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Browse AllThis 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
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 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