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A rocket's velocity in the presence of a gravitational field is decreased by the amount of force exerted by Earth's gravitational field, which opposes the motion of the rocket. If we consider thrust, that is, the force exerted on a rocket by the exhaust gases, then a rocket's thrust is greater in outer space than in the atmosphere or on a launch pad. In fact, gases are easier to expel in a vacuum.
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Rockets range in size from small fireworks that ordinary people use to the enormous Saturn V that once propelled massive payloads toward the Moon. The propulsion of all rockets, jet engines, deflating balloons, and even squids and octopuses are explained by the same physical principle: Newton's third law of motion. The matter is forcefully ejected from a system, producing an equal and opposite reaction on what remains.
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轻量级RepVGG基于固体火箭发动机的交叉模式数据预测方法

Huixin Yang1, Shangshang Zheng1, Xu Wang1

  • 1School of Aerospace Engineering, Shenyang Aerospace University, Shenyang 110136, China.

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概括
此摘要是机器生成的。

一个新的RepVGG深度神经网络使用压力数据预测固体火箭发动机 (SRM) 推力,降低成本和错误. 这种跨模式的方法实现的误差不到5%,有助于航空航天监测.

关键词:
跨模式数据预测方法.压力 压力 压力 压力 压力固体火箭发动机的发动机.推力 推力 推力

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科学领域:

  • 航空航天工程 航空航天工程
  • 数据科学数据科学数据科学
  • 机器学习 机器学习

背景情况:

  • 固体火箭发动机 (SRM) 在航空航天领域至关重要,但性能监测 (压力,推力) 昂贵且容易出错.
  • 在SRM中精确的推力测量通常需要昂贵的设备和手动校正,影响效率.
  • 现有方法在实时,成本效益高的SRM性能监测方面面临挑战.

研究的目的:

  • 开发一种新的,轻量级的,跨模式的SRM数据预测方法.
  • 建立一个端到端的框架,用深度学习来预测SRM绩效指标.
  • 为了减少与SRM推力测量相关的经济和时间成本.

主要方法:

  • 基于RepVGG的深度神经网络架构被设计用于从原始数据中进行特征学习.
  • 该方法将数据转换成不同的模式,用于预测建模.
  • 实施了一个端到端的框架,用于时间序列数据预测.

主要成果:

  • 拟议的方法使用压力数据准确预测SRM推力数据.
  • 与实际数据相比,预测模型的百分比误差低于5%.
  • 使用现场SRM数据进行了验证,证明了实际有效性.

结论:

  • 基于RepVGG的跨模式预测为SRM绩效监测提供了一个有希望的解决方案.
  • 这种方法可以显著提高推力数据预测的准确性和效率.
  • 该方法为涉及SRM的真实世界航空航天应用提供了有价值的工具.