Search Results

Dimensional optimization has always been a time consuming process, especially for aerodynamic bodies, requiring much tuning of dimensions and testing for each sample. Aerodynamic auxiliaries, especially wings, are design dependent on the primary model attached, as they influence the amount of lift or reduction in drag which is beneficial to the model. In this study CFD analysis is performed to obtain pressure counter of wings. For a wing, the angle of attack is essential in creating proper splits to incoming winds, even under high velocities with larger distances from the separation point. In the case of a group of wings, each wing is then mentioned as a wing element, and each wing is strategically positioned behind the previous wing in terms of its vertical height and its self-angle of attack to create maximum lift. At the same time, its drag remains variable to its shape ultimately maximizing the C L /C D ratio.

The study of aerodynamic forces in hypersonic environments is important to ensure the safety and proper functioning of aerospace vehicles. These forces vary with the angle of attack (AOA) and there exists an optimum angle of attack where the ratio of the lift to drag force is maximum. In this paper, computational analysis has been performed on a blunt cone model to study the aerodynamic characteristics when hypersonic flow is allowed to pass through the model. The flow has a Mach number of 8.44 and the angle of attack is varied from 0º to 20º. The commercial CFD solver ANSYS FLUENT is used for the computational analysis and the mesh is generated using the ICEM CFD module of ANSYS. Air is selected as the working fluid. The simulation is carried out for a time duration of 1.2 ms where it reaches a steady state and the lift and drag forces and coefficients are estimated. The pressure, temperature, and velocity contours at different angles of attack are also observed.

Selective Laser Melting (SLM) has gained widespread usage in aviation, aerospace, and die manufacturing due to its exceptional capacity for producing intricate metal components of highly complex geometries. Nevertheless, the instability inherent in the SLM process frequently results in irregularities in the quality of the fabricated components. As a result, this hinders the continuous progress and wider acceptance of SLM technology. Addressing these challenges, in-process quality control strategies during SLM operations have emerged as effective remedies for mitigating the quality inconsistencies found in the final components. This study focuses on utilizing optical emission spectroscopy and IR thermography to continuously monitor and analyze the SLM process within the powder bed, with the aim of strengthening process control and minimizing defects.

Abstract: The present study discusses about the effect of installation torque on the surface and subsurface deformations for thin walled 7075 aluminum alloy used in Aerospace applications. A FE model was constructed to predict the effect of torque induced stresses on thin walled geometry followed with an experimentation. A detailed surface analysis was performed on 7075 aluminum in terms of superficial discontinuities, residual stresses, and grain deformations. The localized strain hardening resulting from increased dislocation density and its effect on surface microhardness was further studied using EBSD and micro indentation. The predicted surface level plastic strain of .25% was further validated with grain deformations measured using optical and scanning electron microscopy.

Abstract: Hydraulic systems in aircrafts largely comprise of metallic components with high strength to weight ratios which comprise of 2024 Aluminum and Titanium Ti-6AL-4V. The selection of material is based on low and high pressure applications respectively. For aircraft fluid conveyance products, hydraulic conduits are fabricated by axisymmetric turning to support flow conditions. The hydraulic conduits further carries groves within for placement of elastomeric sealing components. This article presents a systematic study carried out on common loads experienced by fluid carrying conduits and the failure modes induced. The critical failure locations on fluid carrying conduits of 2024-T351 Aluminum was identified, and the Scanning Electron Microscope (SEM) analysis was carried out to identify the characteristic footprints of failure surfaces and crack initiation. Through this analysis, a load to failure mode correlation is established.

Industries have been increasingly adopting AI based computer vision models for automated asset defect inspection. A challenging aspect within this domain is the inspection of composite assets consisting of multiple components, each of which is an object of interest for inspection, with its own structural variations, defect types and signatures. Training vision models for such an inspection process involves numerous challenges around data acquisition such as insufficient volume, inconsistent positioning, poor quality and imbalance owing to inadequate image samples of infrequently occurring defects. Approaches to augmenting the dataset through Standard Data Augmentation (SDA) methods (image transformations such as flipping, rotation, contrast adjustment, etc.) have had limited success. When dealing with images of such composite assets, it is challenging to correct the data imbalance at the component level using image transformations as they apply to all the components within an image.

