Blended-wing-body Underwater Glider Research Articles

Bi-level task planning strategy for blended-wing-body underwater gliders

ABSTRACT This study focuses on the task planning problem of the blended-wing-body underwater glider (BWBUG), which must autonomously navigate through a sequence of underwater visitation points while ensuring safety and economy. Firstly, the task planning problem of the BWBUG is analysed, with attention given to operational space and task requirements. Subsequently, a bi-level planning framework that incorporates both the bottom-level and top-level planners is proposed. The bottom-level planner optimises the glide path between any two visit points by considering safety and glide angle constraints, with the objective of minimising energy usage. The top-level planner focuses on the task constraints and optimises the global visitation path of the BWBUG to achieve the objective of global energy minimisation. Simulations demonstrate the effectiveness of the bi-level based task planning strategy proposed in this study. The solution algorithms are highly competitive, capable of planning rational global visitation paths for the BWBUG.

Hierarchical decision-making approach for the task planning of the BWBUG cluster with three-dimensional time-varying ocean currents

This study concerns the task planning problem for a Blended-Wing-Body Underwater Glider (BWBUG) cluster with three-dimensional, time-varying ocean currents. The objective is to achieve task allocation and three-dimensional glide path planning for the BWBUG cluster in a manner that ensures safety and cost-effectiveness. First, the task planning problem for the BWBUG cluster is analyzed, detailing the task requirements and operational space. Next, a hierarchical decision-making approach is proposed, which comprises an upstream planner and a downstream planner. The upstream planner aims to determine a feasible task space for the BWBUG cluster while satisfying task constraints. The downstream planner fully considers the impact of ocean currents on BWBUG movement, as well as the safety and kinematic constraints of BWBUGs. It optimizes the three-dimensional movement trajectories of each BWBUG with the objective of minimizing energy consumption. Inspired by the nest-seeking behavior of bees, a novel heuristic algorithm called the Bee Nesting Optimization Algorithm (BNOA) is proposed to solve both upstream and downstream planning problems. The results of the simulations demonstrate the efficacy of the hierarchical decision-making approach developed in this study. Additionally, the proposed BNOA is highly competitive, offering reasonable task allocations and optimal three-dimensional glide paths for the BWBUG cluster.

Three-dimensional deployment strategy for a multi-BWBUG cooperative system at deep depths

ABSTRACT This study investigates the issue of the three-dimensional deployment for blended-wing-body underwater gliders (BWBUGs) at deep depths. Initially, an underwater three-dimensional deployment optimisation model is formulated, considering factors such as the three-dimensional coverage ratio and the balance of node communication energy consumption. Subsequently, a sine-logistic chaos strategy with excellent ergodicity is proposed. Thereafter, the dynamic decision-assisted heuristic method (DDHM) is proposed based on the dynamic decision framework, sine-logistic chaos strategy, and opposition-based learning. The well-designed dynamic decision framework effectively avoids the premature convergence and local optimum of the algorithm. Additionally, the multi-objective variant of DDHM, referred to as MDDHM, is established utilising the archival mechanism. Lastly, the performance of the DDHM and MDDHM is evaluated by simulation experiments. Statistical analysis demonstrates that DDHM is highly effective for the underwater three-dimensional deployment of the multi-BWBUG cooperative system. Furthermore, its overall performance surpasses that of other comparative methods.

Investigation on the Reduced-Order Model for the Hydrofoil of the Blended-Wing-Body Underwater Glider Flow Control with Steady-Stream Suction and Jets Based on the POD Method

The rapid acquisition of flow field characterization information is crucial for closed-loop active flow control. The proper orthogonal decomposition (POD) method is a widely used flow field downscaling modeling method to obtain flow characteristics effectively. Based on the POD method, a flow field reduced-order model (ROM) is constructed in this paper for the flow field control of a hydrofoil of a blended-wing-body underwater glider (BWB-UG) with stabilized suction and blowing forces. Compared with the computational fluid dynamics (CFD) simulation, the computational time required to predict the target flow field using the established POD-ROM is only about 0.1 s, which is significantly less than the CFD simulation time. The average relative error of the predicted surface pressure is not more than 6.9%. These results confirm the accuracy and efficiency of the POD-ROM in reconstructing flow characteristics. The timeliness problem of fast flow field prediction in BWB-UG active flow control is solved by establishing a fast prediction model in an innovative way.

