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901.
着重分析了减损与延寿控制律的综合过程,提出其基于多目标优化方法的理论基础。在建立某型液体火箭发动机系统动力学模型、关键部件之涡轮叶片的结构分析模型及其材料损伤模型的基础上,分析了应用非线性规划法求解减损与延寿控制律的过程。对发动机起动过程实施减损与延寿控制,结果表明在系统性能略微损失的情况下,可以较大幅度地减小涡轮叶片的损伤,从而达到延长发动机工作寿命的目的。 相似文献
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906.
In order to study the influences of confining pressure and strain rate on the mechanical properties of the Nitrate Ester Plasticized Polyether (NEPE) propellant, uniaxial tensile tests were conducted using the self-made confining pressure system and material testing machine. The stress-strain responses of the NEPE propellant under different confining pressure conditions and strain rates were obtained and analyzed. The results show that confining pressure and strain rate have a remarkably influence on the mechanical responses of the NEPE propellant. As confining pressure increases (from 0 to 5.4 MPa), the maximum tensile stress and ultimate strain increase gradually. With the coupled effects of confining pressure and strain rate, the value of the maximum tensile stress and ultimate strain at 5.4 MPa and 0.0667 s−1 is 2.03 times and 2.19 times of their values under 0 MPa and 0.00333 s−1, respectively. Afterwards, the influence mechanism of confining pressure on the NEPE propellant was analyzed. Finally, based on the viscoelastic theory and continuous damage theory, a nonlinear constitutive model considering confining pressure and strain rate was developed. The damage was considered to be rate-dependent and pressure-dependent. The constitutive model was validated by comparing experimental data with predictions of the constitutive model. The whole maximum stress errors of the model predictions are lower than 4% and the corresponding strain errors are lower than 7%. The results show that confining pressure can suppress the damage initiation and evolution of the NEPE propellant and the nonlinear constitutive model can describe the mechanical responses of the NEPE propellant under various confining pressure conditions and strain rates. This research can lay a theoretical foundation for analyzing the structural integrity of propellant grain accurately under working pressure loading. 相似文献
907.
Tracking maneuvering target in real time autonomously and accurately in an uncertain environment is one of the challenging missions for unmanned aerial vehicles(UAVs).In this paper,aiming to address the control problem of maneuvering target tracking and obstacle avoidance,an online path planning approach for UAV is developed based on deep reinforcement learning.Through end-to-end learning powered by neural networks,the proposed approach can achieve the perception of the environment and continuous motion output control.This proposed approach includes:(1)A deep deterministic policy gradient(DDPG)-based control framework to provide learning and autonomous decision-making capa-bility for UAVs;(2)An improved method named MN-DDPG for introducing a type of mixed noises to assist UAV with exploring stochastic strategies for online optimal planning;and(3)An algorithm of task-decomposition and pre-training for efficient transfer learning to improve the generalization capability of UAV's control model built based on MN-DDPG.The experimental simulation results have verified that the proposed approach can achieve good self-adaptive adjustment of UAV's flight attitude in the tasks of maneuvering target tracking with a significant improvement in generalization capability and training efficiency of UAV tracking controller in uncertain environments. 相似文献
908.
As a main oxidizer in solid composite propellants, ammonium perchlorate (AP) plays an important role because its thermal decomposition behavior has a direct influence on the characteristic of solid com-posite propellants. To improve the performance of solid composite propellant, it is necessary to take measures to modify the thermal decomposition behavior of AP. In recent years, transition metal oxides and carbon-supported transition metal oxides have drawn considerable attention due to their extraor-dinary catalytic activity. In this review, we highlight strategies to enhance the thermal decomposition of AP by tuning morphology, varying the types of metal ion, and coupling with carbon analogue. The enhanced catalytic performance can be ascribed to synergistic effect, increased surface area, more exposed active sites, and accelerated electron transportation and so on. The mechanism of AP decom-position mixed with catalyst has also been briefly summarized. Finally, a conclusive outlook and possible research directions are suggested to address challenges such as lacking practical application in actual formulation of solid composite propellant and batch manufacturing. 相似文献
909.
Track tension is a major factor influencing the reliability of a track. In order to reduce the risk of track peel-off, it is necessary to keep track tension constant. However, it is difficult to measure the dynamic tension during off-road operation. Based on the analysis of the relation and external forces depending on free body diagrams of the idler, idler arm, road wheel and road arm, a theoretical estimation model of track tension is built. Comparing estimation results with multibody dynamics simulation results, the rationality of track tension monitor is validated. By the aid of this monitor, a track tension control system is designed, which includes a self-tuning proportional-integral-derivative (PID) controller based on radial basis function neural network, an electro-hydraulic servo system and an idler arm. The tightness of track can be adjusted by turning the idler arm. Simulation results of the vehicle starting process indicate that the controller can reach different expected tensions quickly and accurately. Compared with a traditional PID controller, the proposed controller has a stronger anti-disturbance ability by amending control pa-rameters online. 相似文献
910.
In this paper, based on a bidirectional parallel multi-branch feature pyramid network (BPMFPN), a novel one-stage object detector called BPMFPN Det is proposed for real-time detection of ground multi-scale targets by swarm unmanned aerial vehicles (UAVs). First, the bidirectional parallel multi-branch convolution modules are used to construct the feature pyramid to enhance the feature expression abilities of different scale feature layers. Next, the feature pyramid is integrated into the single-stage object detection framework to ensure real-time performance. In order to validate the effectiveness of the proposed algorithm, experiments are conducted on four datasets. For the PASCAL VOC dataset, the proposed algorithm achieves the mean average precision (mAP) of 85.4 on the VOC 2007 test set. With regard to the detection in optical remote sensing (DIOR) dataset, the proposed algorithm achieves 73.9 mAP. For vehicle detection in aerial imagery (VEDAI) dataset, the detection accuracy of small land vehicle (slv) targets reaches 97.4 mAP. For unmanned aerial vehicle detection and tracking (UAVDT) dataset, the proposed BPMFPN Det achieves the mAP of 48.75. Compared with the previous state-of-the-art methods, the results obtained by the proposed algorithm are more competitive. The experimental results demonstrate that the proposed algorithm can effectively solve the problem of real-time detection of ground multi-scale targets in aerial images of swarm UAVs. 相似文献