Prediction of unpowered diving and floating for large-depth manned submersible based on geometric similarity

Large-depth manned submersibles generally conduct diving and floating operations in an unpowered mode. On the premise of guaranteeing satisfactory diving and floating speed, this mode can significantly reduce energy consumption and prolong underwater operating time, which serves as one of the key foundations for the underwater operational performance of manned submersibles. Therefore, the study on prediction methods for unpowered diving and floating motion is of great engineering significance. At present, existing prediction methods are mainly established on the standard motion model, whose results are extremely sensitive to the handling of hydrodynamic terms in the model, thus limiting the prediction accuracy. In this paper, a prediction method for unpowered diving and floating motion of manned submersibles is proposed based on geometric similarity theory, by deriving the relationship between the submersible's diving and floating speed and its net weight in water. Taking the "Jiao Long" manned submersible as a research object, the established model is employed to predict the 7000 m unpowered diving and floating motion using the 5000 m deep-sea trial data. Comparative analysis indicates that the proposed method achieves higher prediction accuracy than the conventional standard motion model, which preliminarily verifies its feasibility and effectiveness and its general applicability remains to be further examined in follow-up investigations. Meanwhile, many assumptions are adopted in the development of the proposed method. Therefore, this method is only applicable to the quasi-steady segments of the same manned submersible, and is not suitable for transient phases such as water entry, ballast release transitions, or motions involving significant attitude coupling. Furthermore, the method is only intended for operational application phases, rather than the demonstration and design phases of manned submersibles.