Surrogate-assisted evolutionary algorithm with decomposition-based local learning for high-dimensional multi-objective optimization

When the evolutionary algorithm is applied to handle high-dimensional expensive multi-objective optimization problems (MOPs), population evolution is crucial since it controls exploration and exploitation and decides if promising candidate solutions could be generated. However, little attention has been paid to this issue, evolution operators based on Genetic Algorithm (GA) and Differential Evolution (DE) are still the two most common approaches, whose convergence on high-dimensional MOPs with a limited number of fitness evaluations remains challenging. In this paper, we propose a decomposition-based local learning strategy to accelerate convergence in the high-dimensional search space of MOPs. Specifically, an individual is updated by learning from one of the best solutions of its corresponding local area based on the multi-objective decomposition approach. Accordingly, a surrogate-assisted evolutionary algorithm is proposed for better solving expensive high-dimensional MOPs. Experimental studies on MOPs with up to 100 decision variables and with 300 fitness evaluations demonstrate the effectiveness of the proposed method. Furthermore, we use the proposed method to solve a 2-objective shape design problem of the blended wing-body underwater glider (BWBUG) with 39 decision variables, and an impressive solution set is obtained.

A meta-heuristic assisted method for the deployment of the multi-BWBUG cooperative system

Blended-wing-body underwater gliders (BWBUGs) are characterized by low noise and high endurance, and they are well suited for deployment in offshore missions. However, the deployment of the multi-BWBUG cooperative system faces great difficulties due to the complex ocean environment and the difficulty of accurately establishing the deployment model. Thus, a novel meta-heuristic assisted method is proposed for the deployment of the multi-BWBUG cooperative system in this study. First, the deployment optimization model of the multi-BWBUG cooperative system is established by combining the detection coverage ratio and the all-terminal reliability. In addition, Levy flight with visual expansion (LVE) is put forward to effectively enhance the search capability of the search unit. Then, an LVE-enhanced whale optimizer (LVEWO) is proposed for improving the quality of the initial population and the search strategy. The embedding of LVE and the well-designed competition mechanism can improve the convergence speed of LVEWO. Simulation experiments and statistical analysis results convincingly demonstrate the effectiveness of the proposed deployment model and the competency of LVEWO to deploy the multi-BWBUG cooperative system. The comprehensive performance of LVEWO is better than those of the FA, EABC, and WO algorithms.

A flexible and efficient optimization design framework for the shape of blend-wing-body underwater glider

Shape optimization design plays an important role in improving the gliding performance of blended-wing-body underwater glider (BWBUG), but it faces the challenges of how to handle the geometric constraints and improve the optimization efficiency when building the surrogate-based optimization (SBO) framework. To address these issues, a flexible and efficient optimization design framework for the shape of BWBUG is proposed to perform the shape optimization design of BWBUG. In the framework, an axis-driven geometric free-form deformation method is presented to reduce cost of the BWBUG shape deformation by reasonably controlling the scale of optimization variables, and the rapid calculation method of geometric constraints are derived for the proposed shape deformation method. Based on these, a constrained SBO optimization strategy is developed to directly employ the geometric constraints into the training dataset generation and infill criteria. Finally, the shape of one BWBUG example is optimized, and the results show the flexibility and efficiency of the proposed optimization framework.

Chaotic heuristic assisted method for the search path planning of the multi-BWBUG cooperative system

Blended-wing-body underwater gliders (BWBUGs) are low cost and have high endurance. As such, they are highly suitable for underwater search missions. However, the search path planning of BWBUGs presents significant challenges due to the complexities in designing the planning model and solution algorithm. In this study, we propose a novel chaotic heuristic assisted method (CHAM) for search missions involving multi-BWBUG cooperative systems. First, our approach addresses the challenges by introducing a probabilistic sensing model that accurately describes the detection performance of the BWBUG. Then, a search path planning model of the multi-BWBUG cooperative system is developed on the basis of the cumulative detection probability and communication energy consumption ratio. Second, we propose the sine-cosine chaos strategy, which exhibits earlier chaotic state entry and higher ergodicity compared to the sine chaos strategy. Leveraging this, CHAM integrates the sine-cosine chaos strategy with a well-designed hierarchical collaboration mechanism. The proposed algorithm adapts different position update strategies at various stages to enhance convergence speed and avoid local optima. Finally, the simulation experiments and statistical analysis results provide compelling evidence that CHAM excels in performing search path planning for the multi-BWBUG cooperative system, and the comprehensive performance of CHAM is better than that of comparison algorithms.

