Abstract:To accurately describe the dynamic response process of helicopter maneuvering flight, this study combines the engine aero-thermodynamic model with the helicopter flight dynamics model to establish a coupled model of helicopter/engine that reveals the effect of rotor speed variation. Firstly, the rotor flight dynamics model and the engine’s component-level aero-thermodynamic models are established. Then, the rotor/engine coupling model is formulated based on the power matching relationship, ultimately forming a coupled model of helicopter/engine that accounts for the engine’s dynamic characteristics. The UH-60A flight test data are used to validate the trim and dynamic response of the helicopter model. The results show that the helicopter model can accurately describe rotor speed and yaw moment changes. Finally, a simulation was performed on the helicopter bob-up maneuver flight process. The study shows that the helicopter flight state changes rapidly during the bob-up maneuver, and the rapid change in engine load causes a sudden change in rotor speed, leading to a reverse change in yaw angular velocity in the yaw direction, which has adverse effects on the helicopter’s maneuverability. This research has important reference value for a deeper understanding of the coupling relationship between helicopter and engine, improving the design of helicopter/engine coupling control laws, and enhancing helicopter maneuver flight performance.

Abstract:To achieve the classification and recognition of rocket engine targets, it becomes necessary to extract the correlation between engine thrust and the infrared radiation intensity of the rocket exhaust plume. The High-Nitrogen, High-Energy GAP/Cl20/Al (GCA) and High-Nitrogen, Smokeless GAP/ADN/DAAOF (GAD) propellants are taken as the research objects of this study. Three parameters affecting motor thrust are numerically designed including combustion chamber pressure, nozzle expansion ratio and propellant formulation. A full-link computational method consisting of the motor internal flow, reacting flow field and infrared radiation is used to predict plume infrared signatures. On basis of this method, the correlation between the in-band radiation in the 2.7 μm and 4.3 μm bands of the under-expanded plume and motor thrust is established. Results show that motor thrust is log-linearly correlated with the radiant intensity under the variations of combustion chamber pressure and nozzle expansion ratio. At the same thrust level, the radiant intensity of the exhaust plume of GAD propellant is about 3~5 times lower than that of GCA. The combustion chamber pressure and nozzle expansion ratio can also be the dominant factors for thrust. For two types of propellant motors with different ambient conditions, the thrust and plume infrared radiation intensity approximately follow a unified logarithmic distribution relationship. The change in propellant formulation causes a weaker degree of change in motor thrust. Under different ambient pressures, the plume radiation intensity distribution of both high nitrogen propellants exhibits “aggregation” as the motor thrust changes. The radiation intensity in the 4.3 μm band exhibits a layered distribution phenomenon with ambient pressure as a function.

Abstract:To enhance the precision of aircraft maneuver recognition method and improve the real-time of the recognition process, considering the dynamic and temporal nature of tactical maneuver trajectory, through fusing maneuver trajectory segmentation point detection method, an online maneuver trajectory recognition method based on the Mahalanobis distance-based dynamic time warping network is proposed. Firstly, in order to prevent the trained segmentation recognition model from overfitting, the flight parameters of the maneuver trajectory are converted into the maneuver trajectory feature parameters by extracting the maneuver trajectory features, and the maneuver library including 21 maneuver trajectory units is constructed. Secondly, in order to quickly split the maneuver trajectory units, a method is introduced that combines support vector machines and Mahalanobis distance:a maneuver trajectory segmentation point detection method based on Mahalanobis Distance Support Vector Machine. Then, in order to improve the accuracy of maneuver trajectory unit recognition, a method is proposed that combines dynamic time warping based on improved Mahalanobis distance and convolutional neural networks:a maneuver trajectory unit recognition method based on Mahalanobis distance measurement in dynamic time warping neural networks. Finally, by fusing the segmentation point detection model and the trajectory unit recognition model, an online maneuvering trajectory recognition platform is constructed, and the simulation analysis using three maneuver trajectory conditions data is carried out. The experimental results show that the proposed method can detect the segmentation point of the aircraft unit in real time, and the accuracy of the segmentation detection can reach 97.0%. Compared with other maneuver trajectory recognition methods, the proposed method not only meets real-time requirements, but also has high recognition accuracy exceeding 90%. The results validate the effectiveness and real-time performance of the online maneuver recognition model proposed in this research.

