Search Results (23)

During atmospheric reentry, a significant number of chemical reactions are produced inside the high-temperature shock wave formed upstream of the spacecraft. Chemical reactions can significantly alter the flowfield structure surrounding the vehicle and affect surface properties, including heat transfer, pressure, and skin friction coefficients. In this scenario, the primary goal of this investigation is to evaluate the Quantum-Kinetic chemistry model for computing rarefied reactive gas flow over simple and complex geometries. The results are compared with well-established reaction models available for the transitional flow regime. The study focuses on two configurations, a sphere and the Orion capsule, analyzed at different altitudes to assess the impact of chemical nonequilibrium across varying flow rarefaction levels. Including chemical reactions led to lower post-shock temperatures, broader shock structures, and significant species dissociation in both geometries. These effects strongly influenced the surface heat flux, pressure, and temperature distributions. Comparison with results from the literature confirmed the validity of the implemented QK model and highlighted the importance of including chemical kinetics when simulating hypersonic flows in the upper atmosphere. Full article

Hypersonic morphing vehicles (HMVs), renowned for their adaptive structural reconfiguration and cross-domain maneuverability, confront formidable reentry guidance challenges under multiple no-fly zones, stringent path constraints, and nonlinear dynamics exacerbated by morphing-induced aerodynamic uncertainties. To address these issues, this study proposes a hierarchical framework integrating an A-based energy-optimal waypoint planner, a deep deterministic policy gradient (DDPG)-driven morphing policy network, and a quasi-equilibrium glide condition (QEGC) guidance law with continuous sliding mode control. The A* algorithm generates heuristic trajectories circumventing no-fly zones, reducing the evaluation function by 6.2% compared to greedy methods, while DDPG optimizes sweep angles to minimize velocity loss and terminal errors (0.09 km position, 0.01 m/s velocity). The QEGC law ensures robust longitudinal-lateral tracking via smooth hyperbolic tangent switching. Simulations demonstrate generalization across diverse targets (terminal errors < 0.24 km) and robustness under Monte Carlo deviations (0.263 ± 0.184 km range, −12.7 ± 42.93 m/s velocity). This work bridges global trajectory planning with real-time morphing adaptation, advancing intelligent HMV control. Future research will extend this framework to ascent/dive phases and optimize its computational efficiency for onboard deployment. Full article

Thermochemical nonequilibrium significantly affects the accurate simulation of the aerothermal environment surrounding a hypersonic reentry vehicle entering Earth’s atmosphere during deep space exploration missions. The different heat transfer modes corresponding to each internal energy mode and chemical diffusion have not been sufficiently analyzed. The existing dimensionless correlations for stagnation point aerodynamic heating do not account for thermochemical nonequilibrium effects. This study employs an in-house high-fidelity solver PHAROS (Parallel Hypersonic Aerothermodynamics and Radiation Optimized Solver) to simulate the hypersonic thermochemical nonequilibrium flows over a standard sphere under both super-catalytic and non-catalytic wall conditions. The total stagnation point heat flux and different heating modes, including the translational–rotational, vibrational–electronic, and chemical diffusion heat transfers, are all identified and analyzed. Stagnation point aerodynamic heating correlations have been modified to account for the thermochemical nonequilibrium effects. The results further reveal that translational–rotational and chemical diffusion heat transfers dominate the total aerodynamic heating, while vibrational–electronic heat transfer contributes only about 5%. This study contributes to the understanding of aerodynamic heating principles and thermal protection designs for future hypersonic reentry vehicles. Full article

This study starts from the physical perspective of electromagnetic wave propagation in ionosphere media, and the skywave OTH (over-the-horizon) ionosphere channel model is established for hypersonic vehicles based on the ray-tracing method, and this study identifies the key parameters influencing channel characteristics. Secondly, using the re-entry trajectory of the RAM C-II flight experiment as an example, dynamic multipath channel characteristic parameters—such as loss, delay, and Doppler shift—are analyzed in multiple seasons during the noon and midnight periods at a communication frequency of 14 MHz. The results indicate that the settling effect of the ionosphere at midnight makes the changes in the channel more complex, with the irregular sudden appearance and disappearance of multipath numbers. In addition, channel loss is greater in low-elevation propagation mode than in high-elevation propagation mode, indicating that the channel multipath exhibits high loss and low delay characteristics. The skywave communication channel model for hypersonic vehicles, and the dynamic multipath channel characteristic parameters presented in this study offer valuable support for the design, development, and evaluation of long-distance TT&C (Tracking, Telemetering, and Command) communication systems. Full article

