Aerospace

Satellite networks face escalating cybersecurity threats from evolving attack vectors and systemic complexities. This paper proposes SatGuard, a novel framework integrating a three-dimensional penetration testing methodology and a nonlinear risk assessment mechanism tailored for satellite security. To address limitations of conventional tools in handling satellite-specific vulnerabilities, SatGuard employs large language models (LLMs) like GPT-4 and DeepSeek-R1. By leveraging their contextual reasoning and code-generation abilities, SatGuard enables semi-automated vulnerability analysis and exploitation. Validated in a simulated ground station environment, the framework achieved a 73.3% success rate (22/30 attempts) across critical ports, with an average of 5.5 human interactions per test. By bridging AI-driven automation with satellite-specific risk modeling, SatGuard advances cybersecurity for next-generation space infrastructure through scalable, ethically aligned solutions.

Due to the limited difference in maneuverability between the pursuer and the evader in three-dimensional space, it is difficult for a single pursuer to capture the evader. To address this, this paper proposes a strategy where three pursuers intercept one evader and introduces a Q-learning-cover algorithm. Based on the motion models of the pursuers and the evader in three-dimensional space, this paper presents a region coverage scheme based on the Ahlswede ball and analyzes the convergence upper bound of the Q-learning-cover algorithm by designing an appropriate Lyapunov function. Through extensive model training, the successful capture of the evader by the pursuers in a three-on-one scenario was achieved. Finally, numerical simulation experiments and hardware-in-the-loop simulation experiments are presented, both of which demonstrate that the proposed Q-learning-cover algorithm can effectively realize the three-on-one encirclement and interception of the evading target.

This article reviews the physical and chemical mechanisms associated with unsteady swirl-stabilized partially or fully lean premixed combustion. The processes of flame stabilization, mode conversion, swirl number oscillation, equivalence ratio oscillation, and vortex rollup are described. The key challenges associated with flow-flame dynamics for several sources of perturbations are presented and discussed. The Rayleigh criterion is discussed. This article summarizes the scientific knowledge gained on swirling flames dynamics in terms of modeling, theoretical analysis, and transient measurements with advanced diagnostics. The following are specifically documented: (i) the effect of the swirler on swirling flames; (ii) the analytical results, computational modeling, and experimental measurements of swirling flame dynamics; (iii) the influence of flow features on flame response of swirling flames for combustion instabilities studies; and (iv) the identification and description of the combustion dynamics mechanisms responsible for swirl-stabilized combustion instabilities. Relevant elements from the literature in this context for hydrogen fuel are included.

The process of spacecraft entry, deceleration, landing, and ascent requires high specific impulse, high reliability, and high-precision thrust adjustments for the power system. The new hybrid rocket motor adopts a complex-shaped grain and high-energy propellant, offering high-energy characteristics, continuously adjustable thrust, a relatively simple oxidant delivery system, and high reliability, making it an ideal power choice for the above systems. However, due to changes in the characteristic structure of the three-dimensional complex flame surface degradation process, it is difficult to accurately predict the motor performance. In this study, changes in the flow field structure and performance parameters during the operation of the cross-shaped grain hybrid rocket motor are presented using fuel surface reconstruction technology based on a dynamic mesh. The spatial distribution of the fuel surface is analyzed, and the accuracy of the model is verified via firing tests. The results show that the deviations of combustion chamber pressure and thrust are less than 0.6% and 1.7%, respectively. After the test, the deviation between the simulated port area and the CT-scanned port area is less than 3.5%. The accuracy of this model is verified in terms of the above two aspects, establishing a solid foundation for predicting the performance of future hybrid rocket motors with more complex-shaped grains.

Accurate quantification of metallic contaminants in rocket exhaust plumes serves as a critical diagnostic indicator for engine wear monitoring. This paper develops a hybrid method combining atomic emission spectroscopy (AES) theory with a genetic algorithm (GA) optimized backpropagation (BP) network to quantify the metallic element concentrations in liquid-propellant rocket exhaust plumes. The proposed method establishes linearized intensity–concentration mapping through the introduction of a photon transmission factor, which is derived from radiative transfer theory and experimentally calibrated via AES measurement. This critical innovation decouples the inherent nonlinearities arising from self-absorption artifacts. Through the use of the transmission factor, the training dataset for the BP network is systematically constructed by performing spectral simulations of atomic emissions. Finally, the trained network is employed to predict the concentration of metallic elements from the measured atomic emission spectra. These spectra are generated by introducing a solution containing metallic elements into a CH4-air premixed jet flame. The predictive accuracy of the method is rigorously evaluated through 32 independent experimental trials. Results show that the quantification error of metallic elements remains within 6%, and the method exhibits robust performance under conditions of spectral self-absorption, demonstrating its reliability for rocket engine health monitoring applications.

