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.
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.
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.
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.
RAMBHA-LP (Radio Anatomy of Moon Bound Hypersensitive Ionosphere and Atmosphere - Langmuir Probe) is one of the key scientific payloads onboard the Indian Space Research Organization’s (ISRO) Chandrayaan-3 mission. Its objectives were to estimate the plasma density and its variations on the near lunar surface. The probe was initially kept in a stowed condition attached to the lander. A mechanism was designed and realized to meet the functional requirement of deploying the probe at a distance of 1 meter, equivalent to the Debye length of the probe in the moon’s plasma environment. The probe deployment mechanism consists of the Titanium alloy spherical probe with a Titanium Nitride coating on its surface to achieve a constant work function, a long carbon-fiber-reinforced polymer boom, a double torsion spring, a dust-protection box, and a shape-memory alloy-based Frangibolt actuator for low-shock separation. The entire mechanism weighed less than 1.5 kilograms.
The paper presents a theoretical framework for the detection and first-level preliminary identification of potential defects on aero-structure components while employing ultrasonic guided wave based structural health monitoring strategies, systems and tools. In particular, we focus our study on ground inspection using laser-Doppler scan of surface velocity field, which can also be partly reconstructed or monitored using point sensors and actuators on-board structurally integrated. Using direct wave field data, we first question the detectability of potential defects of unknown location, size, and detailed features. Defects could be manufacturing defects or variations, which may be acceptable from design and qualification standpoint; however, those may cause significant background signal artifacts in differentiating structure progressive damage or sudden failure like impact-induced damage and fracture.
Aerospace structural components grapple with the pressing issue of high-cycle fatigue-induced micro-crack initiation, especially in high-performance alloys like Titanium and super alloys. These materials find critical use in aero-engine components, facing a challenging combination of thermo-mechanical loads and vibrations that lead to gradual dislocations and plastic strain accumulation around stress-concentrated areas. The consequential vibration or overload instances can trigger minor cracks from these plastic zones, often expanding unpredictably before detection during subsequent inspections, posing substantial risks. Effectively addressing this challenge demands the capability to anticipate the consequences of operational life and aging on these components. It necessitates assessing the likelihood of crack initiation due to observed in-flight vibration or overload events.
The evaluation of aircraft characteristics through flight test maneuvers is fundamental to aviation safety and understanding flight attributes. This research project proposes a comprehensive methodology to detect and analyze aircraft maneuvers using full flight data, combining signal processing and machine learning techniques. Leveraging the Wavelet Transform, we unveil intricate temporal details within flight data, uncovering critical time-frequency insights essential for aviation safety. The integration of Long Short-Term Memory (LSTM) models enhances our ability to capture temporal dependencies, surpassing the capabilities of machine learning in isolation. These extracted maneuvers not only aid in safety but also find practical applications in system identification, air-data calibration, and performance analysis, significantly reducing pre-processing time for analysts.
Hydrogen has recently become a primary focus as a future carbon-free fuel for transportation, especially for heavy duty commercial vehicles. The hydrogen internal combustion engine (H2 ICE) shows promise, as current manufacturing facilities and vehicle architectures can be largely maintained while keeping the initial purchase price of the vehicle relatively low. However, hydrogen combustions engines have challenges to overcome. One of the main challenges is to provide transient response on par with current diesel engines while maintaining low NOx emissions from the engine. Previously, simulations were performed by AVL List GmbH and SuperTurbo Technologies of a mechanically driven turbocharger, the SuperTurbo, on a 13L H2 ICE. This paper covers follow on work of actual engine testing of the H2 ICE with the SuperTurbo in an effort to reproduce the simulation results with engine test data.
This study experimentally investigates the combustion stability in RCCI engines along with the gaseous (regulated and unregulated) and particle emissions. Multifractal analysis is used to characterize the cyclic combustion variations in the combustion parameters (such as IMEP, CA50, Pmax) of the RCCI engine. The investigation is carried out on a modified single-cylinder diesel engine to operate in RCCI combustion mode. The RCCI combustion mode is tested for different fuel premixing ratio (r_p) and diesel injection timing (SOI) at fixed engine speed (1500rpm) and load (1.5 bar BMEP). The particle number characteristics and gaseous emissions are measured using a differential mobility spectrometer (DMS500) and Fourier Transform Infrared Spectroscopy (FTIR) along with Flame Ionizing Detector (FID), respectively. The results indicate that the NOx emissions decrease with advanced SOI while the methane (CH4) emission increases.