Abstract:The sealing reliability of through-cabin optical fiber connector is curcial for maintaining the stability of air pressure in manned spacecraft cabin and ensuring the safety of manned spacecraft. In order to ensure the reliable application of the through-cabin optical fiber connector in the complicated space application environment and long service life in orbit, a high reliable bi-au-tin optical fiber sealing structure of the through-cabin optical fiber connector is proposed, and the mechanism of optical fiber metallization welding seal is analyzed. Based on the requirements of the cabin connection application sealing capacity≤10^-10 （Pa·m³）/s and ambient temperature -100 ℃ to 100 ℃, the structure of a multi-core through-cabin fiber connector with double gold tin fiber sealing structure is analyzed. The sealing reliability of the multi-core through-cabin fiber connector is ensured by structural design such as pressure degradation, temperature buffer and low stress structure. Through solder selection, CTE matching analysis, secondary sealing process validation analysis, and small batch trial production, research on the metallization fiiber optic welding sealing process is performed to validate the rationality and stability of the double-gold tin fiber seal structure design and process technology. The experimental valudation demonstrates that the optical performance and sealing leakage rate of the multi-core optical fiber connector are qualified after the extreme space environment tests such as temperature shock, rapid pressure drop and high strength sinusoidal vibration. This confirms that the optical fiber connector using double gold tin optical fiber sealing technology can meet the long-term requirements of manned spacecraft in orbit and has good reliability.

Abstract:With the gradual improvement of the intelligent level of the launch vehicle, in order to realize the load monitoring, fault diagnosis and disposal of the flight status of the launch vehicle, it is necessary to carry out fast and real-time calculation of the dangerous section load, that is, the lightweight calculation of the load. First, by combining theoretical formulas and proxy models, a lightweight calculation method is proposed to improve computational efficiency while ensuring computational accuracy. Secondly, the calculation process is re-planned according to the calculation amount, and the sections are classified. Next, for aerodynamic and control loads, the load calculation formula is derived based on the d’Alembert principle to ensure the accuracy of the calculation, and the calculation amount is reduced by pre-calculating the constant-value parameters related to the configuration. Lastly, for the sloshing load, a polynomial proxy model is built based on the simulation data, and the sloshing load is predicted through the proxy model to further reduce the calculation amount. The research results indicate that the proposed lightweight method can significantly improve computational efficiency. By combining the load variation patterns of different flight time periods and selecting the number of calculation sections reasonably, the calculation time can be controlled at the millisecond level. Compared with the finite element method, the calculation deviation of different types of sections is less than 5%. This method has important application value for multidisciplinary simulation and real-time calculation of flight load.

Abstract:To solve the problem of scattering and dose-rate setting for on-site calibration of fixed X, gamma radiation dosimeter, and to ensure the safe and stable operation of nuclear facilities, a portable X-ray irradiation device and self-shielded X-ray irradiation device were established based on X-ray machine. Based on Monte Carlo simulation, machine learning algorithms and experimental methods, the scattering radiation correction of X-ray radiation field was completed and the radiation field dose rate setting technology was studied. Firstly, MCNP was used to simulate the radiation characteristics of portable X-ray reference radiation field and self-shielded X-ray reference radiation field, such as uniformity, scattered radiation and energy spectrum distribution. The obtained results were compared with experimental data to validate the effectiveness of the numerical simulation method and the feasibility of applying the device to field calibration. Secondly, an ambient scattered radiation correction system for on-site calibration was built based on machine learning algorithm, and the mean absolute error (MAE) was used to evaluate the performance of the machine learning model. Finally, on-site calibration experiments were carried out by combining the irradiation device and secondary ionization chamber with the ambient scattered radiation correction system built. The results show that both the portable X-ray irradiation device and self-shielded X-ray irradiation device can provide reference radiation field meeting the requirements of GB/T 12162.1―2000. The relative error of calibration factor and laboratory calibration factor of fixed X, gamma radiation dosimeter calibration based on environmental scattered radiation correction system is less than 6.2%, meeting the requirements of on-site calibration work.

