Computation and analysis of aero-optic imaging deviation of a blunt nosed aircraft with Mach number 0.5–3

Xu, Liang; Zhao, Shiwei; Xue, Wei; Wang, Tao

doi:10.1007/s11801-023-2046-9

Cited by 2 publications

(4 citation statements)

References 10 publications

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“…17 Different concentrations of food in the environment affect the flow of cytoplasm in the venous network of slime molds, resulting in different states of foraging for slime molds. 17 When slime molds approach food, the mathematical model of the SMA can be expressed as follows:…”

Section: Standard Smamentioning

confidence: 99%

“…The SMA mainly simulates the foraging behavior and state change of Physarum polycephalum in nature under different food concentrations 20 . The slime molds mainly secrete enzymes to digest food, with the front end extending into a fan‐shaped shape and the back end surrounded by interconnected venous networks 17 . Different concentrations of food in the environment affect the flow of cytoplasm in the venous network of slime molds, resulting in different states of foraging for slime molds 17 …”

Section: Performance Optimization Of Tmht Based On Ismamentioning

confidence: 99%

“…[12][13][14] In terms of intelligent optimization algorithms, slime mold algorithm (SMA) simulates the behavior strategies of slime molds when searching for food. [15][16][17] It has the characteristics of a simple structure, fast optimization speed, and can achieve a balance between local optimization and global search. It has good performance in handling nonlinear problems.…”

Section: Introductionmentioning

confidence: 99%

“…In nonlinear fitting, back propagation neural network (BPNN), which has strong nonlinear mapping ability, is widely used in modeling 12–14 . In terms of intelligent optimization algorithms, slime mold algorithm (SMA) simulates the behavior strategies of slime molds when searching for food 15–17 . It has the characteristics of a simple structure, fast optimization speed, and can achieve a balance between local optimization and global search.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of geometric parameters of hydraulic turbine runner in turbine mode based on ISMA and BPNN

Wang,

Chen,

Zhang

et al. 2023

Energy Science & Engineering

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The hydraulic turbine in turbine mode (TMHT) has significant advantages in residual energy recovery, but rapid optimization of its runner parameters has always been a challenge. To address this issue, the optimization algorithm ISMA‐BPNN is proposed based on the improved slime mold algorithm (ISMA) and back propagation neural network (BPNN). First, an efficiency characteristic neural network (ECNN) and a water head characteristic neural network (HCNN), which take the geometric parameters of the runner as input, and the efficiency and water head as outputs, respectively, are constructed by combining the orthogonal test‐based sample data, computational fluid dynamics (CFD), and BPNN. Then, the slime mold algorithm is improved and the optimal runner geometric parameters are obtained based on the ISMA, ECNN, and HCNN, to achieve rapid optimization of the TMHT runner. Finally, the CFD‐based numerical calculation accuracy is verified through real machine tests, and the feasibility of the ISMA‐BPNN‐based rapid optimization strategy for TMHT performance is further verified through CFD numerical simulation.

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Section: Standard Smamentioning

confidence: 99%

Section: Performance Optimization Of Tmht Based On Ismamentioning

confidence: 99%