Thermal conductivity and mineral composition of flood basalt in Al Hashimiyya city were correlated. Representative thin sections were optically analyzed for their mineral constituents and micro fractures. Findings of ...Thermal conductivity and mineral composition of flood basalt in Al Hashimiyya city were correlated. Representative thin sections were optically analyzed for their mineral constituents and micro fractures. Findings of this study will contribute to a comprehensive understanding of the correlation between selected petrological characteristics of basalts and their heat conduction properties. It found that a 10% increase of opaque and ferromagnesian minerals volume in the studied basalts leads to a thermal conductivity increasing by 0.4 W•m−1•K−1. This may considerably contribute to provide an alternative to direct measurements of the thermal conductivity in Jordan basalts if a sufficient mineralogical data set is achievable. Thus, the prediction of thermal conductivity through modal mineral composition may become a significant feature for efficient geothermal system exploration in basaltic rocks. The results can be brought together into a petrophysical and hydrogeothermal model for better reservoir characterization. Such models will improve the assessment of the basalt’s suitability as a geothermal reservoir for cooling and heating utilizations.展开更多
Based on the characteristics of 3D bulk forming process, the arbitrary Lagrangian-Eulerian (ALE) formulation-based FEM is studied, and a prediction-correction ALE-based FEM is proposed which integrates the advantages ...Based on the characteristics of 3D bulk forming process, the arbitrary Lagrangian-Eulerian (ALE) formulation-based FEM is studied, and a prediction-correction ALE-based FEM is proposed which integrates the advantages of precisely predicting the boundary configuration of the deformed material, and of efficiently avoiding hexahedron remeshing processes. The key idea of the prediction-correction ALE FEM is elaborated in detail. Accordingly, the strategy of mesh quality control, one of the key enabling techniques for the 3D bulk forming process numerical simulation by the prediction-correction ALE FEM is carefully investigated, and the algorithm for hexahedral element refinement is formulated based on the mesh distortion energy.展开更多
The lower bound of maximum predictable time can be formulated into a constrained nonlinear opti- mization problem, and the traditional solutions to this problem are the filtering method and the conditional nonlinear o...The lower bound of maximum predictable time can be formulated into a constrained nonlinear opti- mization problem, and the traditional solutions to this problem are the filtering method and the conditional nonlinear optimal perturbation （CNOP） method. Usually, the CNOP method is implemented with the help of a gradient descent algorithm based on the adjoint method, which is named the ADJ-CNOP. However, with the increasing improvement of actual prediction models, more and more physical processes are taken into consideration in models in the form of parameterization, thus giving rise to the on–off switch problem, which tremendously affects the effectiveness of the conventional gradient descent algorithm based on the ad- joint method. In this study, we attempted to apply a genetic algorithm （GA） to the CNOP method, named GA-CNOP, to solve the predictability problems involving on–off switches. As the precision of the filtering method depends uniquely on the division of the constraint region, its results were taken as benchmarks, and a series of comparisons between the ADJ-CNOP and the GA-CNOP were performed for the modified Lorenz equation. Results show that the GA-CNOP can always determine the accurate lower bound of maximum predictable time, even in non-smooth cases, while the ADJ-CNOP, owing to the effect of on–off switches, often yields the incorrect lower bound of maximum predictable time. Therefore, in non-smooth cases, using GAs to solve predictability problems is more effective than using the conventional optimization algorithm based on gradients, as long as genetic operators in GAs are properly configured.展开更多
The frequency of heterogeneous nucleation during the solidification of Al-Sibinary alloy was estimated by comparing experimentally obtained macrostructures of castings withnumerically simulated ones. A molten alloy wa...The frequency of heterogeneous nucleation during the solidification of Al-Sibinary alloy was estimated by comparing experimentally obtained macrostructures of castings withnumerically simulated ones. A molten alloy was unidirectionally solidified from a water-cooledcopper chill in an adiabatic mold. The location of columnar to equiaxed transition (CET) in thesolidified alloy ingot was measured. A numerical simulation for grain structure formation based onthe Monte Carlo method was carried out, and the frequency of heterogeneous nucleation in the alloywas evaluated by producing similar structure with the experimental one. The frequency ofheterogeneous nucleation was expressed as a probabilistic function with an exponential form ofundercooling that deter-mines the probability of nucleation event in the simulation. The value ofthe exponent is regarded as the nucleation parameter. The nucleation parameter of Al-Si binary alloyvaried with initial Si content.展开更多