Abstract:To solve the problem of consensus control of industrial cyber-physical systems (ICPS) composed of multiple controlled objects under denial of service (DoS) attack and uncertain external disturbance, optimal H∞ consensus controller is designed by combining multi-agent with ICPS and employing state observer and zero-sum game methods. Firstly, for the ICPS composed of multiple controlled objects, a fixed topology multi-agent ICPS model is constructed based on the information interaction characteristics of agents. Secondly, considering the effects of DoS attacks causing communication channel blockages when acting on sensor channels and actuator channels, the state observer is used to reconstruct the blocked state information, and the multi-agent ICPS model under DoS attack based on the state observer is constructed. Then, the interference suppression level is introduced, and the controller and the disturber are considered as participants in a zero-sum game. Based on the optimization objective of interference suppression level and the condition of system H∞ performance, an optimal design framework based on zero-sum game is constructed. Finally, by imposing constraints on disturbance suppression levels and solving linear matrix inequalities that satisfy H∞ consensus, an optimal consensus controller is designed. Using the control problem of four uninterruptible power supplies as a simulation object, Matlab simulation results demonstrate that compared to design methods without state reconstruction and parameter constraints, the proposed approach achieves shorter consensus time for multi-agent ICPS under dual-channel DoS attacks and exhibits good relative stability.

Abstract:To improve the wind speed prediction accuracy of large-scale wind farm clusters and ensure the safe and stable operation of China’s power grid, a short-term wind speed hybrid prediction model for wind farm groups based on particle swarm optimization combined with projection pursuit clustering and NS-L-Transformer was proposed. Firstly, the collected wind speed dataset was processed by the methods of variational mode decomposition, depseudo-component removal and wavelet transform, and the wind speed dataset after filtering out noise interference was obtained. Secondly, considering the spatial correlation characteristics of wind speed among wind farm groups, according to the wind speed fluctuation characteristics, the particle swarm optimization based on projection pursuit clustering algorithm was used to analyze the spatial correlation between wind farm groups. Using the evaluation metrics obtained from the algorithm, an optimal classification of field group correlation was carried out based on their spatial correlations, and the classified high-dimensional wind speed dataset was constructed. Finally, the self-attention mechanism of the Transformer model combined with the gating unit mechanism of the LSTM model captured the local characteristics of the wind speed time series, and the NS-L-Transformer model was proposed to predict the wind speed of the constructed high-dimensional wind speed dataset with local characteristics. The wind speed data of a wind farm group in southeast China was selected for simulation analysis, and the results show that the prediction accuracy of wind speed prediction using the classified high-dimensional dataset is greatly improved compared with that of the single wind speed dataset. Furthermore, compared with the Transformer model, the NS-L-Transformer model exhibits reduced prediction errors, which validates the effectiveness of the hybrid prediction model proposed in this paper.

