Thrust vectoring innovations are demonstrated ideas that improve the projection of aerospace power with enhanced maneuverability, control effectiveness, survivability, performance, and stealth. Thrust vector control systems following a variety of concepts have been considered for modern aircraft and missiles to enhance their military performance. Short Take-off and Landing (STOL) and control effectiveness at lower aircraft speeds can be achieved by employing Fluidic Thrust Vectoring Control (FTVC). This paper summarizes a range of ideas for FTVC that have been designed and tested both computationally and experimentally to determine the thrust vectoring performance of supersonic propulsion system nozzles. The conventional method of thrust vectoring involves mechanical means to deflect the direction of flow of the exhaust gases, whereas the most recent method involves fluidic-based thrust vectoring techniques. Fluid-based thrust vectoring has the advantages of simplicity and low weight over mechanical-based thrust vectoring, which has complex geometry and adds extra weight to the aircraft. The fluidic vectoring control nozzles are divided into seven categories: shock vector, bypass shock vector, counterflow, co-flow, throat skewing, dual throat, and bypass dual throat nozzle control. This paper provides a summary of each fluidic thrust vectoring technique with its characteristics, design, classification, and different operational criteria developed to date and compares the intrinsic characteristics of each technique. Based on the present literature, it is concluded that among all the fluidic control techniques, the bypass dual-throat nozzle control can achieve better thrust vectoring performance with large vector angles and low thrust loss.

Drones are becoming increasingly valuable tools in wildlife studies due to their ability to access remote areas and offer high-resolution information with minimal human interference. Their application is, however, causing concern regarding wildlife disturbance. This review synthesizes the existing literature on how animals within terrestrial, aerial, and aquatic environments are impacted by drone disturbance in relation to operational variables, sensory stimulation, species-specific sensitivity, and physiological and behavioral responses. We found that drone altitude, speed, approach distance, and noise levels significantly influence wildlife responses, with some species exhibiting increased vigilance, flight responses, or physiological stress. Environmental context and visual cues are also involved in species detection of drones and disturbance thresholds. Although the short-term response to behavior change has been well documented, long-term consequences of repeated drone exposure remain poorly known. This paper identifies the necessity for continued research into drone–wildlife interactions, with an emphasis on the requirement to minimize disturbance by means of improved flight parameters and technology.

Drones are increasingly popular for collecting behaviour data of group‐living animals, offering inexpensive and minimally disruptive observation methods. Imagery collected by drones can be rapidly analysed using computer vision techniques to extract information, including behaviour classification, habitat analysis and identification of individual animals. While computer vision techniques can rapidly analyse drone‐collected data, the success of these analyses often depends on careful mission planning that considers downstream computational requirements—a critical factor frequently overlooked in current studies. We present a comprehensive summary of research in the growing AI‐driven animal ecology (ADAE) field, which integrates data collection with automated computational analysis focused on aerial imagery for collective animal behaviour studies. We systematically analyse current methodologies, technical challenges and emerging solutions in this field, from drone mission planning to behavioural inference. We illustrate computer vision pipelines that infer behaviour from drone imagery and present the computer vision tasks used for each step. We map specific computational tasks to their ecological applications, providing a framework for future research design. Our analysis reveals AI‐driven animal ecology studies for collective animal behaviour using drone imagery focus on detection and classification computer vision tasks. While convolutional neural networks (CNNs) remain dominant for detection and classification tasks, newer architectures like transformer‐based models and specialized video analysis networks (e.g. X3D, I3D, SlowFast) designed for temporal pattern recognition are gaining traction for pose estimation and behaviour inference. However, reported model accuracy varies widely by computer vision task, species, habitats and evaluation metrics, complicating meaningful comparisons between studies. Based on current trends, we conclude semi‐autonomous drone missions will be increasingly used to study collective animal behaviour. While manual drone operation remains prevalent, autonomous drone manoeuvrers, powered by edge AI, can scale and standardise collective animal behavioural studies while reducing the risk of disturbance and improving data quality. We propose guidelines for AI‐driven animal ecology drone studies adaptable to various computer vision tasks, species and habitats. This approach aims to collect high‐quality behaviour data while minimising disruption to the ecosystem.

Modern aircraft and missiles are gradually integrating thrust vector control systems to enhance their military capabilities. Bypass Dual-Throat Nozzle (BDTN) control is a new fluidic thrust vectoring technique capable of achieving superior performance with large vector angles and low thrust loss. In this study, we analyzed the flow characteristics and performance parameters of BDTN by varying the bypass angle, nozzle convergence angle, and bypass width. The flow governing equations are solved according to a finite volume discretization technique of the compressible RANS equations coupled with the Renormalization Group (RNG) k-ϵ turbulence model for Nozzle Pressure Ratio (NPR = 2~10) to capture the significance of under-expanded and over-expanded jets. Results show that by decreasing the bypass angle from 90° to 35°, there is a 6% increase in vectoring angle while the vectoring efficiency is enhanced by 18%. However, a decrease of 3% in the thrust and discharge coefficients is also observed. When the convergence angle was increased from 22° to 37°, vectoring angle, discharge coefficient, and thrust coefficient increased by 2%, 1%, and 0.26%, respectively. Moreover, vectoring efficiency is also enhanced by 8% by reducing the convergence angle from 37° to 22°. Based on the investigated parameters, it is determined that nozzle convergence angle does not significantly influence thrust vectoring performance, however, bypass width and bypass angle have a significant effect on thrust vectoring performance.

Thrust vectoring is a promising technology that offers the potential for improved maneuverability, control efficiency, and stealth characteristics of aircraft. Optimized nozzle design over a range of operating conditions is one of the most crucial factors for maximum thrust vectoring operation. Our goal was to investigate the optimal design of bypass dual throat nozzle to maximize thrust vectoring. We performed a multi-objective optimization study by varying the nozzle bypass angle, convergence angle, and bypass width to see what impact these parameters had on the performance of the bypass dual throat nozzle. A steady numerical simulation has been performed on 55 different nozzle configurations to compare their thrust vectoring performance and losses. In all simulations, the k- ϵ turbulence model is used to determine the vectoring states of the nozzle. The computational fluid dynamics analysis was followed by a multi-objective optimization process using the Response Surface Methodology within the ModeFrontier software. The testing of the optimized nozzle shapes using ANSYS FLUENT verified the accuracy and reliability of the multi-objective optimization algorithm. These findings suggest that nozzle convergence does not significantly affect thrust vectoring. In contrast, bypass width and bypass angle significantly affected thrust vectoring.

Drones are becoming increasingly valuable tools in wildlife studies due to their ability to 1 access remote areas and offer high-resolution information with minimal human interference. Their 2 application is, however, causing concern regarding wildlife disturbance. This review synthesizes the 3 existing literature on how animals within terrestrial, aerial, and aquatic environments are impacted by 4 drone disturbance in relation to operational variables, sensory stimulation, species-specific sensitivity, 5 and physiological and behavioral responses. We found that drone altitude, speed, approach distance, 6 and noise levels significantly influence wildlife responses, with some species exhibiting increased 7 vigilance, flight responses, or physiological stress. Environmental context and visual cues are also 8 involved in species detection of drones and disturbance thresholds. Although the short-term response 9 to behaviour change has been well documented, long-term consequences of repeated drone exposure 10 remain poorly known. This paper identifies the necessity for continued research into drone-wildlife 11 interactions, with an emphasis on the requirement to minimize disturbance by means of improved 12 flight parameters and technology.