The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,th...The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,this study proposes an intelligent decision-making framework based on a deep long short-term memory Q-network.This framework transforms the real-time sequencing for bolter recovery problem into a partially observable Markov decision process.It employs a stacked long shortterm memory network to accurately capture the long-range temporal dependencies of bolter event chains and fuel consumption.Furthermore,it integrates a prioritized experience replay training mechanism to construct a safe and adaptive scheduling system capable of millisecond-level real-time decision-making.Experimental demonstrates that,within large-scale mass recovery scenarios,the framework achieves zero safety violations in static environments and maintains a fuel safety violation rate below 10%in dynamic scenarios,with single-step decision times at the millisecond level.The model exhibits strong generalization capability,effectively responding to unforeseen emergent situations—such as multiple bolters and fuel emergencies—without requiring retraining.This provides robust support for efficient carrier-based aircraft recovery operations.展开更多
Shape memory polymers used in 4D printing only had one permanent shape after molding,which limited their applications in requiring multiple reconstructions and multifunctional shapes.Furthermore,the inherent stability...Shape memory polymers used in 4D printing only had one permanent shape after molding,which limited their applications in requiring multiple reconstructions and multifunctional shapes.Furthermore,the inherent stability of the triazine ring structure within cyanate ester(CE)crosslinked networks after molding posed significant challenges for both recycling,repairing,and degradation of resin.To address these obstacles,dynamic thiocyanate ester(TCE)bonds and photocurable group were incorporated into CE,obtaining the recyclable and 3D printable CE covalent adaptable networks(CANs),denoted as PTCE1.5.This material exhibits a Young's modulus of 810 MPa and a tensile strength of 50.8 MPa.Notably,damaged printed PTCE1.5 objects can be readily repaired through reprinting and interface rejoining by thermal treatment.Leveraging the solid-state plasticity,PTCE1.5 also demonstrated attractive shape memory ability and permanent shape reconfigurability,enabling its reconfigurable 4D printing.The printed PTCE1.5 hinges and a main body were assembled into a deployable and retractable satellite model,validating its potential application as a controllable component in the aerospace field.Moreover,printed PTCE1.5 can be fully degraded into thiol-modified intermediate products.Overall,this material not only enriches the application range of CE resin,but also provides a reliable approach to addressing environmental issue.展开更多
针对目前基于正交时频空调制系统中使用脉冲导频进行信道估计会引入高均峰比和信道估计困难的问题,提出了一种基于双向长短期记忆网络(Bidirectional Long Short-term Memory,BiLSTM)的信道估计算法。该算法首先将低功率导频序列放置于...针对目前基于正交时频空调制系统中使用脉冲导频进行信道估计会引入高均峰比和信道估计困难的问题,提出了一种基于双向长短期记忆网络(Bidirectional Long Short-term Memory,BiLSTM)的信道估计算法。该算法首先将低功率导频序列放置于时频域中,再对发送信号添加功率放大器的非线性衰变模型,对接收信号导频部分的接收信号使用最小二乘法进行粗略信道的计算,之后通过BiLSTM结合卷积块注意力模块对完整信道进行拟合估计,从而得到时频域的完整信道矩阵。为进一步减少误差,通过神经网络对完整信道矩阵进行修正处理。仿真结果表明,该算法求得非线性衰变影响信号的信道矩阵的归一化均方误差提升了3~15 dB,均峰比下降了5~6 dB,误比特率性能提升3~8 dB。展开更多
开展公路隧道结构状态精准预测是掌握隧道结构状态变化、识别潜在安全风险和保障安全运营的重要技术手段。针对隧道监控量测测点的空间分布与时序特性,提出了一种基于河马优化(Hippopotamus Optimization, HO)算法和卷积神经网络(Convol...开展公路隧道结构状态精准预测是掌握隧道结构状态变化、识别潜在安全风险和保障安全运营的重要技术手段。针对隧道监控量测测点的空间分布与时序特性,提出了一种基于河马优化(Hippopotamus Optimization, HO)算法和卷积神经网络(Convolutional Neural Network, CNN)的双向长短期记忆(Bidirectional Long Short Term Memory, BiLSTM)网络公路隧道结构状态预测方法。量化分析测点间关联性,结合温度特征构建模型输入矩阵;利用CNN挖掘各测点的空间关联性,采用BiLSTM提取时间序列特征,引入HO算法优化模型参数;将预测结果映射为隧道结构状态等级,展示隧道整体受力状态。结果表明,建立的HO-CNN-BiLSTM模型能够有效提取空间和温度特征,在预测精度和稳定性方面均优于对比模型,可实现隧道结构状态精确评估,为公路隧道的安全运营及分级管控措施制定提供技术支撑。展开更多
针对变压器故障诊断存在诊断精度不高和麻雀搜索算法(sparrow search algorithm,SSA)存在易陷入局部最优的问题,提出了一种基于融合正余弦和柯西变异的麻雀搜索算法(sine-cosine and Cauchy mutation sparrow search algorithm,SCSSA)...针对变压器故障诊断存在诊断精度不高和麻雀搜索算法(sparrow search algorithm,SSA)存在易陷入局部最优的问题,提出了一种基于融合正余弦和柯西变异的麻雀搜索算法(sine-cosine and Cauchy mutation sparrow search algorithm,SCSSA)优化双向长短期记忆网络(bi-directional long-short term memory,BiLSTM)的变压器故障诊断模型。首先,基于油中溶解气体分析(dissolved gas analysis,DGA)法,以5种特征量作为输入,其次利用正余弦策略和柯西变异策略对麻雀算法进行改进,然后将SCSSA算法、SSA算法和灰狼优化算法(grey wolf optimizer,GWO)在4种测试函数上进行性能对比,验证了SCSSA算法的优越性。最后利用SCSSA算法对BiLSTM网络中的参数进行优化,从而提高BiLSTM网络在变压器故障诊断中的性能。实验结果表明,所提SCSSA-BiLSTM故障诊断模型的综合诊断精度为95.1%,相比于SSA-BiLSTM、GWO-BiLSTM、BiLSTM和LSTM模型分别提高了7.3%、12.2%、14.6%、19.5%,并且SCSSA-BiLSTM模型有着更好的鲁棒性。展开更多
