The last decade has witnessed significant advances in energy harvesting technologies as a possible alternative to provide a continuous power supply for small, low-power devices in applications, such as wireless sensing, data transmission, actuation, and medical implants. Piezoelectric energy harvesting (PEH) has been a salient topic in the literature and has attracted widespread attention from researchers due to its advantages of simple architecture, high power density, and good scalability. This paper presents a comprehensive review on the state-of-the-art of piezoelectric energy harvesting. Various key aspects to improve the overall performance of a PEH device are discussed, including basic fundamentals and configurations, materials and fabrication, performance enhancement mechanisms, applications, and future outlooks.

A comprehensive review on piezoelectric energy harvesting technology: Materials, mechanisms, and applications

It is widely known that the availability of lightweight structures with excellent energy absorption capacity is essential for numerous engineering applications. Inspired by many biological structures in nature, bio-inspired structures have been proved to exhibit a significant improvement over conventional structures in energy absorption capacity. Therefore, use of the biomimetic approach for designing novel lightweight structures with excellent energy absorption capacity has been increasing in engineering fields in recent years. This paper provides a comprehensive overview of recent advances in the development of bio-inspired structures for energy absorption applications. In particular, we describe the unique features and remarkable mechanical properties of biological structures such as plants and animals, which can be mimicked to design efficient energy absorbers. Next, we review and discuss the structural designs as well as the energy absorption characteristics of current bio-inspired structures with different configurations and structures, including multi-cell tubes, frusta, sandwich panels, composite plates, honeycombs, foams, building structures and lattices. These materials have been used for bio-inspired structures, including but not limited to metals, polymers, fibre-reinforced composites, concrete and glass. We also discussed the manufacturing techniques of bio-inspired structures based on conventional methods, and adaptive manufacturing (3D printing). Finally, contemporary challenges and future directions for bio-inspired structures are presented. This synopsis provides a useful platform for researchers and engineers to create novel designs of bio-inspired structures for energy absorption applications.

A review of recent research on bio-inspired structures and materials for energy absorption applications

Aeroelastic energy harvesting: A review

Mechanical design and multifunctional applications of chiral mechanical metamaterials: A review

Harvesting ambient wind energy with an inverted piezoelectric flag

Morphing wings are a typical application of shape-adaptive structures in aviation, which play an important role in improving the comprehensive performance of an aircraft. However, traditional morphing wings based on purely mechanical, rigid-flexible coupling, or purely flexible structures usually cannot achieve a distributed morphing ability and have limitations in weight, intelligence level, and reliability. In this paper, a distributed morphing lattice structure based on variable geometry digital metamaterials is proposed. The innovative structural concept consists of three types of fundamental cells featuring remarkably different mechanical properties and three other types of derived cells. One type of the derived cells embedded with micro-actuators, named an active cell, can autonomously extend or contract. All these cells can be reversibly assembled in a random sequence to form an active distributed morphing lattice structure with the ability to realize different target aerodynamic contours. In addition, taking a simplified variable thickness wing as a designing case, this paper develops a cell combination optimization methodology on the basis of a heuristic algorithm to determine the optimal combination sequence of the six types of basic cells and the actuator inputs of active cells collaboratively. Final results show that the optimized lattice structure can morph its outer surface into a predefined aerodynamic contour with a maximum deviation of 3 mm.

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Design of a Distributedly Active Morphing Wing Based on Digital Metamaterials

The spanwise morphing wing can change its aerodynamic shape to suit its flight environment, thereby having the potential to improve the flight performance of the aircraft, especially in gusty conditions. To investigate the potential of morphing wings, the aerodynamic performance of a spanwise morphing wing with a flapping wingtip in a gust environment was analyzed in this paper. The aerodynamic characteristics of the morphing wing are hard to measure accurately, and thus a wind tunnel test was carried out to study the influences of morphing parameters, such as the morphing length, amplitude and frequency on the gust alleviation effect. The flow mechanism of the designed spanwise morphing wing was analyzed in detail by the instantaneous lift results of the wind tunnel test and the flow field results of the CFD method. The results have shown that with appropriate morphing parameters, the spanwise morphing wing designed in this paper can effectively achieve gust alleviation during flight. The conclusions obtained in this paper can be useful guidance for the design of morphing aircraft.