In this paper, a controller design method based on deep reinforcement learning is proposed for a wide-range variable cycle engine with a turbine interstage mixed architecture. The PID controller is subject to limitations, including single-input single-output limitations, low regulation efficiency, and poor adaptability when confronted with contemporary variable cycle engines that exhibit complex and multi-variable operating conditions. To solve this problem, this paper adopts a deep reinforcement learning method based on a deep deterministic policy gradient algorithm, and it applies an action space pruning technique to optimize the controller, which significantly improves the convergence speed of network training. In order to verify the control performance, two typical flight conditions are selected for simulation experiments as follows: in the first scenario, H = 0 km and Ma = 0, while in the second scenario, H = 10 km and Ma = 0.9. A comparison of the simulation results shows that the proposed deep reinforcement learning controller effectively addresses the engine’s multi-variable coupling control problem. In addition, it reduces response time by an average of 44.5%, while maintaining a similar overshoot level to that of the PID controller.

This paper presents the development of a novel aerodynamic model tailored for the Bio-Inspired Rotating Empennage (BIRE), a non-traditional fixed-wing aircraft empennage inspired by avian flight. The BIRE replaces the conventional vertical stabilizer with an extra degree of freedom for the horizontal stabilizer, which is allowed to rotate about the body-fixed x axis. This empennage is similar to the tail of a bird, and allows control of both longitudinal and lateral moments. However, such a design introduces complex nonlinear longitudinal and lateral aerodynamic interactions, not typically accounted for in most fixed-wing aircraft aerodynamic models below stall. This work presents a nonlinear sinusoidal aerodynamic model that can be used for fixed-wing aircraft with this type of empennage. Although the aerodynamic model is constructed to accurately capture the degrees of freedom of this particular empennage design, similar methods could be used to develop other aerodynamic models for non-traditional control effectors. A large dataset of low-fidelity aerodynamic data was generated using a modern numerical lifting-line algorithm, and these data were fit to the nonlinear sinusoidal aerodynamic model. A method for fitting the data is demonstrated, and the results show that the nonlinear sinusoidal aerodynamic model can be fit to the data with an accuracy of less than 10% of the maximum deviation of the aerodynamic coefficients in root-mean-square error. The underlying physics of many of the longitudinal and lateral nonlinear sinusoidal aerodynamic properties of the aircraft are discussed in detail. The methodology presented here can be extended to other non-traditional control effectors, encouraging innovative approaches in aerodynamic modeling and aircraft design. In contrast, choosing to model control effectors using the traditional, linear approach can obscure key aerodynamic behaviors key for trim and control analyses. The study’s findings underscore the importance of developing adaptable aerodynamic models to support the advancement of next-generation aircraft designs and control systems.

Accurate and robust relative pose estimation is the first step in ensuring the success of an active debris removal mission. This paper introduces a novel method to detect structural markers on the European Space Agency’s Environmental Satellite (ENVISAT) for safe de-orbiting using image processing and Convolutional Neural Networks (CNNs). Advanced image preprocessing techniques, including noise addition and blurring, are employed to improve marker detection accuracy and robustness from a chaser spacecraft. Additionally, we address the challenges posed by eclipse periods, during which the satellite’s corners are not visible, preventing measurement updates in the Unscented Kalman Filter (UKF). To maintain estimation quality in these periods of data loss, we propose a covariance-inflating approach in which the process noise covariance matrix is adjusted, reflecting the increased uncertainty in state predictions during the eclipse. This adaptation ensures more accurate state estimation and system stability in the absence of measurements. The initial results show promising potential for autonomous removal of space debris, supporting proactive strategies for space sustainability. The effectiveness of our approach suggests that our estimation method, combined with robust noise adaptation, could significantly enhance the safety and efficiency of debris removal operations by implementing more resilient and autonomous systems in actual space missions.