Electromechanical actuators (EMAs) play a crucial role in aircraft electrification, offering advantages in terms of aircraft-level weight, rigging and reliability compared to hydraulic actuators. To prevent backdriving, skewed roller braking devices called "no-backs" are employed to provide braking torque. These technology components are continuing to be improved with analysis driven design innovations eg. U.S. Pat. No. 8,393,568. The no-back mechanism has the rollers skewed around their own transverse axis that allow for a combination of rolling and sliding against the stator surfaces. This friction provides the necessary braking torque that prevents the backdriving. By controlling the friction radius and analyzing the Hertzian contact stresses, the brake can be sized for the desired duty cycle. No-backs can be configured to provide braking torque for both tensile and compressive backdriving loads.

Landing of spacecraft on Lunar or Martian surfaces is the last and critical step in inter planetary space missions. The atmosphere on earth is thick enough to slow down the craft but Moon or Mars does not provide a similar atmosphere. Moreover, other factors such as lunar dust, availability of precise onboard navigational aids etc would impact decision making. Soft landing meaning controlling the velocity of the craft from over 6000km/h to zero. If the craft’s velocity is not controlled, it might crash. Various onboard sensors and onboard computing power play a critical role in estimating and hence controlling the velocity, in the absence of GPS-like navigational aids. In this paper, an attempt is made using visual onboard sensor to estimate the velocity of the object. The precise estimation of an object's velocity is a vital component in the trajectory planning of space vehicles, particularly those designed for descent onto lunar or Martian terrains, such as orbiters or landers.

Scramjet-based hypersonic airbreathers are needed for next-generation defense and space applications. Two scramjet configurations, namely, rectangular and axisymmetric, are primarily studied in the literature. However, there is no quantitative comparison of the performance metrics between these two scramjet configurations. This study investigates the aero-thermo-structural performance of rectangular and axisymmetric scramjet engines at Mach 7 and 25 km altitude. A numerical framework involving computational fluid dynamics and computational structural dynamics is established. The aero-thermo-structural loads on the scramjet flow path are estimated using RANS/FANS simulation. A finite element-based coupled thermo-structural analysis is performed to understand the thermo-structural response. Before using the numerical models for the study, CFD and CSD modules are validated with literature data.

It is important to accurately predict the aerodynamic properties for designing applications which involves fluid flows, particularly in the aerospace industry. Traditionally, this is done through complex numerical simulations, which are computationally expensive, resource-intensive and time-consuming, making them less than ideal for iterative design processes and rapid prototyping. Machine learning, powered by vast datasets and advanced algorithms, offers an innovative approach to predict airfoil characteristics with remarkable accuracy, speed, and cost-effectiveness. Machine learning techniques have been applied to fluid dynamics and have shown promising results. In this study, machine learning model called the back-propagation neural network (BPNN) is used to predict key aerodynamic coefficients of lift and drag for airfoils.

This research employs a comprehensive methodology to explore hypersonic boundary layers' stability and transition dynamics, focusing specifically on the influence of sharp and blunt leading edges. The Stanford University Unstructured (SU2) Computational Fluid Dynamics (CFD) solver is utilized to compute the mean flow over a flat plate, establishing a foundational basis for subsequent stability analysis. The extracted boundary layer profiles undergo validation against existing literature, ensuring accuracy and reliability. Further analysis is conducted using a Python code to generate input files for the Linear Stability Solver. The Linear Stability Solver analysis constitutes a crucial phase wherein the research delves into the eigenvalue spectra, identifying dominant modes and closely scrutinizing the role of the modes in the transition process within the hypersonic boundary layers.

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