Causal-relationship-assisted shape design optimization for blended-wing-body underwater gliders

Blended-wing-body underwater gliders (BWBUGs) are a new generation of underwater vehicles that have been successfully applied to long-range missions. It is generally recognized that shapes with a high aspect ratio have a higher lift–drag ratio (LDR) and superior fluid performance. However, the pursuit of higher LDR cannot achieve the optimal overall performance, and there are complex design factors. It is necessary but difficult to conduct a thorough examination of shape layout, energy-carrying and other issues. In this article, the layout and energy models are established, and the causal graph is used as a multidisciplinary analysis method to show the interaction between system variables. A shape design method based on surrogate models and causal graphs is proposed that incorporates knowledge into optimization by combing the information flow and finding the relationship between variables. The results show that causal-assisted optimization can mine the characteristics of variables and discover more valuable shape designs.

Internal layout optimization of the blended-wing-body underwater glider based on a range target

The blended-wing-body underwater glider (BWBUG) has a high gliding efficiency because of its hydrodynamic shape and high lift-to-drag ratio. Because of the complex body shape of the BWBUG, designing the layout of the internal regulating devices is difficult, which can affect the motion performance. In this study, a dynamic model of the BWBUG was established using the Newton–Euler method. A mathematical model of the spatial constraints of the internal regulating devices was established based on the optimised shape obtained from the authors’ previous research. The stability determination condition equation of the BWBUG is established based on the BWBUG dynamic model and vertical surface evaluation model. An energy consumption model is established based on the BWBUG dynamic analysis and gliding performance evaluation index. The optimisation framework of the BWBUG internal regulating device layout is established with the total range as the target, based on the penalty function and genetic algorithm. Finally, an optimised design of the internal regulating devices is developed using a small BWBUG as an example, and the relationship between each variable and the total range is analysed. The optimisation results show that the maximum gliding range after optimization is 2084.3 km, which is 30.4% higher than the initial range, and the energy-carrying capacity is 23.0% higher than before.

Coupled physics analysis of blended-wing-body underwater glider equipped with electromagnetic active flow control

Lift-drag ratio is an important index to determine the hydrodynamic performance, glide performance, and economy of the underwater glider(UG). Limited by many factors, the shape optimization method has been unable to meet the needs of UG hydrodynamic performance optimization. Aiming at this crucial problem, the electromagnetic active flow control method for the UG is proposed in this paper. The coupled physical analysis of the blended-wing-body underwater glider(BWB-UG) equipped with electromagnetic active flow control is carried out by numerical method. The numerical calculation model of electromagnetic active flow control is established. The coupled physical field structure of the BWB-UG equipped with electromagnetic active flow control is studied by Detached-Eddy Simulation. The mechanism of electromagnetic active flow control on the hydrodynamic performance of the BWB-UG is further revealed by coupling physics analysis. The results show that a layer of the periodically distributed electromagnetic force is formed on the surface of BWB-UG, and the boundary layer structure can be effectively controlled. By equipping with electromagnetic active flow control, the lift-drag ratio of BWB-UG can be increased by 11.659%.

3D search path planning for the blended-wing-body underwater glider

A blended-wing-body underwater glider (BWBUG) has the advantages of having low noise and high endurance. As such, it is suitable for underwater search missions. However, the 3D search path planning of the BWBUG faces great difficulties due to the difficulty of accurately establishing the 3D probabilistic sensing model and the complex submarine topography. Therefore, this study proposes a novel multi-stage heuristic method (MSHM) to solve the 3D search path planning problem of the BWBUG. First, a novel 3D probabilistic sensing model is put forward to describe the detection performance of the BWBUG accurately. Second, a model for the 3D search path optimization of the BWBUG is developed by considering the search failure probability, energy consumption, and complex constraints. Then, the MSHM is proposed. This method adopts the position update strategy and multi-stage framework designed in this study, which accelerates the convergence speed and effectively avoids the algorithm from falling into the local optimum. Finally, the simulation experiments and statistical analysis results show that the 3D probabilistic sensing model of the BWBUG proposed in this study is effective. In addition, the MSHM is adequate for the 3D search path planning of the BWBUG, and its comprehensive performance is better than other representative heuristic algorithms.

