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Abstract Constant area section length downstream to the fuel injection point is a crucial dimension of scramjet duct geometry. It has a major contribution in creating the maximum effective pressure inside the combustor that is required for propulsion. The length is limited by the thermal choking phenomenon, which occurs when heat is added in a flow through constant area duct. As per theory, to avoid thermal choking the constant area section length depends upon the inlet conditions and the rate of heat addition. The complexity related to mixing and combustion process inside the supersonic stream makes it difficult to predict the rate of heat addition and in turn the length. Recent efforts of simulating the reacting flow inside scramjet combustors are encouraging and can be useful in this regard. The presented work attempts to use simulation results of scramjet combustion for predicting the constant area section length for a typical scramjet combustor.

Abstract The article considers one of the possible mechanisms of loading the solidity of a cylindrical metal composite high-pressure vessel (MC HPV). This mechanism manifests itself as delamination of a thin-walled metal shell (liner) from a more rigid composite shell causing local buckling. A similar effect can be detected in the manufacturing process of MC HPV, when the composite shell is formed by winding with tension a carbon fiber-reinforced plastic tape on the liner. Pressure transfer from the composite shell to the liner is carried out by the method of temperature analogy, that is, by cooling the composite shell, thermally insulated from the liner. To solve the problem of externally confined liner local buckling an approach is proposed, which is based on three points: the introduction of local technological deviations inherent in actual structures, the determination of the general stress-strain state, and a real-time deforming.

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Abstract The study aims to design a roadmap of the turboelectric propulsion system’s power system architecture, which is based on the Inner Bus Tie Architecture proposed by the National Aeronautics and Space Administration (NASA). The single turboshaft engine shutdown failure mode is analyzed and simulated on MATLAB/Simulink. A General Electric (GE) T700 turboshaft engine is preferred in constructing a turboshaft engine mathematical model. The constructed mathematical model is integrated into NASA’s Inner Bus Tie Architecture. As a result, mechanical to electrical conversion efficiency and power balance of the system is derived. Element sizing is accomplished. Preliminary power requirements for generators, power converters, and electrical motors are calculated to generate a roadmap for future applications. Finally, it is observed that the simulations are found efficient and quite acceptable for such applications.

Abstract The airline industry faces a crisis in the future as consumer demand is increasing, but the environmental effects and depleting resources of kerosene mean that growth is unsustainable. Hydrogen is touted as the leading candidate to replace kerosene, but it needs significant technological and economical endeavors. In such a scenario, cryogenic liquid hydrogen (LH2) is predicted to be the most feasible method of using hydrogen. The major challenge of LH2 as an aircraft fuel is that it requires approximately four times the storage volume of kerosene—due to its lower density. Thus the design of cryogenic storage tanks to handle larger quantities of fuel is becoming increasingly important. But the increase in drag associated with larger storage tanks causes an increase in fuel consumption. Hence, this paper aims to evaluate the aerodynamic performance of different storage configurations and aid in the selection of an economic and efficient storage system.

Abstract The vibration behavior of components exposed to aerodynamic loads must be taken into consideration when designing aerial vehicles. Numerical simulation plays a key role in developing more realistic analytical models for panel flutter analysis. The notable feature of the present research is the use of two methods for the aeroelastic analysis of two-dimensional curved panels with cylindrical bending. In the first approach, the finite volume method (FVM) is used for supersonic viscous flow and nonlinear structural model while full Navier-Stokes equations are discretized. In the second approach, the third-order nonlinear piston theory aerodynamics in addition to mechanical and thermal loads is assumed. Moreover, the semi-analytical weighted residual method for the nonlinear curved panel is utilized. These approaches are concurrently compared with each other for the first time. Furthermore, Hamilton’s principle is used and partial differential equations (PDEs) are derived.