https://www.mdpi.com/2226-4310/10/4/328/pdf?version=1679665347

Gust Response of Spanwise Morphing Wing by Simulation and Wind Tunnel Testing

UAVs rely on mapping the surroundings to gather real-time environmental information. The mapping outcome stands as the key prerequisite for further motion planning. Plenty of work has been done with sophisticated mapping algorithms. However, during UAV navigation, these algorithms are expected to be updated continuously, which consumes a significant amount of memory and computational resources in a large area. To address this limitation, in this paper, we propose an end-to-end method for UAV autonomous motion planning via Reinforcement Learning (RL). In particular, a deep RL network is built as the brain of the intelligent agent, which takes the depth image of the UAV visual feedback as input, and outputs the continuous action as a control decision. A convolutional neural network is employed to process the depth image. In order to implement and validate the proposed method, a high-fidelity 3D simulation environment is established in AirSim, which generates the real-time flight status and depth images during the UAV flight. As a result, the flight simulation demonstrates the effectiveness and efficiency of the RL-based motion planning algorithm in a complex environment. Importantly, the agent trained by the proposed IDDPG could get closer to the destination than that trained by DDPG by about 17 meters on average. Last, the computation time for each step is significantly reduced to 5 ms compared to the classical approach.

An End-to-End Deep Reinforcement Learning Method for UAV Autonomous Motion Planning

This paper presents two adaptive control schemes for the stabilization of a nonlinear prototypical aeroelastic wing section with a trailing-edge and a leading-edge control surface. Using a Lyapunov approach, two types of controllers with high performance are designed based on neural networks. The first scheme accomplishes adaptive compensating control by eliminating uncertainties including structural nonlinearities, but certain parts of the system are required to design a linear feedback controller. Compared to the first scheme, the second does not need to consider internal information of the real system which is globally identified on-line using neural networks, and to improve robustness, approximate error is offset by adding extra terms to adaptive control laws. Results show that despite large uncertainties and unmodelled nonlinearities, the compensating controller is effective for flutter suppression, while the second scheme even have higher flutter speed than the former.

Flutter Suppression of Nonlinear Aeroelastic System using Adaptive Control based on Neural Network

This paper proposes a low-cost, high-efficiency, and generic mid-air autonomous release and recall system for mother-daughter UAVs. Such a design includes a release and recall shelter mechanism, lifting devices, and a gain-scheduled PID control algorithm for the recall of daughter-UAVs. The position feedback is based on the sensor fusion of GPS, UWB, and vision. The control scheme covers all of the different stages of the vehicles during recalling. In order to validate the sensing accuracy and the control logic, we carry out relevant experiments in high-fidelity simulator Gazebo and real-world environment respectively. As a result, the experimental tests demonstrate that the proposed mechanism can realize the lifting, opening and closing properly. In particular, the actual opening time of the rolling-curtain platform is about 8s. In addition, the developed recall algorithm can realize the real-time positioning and accurate control, and the average absolute values in very last stage of the simulated and real-world environment are 3.57cm and 5.13cm respectively, which meet the recall requirements under the hypothetical interference of downwash flow. Such results demonstrate the feasibility of the proposed system for mother-daughter UAVs to release and recall autonomously in mid-air. 

Daochun Li

Papers

Design of a Distributedly Active Morphing Wing Based on Digital Metamaterials

Gust Response of Spanwise Morphing Wing by Simulation and Wind Tunnel Testing

An End-to-End Deep Reinforcement Learning Method for UAV Autonomous Motion Planning

Flutter Suppression of Nonlinear Aeroelastic System using Adaptive Control based on Neural Network

Mid-Air Autonomous Release and Recall System for Mother-Daughter UAVs