This paper presents the predictability of aluminium-manganese alloy exposure time based on its as-cast weight and corrosion rate in sea water environment. The validity of the derived model: α = 26.67γ + 0.55β?- 0.2...This paper presents the predictability of aluminium-manganese alloy exposure time based on its as-cast weight and corrosion rate in sea water environment. The validity of the derived model: α = 26.67γ + 0.55β?- 0.29 is rooted on the core expression: 0.0375α = γ + 0.0206β?- 0.0109 where both sides of the expression are correspondingly approximately equal. Statistical analysis of model-predicted and experimentally evaluated exposure time for each value of as-cast weight and alloy corrosion rate considered shows a standard error of 0.0017% & 0.0044% and 0.0140% & 0.0150% respectively. The depths of corrosion penetration (at increasing corrosion rate: 0.0104 - 0.0157 mm/yr) as predicted by derived model and obtained from experiment are 0.7208 × 10-4 & 1.0123 × 10-4 mm and 2.5460 × 10-4 & 1.8240 × 10-4 mm (at decreasing corrosion rate: 0.0157 - 0.0062 mm/yr) respectively. Deviational analysis indicates that the maxi- mum deviation of the model-predicted alloy exposure time from the corresponding experimental value is less than 10%.展开更多
Aluminum Metal Matrix Composites (MMCs) sought over other conventional materials in the field of aerospace, automotive and marine applications owing to their excellent improved properties. These materials are of much ...Aluminum Metal Matrix Composites (MMCs) sought over other conventional materials in the field of aerospace, automotive and marine applications owing to their excellent improved properties. These materials are of much interest to the researchers from few decades. These composites initially replaced Cast Iron and Bronze alloys but owing to their poor wear and seizure resistance, they were subjected to many experiments and the wear behavior of these composites were explored to a maximum extent and were reported by number of research scholars for the past 25 years. In this paper an attempt has been made to consolidate some of the aspects of mechanical and wear behavior of Al-MMCs and the prediction of the Mechanical and Tribological properties of Aluminum MMCs.展开更多
随着空中交通流量的增长,尾流间隔精细化、动态化缩减成为民航发展的一种趋势,研究尾流演化过程也成为民航领域关注的前沿科学问题。基于此,采用雷诺平均N-S方程方法研究了B737-800飞机有无融合式翼梢小翼对飞机尾涡的演化过程影响。利...随着空中交通流量的增长,尾流间隔精细化、动态化缩减成为民航发展的一种趋势,研究尾流演化过程也成为民航领域关注的前沿科学问题。基于此,采用雷诺平均N-S方程方法研究了B737-800飞机有无融合式翼梢小翼对飞机尾涡的演化过程影响。利用美国国家航空航天局(National Aeronautics and Space Administration,NASA)动态尾流系统中尾涡消散模型(aircraft vortex spacing system prediction algorithm,APA)计算了不同气象环境参数下有无小翼的尾涡环量变化。结果表明:融合式翼梢小翼可以分割翼尖涡,有效改变翼尖气流的流动特性,增大速度梯度,减小尾涡速度、尾涡能量集中程度和尾涡强度;不同大气湍流耗散率和大气层结稳定度下,小翼对尾涡强度的减小量不同。展开更多
In recent years,there has been a significant increase in the utilization of Al/SiC particulate composite materials in engineering fields,and the demand for accurate machining of such composite materials has grown acco...In recent years,there has been a significant increase in the utilization of Al/SiC particulate composite materials in engineering fields,and the demand for accurate machining of such composite materials has grown accordingly.In this paper,a feed-forward multi-layered artificial neural network(ANN)roughness prediction model,using the Levenberg-Marquardt backpropagation training algorithm,is proposed to investigate the mathematical relationship between cutting parameters and average surface roughness during milling Al/SiC particulate composite materials.Milling experiments were conducted on a computer numerical control(C N C)milling machine with polycrystalline diamond(PCD)tools to acquire data for training the ANN roughness prediction model.Four cutting parameters were considered in these experiments:cutting speed,depth of cut,feed rate,and volume fraction of SiC.These parameters were also used as inputs for the ANN roughness prediction model.The output of the model was the average surface roughness of the machined workpiece.A successfully trained ANN roughness prediction model could predict the corresponding average surface roughness based on given cutting parameters,with a 2.08%mea n relative error.Moreover,a roughness control model that could accurately determine the corresponding cutting parameters for a specific desired roughness with a 2.91%mean relative error was developed based on the ANN roughness prediction model.Finally,a more reliable and readable analysis of the influence of each parameter on roughness or the interaction between different parameters was conducted with the help of the ANN prediction model.展开更多
文摘Thermal conductivity and mineral composition of flood basalt in Al Hashimiyya city were correlated. Representative thin sections were optically analyzed for their mineral constituents and micro fractures. Findings of this study will contribute to a comprehensive understanding of the correlation between selected petrological characteristics of basalts and their heat conduction properties. It found that a 10% increase of opaque and ferromagnesian minerals volume in the studied basalts leads to a thermal conductivity increasing by 0.4 W•m−1•K−1. This may considerably contribute to provide an alternative to direct measurements of the thermal conductivity in Jordan basalts if a sufficient mineralogical data set is achievable. Thus, the prediction of thermal conductivity through modal mineral composition may become a significant feature for efficient geothermal system exploration in basaltic rocks. The results can be brought together into a petrophysical and hydrogeothermal model for better reservoir characterization. Such models will improve the assessment of the basalt’s suitability as a geothermal reservoir for cooling and heating utilizations.