Abstract:To address the issue of class imbalance in datasets, a natural neighborhood hypersphere oversampling method (NNHOS) for high performance classification of imbalanced data sets is proposed in this paper. First, for each sample point in the imbalanced data sets, its natural neighbors are searched until a stable natural neighborhood is formed. Then, based on the label characteristics of the natural neighbors of each sample point, all sample points are classified into five regions: outliers, noise points, safe points of the majority class, safe points of the minority class, and boundary points of the minority class. Subsequently, a hypersphere is constructed for each boundary point of the minority class. At the same time, the small hyperspheres that are completely within the large hypersphere are merged to form a set of hyperspheres. Finally, to achieve a balanced data set, each hypersphere is adaptively assigned a sampling ratio based on the hypersphere radius and a specified number of new sample points are generated within each hypersphere. The results indicate that this method utilizes oversampling on synthetic and real datasets to generate a new sample set. Experiments are conducted using the CART, SVM, and KNN classifiers, and compared with eight other commonly used methods. Additionally, AUC, F₁, and G_m are used as evaluation metrics to further demonstrate that this method can more effectively classify imbalanced datasets.

Abstract:To fulfill the demands of achieving low-noise and high-precision detection of capacitive MEMS gyroscopes, this paper presents a fully differential sensing circuit based on a switched capacitor structure. The design consists of two parts: capacitance detection and digital quantization processing. The discrete-time capacitor to voltage (C/V) conversion scheme is used for capacitance detection. The method of combining high frequency carrier modulation and correlated double sampling technique is proposed to reduce the low frequency noise. The quantization circuit adopts a bandpass ΔΣ modulator with a 4th-order, 4-bit single-loop feedforward structure. The input signal feedforward pathway is introduced to improve the stability of the system. The internal multi-bit quantizer is used to improve the signal-to-noise ratio. The system achieves high precision with low power consumption. Based on the 0.18 μm BCD process, the overall circuit is simulated and verified under a 5 V power supply. The simulation results demonstrate that the sensitivity of the detection circuit can reach 10 mV/fF, and the equivalent input capacitive noise is 0.062 aF/Hz at 5 kHz. The quantization accuracy of the readout signal can be up to 15 bits in the bandwidth range of 100 Hz. Compared with the traditional MEMS gyroscope sensing circuit, this circuit has lower noise and higher quantization accuracy. It is suitable for high-precision detection.

Abstract:In order to reduce the repeated disassembly and reassembly in aero-engine fuel nozzle assembly and improve the success rate of one assembly, a key part selective assembly method based on atomization performance prediction was proposed. First, based on the historical assembly data of nozzle, the nozzle geometric precision-atomization performance case library was constructed. Next, considering the impact of large fluctuations in sample space size and nozzle geometric accuracy, as well as poor consistency, the sample space was expanded by adaptive comprehensive oversampling method, and simultaneously the continuous attribute was discretized by improved K-means clustering algorithm. Finally, the correlation between geometric accuracy and atomization performance was established by association rule mining algorithm, and the accuracy of each rule was quantified by rule fitness evaluation method. Based on these association rule sets, the nozzle atomization performance prediction model was constructed to guide nozzle assembly. The research results show that the nozzle atomization performance prediction model proposed in this paper has the best prediction effect, with a prediction accuracy of up to 98.33%, compared with methods such as decision tree, support vector machine, and artificial neural network, which can effectively predict the atomization performance of different parts combination, thus reducing invalid assembly and improving the assembly efficiency of nozzle.

Abstract:This research tackles the challenge of action fluctuation in articulated vehicle trajectory tracking control, aiming to enhance both accuracy and smoothness. It introduces a novel approach: a smooth tracking control methodology grounded in reinforcement learning (RL). Firstly, to improve the control accuracy, we incorporate trajectory preview information as input to both the policy and value networks and establish a predictive policy iteration framework. Then, to ensure control smoothness, we employ the LipsNet network to approximate the policy function, to realize the adaptive restriction of the Lipschitz constant of the policy network. Finally, coupled with distributional RL theory, we formulate an articulated vehicle trajectory tracking control method, named smooth distributional soft actor-critic (SDSAC), focusing on achieving synergistic optimization of both control precision and action smoothness. The simulation results demonstrate that the proposed method can maintain good action smoothing ability under six different noise levels, and has strong noise robustness and high tracking accuracy. Compared with traditional value distribution reinforcement learning distributional soft actor-critic (DSAC), SDSAC improves action smoothness by more than 5.8 times under high noise conditions. In addition, compared with model predictive control, SDSAC’s average single-step solution speed is improved by about 60 times, and it has higher online computing efficiency.

