall temperature on the flow characteristics of the shock train under high back pressure ratios is significantly different from that under low back pressure ratios. The leading edge of the shock train exhibits an upstream displacement ranging from 5.8% to 90.0% as wall temperatures increase from 120 K to 1100 K under low back pressure ratios, whereas it shifts downstream by 5.87% under high back pressure ratios. The study demonstrates a coupling relationship between wall temperature and back pressure ratios, highlighting distinct influence patterns on shock train characteristics at low and high back pressure ratios, providing essential insights for the optimal design of isolators in hypersonic vehicles.

Abstract This paper studies a distributed fixed-time dynamic event-triggered formation control framework for a group of hypersonic gliding vehicles (GHGVs) suffering from internal uncertainties and non-affine properties. The main challenge is strong coupling of non-affine nonlinear dynamic with hypervelocity characteristics and multi-source uncertainties make it difficult to design the control protocol. Firstly, by integrating the distributed consensus control strategy, fractional order control theory and dynamic event-triggered mechanism, a framework of fixed-time formation control for GHGVs system is constructed. Secondly, to mitigate the issue of ‘explosion of complexity’ (EI), a fixed-time command filter (FCF) is proposed and a compensative strategy is formulated to tackle the impact of filtering errors. Thirdly, an additional auxiliary differential equation (ADE) is developed to decouple the control input from the status variable. Several radial base function neural networks (RBFNN) are utilised to handle the unknown internal uncertainties. Furthermore, a unique dynamic event-triggered mechanism (DTEM) is introduced for each follower, facilitating seamless transitions between two distinct dynamic threshold strategies. Analysis based on Lyapunov function illustrates that the output tracking error of followers exponentially converges to a small range within a fixed time, and Zeno behaviour is prevented. Finally, several numerical simulations are presented to demonstrate the practicability and meliority of the suggested approach.

Abstract For the hypersonic vehicle interception problem, this paper proposes a cooperative guidance law identification and trajectory prediction method based on bearing-only measurement information. Firstly, under the condition that dual interceptor vehicles can obtain bearing-only measurement information, a nonlinear state and observation model based on the cooperative observation of the dual interceptor vehicles is constructed. Then, the cubature Kalman filter is used for state estimation and guidance law identification. Finally, trajectory prediction is performed based on the estimation results, and interceptor vehicles adopt proportional guidance for interception. The results show that the method can realize the accurate estimation of the guidance law parameters and state information of the hypersonic vehicle without relying on the distance measurement information, and the trajectory prediction based on the estimation results meets certain accuracy and speed requirements.

Fast and accurate aerothermal prediction is essential for hypersonic vehicles. In this study, a Point Cloud-Embedded Multi-Fidelity Network (PC-MFNet) is proposed for aerothermal prediction with variable configurations. Within this framework, low-fidelity heat flux data generated by Eckert's reference enthalpy method are embedded into a point cloud neural network, enabling effective multi-fidelity data fusion to enhance prediction accuracy and computational efficiency. To construct the multi-fidelity dataset, four representative hypersonic configurations—the double ellipsoid, blunt cone, blunt biconic, and lifting body—were selected based on computational fluid dynamics (CFD) simulations and engineering calculations. The generalization performance of PC-MFNet was evaluated through multi-dimensional test cases with totally different configurations. Results show that PC-MFNet maintains prediction errors below 4% under various angles of attack and Mach numbers for the trained configurations. For unseen configurations outside the training set, the model achieves average prediction errors for heat flux below 14%, demonstrating strong generalization performance. Moreover, PC-MFNet requires only 0.2% of the prediction time compared to CFD simulations while maintaining near-CFD-level accuracy.

The design of hypersonic vehicles is primarily driven by thermal considerations. Additionally, due to the lack of turbomachinery in these vehicles, the provision of electrical energy to vehicle systems is done almost exclusively by batteries. This paper outlines the analysis of photovoltaic cells embedded in the skin of a hypersonic vehicle to reduce the heat transfer from the hot external aerodynamic surface and the internal vehicle structure and to produce electrical energy from a portion of the thermal radiation. By treating each of the surfaces involved in the radiative exchange as coplanar surfaces in local thermodynamic equilibrium, the equations governing the transfer of thermal energy through the skin structure are derived. Two nominal refractory metals and a nominal ceramic matrix composite material are analyzed at a range of temperatures to determine the relative effectiveness of a thermophotovoltaic (TPV) skin in heat flux reduction and electrical energy generation compared to a simple skin structure. A TPV skin is shown to reduce the heat flux to the vehicle interior by more than an order of magnitude and simultaneously convert more than 10% of the external heat load into electrical energy at high temperatures.