Abstract The main focus of this article is the online estimation of the terminal airspace sector capacity from the Air Traffic Controller 0ATC) dynamical neural model using Neural Partial Differentiation (NPD) with permissible safe separation and affordable workload. For this purpose, a primarily neural model of a multi-input-single-output (MISO) ATC dynamical system is established, and the NPD method is used to estimate the model parameters from the experimental data. These estimated parameters have a less relative standard deviation, and hence the model validation results show that the predicted neural model response is well matched with the intervention of the ATC workload. Moreover, the proposed neural network-based approach works well with the experimental data online as it does not require the initial values of model parameters, which are unknown in practice.

According to the International Civil Aviation Organization, the world aviation air traffic has grown by an average yearly rate of 5% over the last thirty years, until the devastating downturn brought on by the COVID crisis of 2020. Regardless of the current situation, there are still a number of issues and challenges that the industry is confronted with, not the least of which are related to sustainability, the conversion to electrical usage, the challenge of increasing propulsion efficiency in conventional propulsion, the digital transformation of the entire ecosystem, etc. In response, system developers and researchers in the field are working on a number of key technologies and methodologies to solve some of these issues. The Sustainable Aviation Research Society (SARES), a global organization that seeks to encourage research in this area and helps disseminate knowledge via conferences and symposia, has been organizing meetings to promote sustainable aviation over the five years.

Abstract Analysis and design of total temperature probes for accurate measurements in hot, high-speed flows remains a topic of great interest in aerospace propulsion and a number of other engineering areas. One can apply detailed computational methods for simultaneous convection, conduction and radiation heat transfer, but such approaches are not suitable for rapid, routine analysis and design studies. For these studies, there is still a place for low-order approximate methods, and that is the subject of this paper. Here, an enhanced, low-order model is presented that includes conduction with variable thermal conductivity, convection with varying convection coefficient, varying diameter (and thus area) along the length of the sensor and radiation, all implemented in a convenient MATLAB code.

Abstract The purpose of this research is to fabricate a multipurpose drone with different lightweight materials that are used for water irrigation as well as pesticide spraying in agricultural fields. Components are collected and the drone is fabricated based on the parameters required for payload, weight, and design calculations. After the completion of fabrication, the drone is tested using different masses of payload for better endurance. The drone arms are made of balsa wood and stands are fabricated with polyvinyl chloride (PVC) and carbon fiber. The obtained results proved that a full payload is able to fly for 7 min; at the same time if we reduce the payload to 50%, the endurance will be increased double the time. In this study, the same drone and pumping configuration is used to perform the water and pesticide irrigation over various areas on agricultural land, which is achieved by changing the tank quantity.

Abstract At supersonic aircraft speeds, aerodynamically heated surfaces, e.g., nose, wing leading edges, are infrared (IR) signature sources from the tactically crucial frontal aspect. This study numerically predicts and then illustrates the minimization of IR contrast between the nose and background sky radiance by the emissivity optimization (εw,opt) technique, which has the least performance penalties. The IR contrast between the aircraft nose and its replaced background in 1.9-2.9 μm short-wave IR (SW-IR), 3-5 μm medium-wave IR (MW-IR), and 8-12 μm long-wave IR (LW-IR) bands are obtained. The IR contrast especially in LW-IR (i) increases with flight Mach number (M ∞) for a given flight altitude (H) and εw (ii) decreases with increasing H for a given M ∞ and εw. The εw,opt for a flight altitude of 5 km is found to decrease from 0.99 at M ∞ = 0.001 (low subsonic) in all three bands to 2 × 10−4 in MW-IR and 0.0213 in LW-IR bands at M ∞ = 3 (high supersonic).

Abstract This article explores the value of simulation for autonomous-vehicle research and development. There is ample research that details the effectiveness of simulation for training humans to fly and drive. Unfortunately, the same is not true for simulations used to train and test artificial intelligence (AI) that enables autonomous vehicles to fly and drive without humans. Research has shown that simulation “fidelity” is the most influential factor affecting training yield, but psychological fidelity is a widely accepted definition that does not apply to AI because it describes how well simulations engage various cognitive functions of human operators. Therefore, this investigation reviewed the literature that was published between January 2010 and May 2022 on the topic of simulation fidelity to understand how researchers are defining and measuring simulation fidelity as applied to training AI.