In this study, two-dimensional airfoil shapes obtained in aerodynamic optimizations are converted to three-dimensional wing models and then their aerodynamic and sonic boom performance are evaluated. The airfoil shapes analyzed are the diamond, Busemann, new supersonic biplane (NSB), and triplane airfoil configurations. The NSB is a modified version of the Busemann biplane airfoil proposed in previous studies. The triplane airfoil configuration is obtained in this study by maximizing the lift-to-drag ratio using an aerodynamic topology optimization method. Based on the obtained two-dimensional airfoil shapes, three-dimensional multiple (biplane/triplane) wing configurations are designed. The aerodynamic and sonic boom performance of these configurations is evaluated in detail through three-dimensional flow analyses as well as acoustic propagation analyses. The aerodynamic superiority of the multiple wing configurations is confirmed in this study.

The load spectrum serves as the foundation for the life analysis of aero-engine turbine disks. To enhance the accuracy of life assessments for turbine disks, this study compiles a time-varying load spectrum for turbine disks. Firstly, a surrogate model for transient processes at the critical points of turbine disks is established, enabling the rapid evaluation of the transient temperature and thermal stress at these points under complex loading histories. Secondly, a performance degradation model is established based on real engine test data, explicitly describing the general trend of performance degradation characteristics with respect to the cycle number and engine power. Finally, a time-varying load spectrum for turbine disks is compiled, considering both short-term transient processes and long-term performance degradation. The life of turbine disks at the fir-tree slot root and disk bore is assessed using the Manson–Coffin equation, Wilshire equation, and linear damage accumulation rule. The results indicate that neglecting transient processes leads to conservative life assessment results while neglecting performance degradation leads to dangerous life assessment results. Compared with traditional methods, the time-varying load spectrum significantly improves the accuracy and scientific nature of turbine disk life assessment.

Low-Earth-Orbit (LEO) satellite networks offer a promising avenue for achieving global connectivity, despite certain technical and economic challenges such as high implementation costs and the complexity of network management. Nonetheless, real-time routing remains challenging because of rapid topology changes and strict energy constraints. This paper proposes a GPU-accelerated Eclipse-Aware Routing (EAR) method that simultaneously minimizes hop count and balances energy consumption for real-time routing on an onboard computer (OBC). The approach first employs a Breadth-First Search (BFS)–based K-Shortest Paths (KSP) algorithm to generate candidate routes and then evaluates battery usage to select the most efficient path. In large-scale networks, the computational load of the KSP search increases substantially. Therefore, CUDA-based parallel processing was integrated to enhance performance, resulting in a speedup of approximately 3.081 times over the conventional CPU-based method. The practical applicability of the proposed method is further validated by successfully updating routing tables in a SpaceWire network.

Efficient and adaptive mission planning for Earth Observation Satellites (EOSs) remains a challenging task due to the growing complexity of user demands, task constraints, and limited satellite resources. Traditional heuristic and metaheuristic approaches often struggle with scalability and adaptability in dynamic environments. To overcome these limitations, we introduce AEM-D3QN, a novel intelligent task scheduling framework that integrates Graph Neural Networks (GNNs) with an Adaptive Exploration Mechanism-enabled Double Dueling Deep Q-Network (D3QN). This framework constructs a Directed Acyclic Graph (DAG) atlas to represent task dependencies and constraints, leveraging GNNs to extract spatial–temporal task features. These features are then encoded into a reinforcement learning model that dynamically optimizes scheduling policies under multiple resource constraints. The adaptive exploration mechanism improves learning efficiency by balancing exploration and exploitation based on task urgency and satellite status. Extensive experiments conducted under both periodic and emergency planning scenarios demonstrate that AEM-D3QN outperforms state-of-the-art algorithms in scheduling efficiency, response time, and task completion rate. The proposed framework offers a scalable and robust solution for real-time satellite mission planning in complex and dynamic operational environments.

Path planning for stratospheric airships in dynamic wind fields is challenging due to complex wind variations and strict nighttime energy constraints. This paper proposes a hybrid Level-Set Particle Swarm Optimization (LS-PSO) framework. Firstly, it employs PSO to search iteratively for a propulsion velocity sequence in the velocity domain, with a multi-objective fitness function that integrates reachability, energy consumption and time cost to evaluate each velocity sequence. Then, the reachability of each candidate sequence is numerically solved by the Level Set forward evolution. To improve optimization efficiency, we proposed a multi-resolution grid adaptive strategy for forward evolutions. Finally, with the optimal velocity sequence, the optimal path is generated once by the Level Set backtrack processing. To validate the resulting methodology, we used a benchmark case of a dynamic complex four-gyre flow, described by mathematical formulas, with the optimal day–night path identified by GPOPS-II. The results show the LS-PSO solution has comparable accuracy, with a trajectory deviation less than 3%. Then, we tested the methodology in the stratospheric wind flows using ERA5 reanalysis data. The results demonstrate that our path planning methodology provides a computationally efficient and optimal energy–time solution for autonomous stratospheric airships, while conforming to reachability and strict nighttime energy constraints.

