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The purpose of this study is to identify the possible relation between the vibration and the stall by using the vibration response of the airfoil. For this purpose, the root mean square values of the acceleration signals are evaluated to demonstrate the compatibility between the stall angles and the vibration levels.

An experimental study is conducted on NACA 4415 airfoil at Reynolds numbers 69e3, 77e3 and 85e3. Experiments are performed from 0° to 25° of the angles of attack (AoA) for each Reynolds number condition. To observe the change of the vibration values at the stall region clearly, experiments are performed with the AoA ranging from 10° to 25° in 1° increments. Three acceleration sensors are used to obtain the vibration data.

The results show that the increase in the amplitude of the vibration is directly related to the decrease in lift. These findings indicate that this approach could be beneficial in detecting stall on airfoil-type structures.

This study proposes a new approach for detecting stall over the airfoil using the vibration data.

Regular monitoring of bacteria, especially Escherichia coli, in wastewater is crucial to ensure the maintenance of public health. Amperometric detection proves to be a fast, sensitive and economically viable solution for E. coli enumeration. This paper reported a prototype amperometric sensor based on PANI-ZnO-NiO nanocomposite thin films prepared by sol–gel method and irradiated with gamma ray. The purpose of this study is to investigate the sensor performance of PANI-ZnO-NiO nanocomposite thin films to detect E. coli in water.

The films were varied with different compositions of ZnO and NiO by using the formula PANI-(ZnO)_1-x-(NiO)_x, with x = 0.2, 0.4, 0.6 and 0.8. PANI-ZnO-NiO nanocomposite thin films were characterized by using X-ray diffraction (XRD) and atomic force microscopy (AFM) to study the crystallinity and surface morphology of the films. The sensor performance was conducted using the current–voltage (I-V) measurement by testing the films in clean water and E. coli solution.

XRD diffractograms show the peaks of ZnO (1 0 0) and NiO (1 0 2). AFM analysis shows the surface roughness, and the grain size of PANI-ZnO-NiO thin films decreases when the concentration ratios of NiO increased. I-V curves show the difference in current flow, where the current in E. coli solution is higher than the clean water.

PANI-(ZnO)_1-x-(NiO)_x nanocomposite thin film with the highest concentration of ZnO performed the highest sensitivity among the other concentrations, which can be used to indicate the presence of E. coli bacteria in water.

Vision, audition, olfactory, tactile and taste are five important senses that human uses to interact with the real world. As facing more and more complex environments, a sensing system is essential for intelligent robots with various types of sensors. To mimic human-like abilities, sensors similar to human perception capabilities are indispensable. However, most research only concentrated on analyzing literature on single-modal sensors and their robotics application.

This study presents a systematic review of five bioinspired senses, especially considering a brief introduction of multimodal sensing applications and predicting current trends and future directions of this field, which may have continuous enlightenments.

This review shows that bioinspired sensors can enable robots to better understand the environment, and multiple sensor combinations can support the robot’s ability to behave intelligently.

The review starts with a brief survey of the biological sensing mechanisms of the five senses, which are followed by their bioinspired electronic counterparts. Their applications in the robots are then reviewed as another emphasis, covering the main application scopes of localization and navigation, objection identification, dexterous manipulation, compliant interaction and so on. Finally, the trends, difficulties and challenges of this research were discussed to help guide future research on intelligent robot sensors.

Identification of the direction of the sound source is very important for human–machine interfacing in the applications such as target detection on military applications and wildlife conservation. Considering its vast applications, this study aims to design, simulate, fabricate and test a bidirectional acoustic sensor having two cantilever structures coated with piezoresistive material for sensing has been designed, simulated, fabricated and tested.

The structure is a piezoresistive acoustic pressure sensor, which consists of two Kapton diaphragms with four piezoresistors arranged in Wheatstone bridge arrangement. The applied acoustic pressure causes diaphragm deflection and stress in diaphragm hinge, which is sensed by the piezoresistors positioned on the diaphragm. The piezoresistive material such as carbon or graphene is deposited at maximum stress area. Furthermore, the Wheatstone bridge arrangement has been formed to sense the change in resistance resulting into imbalanced bridge and two cantilever structures add directional properties to the acoustic sensor. The structure is designed, fabricated and tested and the dimensions of the structure are chosen to enable ease of fabrication without clean room facilities. This structure is tested with static and dynamic calibration for variation in resistance leading to bridge output voltage variation and directional properties.