In detecting hypersonic vehicles, the radar echo signal is coupled with an intra-pulse Doppler frequency (I-D frequency) component caused by relative motion of a plasma sheath (PSh) and the vehicle, which can induce the phenomenon of a ghost target in a one-dimensional range profile. In order to investigate the I-D frequency generated by the relative motion of a PSh, this study transforms a linear frequency modulated (LFM) signal into a single carrier frequency signal based on echo signal equivalent time delay-dechirp processing and realizes high resolution and fast extraction of the I-D frequency coupled with the frequency-domain echo signal. Furthermore, by relying on the computation of the surface flow field of the RAMC-II Blunt Cone Reentry Vehicle, the coupled I-D frequency in the radar echo signal of a PSh-enveloped target under circumstances of typical altitudes and carrier frequencies is extracted and further investigated, revealing the variation law of I-D frequency. The key findings of this study provide a novel approach for suppressing anomalies in radar detection of PSh-enveloped targets as well as effective detecting and as robust target tracking. Full article

To meet the time-coordinated requirement of hypersonic gliding vehicles to reach a single target simultaneously in the presence of no-fly-zone constraints, this paper proposes a time-coordinated A* path planning method considering multiple constraints. The path planning method is designed based on an analytical steady gliding path model and the framework of the A* algorithm. Firstly, an analytical steady gliding path model is designed based on a quadratic function-type altitude-velocity profile. It can derive the control commands explicitly according to the desired terminal altitude and velocity, thus establishing a mapping between the terminal states and the control commands. Secondly, the node extension method of the A* algorithm is improved based on the mapping. Taking the terminal states as new design variables, a feasible path-node set is produced by a one-step integration using the control commands derived according to different terminal states. This node extension method ensures the feasibility of the path nodes while satisfying terminal constraints. Next, the path evaluation function of the A* algorithm is modified by introducing a heuristic switching term to select the most proper node as a waypoint, aiming to minimize the arrival time deviation. Meanwhile, introducing the penalty items into the path evaluation function satisfies the no-fly-zone constraints, process constraints, and control variable constraints. Finally, an online time-coordinated method is proposed to determine a commonly desired arrival time for several hypersonic gliding vehicles. It eliminates the need to specify the arrival time in advance and improves the capability to deal with sudden threats, increasing the path planning method’s online application capability. The proposed method can achieve online time-coordinated multi-constraint path planning for several hypersonic gliding vehicles, whose effectiveness and superiority are verified by simulations. Full article

The reentry trajectory planning problem of hypersonic vehicles is generally a continuous and nonconvex optimization problem, and it constitutes a critical challenge within the field of aerospace engineering. In this paper, an improved sequential convexification algorithm is proposed to solve it and achieve online trajectory planning. In the proposed algorithm, the Chebyshev pseudo-spectral method with high-accuracy approximation performance is first employed to discretize the continuous dynamic equations. Subsequently, based on the multipliers and linearization methods, the original nonconvex trajectory planning problem is transformed into a series of relaxed convex subproblems in the form of an augmented Lagrange function. Then, the interior point method is utilized to iteratively solve the relaxed convex subproblem until the expected convergence precision is achieved. The convex-optimization-based and multipliers methods guarantee the promotion of fast convergence precision, making it suitable for online trajectory planning applications. Finally, numerical simulations are conducted to verify the performance of the proposed algorithm. The simulation results show that the algorithm possesses better convergence performance, and the solution time can reach the level of seconds, which is more than 97% less than nonlinear programming algorithms, such as the sequential quadratic programming algorithm. Full article