High-test peroxide (HTP) monopropellant thrusters are being considered for spacecraft lander missions due to their simplicity and reduced toxicity compared to traditional propellants. Pulse-Width Modulation (PWM) throttling is a key technique for precise thrust control in such systems. However, PWM throttling can lead to pressure surges and oscillations in the propellant feed system, potentially compromising system reliability. This study investigates the influence of PWM parameters, specifically duty cycle and frequency, on pressure surges and oscillations in a 50-newton-class HTP monopropellant thruster. The objective is to identify stable operating conditions that mitigate these effects, thereby enhancing the reliability of PWM throttling for lander applications. An experimental setup was developed, including a 50-newton-class thruster with a MnO2/La/Al2O3 catalyst and a solenoid valve for PWM control. Cold flow tests using water characterized the valve response and water hammer effects, while hot fire tests with 90 wt.% HTP were used to evaluate thruster performance under steady-state and PWM conditions. Analytical methods, including Joukowsky’s equation and power spectral density analysis, were used to interpret the data and understand the underlying mechanisms. The results showed that while surge pressures generally aligned with steady-state values, specific PWM conditions led to amplified surges, particularly at low duty cycles. Additionally, high duty cycles induced chugging instability. The natural frequencies of the feed system were found to play a crucial role in these phenomena. Stable operating conditions were identified by avoiding duty cycles that cause constructive interference of pressure waves. This research demonstrates that by carefully selecting PWM parameters based on the feed system’s dynamic characteristics, pressure surges and oscillations can be minimized, ensuring reliable operation of HTP monopropellant thrusters in PWM throttling mode. These findings contribute to the development of more efficient and safer propulsion systems for spacecraft landers.

Aircraft engines are regularly inspected with borescopes to detect faults at an early stage and maintain airworthiness. A critical part of this inspection process is accurately measuring any detected damage to determine whether it exceeds allowable limits. Current state-of-the-art borescope measurement techniques—primarily stereo camera systems and pattern projection—face significant challenges when engines lack sufficient surface features or when illumination is inadequate for reliable stereo matching. MEMS-based 3D scanners address these issues by focusing laser light onto a small spot, reducing dependency on surface texture and improving illumination. However, miniaturized MEMS-based scanner borescopes that can pass through standard engine inspection ports are not yet available. This work examines the essential steps to downsize MEMS 3D scanners for direct integration into borescope inspections, thereby enhancing the accuracy and reliability of aircraft engine fault detection.

This paper proposes two compact, efficient, and lightweight heat exchangers based on triply periodic minimal surfaces (TPMSs). Designed in an annular configuration, the heat exchangers meet the requirements of micro gas turbines for compactness. Two prototypes of Diamond and Gyroid modular TPMS heat exchangers were fabricated using selective laser melting (SLM) with stainless steel. The flow and heat transfer experimental results indicate that, within a Reynolds number range of 200 to 800, the effectiveness of both heat exchangers remained above 0.62, and the average Nusselt numbers of the Diamond and Gyroid structures reached 3.60 and 4.06 times that of the printed circuit heat exchanger (PCHE), respectively. Although both heat exchangers exhibited relatively high friction factors, their overall performance surpassed that of conventional heat exchangers. Additionally, performance comparisons with existing TPMS heat exchangers revealed that smaller lattice sizes contribute to improved volume-based power density, although they result in increased pressure loss. Simulation results indicated that the “merge–split” effect present in both structures enhances heat transfer between the fluid and the wall. Furthermore, the complex channels of the TPMS structures ensure that the fluid maintains strong turbulence intensity throughout the heat exchanger. This study demonstrates that stainless steel TPMS structures can serve as excellent candidates for applications in micro gas turbines.

