Journal of Bionic Engineering最新文献

Serving as the initiating explosive devices between the propellant tank and the engines, metal-based rupture diaphragms are widely used in ramjet igniters owing to the advantages provided by their simple structure, small size, and low cost. However, the reliability of rupture pressure directly affects the success of engine ignition and rocket launch, which is mainly influenced by factors like material, structure, and residual thickness of the surface notch of the diaphragm. Among those, the geometry of the notch is easy to define and control when compared to the mechanical parameters of the ruptured diaphragm. Thus, to make the diaphragm rupture (1A30 Al) within the required pressure range (0.4 MPa ± 3.5%) with highly sensitive and reliability, we draw inspiration from the arthropod’s force-sensitive slit organ which encompasses curved microgrooves to design a Ω-shaped notch for the rupture diaphragm. Finite element analysis is used to study the relationship between the burst pressure and geometric dimension of the Ω-shaped and bioinspired microgroove. Based on that, metal-based rupture diaphragms are fabricated by femtosecond laser processing technology, followed by rupture tests. Experiment results demonstrate that the practical rupture pressure of the diaphragm is highly consistent with the finite element analysis results, which verifies the effectiveness of the bionic design.

The averaged equation of motion for linear acceleration in the BCF swimming mode was derived using the Elongated Body Theory (EBT) through time averaging. An analytical solution for the linear acceleration swimming velocity was obtained, revealing that the average velocity follows a hyperbolic tangent function of time, which can be considered a semi-empirical formula for linear acceleration swimming. The formula’s parameters, such as the steady swimming velocity and the acceleration time constant, can be determined by conducting experiments on linear acceleration, enabling the estimation of drag coefficient, effective added mass, thrust, and drag force. We developed a tensegrity robotic tuna and conducted a linear acceleration experiment. The results confirmed both the averaged equation of motion and its empirical formula, indicating that the formula is not limited by EBT and can be extended to large amplitude swimming and thunniform swimmers with large aspect ratio caudal fins. This provides researchers with an efficient and easy-to-implement method to estimate the swimming thrust and drag forces through linear acceleration experiments, without the need for complex and expensive flow field and force measurement equipment.

This paper presents a literature review exploring the potential of piezoelectric field-effect transistors (piezo-FETs) as bionic microelectromechanical systems (MEMS). First, piezo-FETs are introduced as bionic counterparts to natural mechanoreceptors, highlighting their classic configuration and working principles. Then, this paper summarizes the existing research on piezo-FETs as sensors for pressure, inertial, and acoustic sensors. Material selections, design characteristics, and key performance metrics are reviewed to demonstrate the advantage of piezo-FETs over traditional piezoelectric sensors. After identifying the limitations in these existing studies, this paper proposes using bionic piezoelectric coupling structures in piezo-FETs to further enhance the sensing capabilities of these artificial mechanoreceptors. Experimentally validated manufacturing methods for the newly proposed piezo-FET structures are also reviewed, pointing out a novel, feasible, and impactful research direction on these bionic piezoelectric MEMS sensors.

The anthropometric differences between European/American and Chinese population are remarkable and have significant influences on pedestrian kinematics and injury response in vehicle crashes. Therefore, the current study aims to develop and validate a Finite Element (FE) human body model representing the anthropometry of Chinese 50th percentile adult male for pedestrian safety analysis and development of Chinese ATDs (Anthropomorphic Test Devices). Firstly, a human body pedestrian model, named as C-HBM (Chinese Human Body Model), was developed based on the medical image data of a volunteer selected according to both anthropometry and anatomy characteristics of 50th percentile Chinese adult male. Then, the biofidelity of the C-HBM pedestrian model was validated against cadaver impact test data reported in the literature at the segment and full-body level. Finally, the validated C-HBM pedestrian model was employed to predict Chinese pedestrian injuries in real world vehicle crashes. The results indicate that the C-HBM pedestrian model has a good capability in predicting human body mechanical response in cadaver tests and Chinese leg and thorax injuries in vehicle crashes. Kinematic analysis shows that the C-HBM pedestrian model has less sliding on the hood surface, shorter movement in the horizontal direction, and higher pelvis displacement in the vertical direction than cadavers and the pedestrian model in the anthropometry of westerner due to anthropometric differences in the lower limbs. The currently developed C-HBM pedestrian model provides a basic tool for vehicle safety design and evaluation in China market, and for development of Chinese ATDs.

Gecko-inspired robots have significant potential applications; however, deviations in the yaw direction during locomotion are inevitable for legged robots that lack external sensing. These deviations cause the robot to stray from its intended path. Therefore, a cost-effective and straightforward solution is essential for reducing this deviation. In nature, the tail is often used to maintain balance and stability. Similarly, it has been used in robots to improve manoeuvrability and stability. Our aim is to reduce this deviation using a morphological computation approach, specifically by adding a tail. To test this hypothesis, we investigated four different tails (rigid plate, rigid gecko-shaped, soft plate, and soft gecko-shaped) and assessed the deviation of the robot with these tails on different slopes. Additionally, to evaluate the influence of different tail parameters, such as material, shape, and linkage, we investigated the locomotion performance in terms of the robot’s climbing speed on slopes, its ability to turn at narrow corners, and the resistance of the tails to external disturbances. A new auto-reset joint was designed to ensure that a disturbed tail could be quickly reset. Our results demonstrate that the yaw deviation of the robot can be reduced by applying a tail. Among the four tails, the soft gecko-shaped tail was the most effective for most tasks. In summary, our findings demonstrate the functional role of the tail in reducing yaw deviation, improving climbing ability and stability and provide a reference for selecting the most suitable tail for gecko-inspired robots.