Materials used in aircraft engines, gas turbines, nuclear reactors, re-entry vehicles, and hypersonic structures are subject to severe environmental conditions that present significant challenges. With their remarkable properties, such as high melting temperatures, strong resistance to oxidation, corrosion, and ablation, minimal creep, and advantageous thermal cycling behavior, ceramic matrix composites (CMCs) show great promise as a material to meet the strict requirements in these kinds of environments. Furthermore, the addition of boron nitride nanoparticles with continuous fibers to the CMCs can offer thermal resistivity in harsh conditions, which will improve the composites’ strength and fracture toughness. Therefore, in extreme situations, it is crucial to understand the thermal resistivity period of composite materials. To forecast the ablation performance of composites, we developed six machine learning regression methods in this study: decision tree, random forest, support vector machine, gradient boosting, extreme gradient boosting, and adaptive boosting. When evaluating model performance using metrics including R2 score, root mean square error, mean absolute error, and mean absolute percentage error, the gradient boosting and extreme gradient boosting machine learning regression models performed better than the others. The effectiveness of machine learning models as a useful tool for forecasting the ablation behavior of ceramic matrix composites was effectively explained by this study. Full article

During reentry, the high temperatures experienced by near-space hypersonic vehicles result in surface ablation, generating ablative particles. These particles become part of a plasma, commonly referred to as a “dusty plasma sheath” in radar remote sensing. The dusty plasma model, integral in radar studies, involves extensive charge and dynamic interactions among dust particles. Previous derivations assumed that the dust particle radius significantly surpassed the Debye radius, leading to the neglect of dust radius effects. This study, however, explores scenarios where the dust particle radius is not markedly smaller than the Debye radius, thereby deducing the charging process of dusty plasma. The derived equations encompass the Debye radius, charging process, surface potential, and charging frequency, particularly considering larger dust particle radii. Comparative analysis of the dusty plasma model, both before and after modification, reveals improvements when dust particles approach or exceed the Debye length. In essence, our study provides essential equations for understanding dusty plasma under realistic conditions, offering potential advancements in predicting electromagnetic properties and behaviors, especially in scenarios where dust particles closely align with or surpass the Debye radius. Full article

A large amount of heat flux from aerodynamic heating acts on reusable spacecraft; thus, an accurate heat flux prediction around spacecraft reentry is essential for developing a high-performance reusable spacecraft. Although the approximate convective-heating equations can calculate the heat flux with high efficiency and sufficient fidelity, the heat flux should be evaluated over a thousand times for the entire trajectory in multidisciplinary analyses. For these reasons, it is necessary to develop an efficient method for calculating the heat flux for multidisciplinary analysis. In this paper, an efficient method for heat flux calculation that is adoptable by multidisciplinary analyses for hypersonic vehicles, such as spacecraft, is developed. Approximate convective-heating equations were adopted to relieve the computational cost of estimating the heat flux, and an adaptive time step method for heat flux calculations was developed to reduce the number of heat flux calculations required across the entire flight trajectory. A dynamic factor was introduced that adjusts the time step between each instance of the heat flux calculation. Since the time step using this factor could increase under low heat flux conditions, the number of heat flux calculations decreases by approximately one-tenth with over 90% accuracy. Therefore, the efficiency was improved with high accuracy using the adaptively-determined time step according to this dynamic factor. Full article

This work deals with the numerical solution of hypersonic flow of viscous fluid over a compressible ramp. The solved case involves very important and complicated phenomena such as the interaction of the shock wave with the boundary layer or the transition from a laminar to a turbulent state. This type of problem is very important as it is often found on re-entry vehicles, engine intakes, system and sub-system junctions, etc. Turbulent flow is modeled by the system of averaged Navier–Stokes equations, which is completed by the explicit algebraic model of Reynolds stresses (EARSM model) and further enhanced by the algebraic model of bypass transition. The numerical solution is obtained by the finite volume method based on the rotated-hybrid Riemann solver and explicit multistage Runge–Kutta method. The numerical solution is then compared with the results of a direct numerical simulation. Full article

