Machine learning in High Speed Craft

Document Type : Original Article

Author

Malek Ashtar University of Technology

Abstract

Fast vessels can perhaps be considered the most obvious characteristic of the Islamic Republic of Iran's maritime power in the Persian Gulf and Strait of Hormuz region, and at the same time, the biggest operational and tactical challenge for the US Navy fleet in this region. High-speed boats are used in other parts of the world, but what happened in Iran is considered the production of science. In this regard, the use and application of emerging technologies (artificial intelligence, machine learning, reinforcement learning, deep learning,...) in the field of high-speed vessels is very important. Machine learning is becoming a powerful tool for designers and builders of speedboats. This technology can be applied at various stages of the design and construction process to result in more efficient, safer and cost-effective vessels. Machine learning can be used to predict float performance under various operating conditions. This can help designers and manufacturers to create vessels that meet the specific needs of customers. Machine learning can be used to optimize the performance of floating propulsion systems, such as engines and propellers. This can help reduce fuel consumption. Machine learning can be used to analyze sensor data from the vessel to predict potential failures. This can help with preventive maintenance and reduce downtime. In this article, the application of machine learning in the design and construction of high-speed vessels is discussed, the most cited articles are reviewed and products based on artificial intelligence and machine learning are reported in this field.

Keywords

Main Subjects


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