This paper provides the experimental results that indicate sensor output variation in terms of a Wheatstone bridge output voltage from 0.45 V to 1.618 V for a variation in pressure from 0.59 mbar to 100 mbar. The device is also tested for directionality using vibration source and was found to respond as per the design.

The fabricated devices could not be tested for practical acoustic sources due to lack of facilities. They have been tested for a vibration source in place of acoustic source.

The piezoresistive bidirectional sensor can be used for detection of direction of the sound source.

In defense applications, it is important to detect the direction of the acoustic signal. This sensor is suited for such applications.

The present paper discusses a novel yet simple design of a cantilever beam-based bidirectional acoustic pressure sensor. This sensor fabrication does not require sophisticated cleanroom for fabrication and characterization facility for testing. The fabricated device has good repeatability and is able to detect the direction of the acoustic source in external environment.

Since most of the existing literature do not disclose the node coordinate data of its fixed-wing aircraft airfoil, in order to develop and obtain a practical and suitable deformation airfoil for fixed-wing micro air vehicle (MAV), this paper proposes an improved airfoil design method of fixed-wing MAV based on the profile data of S5010 airfoil.

Combined with the body shape variation of the stingray in the propulsion process, the parametric study of the aerodynamic shape of the original design airfoil is carried out to explore the influence of a single parameter change on the aerodynamic performance of the airfoil. Then, according to the influence law of single parameter variation on the aerodynamic performance of the airfoil, the original airfoil is synthetically deformed by changing multiple parameters.

By comparing the aerodynamic performance of the multi-parameter deformed airfoil with the original airfoil, it is found that the lift coefficient of the multi-parameter deformed airfoil changes from negative to positive value when AOA = 0°. When AOA = 2°, the lift coefficient growth rate is the largest, which is 47.27%, and the lift-to-drag ratio is increased by 50.00%. At other angles of attack, the lift, drag, and torque coefficients of the multi-parameter deformed airfoil are optimized to some extent.

Combined the body shape variation of the stingray in the propulsion process, the parametric study of the aerodynamic shape of the original design airfoil is carried out to explore the influence of a single parameter change on the aerodynamic performance of the airfoil.

This paper aims to present numerical simulations for a series of flight processes for the postlaunching stage of the “balloon-borne UAV system.” It includes the balloon further ascent motion after airborne launching. In terms of unmanned aerial vehicles (UAVs), the tailspin state and the charge-out process with an anti-tailspin parachute-assisted suspending are analyzed. Then, the authors conduct trajectory optimization simulations for the long-distance gliding process.

The balloon kinematics model and the parachute Kane multibody dynamic model are established. Using steady-state tailspin to reduced-order analysis and achieving change-out simulation by parachute suspension dynamic model. A reentry optimization control problem is developed and the Radau pseudo-spectral method is used to calculate the glide trajectory.

The established dynamic model and trajectory optimization method can effectively simulate the motion process of balloons and UAVs. The system mass reduction for launching UAVs will not cause damage to the balloon structure. The anti-tailspin parachute can reduce the UAV attack angles effectively. The UAV can glide to the designated target position by adjusting the attack angle and sideslip angle. The farthest flight distance after launching from 20 km height is 94 km and the gliding time is 40 min, which demonstrates the potential application advantage of high-altitude launching.

The research content and related conclusions of this article achieve a closed-loop analysis of the flight mission chain for the “balloon-borne UAV system,” which provides simulation references for relevant balloon launching experiments.

This paper establishes a complete set of numerical simulation models and can effectively analyze various postlaunching behaviors.

This study aims to present a unique hybrid metaheuristic approach to solving the nonlinear analysis of hall currents and electric double layer (EDL) effects in multiphase wavy flow by merging the firefly algorithm (FA) and the water cycle algorithm (WCA).

Nonlinear Hall currents and EDL effects in multiphase wavy flow are originally described by partial differential equations, which are then translated into an ordinary differential equation model. The hybrid FA-WCA technique is used to take on the optimization challenge and find the best possible design weights for artificial neural networks. The fitness function is efficiently optimized by this hybrid approach, allowing the optimal design weights to be determined.