Construction of a digital twin system for the blended-wing-body underwater glider

The complexity of the marine environment adds unpredictable difficulties to researchers’ exploration. To study the application prospects of high-fidelity digital models to the maritime field, this study adopts the blended-wing-body underwater glider (BWBUG) as an example. A novel construction approach of an underwater simulation platform is proposed based on digital twin (DT) technology. The building process is described from the perspectives of geometry, physics, environment and behavior. Based on the Gazebo simulation platform, the proposed approach includes the construction of a high-fidelity model for underwater scenarios and is equipped with a vehicle body containing various sensors. Under the influence of ocean currents, the twin sensor data are integrated in the proportional–integral–derivative (PID) control algorithm, combined with a robot operating system (ROS), to achieve its pitch attitude control. The overshoot was reduced to at most 0.06% and the settling time is reduced to 29.5 s compared with the traditional PID. The results demonstrate that the proposed framework not only validates the implementability of control algorithms for unmanned systems in actual marine environment, but also has the potential for performance prediction, design improvement, and safety assessment in the next generation vehicle design process.

Surrogate-assisted hierarchical learning water cycle algorithm for high-dimensional expensive optimization

Excessive function evaluations are the main obstacle preventing the application of metaheuristic algorithms to expensive real-world problems. Although many surrogate-assisted metaheuristic algorithms have been proposed to tackle this challenge, most of them still suffer from low prediction accuracy on high-dimensional problems. This article presents a surrogate-assisted hierarchical learning water cycle algorithm (SA-HLWCA) for high-dimensional expensive optimization problems. SA-HLWCA utilizes two searching modes, namely, global search and local search, to cooperatively search for the optimal solution. A global search is conducted by a surrogate using a global diverse subset and aims to locate promising optimal areas. A local search is conducted by a surrogate built in the neighboring region of the current best and aims to refine the optimal solution. To validate the performance, comprehensive studies of the impacts of major components of SA-HLWCA are conducted. The proposed algorithm is then compared with three state-of-the-art algorithms on a series of tests on problems that range from 20 dimensions to 100 dimensions, and the results show that SA-HLWCA performs better in terms of both effectiveness and robustness. In addition, SA-HLWCA is applied to the shape optimization of a blended-wing-body underwater glider (BWBUG), and the lift-drag ratio of the optimized shape is improved by 7.66% compared with that of the initial shape.

A model-based multidisciplinary conceptual design for blended-wing-body underwater gliders

ABSTRACT Blended-wing-body underwater gliders (BWBUGs) can achieve superior performance for their shapes. Their overall performance is affected by shapes and shows correlations with other disciplines. In this paper, a universal classification and parameterisation model was proposed, including the shape, layout, structure, and energy consumption. To overcome the coupling between the multidisciplinary design in conceptual design, a model-based multidisciplinary system design was conducted for BWBUGs. This system adopted a parameterisation model to design the shape, layout, structure and employed multiple simulation modules to evaluate the system performance, which can facilitate the convenient definition of overall performance and design constraints, obtain various feasible system schemes rapidly, and boost the series development and agile design of BWBUG.

Surrogate-based bilevel shape optimization for blended-wing–body underwater gliders

For blended-wing–body underwater gliders (BWBUGs), a high-performance shape design necessitates not only a higher lift-to-drag ratio (LDR), but also additional carrying space. However, regardless of how the parameters alter with the fixed layout, some potential optimal solutions may be lost. It is feasible to achieve the genuine global optimum when the parameter space of the layout is liberated. On the other hand, the shape and layout parameters have different physical properties. The complexity of optimization can be reduced if these can be organized according to their properties. Therefore, a surrogate-based bilevel shape optimization (SBSO) method is presented, in which surrogate models are updated iteratively until the optimum is found. The upper level aims at maximizing the LDR, while the lower level aims to maximize the volume. SBSO is tested on 12 benchmark cases, several existing algorithms and shape optimization of a BWBUG, and all show excellent performance.

