The rapid prediction of aerodynamic performance is critical in the conceptual and preliminary design of hypersonic vehicles. This study focused on axisymmetric body configurations commonly used in such vehicles and pr...The rapid prediction of aerodynamic performance is critical in the conceptual and preliminary design of hypersonic vehicles. This study focused on axisymmetric body configurations commonly used in such vehicles and proposed a multi-fidelity neural network (MFNN) framework to fuse aerodynamic data of varying quality. A data-driven prediction model was constructed using a pointwise modeling method based on generating lines to input geometric features into the network. The MFNN framework combined low-fidelity and high-fidelity networks, trained on aerodynamic performance data from engineering rapid computation methods and CFD, respectively, using spherically blunted cones as examples. The results showed that the MFNN effectively integrated multi-fidelity data, achieving prediction accuracy close to CFD results in most regions, with errors under 5% in key stagnation areas. The model demonstrated strong generalization capabilities for varying cone dimensions and flight conditions. Furthermore, it significantly reduced dependence on high-fidelity data, enabling efficient aerodynamic performance predictions with limited datasets. This study provides a novel methodology for rapid aerodynamic performance prediction, offering both accuracy and efficiency, and contributes to the design of hypersonic vehicles.展开更多
In this paper,a data-driven method to model the three-dimensional engineering structure under the cyclic load with the one-dimensional stress-strain data is proposed.In this method,one-dimensional stress-strain data o...In this paper,a data-driven method to model the three-dimensional engineering structure under the cyclic load with the one-dimensional stress-strain data is proposed.In this method,one-dimensional stress-strain data obtained under uniaxial load and different loading history is learned offline by gate recurrent unit(GRU)network.The learned constitutive model is embedded into the general finite element framework through data expansion from one dimension to three dimensions,which can perform stress updates under the three-dimensional setting.The proposed method is then adopted to drive numerical solutions of boundary value problems for engineering structures.Compared with direct numerical simulations using the J2 plasticity model,the stress-strain response of beam structure with elastoplastic materials under forward loading,reverse loading and cyclic loading were predicted accurately.Loading path dependent response of structure was captured and the effectiveness of the proposed method is verified.The shortcomings of the proposed method are also discussed.展开更多
水泥生产立磨出风口温度是判断立磨运行状态是否安全稳定的关键参数,对该参数提前预测可以减少立磨振动,提高运行稳定性,增加产量,降低能耗及相关碳排放。水泥立磨系统具有多参数、大时滞和非线性等复杂特性。针对上述问题,提出了基于...水泥生产立磨出风口温度是判断立磨运行状态是否安全稳定的关键参数,对该参数提前预测可以减少立磨振动,提高运行稳定性,增加产量,降低能耗及相关碳排放。水泥立磨系统具有多参数、大时滞和非线性等复杂特性。针对上述问题,提出了基于互相关延时分析优化的非线性自回归外部输入(Nonlinear AutoRegressive with eXogenous inputs,NARX)神经网络,并用于立磨出风口温度预测。首先,采用皮尔逊相关性分析从多个参数中确定影响立磨出风口温度的关键参数。同时,利用互相关延时分析进行时滞分析,解决大时滞问题。其次,通过优化的NARX神经网络,实现非线性工况下温度的精准预测。案例验证结果表明,所提出模型的拟合度达到了0.99967,均方误差为0.56483,预测精度达到了98.4%以上。预测模型结果可指导立磨操作人员及时控制立磨振动,提高水泥产量并降低能耗和碳排放。展开更多
文摘The rapid prediction of aerodynamic performance is critical in the conceptual and preliminary design of hypersonic vehicles. This study focused on axisymmetric body configurations commonly used in such vehicles and proposed a multi-fidelity neural network (MFNN) framework to fuse aerodynamic data of varying quality. A data-driven prediction model was constructed using a pointwise modeling method based on generating lines to input geometric features into the network. The MFNN framework combined low-fidelity and high-fidelity networks, trained on aerodynamic performance data from engineering rapid computation methods and CFD, respectively, using spherically blunted cones as examples. The results showed that the MFNN effectively integrated multi-fidelity data, achieving prediction accuracy close to CFD results in most regions, with errors under 5% in key stagnation areas. The model demonstrated strong generalization capabilities for varying cone dimensions and flight conditions. Furthermore, it significantly reduced dependence on high-fidelity data, enabling efficient aerodynamic performance predictions with limited datasets. This study provides a novel methodology for rapid aerodynamic performance prediction, offering both accuracy and efficiency, and contributes to the design of hypersonic vehicles.
文摘In this paper,a data-driven method to model the three-dimensional engineering structure under the cyclic load with the one-dimensional stress-strain data is proposed.In this method,one-dimensional stress-strain data obtained under uniaxial load and different loading history is learned offline by gate recurrent unit(GRU)network.The learned constitutive model is embedded into the general finite element framework through data expansion from one dimension to three dimensions,which can perform stress updates under the three-dimensional setting.The proposed method is then adopted to drive numerical solutions of boundary value problems for engineering structures.Compared with direct numerical simulations using the J2 plasticity model,the stress-strain response of beam structure with elastoplastic materials under forward loading,reverse loading and cyclic loading were predicted accurately.Loading path dependent response of structure was captured and the effectiveness of the proposed method is verified.The shortcomings of the proposed method are also discussed.
文摘水泥生产立磨出风口温度是判断立磨运行状态是否安全稳定的关键参数,对该参数提前预测可以减少立磨振动,提高运行稳定性,增加产量,降低能耗及相关碳排放。水泥立磨系统具有多参数、大时滞和非线性等复杂特性。针对上述问题,提出了基于互相关延时分析优化的非线性自回归外部输入(Nonlinear AutoRegressive with eXogenous inputs,NARX)神经网络,并用于立磨出风口温度预测。首先,采用皮尔逊相关性分析从多个参数中确定影响立磨出风口温度的关键参数。同时,利用互相关延时分析进行时滞分析,解决大时滞问题。其次,通过优化的NARX神经网络,实现非线性工况下温度的精准预测。案例验证结果表明,所提出模型的拟合度达到了0.99967,均方误差为0.56483,预测精度达到了98.4%以上。预测模型结果可指导立磨操作人员及时控制立磨振动,提高水泥产量并降低能耗和碳排放。
