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Browse AllFor brake and clutch components of aircraft vehicles which require higher mechanical strength and wear resilient, light-weight aluminium composites were developed infusing solid lubricant. In this study, hybrid composites were developed using powder metallurgy route with aluminum alloy AA356 and various amounts of zirconium oxide (ZrO2) (0, 5, 10, 15, and 20 wt.%) as reinforcements. A solid lubricant hexagonal boron nitride (hBN) at a fixed 5 wt.% is considered. Following the appropriate ASTM guidelines, the specimens were mechanically characterized by measuring their density, porosity, micro-hardness, compression strength, impact strength, and flexural strength, among other properties. The findings showed that the composites' mechanical and physical behaviour were greatly affected by the inclusion of ZrO2. Porosity increased as a result of particle clustering and interfacial voids, while density increased gradually as ceramic content increased. Consistently increasing ZrO2 addition
Porosity in carbon fibre reinforced polymers (CFRP) remains a critical concern for aerospace engineers, as even minor voids introduced during manufacturing can undermine the reliability of structural components. This work explores the influence of Interply porosity on composite panel behavior, employing a multiscale simulation approach that bridges material characterization and full-scale structural analysis. The study begins with virtual coupon testing using Digimat-VA and Digimat-MF, enabling the prediction of material allowable and the assessment of defect variability. Homogenized material properties derived from these simulations are then applied to detailed panel models constructed in MSC Apex, ensuring accurate representation of layup and orthotropic behavior. The workflow can support a range of structural load cases, allowing for the evaluation of stiffness, buckling, or other relevant scenarios as dictated by aerospace certification requirements. Nonlinear finite element
The aerospace industry is undergoing a significant digital transformation in the way system requirements are defined, communicated, and managed. Major OEMs are moving towards fully model-based development processes, with plans to deliver requirements exclusively in the form of models. It is no longer sufficient to manage requirements using traditional document-based approaches; instead, organizations must adopt tools and processes that enable the consumption, interpretation, and implementation of model-based requirements. However, MBSE itself does not ensure that the requirements defined within the model are complete or consistent. Without rigorous validation techniques, even well-structured models can carry forward poorly defined or conflicting requirements — leading to errors that propagate throughout the development lifecycle. This work proposes an approach that integrates formal methods into MBSE workflows by enabling completeness and consistency checks of SysML-based requirements
This SAE Aerospace Recommended Practice (ARP) establishes the overall component and system function guidelines and minimum performance levels for a TPMS. These guidelines include, but are not limited to: Design recommendations for system components, which: Monitor tire inflation Are located in/on the tire/wheel assembly, landing gear axle, and/or aircraft avionics compartment Recommended performance and safety guidelines for a TPMS.
This research investigates the fabrication and evaluation of Delrin (polyoxymethylene, POM) composites reinforcing 5-20 wt.% chopped ramie fiber (RF). The polymer composites were fabricated via the injection moulding technique. Glass transition temperature (Tg), thermal conductivity, Vicat softening temperature (VST), heat deflection temperature (HDT), melt flow index (MFI), and coefficient of linear thermal expansion (CLTE) were the various thermal characteristics of the sustainable composites that were systematically evaluated as per the ASTM standards. The addition of RF drastically altered the Delrin matrix's performance. Among the formulations, the composite with 15 wt.% RF had the best combination of properties: higher VST and HDT values, which provide greater dimensional stability at high temperatures; lower CLTE, resulting in less thermal expansion; comparatively better thermal conductivity; and improved heat dissipation. Eventually, there was a moderate drop in the MFI
Dynamic responses at critical locations of a spacecraft due to excitations expected during the ascent phase of a launch vehicle mission are usually estimated through a Coupled Loads Analysis (CLA) using the structural dynamic finite element model of the launch vehicle coupled with that of the spacecraft. Generally, the full physical structural dynamic model of a spacecraft has lakhs of degrees-of-freedom (DOFs). Coupling such a model with a similar model for the launch vehicle results in exorbitantly high computational costs for CLA. Hence, dynamic analysis of such large and complex structural assemblies usually employ sub-structure coupling or Component Mode Synthesis (CMS) methods. The most widely used CMS method for dynamic analyses is the Craig-Bampton (CB) method. Conventionally, a full launch vehicle CLA involves one level of CB-reduction wherein a reduced-order dynamic model of the spacecraft is first generated using the fixed-interface CB-method. This reduced-order model is
This study presents a data-driven approach for strengthening aviation safety by integrating human factors assessment with modern predictive modeling techniques. The work focuses on understanding how human performance, operational conditions, and system-level interactions collectively influence safety risk, and how these interactions can be quantified to support improved design and decision-making. Unlike previous studies that address human factors or predictive modeling in isolation, this research offers a unified framework that links causal human factors indicators with statistical modeling, feature extraction, and machine learning based risk estimation. The novelty of this work lies in the structured pipeline that transforms raw categorical and narrative human factors information into measurable predictors that can be analyzed using structural modeling and machine learning. The methodology includes data preparation, dimensionality reduction, latent pattern discovery, dependence