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The issue of greenhouse gas (GHG) emissions from the transportation sector is widely acknowledged. Recent years have witnessed a push towards the electrification of cars, with many considering it the optimal solution to address this problem. However, the substantial battery packs utilized in electric vehicles contribute to a considerable embedded ecological footprint. Research has highlighted that, depending on the vehicle's size, tens or even hundreds of thousands of kilometers are required to offset this environmental burden. Human-powered vehicles (HPVs), thanks to their smaller size, are inherently much cleaner means of transportation, yet their limited speed impedes widespread adoption for mid-range and long-range trips, favoring cars, especially in rural areas. This paper addresses the challenge of HPV speed, limited by their low input power and non-optimal distribution of the resistive forces.

Electric vehicles offer cleaner transportation with lower emissions, thus their increased popularity. Although, electric powertrains contribute to quieter vehicles, the shift from internal combustion engines to electric powertrains presents new Noise, Vibration, and Harshness challenges. Unlike traditional engines, electric powertrains produce distinctive tonal noise, notably from motor whistles and gear whine. These tonal components have frequency content, sometimes above 10 kHz. Furthermore, the housing of the powertrain is the interface between the excitation from the driveline via the bearings and the radiated noise (NVH). Acoustic features of the radiated noise can be predicted by utilising the transmitted forces from the bearings. Due to tonal components at higher frequencies and dense modal content, full flexible multibody dynamics simulations are computationally expensive.

Squeak and rattle (SAR) noise audible inside a passenger car causes the product quality perceived by the customer to deteriorate. The consequences are high warranty costs and a loss in brand reputation for the vehicle manufacturer in the long run. Therefore, SAR noise must be prevented. This research shows the application and experimental validation of a novel method to predict SAR noise on an actual vehicle interior component. The novel method is based on non-linear theories in the frequency domain. It uses the harmonic balance method in combination with the alternating frequency/time domain method to solve the governing dynamic equations. The simulation approach is part of a process for SAR noise prediction in vehicle interior development presented herein. In the first step, a state-of-the-art linear frequency-domain simulation estimates an empirical risk index for SAR noise emission. Critical spots prone to SAR noise generation are located and ranked.

Composite materials, pioneered by aerospace engineering due to their lightweight, strength, and durability properties, are increasingly adopted in the high-performance automotive sector. Besides the acknowledged composite components’ performance, enabled lightweighting is becoming even more crucial for energy efficiency, and therefore emissions along vehicle use phase from a decarbonization perspective. However, their use entails energy-intensive and polluting processes involved in raw material production, in manufacturing processes, and, in particular, in end-of-life disposal. Carbon footprint is the established indicator to assess the environmental impact of climate-changing factors on products or services. Research on different carbon footprint sources reduction is increasing, and even the European Composites Industry Association is demanding the development of specific Design for Sustainability approaches.

As environmental concerns have taken the spotlight, electrified powertrains are rapidly being integrated into vehicles across various brands, boosting their market share. With the increasing adoption of electric vehicles, market demands are growing, and competition is intensifying. This trend has led to stricter standards for noise and vibration as well. To meet these requirements, it is necessary to not only address the inherent noise and vibration sources in electric powertrains, primarily from motors and gearboxes, but also to analyze the impact of the spline power transmission structure on system vibration and noise. Especially crucial is the consideration of manufacturing discrepancies, such as pitch errors in splines, which various studies have highlighted as contributors to noise and vibration in electric powertrains. This paper focuses on comparing and analyzing the influence of spline pitch errors on two layout configurations of motor and gearbox spline coupling structures.

A gerotor pump is a positive displacement pump consisting of inner and outer rotors, with axis of inner rotor offset from axis of outer rotor. Both rotors rotate about their respective axes. The volume between the rotors changes dynamically, due to which suction and compression occurs. A gerotor pump may be subject to erosion due to cavitation. This paper details about the CFD methodology that has been used to capture cavitation bubbles which might form during the operation of gerotor pump. A full scale (3D) transient CFD model for gerotor pump has been developed using commercial CFD code ANSYS FLUENT. The most challenging part of this CFD flow modeling is to create a dynamic volume mesh that perfectly represents the dynamically changing rotor fluid volume of the gerotor pump. Two different approaches have been used to model this dynamic mesh analysis in the Ansys Fluent tool - one method by using the traditional UDF script and, another method by using Python automation script.

