Projectile Balloting Attributable to Gun Tube Curvature

Transverse motion of a projectile during launch is detrimental to firing accuracy, structural integrity, and/or on-board electronics performance of the projectile. One manifest contributing factor to the undesired motion is imperfect bore centerline straightness. This paper starts with the presentation of a deterministic barrel model that possesses both vertical and lateral deviations from centerline in accordance with measurement data, followed by a novel approach to simulating comprehensive barrel centerline variations for the investigation of projectile balloting^1 motions. A modern projectile was adopted for this study. In-bore projectile responses at various locations of the projectile while traveling through the simulated gun tubes were obtained. The balloting was evaluated in both time and frequency domains. Some statistical quantities and the significance were outlined.

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A Design of Comfortable Dental Treatment Sound Based on Chord-forming in Time and Frequency Domains

IEEJ Transactions on Electronics Information and Systems ◽

10.1541/ieejeiss.135.1565 ◽

2015 ◽

Vol 135 (12) ◽

pp. 1565-1573

Author(s):

Yoshitaka Ohshio ◽

Daisuke Ikefuji ◽

Yuko Suhara ◽

Masato Nakayama ◽

Takanobu Nishiura

Keyword(s):

Dental Treatment ◽

Frequency Domains ◽

Time And Frequency Domains

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Fuzzy Extreme Analysis in Time- and Frequency Domains

International Journal of Electronics and Telecommunications ◽

10.2478/v10177-011-0072-1 ◽

2011 ◽

Vol 57 (4) ◽

Author(s):

Włodzimierz Pogribny ◽

Marcin Drzycimski ◽

Zdzisław Drzycimski

Keyword(s):

Frequency Domains ◽

Time And Frequency Domains

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Application of Deep Reinforcement Learning to Predict Shaft Deformation Considering Hull Deformation of Medium-Sized Oil/Chemical Tanker

Journal of Marine Science and Engineering ◽

10.3390/jmse9070767 ◽

2021 ◽

Vol 9 (7) ◽

pp. 767

Author(s):

Shin-Pyo Choi ◽

Jae-Ung Lee ◽

Jun-Bum Park

Keyword(s):

Reinforcement Learning ◽

Reaction Force ◽

Measurement Data ◽

System Stability ◽

Main Bearing ◽

Shaft System ◽

Novel Approach ◽

Design Changes ◽

Prediction Techniques ◽

Shaft Deformation

The enlargement of ships has increased the relative hull deformation owing to draft changes. Moreover, design changes such as an increased propeller diameter and pitch changes have occurred to compensate for the reduction in the engine revolution and consequent ship speed. In terms of propulsion shaft alignment, as the load of the stern tube support bearing increases, an uneven load distribution occurs between the shaft support bearings, leading to stern accidents. To prevent such accidents and to ensure shaft system stability, a shaft system design technique is required in which the shaft deformation resulting from the hull deformation is considered. Based on the measurement data of a medium-sized oil/chemical tanker, this study presents a novel approach to predicting the shaft deformation following stern hull deformation through inverse analysis using deep reinforcement learning, as opposed to traditional prediction techniques. The main bearing reaction force, which was difficult to reflect in previous studies, was predicted with high accuracy by comparing it with the measured value, and reasonable shaft deformation could be derived according to the hull deformation. The deep reinforcement learning technique in this study is expected to be expandable for predicting the dynamic behavior of the shaft of an operating vessel.

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