基金the National Natural Science Foundation of China(No.50275094).
文摘Based on the characteristics of 3D bulk forming process, the arbitrary Lagrangian-Eulerian (ALE) formulation-based FEM is studied, and a prediction-correction ALE-based FEM is proposed which integrates the advantages of precisely predicting the boundary configuration of the deformed material, and of efficiently avoiding hexahedron remeshing processes. The key idea of the prediction-correction ALE FEM is elaborated in detail. Accordingly, the strategy of mesh quality control, one of the key enabling techniques for the 3D bulk forming process numerical simulation by the prediction-correction ALE FEM is carefully investigated, and the algorithm for hexahedral element refinement is formulated based on the mesh distortion energy.
基金supported bythe National Natural Science Foundation of China(Grant Nos40975063 and 40830955)
文摘The lower bound of maximum predictable time can be formulated into a constrained nonlinear opti- mization problem, and the traditional solutions to this problem are the filtering method and the conditional nonlinear optimal perturbation （CNOP） method. Usually, the CNOP method is implemented with the help of a gradient descent algorithm based on the adjoint method, which is named the ADJ-CNOP. However, with the increasing improvement of actual prediction models, more and more physical processes are taken into consideration in models in the form of parameterization, thus giving rise to the on–off switch problem, which tremendously affects the effectiveness of the conventional gradient descent algorithm based on the ad- joint method. In this study, we attempted to apply a genetic algorithm （GA） to the CNOP method, named GA-CNOP, to solve the predictability problems involving on–off switches. As the precision of the filtering method depends uniquely on the division of the constraint region, its results were taken as benchmarks, and a series of comparisons between the ADJ-CNOP and the GA-CNOP were performed for the modified Lorenz equation. Results show that the GA-CNOP can always determine the accurate lower bound of maximum predictable time, even in non-smooth cases, while the ADJ-CNOP, owing to the effect of on–off switches, often yields the incorrect lower bound of maximum predictable time. Therefore, in non-smooth cases, using GAs to solve predictability problems is more effective than using the conventional optimization algorithm based on gradients, as long as genetic operators in GAs are properly configured.
文摘The frequency of heterogeneous nucleation during the solidification of Al-Sibinary alloy was estimated by comparing experimentally obtained macrostructures of castings withnumerically simulated ones. A molten alloy was unidirectionally solidified from a water-cooledcopper chill in an adiabatic mold. The location of columnar to equiaxed transition (CET) in thesolidified alloy ingot was measured. A numerical simulation for grain structure formation based onthe Monte Carlo method was carried out, and the frequency of heterogeneous nucleation in the alloywas evaluated by producing similar structure with the experimental one. The frequency ofheterogeneous nucleation was expressed as a probabilistic function with an exponential form ofundercooling that deter-mines the probability of nucleation event in the simulation. The value ofthe exponent is regarded as the nucleation parameter. The nucleation parameter of Al-Si binary alloyvaried with initial Si content.
文摘This paper presents the predictability of aluminium-manganese alloy exposure time based on its as-cast weight and corrosion rate in sea water environment. The validity of the derived model: α = 26.67γ + 0.55β?- 0.29 is rooted on the core expression: 0.0375α = γ + 0.0206β?- 0.0109 where both sides of the expression are correspondingly approximately equal. Statistical analysis of model-predicted and experimentally evaluated exposure time for each value of as-cast weight and alloy corrosion rate considered shows a standard error of 0.0017% & 0.0044% and 0.0140% & 0.0150% respectively. The depths of corrosion penetration (at increasing corrosion rate: 0.0104 - 0.0157 mm/yr) as predicted by derived model and obtained from experiment are 0.7208 × 10-4 & 1.0123 × 10-4 mm and 2.5460 × 10-4 & 1.8240 × 10-4 mm (at decreasing corrosion rate: 0.0157 - 0.0062 mm/yr) respectively. Deviational analysis indicates that the maxi- mum deviation of the model-predicted alloy exposure time from the corresponding experimental value is less than 10%.