Abstract:With the increasing density of maritime traffic, offshore wind turbines face the risk of collisions with vessels. To enhance the collision resistance performance of offshore wind turbines and maintain their safe and stable operation, as well as extend their service life, a novel biomimetic fractal-based protective device inspired by the lotus leaf vein structure is proposed for offshore wind turbines. Using LS-DYNA, a nonlinear finite element model is employed to simulate the collision process between the offshore wind turbine foundation and a vessel traveling at 2 m/s. The protective performance of the biomimetic fractal protective device is analyzed and compared with a conventional traditional protective device, including the system contact force, tower stress, stress in the high-stress zone of the column and foundation connection, and energy conversion. The simulation results show that compared to conventional protective devices, the biomimetic leaf vein structure can reduce the maximum contact force applied to the wind turbine, resulting in a smaller high-stress region in the contact area and a significant overall reduction in stress. Additionally, the biomimetic leaf vein structure enhances the protective capabilities of the device at the connection between the column and the foundation, reducing the maximum stress at the connection and significantly lowering the average stress. Compared to ordinary protective devices, the main protective material of the biomimetic fractal structure absorbs more kinetic energy while experiencing a smaller increase in internal energy, leading to greater energy dissipation and significantly improved energy absorption efficiency of the steel shell. Overall, the biomimetic fractal structure protective device demonstrates superior comprehensive collision resistance performance compared to traditional conventional protective devices.

Abstract:To improve the economy and robustness of building integrated photovoltaic (BIPV) energy system, and mitigate the negative impacts of uncertainty in photovoltaic output and electric heating and cooling loads on the economic and stable operation of BIPV energy system, this paper proposes an integrated prediction-configuration-scheduling strategy to achieve robust optimal scheduling for BIPV energy systems. Firstly, the correlation between photovoltaic output and electric heating and cooling loads is comprehensively considered by multi-task Gaussian process (MTGP), and the accurate source load prediction results and their probability information are obtained. On this basis, the MTGP-based source load uncertainty set is established. Secondly, the energy system structure is designed according to the building type and load requirements, and the economic optimal capacity allocation model is established and solved with the minimum annual investment cost and annual operational cost of the system as the objective function. Finally, the two-stage robust optimal scheduling model of BIPV energy system is established on the basis of determining the uncertainty set of source-load and equipment capacity allocation. The economic optimal scheduling scheme of the system is obtained by solving the model through column constraint generation algorithm and KKT condition. In this paper, the electric heating and cooling loads data of a teaching building in Arizona State University are used to validate the proposed optimization strategy. The simulation experimental results showed that the proposed optimization strategy can flexibly adjust the conservatism of the scheduling scheme by changing the uncertainty set confidence. Under considering the time-of-use electricity price, the energy storage devices give full play to the role of peak shaving and valley filling, which reduces the system operation cost. The proposed optimization strategy improves the economy and robustness of the scheduling scheme, compared with the traditional deterministic optimization strategy and the robust optimization strategy using box uncertainty sets.