An Efficient Surrogate-Based Optimization Method for BWBUG Based on Multifidelity Model and Geometric Constraint Gradients

Performing shape optimization of blended-wing-body underwater glider (BWBUG) can significantly improve its gliding performance. However, high-fidelity CFD analysis and geometric constraint calculation in traditional surrogate-based optimization methods are expensive. An efficient surrogate-based optimization method based on the multifidelity model and geometric constraint gradient information is proposed. By establishing a shape parameterized model, deriving analytical expression of geometric constraint gradient, constructing multifidelity surrogate model, the calculation times of high-fidelity CFD model and geometric constraints are reduced during the shape optimization process of BWBUG, which greatly improve the optimization efficiency. Finally, the effectiveness and efficiency of the proposed method are verified by performing the shape optimization of a BWBUG and comparing with traditional surrogate-based optimization methods.

SGOP: Surrogate-assisted global optimization using a Pareto-based sampling strategy

In this paper, we present a new global optimization algorithm SGOP for computationally intensive black-box problems. Considering that multiple surrogates concurrently used in an optimization process can have more robust performance in most cases, a Pareto-based multi-point sampling strategy is presented to improve iterative efficiency. Ideally, a group of samples having best predictive values on all the surrogates and meanwhile keeping better space-filling feature are most appropriate to be selected in each cycle. Therefore, a four-objective optimization formula is presented, where Kriging, radial basis function, quadratic response surface and a sampling density function are defined as objective functions, respectively. The non-dominated sorting strategy is used to capture the Pareto solutions of the multi-objective problem and the new promising samples are adaptively chosen from their Pareto solutions set to drive the optimization cycle. Moreover, a dynamic monitor is presented to check the premature convergence. Once the trigger is activated, the search will focus on unexplored area. SGOP can not only build a reasonable balance between global exploration and local exploitation, but also has remarkable advantages in sampling efficiency. Finally, the new algorithm is tested on 17 benchmark cases and compared with several existing algorithms. The results show SGOP’s superior performance and strong robustness. Besides, SGOP is used for the shape optimization of a blended-wing-body underwater glider (BWBUG), and the lift–drag-ratio gets remarkable improvement.

Surrogate based blended-wing-body underwater glider shape optimization design

In order to improve the design quality and optimization efficiency for blended-wing-body underwater glider(BWBUG) shape design optimization problems, a surrogate-based blended-wing-body underwater glider shape optimization(SBUGSO) framework is proposed. The aim is to maximize the lift to drag ratio(LDR) of BWBUG with the constrain that the displacement volume of the optimal shape is larger than that of the initial shape. The LDR of the optimal BWBUG is improved by 24.32% with acceptable computational resources. The optimization results show that the present SBUGSO framework can efficiently decrease the computational resource, and improve the hydrodynamic performance and loading capacity of BWBUG. Comparing with the other optimization algorithms, SBUGSO framework shows the significant superiority.

Kriging-based shape optimization framework for blended-wing-body underwater glider with NURBS-based parametrization

Lift-drag ratio has an important impact on the horizontal range of Blended-Wing-Body Underwater Glider (BWBUG). It is significant to increase the lift-drag ratio of BWBUG for a longer horizontal range. In this paper, a Kriging-based shape optimization framework for the BWBUG with NURBS-based parametrization is proposed to maximize the lift-drag ratio and to improve the optimization efficiency. In this framework, a shape parameterization method of BWBUG is presented based on the NURBS method, to precisely calculate the geometric constraints and their gradient information. The CFD method is applied to accurately compute the hydrodynamic parameters of BWBUG in the whole optimization design. A Kriging-based optimization framework is built with dynamically infilling sample points to update the Kriging model by the aid of the gradient information of geometric constraints, which further improves the optimization efficiency Finally, the effectiveness of the proposed optimization framework is verified by performing the shape optimization of a BWBUG.