The vigorous development of urban air mobility (UAM) is reshaping the urban travel landscape, but it also poses severe challenges to the safe and efficient operation of dense and complex airspace. Potential conflicts between flight plans have become a core bottleneck restricting its development. Traditional flight plan adjustment and management methods often rely on deterministic trajectory predictions, ignoring the inherent temporal uncertainties in actual operations, which may lead to the underestimation of potential risks. Meanwhile, existing global optimization strategies often face issues of inefficiency and overly broad adjustment scopes when dealing with large-scale plan conflicts. To address these challenges, this study proposes an innovative flight plan conflict management framework. First, by introducing a probabilistic model of flight time errors, a new conflict detection mechanism based on confidence intervals is constructed, significantly enhancing the ability to foresee non-obvious conflict risks. Furthermore, based on complex network theory, the framework accurately identifies a small number of “critical flight plans” that play a core role in the conflict network, revealing their key impact on chain reactions of conflicts. On this basis, a phased optimization strategy is adopted, prioritizing the adjustment of spatiotemporal parameters (departure time and speed) for these critical plans to systematically resolve most conflicts. Subsequently, only fine-tuning the speeds of non-critical plans is required to address remaining local conflicts, thereby minimizing interference with the overall operational order. Simulation results demonstrate that this framework not only significantly improves the comprehensiveness of conflict detection but also effectively reduces the total number of conflicts. Additionally, the proposed phased artificial lemming algorithm (ALA) outperforms traditional optimization algorithms in terms of solution quality. This work provides an important theoretical foundation and a practically valuable solution for developing robust and efficient UAM dynamic scheduling systems, holding promise to support the safe and orderly operation of large-scale urban air traffic in the future.

Aviation is highly regulated with a strong focus on safety. If machine learning models are to be used in aviation, their correctness must be proven as part of the certification process. As the number of data points is limited in real-world applications, a new approach is needed to ensure that the behaviour between the test points is correct. Due to the complexity, it is unlikely that a method for a complete evaluation with a reasonable runtime will be found. It is demonstrated in this methodology study how, in addition to the data set, the expected behaviour of the system the model is designed for can be considered. Using domain knowledge, a specific “behaviour envelope” defines the area the model is expected to stay within. In case the model stays within the behaviour envelope, which can be mathematically evaluated, it can be ensured that the behaviour between the test points is always physically meaningful. Since the effort for the evaluation increases with the complexity, it is proposed to use symbolic regression, a method where a search procedure combines elementary functions to create a compact symbolic model. This shifts the effort more towards model creation and simplifies the subsequent validation.

In the present paper, a reduced-order modeling (ROM) approach based on a hybrid neural network is presented in order to calculate wing buffet pressure distributions due to structural eigenmode-based deformations. The accurate prediction of unsteady surface pressure distributions is crucial for assessing aeroelastic stability and preventing structural failure, but full-order simulations are computationally expensive; the proposed ROM provides a fast and efficient alternative with a sufficient level of accuracy. The hybrid ROM is defined by a series connection of a convolutional autoencoder (CNN-AE) and a long short-term memory (LSTM) neural network. As a test case, the NASA Common Research Model (CRM) configuration for the transonic buffet condition is applied. Forced-motion computational fluid dynamics (CFD) simulations are conducted in order to obtain the aerodynamic responses induced by the eigenmode-based deformations. For the unsteady simulations, the triangular adaptive upwind (TAU) solver of the German Aerospace Center (DLR), is used. Based on a selected structural model, symmetric and asymmetric eigenmode-based deformations of the wing structure are implemented and considered for performance evaluation. Comparing the pressure loads modeled by the hybrid ROM and the reference full-order numerical solution, an overall good prediction performance is indicated with mean squared error (MSE) values mostly below 3%, reaching local maxima of about 12%, due to strong pressure gradients associated with pronounced shock oscillations.

A novel deployable reflector antenna for small satellites has been designed, fabricated, and experimentally validated. The reflector utilizes a doubly curved flexible surface manufactured from a triaxially woven fabric-reinforced silicone (TWFS) composite. By leveraging high-strain composite materials, the design enables a highly compact stowed configuration while maintaining precise surface accuracy upon deployment. The deployment mechanism is proposed to accommodate a 0.6 m diameter parabolic reflector within a minimal stowed volume, optimizing space efficiency for satellite integration. To validate this concept, a prototype of the reflector antenna has been fabricated and demonstrated the feasibility and effectiveness of the proposed approach.