Due to their remarkable efficiency and efficacy, chevrons have emerged as a prominent subject of investigation within the Aviation Industry, primarily aimed at mitigating aircraft noise levels and achieving a quieter airborne experience. Extensive research has identified the engine as the primary source of noise in aircraft, prompting the implementation of chevrons within the engine nozzle. These chevrons function by inducing streamwise vortices into the shear layer, thereby augmenting the mixing process and resulting in a noteworthy reduction of low-frequency noise emissions. Our paper aims to conduct a comparative computational analysis encompassing seven distinct chevron designs and a design without chevrons. The size and configuration of the chevrons with the jet engine nacelle were designed to match the nozzle diameter of 100.48mm and 56.76mm, utilizing the advanced SolidWorks CAD modeling software.

Despite being ubiquitous elements in aerospace structures, thin cylindrical shells’ catastrophic buckling response under axial compression has still remained an enigma. The recent advancements in theoretical and numerical studies aided in realising the role of localisation in shell buckling. However, the buckling being instantaneous made it unfeasible for the experimental observations to corroborate the numerical results. This necessitates high-fidelity shell buckling experiments using full-filed measurement techniques. Cut-outs are deliberate and inevitable geometrical imperfections in actual structures that could dictate the buckling response. Additive manufacturing makes it feasible to fabricate shells with tailored imperfections and study various conceivable designs.

The present study discusses about the determination of the Seal drag force in the application where elastomeric seal is used with metallic interface in the presence of different fluids. An analytical model was constructed to predict the seal drag force and experimental test was performed to check the fidelity of the analytical model. A Design of Experiment (DoE) was utilized to perform experimental test considering different factors affecting the Seal drag force. Statistical tools such as Test for Equal Variances and One way Analysis of Variance (ANOVA) were used to draw inferences for population based on samples tested in the DoE test. It was observed that Glycol based fluids lead to lubricant wash off resulting into increased seal drag force. Additionally, non-lubricated seals tend to show higher seal drag force as compared to lubricated seals. Keywords: Seal Drag, DoE, ANOVA

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.

The Selective Laser Melting (SLM) process is employed in high-precision layer-by-layer Additive Manufacturing (AM) on powder bed and aims to fabricate high-quality structural components. To gain a comprehensive understanding of the process and its optimization, both modeling and simulation in conjunction with extensive experimental studies along with laser calibration studies have been attempted. Multiscale and multi-physics-based simulations have the potential to bring out a new level of insight into the complex interaction of laser melting, solidification, and defect formation in the SLM parts. SLM process encompasses various physical phenomena during the formation of metal parts, starting with laser beam incidence and heat generation, heat transfer, melt/fluid flow, phase transition, and microstructure solidification. To effectively model this Multiphysics problem, it is imperative to consider different scales and compatible boundary conditions in the simulations.

The descent phase of GAGANYAAN (Indian Manned Space Mission) culminates with a crew module impacting at a predetermined site in Indian waters. During water impact, huge amount of loads are experienced by the astronauts. This demands an impact attenuation system which can attenuate the impact loads and reduce the acceleration experienced by astronauts to safe levels. Current state of the art impact attenuation systems use honeycomb core, which is passive, expendable, can only be used once (at touchdown impact) during the entire mission and does not account off-nominal impact loads. Active and reusable attenuation systems for crew module is still an unexplored territory. Three configurations of impact attenuators were selected for this study for the current GAGANYAAN crew module configuration, namely, hydraulic damper, hydro-pneumatic damper and airbag systems.

In applications demanding high performance under extreme conditions of pressure and temperature, a range of Mechanically Attached Fittings (MAFs) is offered by various Multinational Corporations (MNCs). These engineered fittings have been innovatively designed to meet the rigorous requirements of the aerospace industry, offering a cost-effective and lightweight alternative to traditional methods such as brazing, welding, or other mechanically attached tube joints. One prominent method employed for attaching these fittings to tubing is through Internal Swaging, a mechanical technique. This process involves the outward formation of rigid tubing into grooves within the fitting. One of the methods with which this intricate operation is achieved is by using a drawbolt - expander assembly within an elastomeric swaging machine.