The constitutive models of shape memory alloys(SMAs)play an important role in facilitating the widespread application of such types of alloys in various engineering fields.However,to accurately describe the deformatio...The constitutive models of shape memory alloys(SMAs)play an important role in facilitating the widespread application of such types of alloys in various engineering fields.However,to accurately describe the deformation behaviors of SMAs,the concepts in classical plasticity are employed in the existing constitutive models,and a series of complex mathematical equations are involved.Such complexity brings inconvenience for the construction,implementation,and application of the constitutive models.To overcome these shortcomings,a data-driven constitutive model of SMAs is developed in this work based on the artificial neural network(ANN).In the proposed model,the components of the strain tensor in principal space,ambient temperature,and the maximum equivalent strain in the deformation history from the initial state to the current loading state are chosen as the input features,and the components of the stress tensor in principal space are set as the output.The proposed ANN-based constitutive model is implemented into the finite element program ABAQUS by deriving its consistent tangent modulus and writing a user-defined material subroutine.The stress-strain responses of SMA material under various loading paths and at different ambient temperatures are used to train the ANN model,which is generated from the existing constitutive model(numerical experiments).To validate the capability of the proposed model,the predicted stress-strain responses of SMA material,and the global and local responses of two typical SMA structures are compared with the corresponding numerical experiments.This work demonstrates a good potential to obtain the constitutive model of SMAs by pure data and avoid the need for vast stores of knowledge for the construction of constitutive models.展开更多
Complicated loads encountered by floating offshore wind turbines(FOWTs)in real sea conditions are crucial for future optimization of design,but obtaining data on them directly poses a challenge.To address this issue,w...Complicated loads encountered by floating offshore wind turbines(FOWTs)in real sea conditions are crucial for future optimization of design,but obtaining data on them directly poses a challenge.To address this issue,we applied machine learning techniques to obtain hydrodynamic and aerodynamic loads of FOWTs by measuring platform motion responses and wave-elevation sequences.First,a computational fluid dynamics(CFD)simulation model of the floating platform was established based on the dynamic fluid body interaction technique and overset grid technology.Then,a long short-term memory(LSTM)neural network model was constructed and trained to learn the nonlinear relationship between the waves,platform-motion inputs,and hydrodynamic-load outputs.The optimal model was determined after analyzing the sensitivity of parameters such as sample characteristics,network layers,and neuron numbers.Subsequently,the effectiveness of the hydrodynamic load model was validated under different simulation conditions,and the aerodynamic load calculation was completed based on the D'Alembert principle.Finally,we built a hybrid-scale FOWT model,based on the software in the loop strategy,in which the wind turbine was replaced by an actuation system.Model tests were carried out in a wave basin and the results demonstrated that the root mean square errors of the hydrodynamic and aerodynamic load measurements were 4.20%and 10.68%,respectively.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.62403486)。
文摘The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,this study proposes an intelligent decision-making framework based on a deep long short-term memory Q-network.This framework transforms the real-time sequencing for bolter recovery problem into a partially observable Markov decision process.It employs a stacked long shortterm memory network to accurately capture the long-range temporal dependencies of bolter event chains and fuel consumption.Furthermore,it integrates a prioritized experience replay training mechanism to construct a safe and adaptive scheduling system capable of millisecond-level real-time decision-making.Experimental demonstrates that,within large-scale mass recovery scenarios,the framework achieves zero safety violations in static environments and maintains a fuel safety violation rate below 10%in dynamic scenarios,with single-step decision times at the millisecond level.The model exhibits strong generalization capability,effectively responding to unforeseen emergent situations—such as multiple bolters and fuel emergencies—without requiring retraining.This provides robust support for efficient carrier-based aircraft recovery operations.