文摘Aluminum Metal Matrix Composites (MMCs) sought over other conventional materials in the field of aerospace, automotive and marine applications owing to their excellent improved properties. These materials are of much interest to the researchers from few decades. These composites initially replaced Cast Iron and Bronze alloys but owing to their poor wear and seizure resistance, they were subjected to many experiments and the wear behavior of these composites were explored to a maximum extent and were reported by number of research scholars for the past 25 years. In this paper an attempt has been made to consolidate some of the aspects of mechanical and wear behavior of Al-MMCs and the prediction of the Mechanical and Tribological properties of Aluminum MMCs.
文摘针对传统极限学习机易陷入局部最优解的缺点以及环境变化导致光伏出力波动的特点,构建了一种基于自适应噪声完全集成经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)算法,结合黑猩猩优化算法优化极限学习机神经网络的光伏出力短期预测模型。首先利用CEEMDAN算法将影响光伏输出功率的关键环境因素序列进行分解,得到数据信号在不同时间尺度的局部特征,降低环境因素序列的非平稳性,然后将各分解子序列和光伏历史数据序列作为黑猩猩算法优化的极限学习机预测模型输入进行预测。最后,选用DKASC Solar Centre光伏电站数据集对不同预测模型进行验证对比。实例仿真结果表明,构建的改进光伏出力预测组合模型的各项指标预测效果更好,且适用不同环境的光伏发电预测。
文摘随着空中交通流量的增长,尾流间隔精细化、动态化缩减成为民航发展的一种趋势,研究尾流演化过程也成为民航领域关注的前沿科学问题。基于此,采用雷诺平均N-S方程方法研究了B737-800飞机有无融合式翼梢小翼对飞机尾涡的演化过程影响。利用美国国家航空航天局(National Aeronautics and Space Administration,NASA)动态尾流系统中尾涡消散模型(aircraft vortex spacing system prediction algorithm,APA)计算了不同气象环境参数下有无小翼的尾涡环量变化。结果表明:融合式翼梢小翼可以分割翼尖涡,有效改变翼尖气流的流动特性,增大速度梯度,减小尾涡速度、尾涡能量集中程度和尾涡强度;不同大气湍流耗散率和大气层结稳定度下,小翼对尾涡强度的减小量不同。
基金This work was supported by the National Natural Science Foundation of China(No.51975330)the Key Research and Development Program of Shandong Province,China(No.2021ZLGX01)the Project of Colleges and Universities Innovation Team of Jinan City,China(No.2021GXRC030).
基金This work was supported by the National High Technology Research and Development Plan of China(Grant No.2015AA043505)the Equipment Advanced Research Funds(Grant No.61402100401)+1 种基金the Equipment Advanced Research Key Laboratory Funds(Grant No.6142804180106)Shenzhen Fundamental Research Funds(Grant No.JCYJ20180508151910775).
文摘In recent years,there has been a significant increase in the utilization of Al/SiC particulate composite materials in engineering fields,and the demand for accurate machining of such composite materials has grown accordingly.In this paper,a feed-forward multi-layered artificial neural network(ANN)roughness prediction model,using the Levenberg-Marquardt backpropagation training algorithm,is proposed to investigate the mathematical relationship between cutting parameters and average surface roughness during milling Al/SiC particulate composite materials.Milling experiments were conducted on a computer numerical control(C N C)milling machine with polycrystalline diamond(PCD)tools to acquire data for training the ANN roughness prediction model.Four cutting parameters were considered in these experiments:cutting speed,depth of cut,feed rate,and volume fraction of SiC.These parameters were also used as inputs for the ANN roughness prediction model.The output of the model was the average surface roughness of the machined workpiece.A successfully trained ANN roughness prediction model could predict the corresponding average surface roughness based on given cutting parameters,with a 2.08%mea n relative error.Moreover,a roughness control model that could accurately determine the corresponding cutting parameters for a specific desired roughness with a 2.91%mean relative error was developed based on the ANN roughness prediction model.Finally,a more reliable and readable analysis of the influence of each parameter on roughness or the interaction between different parameters was conducted with the help of the ANN prediction model.