To meet the increasingly stringent performance indicators of gas turbines, the turbine inlet temperature has increased, and variable geometry turbine technology is widely applied. Therefore, this study developed a quasi-two-dimensional (quasi-2D) method for variable geometry turbine performance considering cooling air mixing based on the elementary blade method and the cooling airflow mixing model. To address the high-dimensional, multi-variable data fitting problem of variable geometry turbines considering the effects of cooling air, this study adopted a BP neural network to further establish a surrogate model for variable geometry turbine performance. A sensitivity analysis of a single-stage turbine was conducted. The variable geometry cooling performance of a single-stage turbine and an E3 five-stage low-pressure air turbine were calculated, and the corresponding surrogate models were established. The relative errors between the calculated mass flow rate and efficiency of the single-stage turbine and the experimental values were no more than 0.70% and 4.44%, respectively; for the five-stage air turbine, the maximum relative errors in mass flow rate and efficiency were no more than 1.67% and 1.385%, respectively. When the throat area of the single-stage turbine nozzle changed by ±30%, the maximum relative errors between the calculated mass flow rate and efficiency and their experimental values were 3.602% and 4.228%, respectively; thus, the determination coefficients of the constructed BP neural network model for the training samples were all greater than 0.999, indicating that the surrogate model has high prediction accuracy and strong generalization ability and can quickly predict variable geometry turbine cooling performance.

The motion of a copter with a suspended payload in a vertical plane is considered. The payload has a spherical shape and contains a concentric spherical cavity partially filled with ideal liquid. The system is subjected to horizontal stationary wind. The aerodynamic load on the payload is described within the framework of a quasi-steady approach. The dynamics of the liquid are simulated using the phenomenological pendulum model. The points of this study are the controllability and observability of a stationary flight of a copter with the payload. A control strategy is proposed, which aims to bring the system from a certain initial state to a certain final state, such that the center of mass of the copter moves along a given sufficiently smooth curve. The control is designed to ensure the suppression of oscillations of the payload and the liquid along the entire trajectory.

Aiming to reduce energy demand and carbon footprint, minimize noise impact, and enhance flight safety and efficiency during aerotow operations, this study integrates an electric propulsion system and an automatic flight control system (AFCS) into the electric research aircraft e-Genius. An advanced propulsion system is developed using high-performance batteries and available electric drive components, while the AFCS is designed following a systematic process of developing flight control algorithms. Flight tests are then conducted to evaluate the performance of individual components and the overall system. The test results demonstrate that the upgraded propulsion system provides sufficient power to launch sailplanes, even with the maximum takeoff mass, while significantly reducing energy demand when compared to contemporary fossil fueled towplanes. Additionally, the AFCS proves to be stable and robust, successfully following specified commanded states, executing path tracking, and performing aerotow operations.

We present a novel approach to generating a cooperative guidance strategy using deep reinforcement learning to address the challenge of cooperative multi-missile strikes under uncontrollable velocity conditions. This method employs the multi-agent proximal policy optimization (MAPPO) algorithm to construct a continuous action space framework for intelligent cooperative guidance. A heuristically reshaped reward function is designed to enhance cooperative guidance among agents, enabling effective target engagement while mitigating the low learning efficiency caused by sparse reward signals in the guidance environment. Additionally, a multi-stage curriculum learning approach is introduced to smooth agent actions, effectively reducing action oscillations arising from independent sampling in reinforcement learning. Simulation results demonstrate that the proposed deep reinforcement learning-based guidance law can successfully achieve cooperative attacks across a range of randomized initial conditions.

The unsteady interactions in rotating detonation turbine engines (RDTE) remain poorly understood. To address this, a 2D numerical model integrating a rotating detonation combustor (RDC) with a first-stage turbine is established to analyze flow structures and aerodynamics under various detonation modes. Proper orthogonal decomposition (POD) reveals intrinsic links between flow features and performance metrics. Results show that while the RDC generates total pressure gain, it induces significant unsteady flow. Guide vanes partially suppress pressure fluctuations but cannot eliminate total pressure losses or circumferential non-uniformity, reducing rotor efficiency. Increasing detonation wave numbers decreases total pressure gain at rotor inlet but improves flow uniformity: the counterclockwise double-wave mode exhibits optimal performance (27.9% work gain, 5.0% instability, 86.4% efficiency), whereas the clockwise single-wave mode shows the poorest (20.9% work gain, 11.8% instability, 84.0% efficiency). POD analysis indicates first-order modes represent time-averaged flow characteristics, while low-order modes capture non-uniform pressure distributions and pairing phenomena, reconstructing wave propagation. The study highlights discrepancies between turbine inlet’s actual unsteady flow and conventional quasi-steady design assumptions, proposing enhancing mean flow characteristics and increasing first-mode energy proportion to improve work extraction. These findings clarify the detonation wave mode–turbine performance correlation, offering insights for RDTE engineering applications.