Abstract:To improve the heating quality and efficiency of reclaimed asphalt pavement (RAP), reduce the installed power of microwave heating, and promote the application of ultra-high power microwave in hot recycling equipment, a microwave-hot air mixed heating method was proposed. Finite element analysis of microwave, hot air and microwave-low temperature hot air mixed heating demonstrated the advantages of microwave-low temperature hot air mixed heating over separate heating. The microwave-hot air hybrid heating process was analyzed, the multi-feeder microwave-hot air mixed heating chamber and antenna were designed, and the mixed heating finite element model was constructed on this basis. Factors affecting the mixing heating effect were simulated and analyzed, such as different heating heights, hot air temperatures, and hot air velocities. The heating effects of RAP in microwave heating, hot air heating and microwave-hot air mixed heating were compared and analyzed. The results show that the heating height of RAP can cause microwave interference in the mixed heating chamber and significantly affect the heating effect. Excessively high air temperature can lead to rapid surface scorching and aging of RAP. At heating distance of 50 mm and 100 mm, hybrid heating outperforms microwave or hot air heating alone, with efficiency improvements of 9.32% and 11.75%, respectively, compared to microwave heating, and surface heating efficiency enhancements of 124.7% and 92.9%, and underside heating efficiency improvements of 38.8% and 34.8%, respectively, compared to hot air heating. Additionally, uniformity is improved to varying degrees. This indicates that microwave-hot air mixed heating can significantly improve the heating efficiency of RAP, addressing the problems of low heating efficiency and poor uniformity of microwave heating alone. By combining the strengths of both methods, this approach ensures the heating quality of RAP, and provides a new method for the regeneration heating of RAP.

Abstract:The HPR1000 nuclear power unit in China utilizes a double-layer reinforced concrete containment and incorporates a safety design concept that combines both active and passive safety measures. This design significantly enhances the safety of the nuclear power system. However, the construction cost of HPR1000 is considerably higher compared to second-generation units, thereby impacting its economic competitiveness. In order to further improve the safety of nuclear power and to solve the contradiction between safety and economy, this paper proposes a new type of passive containment accident mitigation scheme based on HPR1000. A multifunctional pool (MP), which employs the condensation of steam jet injection in submerged condition to absorb the steam produced during a containment accident, serving as a means of pressure suppression. Furthermore, the water sources for the safety injection system, reactor cavity injection system, and core exchange system are all consolidated within the pool, resulting in significant simplification of the systems and equipment of the nuclear power unit. The performance of the scheme against large break loss-of-coolant accident (LBLOCA) is evaluated by the critical incident analysis program simulation. The results show that by appropriately compensating the volume of air space within the MP, it is possible to effectively impede the increase in pressure within the shell. Compared with HPR1000, the containment size can be reduced by nearly 47% and the total water capacity of the containment system is reduced by about 1 700 m³ while maintauning safety performance. The MP can store non-condensable gases in the containment after the accident, enhancing the heat transfer performance of the passive containment heat removal system (PCS). The core is adequately cooled, and the fuel cladding’s outer surface reaches a maximal temperature of 1 389 K, which is below the embrittlement failure temperature of 1 477 K, ensuring the integrity of the reactor core’s.

Abstract:To understand ship roll mechanisms and explore effects of forward speed on roll damping, roll free decay process of the DTMB5512 ship model was simulated. The reliability of different roll motion analysis methods was also evaluated according to short-term prediction results. By employing overset grid technology in computational fluid dynamics (CFD) and releasing roll, sway and heave degrees of freedom, roll free decay curves at various speeds and initial heel angles were obtained. The simulated roll amplitudes and periods were in good agreement with experimental results, confirming the reliability of the numerical method. Linear and quadratic roll damping coefficients were derived from fitting of roll free decay curves. Furthermore, roll single significant values were predicted at various sea states based on potential theory. The results indicate that as the Froude number (Fn) increases from 0.138 to 0.410, the roll decay rate significantly accelerates, accompanied by an enhanced wave elevation around the hull. At the same speed, with initial heel angle increasing from 10° to 20°, there shows no obvious differences in linear damping coefficients. For the same initial heel angle, higher speeds corresponds to larger linear damping coefficients. The equivalent damping is derived from combination of linear and quadratic damping at corresponding roll amplitude, which can effectively weaken the interference from roll extinction curve fitting. According to short-term roll prediction results, for speeds corresponding to Fn=0.138 and Fn=0.280, roll damping coefficients obtained from CFD method are both more accurate than that from empirical formula method, especially obvious at higher sea states. The equivalent damping coefficients are more suitable for quantitative analysis of roll motion, increasing with speed.