基金supported by the National Natural Science Foundation of China(Nos.52473080,52403167 and 52173079)the Fundamental Research Funds for the Central Universities(Nos.xtr052023001 and xzy012023037)+1 种基金the Postdoctoral Research Project of Shaanxi Province(No.2024BSHSDZZ054)the Shaanxi Laboratory of Advanced Materials(No.2024ZY-JCYJ-04-12).
文摘Shape memory polymers used in 4D printing only had one permanent shape after molding,which limited their applications in requiring multiple reconstructions and multifunctional shapes.Furthermore,the inherent stability of the triazine ring structure within cyanate ester(CE)crosslinked networks after molding posed significant challenges for both recycling,repairing,and degradation of resin.To address these obstacles,dynamic thiocyanate ester(TCE)bonds and photocurable group were incorporated into CE,obtaining the recyclable and 3D printable CE covalent adaptable networks(CANs),denoted as PTCE1.5.This material exhibits a Young's modulus of 810 MPa and a tensile strength of 50.8 MPa.Notably,damaged printed PTCE1.5 objects can be readily repaired through reprinting and interface rejoining by thermal treatment.Leveraging the solid-state plasticity,PTCE1.5 also demonstrated attractive shape memory ability and permanent shape reconfigurability,enabling its reconfigurable 4D printing.The printed PTCE1.5 hinges and a main body were assembled into a deployable and retractable satellite model,validating its potential application as a controllable component in the aerospace field.Moreover,printed PTCE1.5 can be fully degraded into thiol-modified intermediate products.Overall,this material not only enriches the application range of CE resin,but also provides a reliable approach to addressing environmental issue.
文摘针对不同磁密幅值、频率、谐波组合等复杂激励工况下磁致伸缩建模面临的精准性问题,该文利用空间注意力机制(spatial attention mechanism,SAM)对传统的卷积神经网络(convolutional neural network,CNN)进行改进,将SAM嵌套入CNN网络中,建立SAMCNN改进型网络。再结合双向长短期记忆(bidirectional long short-term memory,BiLSTM)网络,提出电工钢片SAMCNN-BiLSTM磁致伸缩模型。首先,利用灰狼优化算法(grey wolf optimization,GWO)寻优神经网络结构的参数,实现复杂工况下磁致伸缩效应的准确表征;然后,建立中低频范围单频与叠加谐波激励等复杂工况下的磁致伸缩应变数据库,开展数据预处理与特征分析;最后,对SAMCNN-BiLSTM模型开展对比验证。对比叠加3次谐波激励下的磁致伸缩应变频谱主要分量,SAMCNN-BiLSTM模型计算值最大相对误差为3.70%,其比Jiles-Atherton-Sablik(J-A-S)、二次畴转等模型能更精确地表征电工钢片的磁致伸缩效应。
文摘开展公路隧道结构状态精准预测是掌握隧道结构状态变化、识别潜在安全风险和保障安全运营的重要技术手段。针对隧道监控量测测点的空间分布与时序特性,提出了一种基于河马优化(Hippopotamus Optimization, HO)算法和卷积神经网络(Convolutional Neural Network, CNN)的双向长短期记忆(Bidirectional Long Short Term Memory, BiLSTM)网络公路隧道结构状态预测方法。量化分析测点间关联性,结合温度特征构建模型输入矩阵;利用CNN挖掘各测点的空间关联性,采用BiLSTM提取时间序列特征,引入HO算法优化模型参数;将预测结果映射为隧道结构状态等级,展示隧道整体受力状态。结果表明,建立的HO-CNN-BiLSTM模型能够有效提取空间和温度特征,在预测精度和稳定性方面均优于对比模型,可实现隧道结构状态精确评估,为公路隧道的安全运营及分级管控措施制定提供技术支撑。
文摘针对变压器故障诊断存在诊断精度不高和麻雀搜索算法(sparrow search algorithm,SSA)存在易陷入局部最优的问题,提出了一种基于融合正余弦和柯西变异的麻雀搜索算法(sine-cosine and Cauchy mutation sparrow search algorithm,SCSSA)优化双向长短期记忆网络(bi-directional long-short term memory,BiLSTM)的变压器故障诊断模型。首先,基于油中溶解气体分析(dissolved gas analysis,DGA)法,以5种特征量作为输入,其次利用正余弦策略和柯西变异策略对麻雀算法进行改进,然后将SCSSA算法、SSA算法和灰狼优化算法(grey wolf optimizer,GWO)在4种测试函数上进行性能对比,验证了SCSSA算法的优越性。最后利用SCSSA算法对BiLSTM网络中的参数进行优化,从而提高BiLSTM网络在变压器故障诊断中的性能。实验结果表明,所提SCSSA-BiLSTM故障诊断模型的综合诊断精度为95.1%,相比于SSA-BiLSTM、GWO-BiLSTM、BiLSTM和LSTM模型分别提高了7.3%、12.2%、14.6%、19.5%,并且SCSSA-BiLSTM模型有着更好的鲁棒性。
基金supported by the National Natural Science Foundation of China(NSFC)(Grant No.12322203).