The proposed strategy is shown to be effective by taking into account multiple variables to arrive at a single answer. The numerical results obtained from the proposed method exhibit good agreement with the reference solution within finite intervals, showcasing the accuracy of the approach used in this study. Furthermore, a comparison is made between the presented results and the reference numerical solutions of the Hall Currents and electroosmotic effects in multiphase wavy flow problem.

This comparative analysis includes various performance indices, providing a statistical assessment of the precision, efficiency and reliability of the proposed approach. Moreover, to the best of the authors’ knowledge, this is a new work which has not been explored in existing literature and will add new directions to the field of fluid flows to predict most accurate results.

Fuselage structures are subjected to combinations of axial, bending, shear and differential pressure loads. The validation of advanced metallic and composite fuselage designs against such loads is based on the full-scale testing of the fuselage barrel, which, however, is highly demanding from a time and cost viewpoint. This paper aims to assist in scaling-down the experimentation to the stiffened panel level which presents the opportunity to validate state-of-the-art designs at higher rates than previously attainable.

Development of a methodology to successfully design tests at the stiffened panel level and realize them using advanced, complex and adaptable test-rigs that are capable of introducing independently a set of distinct load types (e.g. internal overpressure, tension, shear) while applying appropriate boundary conditions at the edges of the stiffened panel.

A baseline test-rig configuration was developed after extensive parametric modelling studies at the stiffened panel level. The realization of the loading and boundary conditions on the test-rig was facilitated through innovative supporting and loading system set-ups.

The proposed test bench is novel and compared to the conventional counterparts more viable from an economic and manufacturing point of view. It leads to panel responses, which are as close as possible to those of the fuselage barrel in-flight and can be used for the execution of static or fatigue tests on metallic and thermoplastic curved integrally stiffened full-scale panels, representative of a business jet fuselage.

The purpose of this study to find an alternate method to minimize waste i.e., eggshell and rice husk ash. In this paper, eggshell (ES) and rice husk ash (RHA) particles are used as reinforcements for examining their effect on the coefficient of thermal expansion (CTE), grain size (GS) and corrosion behavior for developed composite material.

In this investigation, 5 Wt.% each of ES and RHA reinforcement particles have been introduced. To investigate the microstructures of the developed composite material, scanning electron microscope was used. Physical and mechanical properties of composite material are tensile strength and hardness that have been examined.

The result of this paper shows that number of grains per square inch for composition Al/5% ES/5% RHA composite was found to be 1,243. Minimum value of the volume CTE was found to be 6.67 × 10–6/°C for Al/5% ES/5% RHA composite. The distribution of hard phases of ES particles in metal matrix is responsible for improvements in tensile strength and hardness. These findings demonstrated that using carbonized ES as reinforcement provides superior mechanical and physical properties than using uncarbonized ES particles.

There are several articles examining the impact of varying Wt.% of carbonized ES and rice husk reinforcement on the microstructures and mechanical characteristics of metal composites. CTE, GS and corrosion behavior are among of the features that are examined in this paper.

Ensuring the early detection of structural issues in aircraft is crucial for preserving human lives. One effective approach involves identifying cracks in composite structures. This paper employs experimental modal analysis and a multi-variable Gaussian process regression method to detect and locate cracks in glass fiber composite beams.

The present study proposes Gaussian process regression model trained by the first three natural frequencies determined experimentally using a roving impact hammer method with crystal four-channel analyzer, uniaxial accelerometer and experimental modal analysis software. The first three natural frequencies of the cracked composite beams obtained from experimental modal analysis are used to train a multi-variable Gaussian process regression model for crack localization. Radial basis function is used as a kernel function, and hyperparameters are optimized using the negative log marginal likelihood function. Bayesian conditional probability likelihood function is used to estimate the mean and variance for crack localization in composite structures.

The efficiency of Gaussian process regression is improved in the present work with the normalization of input data. The fitted Gaussian process regression model validates with experimental modal analysis for crack localization in composite structures. The discrepancy between predicted and measured values is 1.8%, indicating strong agreement between the experimental modal analysis and Gaussian process regression methods. Compared to other recent methods in the literature, this approach significantly improves efficiency and reduces error from 18.4% to 1.8%. Gaussian process regression is an efficient machine learning algorithm for crack localization in composite structures.

The experimental modal analysis results are first utilized for crack localization in cracked composite structures. Additionally, the input data are normalized and employed in a machine learning algorithm, such as the multi-variable Gaussian process regression method, to efficiently determine the crack location in these structures.