To better study the biology and ecology of hadal trenches for marine scientists, the Hadal Science and Technology Research Center (HAST) of Shanghai Ocean University proposed to construct a movable laboratory that includes a mothership, several full-ocean-depth (FOD) submersibles, and FOD landers to obtain samples in the hadal trenches. Among these vehicles, the project of an FOD autonomous and remotely-operated vehicle (ARV) named “Dream Chaser” was started in July 2018. The ARV could work in both remotely-operated and autonomous-operated modes, and serves large-range underwater observation, on-site sampling, surveying, mapping, etc. This paper proposed a novel three-body design of the FOD ARV. A detailed illustration of the whole system design method is provided. Numerical simulations and experimental tests for various sub-systems and disciplines have been conducted, such as resistance analysis using the computational fluid mechanics method and structural strength analysis for FOD hydrostatic pressure using the finite element method and pressure chamber tests. In addition, components tests and the entire system tests have been performed on land, underwater, and in the pressure chamber in the laboratory of HAST, and the results are discussed. Extensive experiments of two critical components, i.e., the thrusters and ballast-abandoning system, have been conducted and further analyzed in this paper. Finally, the procedures and results of lake trials, South China Sea trials and the first phase of Mariana Trench sea trials of the ARV in 2020 are also introduced. This paper provides a design method for the novel three-body FOD ARV. More importantly, the lessons learned from the FOD pressure test, lake tests, and sea trials, no matter the success or failure, will guide future endeavors and the application of ARV Dream Chaser and underwater vehicles of this kind.

The underwater motion of the ROV is affected by various environmental factors, such as wind, waves, and currents. The complex relationship between these disturbance variables results in non-Gaussian noise distribution, which cannot be handled by the classical Kalman filter. For the accurate and real-time observation of ROV climbing, and, at the same time, to reduce the influence of the uncertainty of the noise distribution, the ROV state filter is designed based on the mixture of Gaussian model theory with the expectation-maximization cubature particle filter (EM-MOGCPF). The EM-MOGCPF considers different sensor measurement noises, and the addition of mixture of Gaussian (MOG) improves the fineness and real-time properties, while the expectation-maximization (EM) reduces the complexity of the algorithm. To estimate the ROV xyz-axis and yaw angular states, we establish a four-degree-of-freedom (4-DOF) ROV kinetics model, which uses a simulation platform for multiple sea state degrees. The results show that the EM-MOGCPF effectively improves the estimation accuracy and exhibits strong adaptability to nonlinear and non-Gaussian environments. We believe that this algorithm holds promise in solving the state estimation challenge in these difficult environments.

Solid oxide fuel cell (SOFC) and gas turbine (GT) hybrid systems exhibit inherent system uncertainties and unmodeled dynamics during operation, which compromise the accuracy of predicting gas turbine power. This poses challenges for system operation analysis and energy management. To enhance the prediction accuracy and stability of gas turbine power in SOFC/GT hybrid systems, a power prediction method capable of incorporating total system disturbance information is investigated. This study constructs a high-fidelity simulation model of an SOFC/GT hybrid system to generate gas turbine power prediction datasets. With fuel utilization (FU) as the input and gas turbine power as the output, this system is assumed to be a first-order dynamic system. Building upon this foundation, an extended state observer (ESO) is employed to extract the total system disturbance ($f$) that affects the power output of the gas turbine, excluding fuel utilization. The total disturbance $f$ and fuel utilization are used as inputs to a Backpropagation (BP) neural network to construct a disturbance-aware power prediction model. The predictive performance of the proposed method is evaluated by comparison with a BP neural network without disturbance estimation information and several benchmark models. Simulation results indicate that incorporating the disturbance term estimated by ESO enhances both the accuracy and stability of the BP neural network’s power prediction, particularly under operating conditions characterized by significant power fluctuations. Quantitatively, when comparing the predictive model with disturbance included to the model without disturbance, including the disturbance reduces the prediction error by approximately 89.33% (MSE) and 67.34% (RMSE), while the coefficient of determination R² increases by 0.1132, demonstrating a substantial improvement in predictive performance under the same test conditions. The research findings indicate that incorporating disturbance information into data-driven prediction models represents a viable modeling approach, providing effective support for predicting gas turbine power in SOFC/GT hybrid systems.