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.

Bio-composites have gained significant attention within the aerospace industry due to their potential as a sustainable solution that addresses the demand for lightweight materials with reduced environmental impact. These materials blend natural fibers sourced from renewable origins, such as plant-based fibers, with polymer matrices to fabricate composite materials that exhibit desirable mechanical properties and environmental friendliness. The aerospace sector's growing interest in bio-composites originates from those composites’ capacity to mitigate the industry's carbon footprint and decrease dependence on finite resources. This study aims to investigate the suitability of utilizing plant derived flax fabric/PLA (polylactic acid) matrix-based bio-composites in aerospace applications, as well as the recyclability potential of these composites in the circular manufacturing economy.

A typical high-pressure hose assembly consists of hose made with synthetic polymer braids and Teflon tube crimped with metallic fittings. These hose assemblies are mainly used for aircraft landing gear application considering its high-pressure sustenance and better flexibility. The proposed study investigates the effect of thermo-mechanical stress generated due to cyclic soaking and flexibility testing at thermostatic subzero (-65°F) and high temperature (+275°F) on performance of high-pressure hose assembly. This effect is further studied through hose tear-down which was envisioned to investigate the hose layer degradation and focused on changes in inner PTFE tube, which ultimately leads to product performance issues. Keywords: braids, tear down analysis, thermo-mechanical, inter-layer abrasion.

In today's industrial sphere, machines are the key supporting various sectors and their operations. Over time, due to extensive usage, these machines undergo wear and tear, introducing subtle yet consequential faults that may go unnoticed. Given the pervasive dependence on machinery, the early and precise detection of these faults becomes a critical necessity. Detecting faults at an early stage not only prevents expensive downtimes but also significantly improves operational efficiency and safety standards. This research focuses on addressing this crucial need by proposing an effective system for condition monitoring and fault detection, leveraging the capabilities of advanced deep learning techniques. The study delves into the application of five diverse deep learning models—LSTM, Deep LSTM, Bi LSTM, GRU, and 1DCNN—in the context of fault detection in bearings using accelerometer data. Accelerometer data is instrumental in capturing vital vibrations within the machinery.

Fastener joints play a critical role within aircraft engine structures by connecting vital structural members and withstanding various load scenarios, including impact occurrences like foreign object damage (FOD) on engine nacelles. The precise modeling and simulation of fastener joint behavior under dynamic loads are pivotal to ensuring their structural integrity and functionality. Simulation is essential for minimizing costly experiments in evaluating the challenging design aspect of containing FOD. Prior investigations on fastener joints have predominantly focused on quasi-static or in-plane dynamic loads. This study introduces a comprehensive methodology to simulate the impact dynamics of fastener joints, accommodating both in-plane and out-of-plane loads. The approach employs a fully self-consistent 3D viscoplastic finite element formulation-based simulation using a newly developed code.

In recent decades, research based innovative system-on-chip (SoC) design has been a very important issue, due to the emerging trends and application challenges. The SoCs encompass digital, analog and mixed-signal hardware and software components and even sensors and actuators. Modelling and simulation constitute a powerful method for designing and evaluating complex systems and processes for many analysts and project managers as they engage in state of-the-art research and development. Modelling and simulations not only help them with the algorithm or concept realization and design feasibility, but it also allows experimentation, optimization, interpretation of results and validation of model.

Modern combat aircraft demands efficient maintenance strategies to ensure operational readiness while minimizing downtime and costs. Innovative approaches using Digital Twining models are being explored to capture inter system behaviours and assessing health of systems which will help maintenance aspects. This approach employs advanced deep learning protocols to analyze the intricate interactions among various systems using the data collected from various systems. The research involves extensive data collection from sensors within combat aircraft, followed by data preprocessing and feature selection, using domain knowledge and correlation analysis. Neural networks are designed for individual systems, and hyper parameter tuning is performed to optimize their performance. By combining the outputs of these during the model integration phase, an overall health assessment of the aircraft will be generated.