Micro- and nano-structures are intentionally incorporated into various biological surfaces, such as fish scales, snakeskin, and burr-covered plant leaves, to enhance their interactions with other surfaces. The mechanical anisotropy affects friction, interlocking, propulsion, and mobility on substrates. This study investigates a novel method for developing a robust, stratified, soft, lubricating coating on a surface. 3-Methacryloyloxypropyl-trimethoxysilane (MPS) is a cohesive adhesion promoter that functions by infiltrating Polydimethylsiloxane (PDMS) silicone elastomers to maintain low friction levels and high mechanical load-bearing capacity. MPS makes it easier for organic and inorganic materials to adhere to the surface of the initiator layer P(AAm-co-AA-co-PDMS/Fe). We investigate how the tough hydrogel layer of the module impacts the lubricating ability of the multilayer coating when the tough hydrogel layer of the module adheres to the bio-based polyurethane substrate. After 1,000 sliding cycles with a 1 N load, the improved PDMS’s Coefficient of Friction (COF) remains steady and low (COF < 0.81). We recommend using the suggested structure and a standard set of optimal variables to enhance the functional efficiency of such systems. In conclusion, we have demonstrated the optimal simulation of these parameters for stimulus-responsive, adjustable surface systems.

This paper proposes an improved version of the Partial Reinforcement Optimizer (PRO), termed LNPRO. The LNPRO has undergone a learner phase, which allows for further communication of information among the PRO population, changing the state of the PRO in terms of self-strengthening. Furthermore, the Nelder-Mead simplex is used to optimize the best agent in the population, accelerating the convergence speed and improving the accuracy of the PRO population. By comparing LNPRO with nine advanced algorithms in the IEEE CEC 2022 benchmark function, the convergence accuracy of the LNPRO has been verified. The accuracy and stability of simulated data and real data in the parameter extraction of PV systems are crucial. Compared to the PRO, the precision and stability of LNPRO have indeed been enhanced in four types of photovoltaic components, and it is also superior to other excellent algorithms. To further verify the parameter extraction problem of LNPRO in complex environments, LNPRO has been applied to three types of manufacturer data, demonstrating excellent results under varying irradiation and temperatures. In summary, LNPRO holds immense potential in solving the parameter extraction problems in PV systems.

Biomimicry is an interdisciplinary field that aims to provide sustainable solutions to technical issues. However, learners often encounter challenges in the application of biomimicry due to the multidisciplinary requisites and abstract thinking skills required. Although multiple hands-on activities and teaching strategies have been explored, significant obstacles remain. Recently, generative artificial intelligent tools have become readily accessible to the general public, among which is ChatGPT. ChatGPT is known for generating detailed responses to user inquiries and has demonstrated efficacy in enhancing learning, although its specific application to biomimicry education has yet to be explored. To bridge this knowledge gap, this study seeks to evaluate the capabilities of ChatGPT in helping its users identify biomimetic solutions. It is found that the effectiveness of ChatGPT in biomimicry education significantly depends on the user’s ability to formulate knowledgeable and effective prompts. Although, a novice user can use ChatGPT to get a fundamental overview of the technical challenge and explore potential sources of bioinspiration. The study proposes a theoretical framework to guide users in the effective use of ChatGPT for biomimicry education and application. In addition, users are cautioned against ChatGPT responses and advised to employ it as a tool to complement their own knowledge gaps. The results from this study can offer insights for teachers and self-directed learners on the effective use of prompts in ChatGPT for biomimicry education. Future investigations will seek to validate this framework by evaluating users’ experiences and feedback on its application in creating prototypes.

Flexible sensors, a class of devices that can convert external mechanical or physical signals into changes in resistance, capacitance, or current, have developed rapidly since the concept was first proposed. Due to the special properties and naturally occurring excellent microstructures of biomaterials, it can provide more desirable properties to flexible devices. This paper systematically discusses the commonly used biomaterials for bio-based flexible devices in current research applications and their deployment in preparing flexible sensors with different mechanisms. According to the characteristics of other properties and application requirements of biomaterials, the mechanisms of their functional group properties, special microstructures, and bonding interactions in the context of various sensing applications are presented in detail. The practical application scenarios of biomaterial-based flexible devices are highlighted, including human-computer interactions, energy harvesting, wound healing, and related biomedical applications. Finally, this paper also reviews in detail the limitations of biobased materials in the construction of flexible devices and presents challenges and trends in the development of biobased flexible sensors, as well as to better explore the properties of biomaterials to ensure functional synergy within the composite materials.

Combining deep-learning image inpainting algorithms with the microfluidic technology, the paper proposes a method to achieve dynamic stealth and camouflage by using a microfluidic vision camouflage system simulating the chameleon skin. The basic principle is to perceive color changes in the external environment and collect ambient image information, and then utilize the image inpainting algorithm to adjust the control signals of the microfluidic system in real time. The detailed working principle of the microfluidic vision camouflage system is presented, and the mechanism of generating control signals for the system through deep-learning image inpainting algorithms and image-processing techniques is elucidated. The camouflage effect of the chameleon skin is analyzed and evaluated using color similarity. Results indicate that the camouflaged images are consistent with the background environment, thereby improving the target’s stealth and maneuvering characteristics. The camouflage technology developed in the paper based on the microfluidic vision camouflage system can be applied to several situations, such as military camouflage uniforms, robot skins, and weapon equipment.