A high-temperature plasma sheath is generated on the surface of the re-entry vehicle through the conversion of kinetic energy to thermal and chemical energy across a strong shock wave at the hypersonic speed. Such a condition results in the forming of a blackout which significantly affects the communication components. To analyze the re-entry vehicle at the hypersonic speed, an unconditionally stable system incorporated factorization-splitting (SIFS) algorithm is proposed in finite-difference time-domain (FDTD) grids. The proposed algorithm shows advantages in the entire performance by simplifying the update implementation in multi-scale problems. The plasma sheath is analyzed based on the modified auxiliary difference equation (ADE) method according to the integer time step scheme in the unconditionally stable algorithm. Higher order perfectly matched layer (PML) formulation is modified to simulate open region problems. Numerical examples are carried out to demonstrate the performance of the algorithm from the aspects of target characteristics and antenna model. From resultants, it can be concluded that the proposed algorithm shows considerable accuracy, efficiency, and absorption during the simulation. Meanwhile, plasma sheath significantly affects the communication and detection of the re-entry vehicle. Full article

Aiming at the cooperative guidance problem of multi-hypersonic glide vehicles, a cooperative guidance method based on a parametric design and an analytical solution of time-to-go is proposed. First, the hypersonic reentry trajectory optimization problem was transformed into a parameter optimization problem. The parameters were optimized to determine the angle of attack profile and the time to enter the altitude velocity reentry corridor. Then, using the quasi-equilibrium glide condition, the estimation form of the remaining flight time was analytically derived to satisfy accurately the cooperative time constraint. Using the remaining time-to-go and range-to-go, combined with the heading angle deviation corridor, the bank angle command was further calculated. Finally, the swarm intelligence optimization algorithm was used to optimize the design parameters to obtain the cooperative guidance trajectory satisfying the time constraint. Simulations showed that the analytical time reentry cooperative guidance algorithm proposed in this paper can accurately meet the time constraints and cooperative flight accuracy. Monte Carlo simulation experiments verified that the proposed algorithm demonstrates a robust performance. Full article

To facilitate future Hypersonic Flight Vehicle (HFV) implementation with high maneuverability throughout its reentry trajectory, an Active Jets-Matrix (AJM) is designed to serve as the flapless pitch control surface. The AJM consists of four control groups composed in total of 48 supersonic nozzles. The AJM aims to utilize the jet flow-interaction-induced additional control moment to improve the control efficiency during atmospheric entry. A comparative research method is employed to study the eight simulation cases for three different HFV configurations (baseline, mechanical control surface with 30° deflection, and the AJM configuration) and two AJM control moment adjustment strategies (nozzle chamber pressure regulation and discretized nozzle group on–off control). A conventional in-house computational fluid dynamics (CFD) solver with the two-equation SST turbulence model is employed to undertake the simulation tasks. Simulation results indicate that: (a) only the AJM configuration is capable of trimming the HFV in pitch channel; (b) nonlinearity exists between the augmentation moment and the specific control variable from respective adjustment strategies; (c) the chamber pressure regulation strategy bears higher overall efficiency, while the discretized control strategy induces more intense local jet-flow interaction. With a maximum control moment augmentation of 1.58, the AJM presents itself as a competitive candidate for future HFV flapless control methods. Full article

The mission of spacecraft usually faces the problem of an unknown deep space environment, limited long-distance communication and complex environmental dynamics, which brings new challenges to the intelligence level and real-time performance of spacecraft onboard trajectory optimization algorithms. In this paper, the optimal control theory is combined with the neural network. Then, the state–control sample pairs and the state–costate sample pairs obtained from the high-fidelity algorithm are used to train the neural network and further drive the spacecraft to achieve optimal control. The proposed method is used on two typical spacecraft missions to verify the feasibility. First, the system dynamics of the hypersonic reentry problem and fuel-optimal moon landing problem are described and then formulated as highly nonlinear optimal control problems. Furthermore, the analytical solutions of the optimal control variables and the two-point boundary value problem are derived based on Pontryagin’s principle. Subsequently, optimal trajectories are solved offline using the pseudospectral method and shooting methods to form large-scale training datasets. Additionally, the well-trained deep neural network is used to warm-start the indirect shooting method by providing accurate initial costates, and thus the real-time performance of the algorithm can be greatly improved. By mapping the nonlinear functional relationship between the state and the optimal control, the control predictor is further obtained, which provides a backup optimal control variables generation strategy in the case of shooting failure, and ensures the stability and safety of the onboard algorithm. Numerical simulations demonstrate the real-time performance and feasibility of the proposed method. Full article