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In an experiment conducted during a Mars mission, a rover propels a projectile with an initial velocity, and the projectile rebounds after colliding with the Martian surface. To ascertain the maximum height attained by the projectile after this collision, the known restitution coefficient and acceleration due to gravity are employed.
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Predicting projectile residual velocities using an advanced artificial neural network model.

Afsar Husain1, Mohd Danish2, Sanan H Khan1

  • 1Department of Mechanical and Aerospace engineering, United Arab Emirates University, Al-Ain, Abu Dhabi, 15551, United Arab Emirates.

Heliyon
|July 1, 2024
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Summary

An Artificial Neural Network (ANN) model accurately predicts projectile residual velocity, outperforming traditional methods. This innovation aids in designing advanced protective barriers and armor systems.

Keywords:
Absorbed energyArtificial neural network (ANN)Projectile impactsProtective barriersRecht-Ipson modelResidual velocity

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Area of Science:

  • Ballistics and Impact Mechanics
  • Computational Modeling and Simulation

Background:

  • Residual velocity is critical for designing protective barriers.
  • Traditional methods for measuring residual velocity are often inefficient and inconsistent.

Purpose of the Study:

  • To develop and validate an Artificial Neural Network (ANN) model for predicting projectile residual velocity.
  • To compare the accuracy of the ANN model against traditional methods like the Recht-Ipson model.

Main Methods:

  • Development of an Artificial Neural Network (ANN) model using MATLAB R2021a.
  • Training the ANN model on a dataset including initial projectile speed, material, shape, and target thickness.
  • Validation of the ANN model's predictive performance.

Main Results:

  • The ANN model demonstrated superior accuracy with low Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) during training and validation.
  • The ANN model significantly outperformed the Recht-Ipson model in accuracy.
  • The model showed potential for predicting absorbed energy.

Conclusions:

  • The developed ANN model offers a more accurate and efficient method for predicting projectile residual velocity.
  • This approach has significant implications for the design of protective structures and armor systems.
  • The ANN model's versatility extends to predicting absorbed energy, enhancing its applicability.