文摘The constitutive models of shape memory alloys(SMAs)play an important role in facilitating the widespread application of such types of alloys in various engineering fields.However,to accurately describe the deformation behaviors of SMAs,the concepts in classical plasticity are employed in the existing constitutive models,and a series of complex mathematical equations are involved.Such complexity brings inconvenience for the construction,implementation,and application of the constitutive models.To overcome these shortcomings,a data-driven constitutive model of SMAs is developed in this work based on the artificial neural network(ANN).In the proposed model,the components of the strain tensor in principal space,ambient temperature,and the maximum equivalent strain in the deformation history from the initial state to the current loading state are chosen as the input features,and the components of the stress tensor in principal space are set as the output.The proposed ANN-based constitutive model is implemented into the finite element program ABAQUS by deriving its consistent tangent modulus and writing a user-defined material subroutine.The stress-strain responses of SMA material under various loading paths and at different ambient temperatures are used to train the ANN model,which is generated from the existing constitutive model(numerical experiments).To validate the capability of the proposed model,the predicted stress-strain responses of SMA material,and the global and local responses of two typical SMA structures are compared with the corresponding numerical experiments.This work demonstrates a good potential to obtain the constitutive model of SMAs by pure data and avoid the need for vast stores of knowledge for the construction of constitutive models.
基金This work is supported by the National Key Research and Development Program of China(No.2023YFB4203000)the National Natural Science Foundation of China(No.U22A20178)
文摘Complicated loads encountered by floating offshore wind turbines(FOWTs)in real sea conditions are crucial for future optimization of design,but obtaining data on them directly poses a challenge.To address this issue,we applied machine learning techniques to obtain hydrodynamic and aerodynamic loads of FOWTs by measuring platform motion responses and wave-elevation sequences.First,a computational fluid dynamics(CFD)simulation model of the floating platform was established based on the dynamic fluid body interaction technique and overset grid technology.Then,a long short-term memory(LSTM)neural network model was constructed and trained to learn the nonlinear relationship between the waves,platform-motion inputs,and hydrodynamic-load outputs.The optimal model was determined after analyzing the sensitivity of parameters such as sample characteristics,network layers,and neuron numbers.Subsequently,the effectiveness of the hydrodynamic load model was validated under different simulation conditions,and the aerodynamic load calculation was completed based on the D'Alembert principle.Finally,we built a hybrid-scale FOWT model,based on the software in the loop strategy,in which the wind turbine was replaced by an actuation system.Model tests were carried out in a wave basin and the results demonstrated that the root mean square errors of the hydrodynamic and aerodynamic load measurements were 4.20%and 10.68%,respectively.
文摘为提高高超声速滑翔飞行器(HGV)轨迹预测的精度,提出一种基于时域卷积网络(temporal convolutional network,TCN)和双向长短时记忆网络(bidirectional long short-term memory network,BiLSTM)结合的HGV轨迹预测方法.该方法利用TCN的因果膨胀卷积提取HGV轨迹多尺度动态特征,融合BiLSTM的双向循环机制挖掘轨迹长时依赖与上下文关联,通过全连接层将预测结果映射到样本空间.引入贝叶斯优化(Bayesian optimization,BO)与灰狼优化(grey wolf optimization,GWO)组合优化模式,实现了网络超参数的全局优化,据此建立了深度学习框架下的HGV轨迹预测模型.数值仿真结果表明,在训练完备条件下,建立的预测模型能够有效预测HGV未来时刻的位置状态,相较于4种对比模型,该预测模型的均方根误差平均降低62.10%,平均绝对误差平均降低61.66%.
