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相关概念视频

Electro-mechanical Systems01:19

Electro-mechanical Systems

954
Electromechanical systems are intricate configurations that effectively combine electrical and mechanical elements to achieve a desired outcome. Central to many of these systems is the DC motor, a device that converts electrical energy into mechanical motion, enabling various applications ranging from simple fans to complex robotic mechanisms.
A key component of the DC motor is the armature, a rotating circuit positioned within a magnetic field. As an electric current passes through the...
954
Transmission Shafts: Problem Solving01:09

Transmission Shafts: Problem Solving

232
Designing a solid shaft that transmits power from a motor to a machine tool involves a series of calculations to ensure the shaft can withstand the stresses applied by bending moments and torques. First, calculate the torque exerted on the gear, considering the power transmitted by the shaft and its rotational speed. Following this, compute the tangential forces acting on the gears, which directly relate to the torque and the gear radius.
Next, use bending moment diagrams for the shaft to...
232
Angle of Twist: Problem Solving01:13

Angle of Twist: Problem Solving

273
An electric motor applies a torque of 700 N·m to an aluminum shaft, triggering a stable rotation. Two pulleys, B and C, are subjected to torques of 300 N·m and 400 N·m, respectively. The modulus of rigidity is provided as 25 GPa. With the knowledge of the length and diameter of each segment, the twist angle between the two pulleys can be computed. First, a section cut is made between pulleys B and C, and the cut cross-section is analyzed using a free-body diagram. Given that the...
273
Design of Transmission Shafts01:16

Design of Transmission Shafts

300
The design of a transmission shaft is governed by two primary specifications: the power it transmits and its rotational speed. These parameters guide the selection of the shaft's material and cross-sectional dimensions, ensuring that the material's maximum shearing stress remains within the elastic limit while transmitting the desired power at the given speed. The system's power is intrinsically linked to the applied torque. The torque applied to the shaft can be calculated by...
300
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

645
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
645
PD Controller: Design01:26

PD Controller: Design

229
In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
229

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优化无刷直流电机设计:多目标通用正常分布优化的应用.

Sundaram B Pandya1, Pradeep Jangir2, Miroslav Mahdal3

  • 1Department of Electrical Engineering, Shri K.J. Polytechnic, Bharuch 392 001, India.

Heliyon
|February 26, 2024
PubMed
概括

一种新的多目标通用正常分布优化 (MOGNDO) 方法提高了无刷直流 (BLDC) 电机设计. 这种仿生算法优化了效率并最大限度地减少了质量,在实际应用中优于现有的技术.

关键词:
在 BLDC 电机上.电磁学 电磁学 电磁学 电磁学这是一种元启发式 (metaheuristic) 启发式.非主导分类一般化正常分布优化优化非主导分类

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科学领域:

  • 电气工程 电气工程
  • 计算智能是一种计算智能.
  • 优化算法 优化算法

背景情况:

  • 无刷直流 (BLDC) 电机设计需要平衡多个,往往相互冲突的目标,如效率和质量.
  • 现有的优化算法可能无法为电机设计中的复杂多目标问题提供足够优化的解决方案.

研究的目的:

  • 引入和评估多目标通用正常分布优化 (MOGNDO) 方法,以优化BLDC电机设计.
  • 将MOGNDO的性能与该特定应用中的其他最先进的优化算法进行比较.

主要方法:

  • 该研究使用了BLDC电机设计的既定分析模型.
  • 开发了多目标通用正常分布优化 (MOGNDO) 方法,这是一个包含帕雷托最佳性,主导性和外部存档的仿生方法.
  • 在应用到BLDC电机设计之前,MOGNDO最初在标准的多目标基准函数上进行了验证.

主要成果:

  • MOGNDO在基准函数方面表现强.
  • 当应用于BLDC电机设计时,MOGNDO的性能始终优于狮优化器 (ALO),离子运动优化 (IMO) 和正弦共弦算法 (SCA).
  • 该算法在最大限度地提高运行效率和最大限度地减少电机质量方面取得了卓越的结果,提供了实用的设计解决方案.

结论:

  • MOGNDO算法是BLDC电机设计中多目标优化的高效方法.
  • 它在现有技术上提供了显著的进步,在关键绩效指标之间产生了最佳的权衡.
  • MOGNDO代码的开源可用性有助于其采用和进一步研究.