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Although structural intensity was introduced in the 80's, this concept never found practical applications, neither for numerical nor experimental approaches. Quickly, it has been pointed out that only the irrotational component of the intensity offers an easy interpretation of the dynamic behavior of structures by visualizing the vibration energy flow. This is especially valuable at mid and high frequency where the structure response understanding can be challenging. A new methodolodgy is proposed in order to extract this irrotational intensity field from the Finite Element Model of assembled structures such as Bodies In White. This methodology is hybrid in the sense that it employs two distinct solvers: a dynamic solver to compute the structural dynamic response and a thermal solver to address a diffusion equation analogous to the thermal conduction built from the previous dynamic response.

Gas bearings are an effective solution to high-speed rotor applications for its contamination free, reduced maintenance and higher reliability. However, low viscosity of gas leads to lower dynamic stiffness and damping characteristics resulting in low load carrying capacity and instability at higher speeds. Gas bearings can be enhanced by adding a foil structure commonly known as gas foil bearings (GFBs), whose dynamic stiffness can be tailored by modifying the geometry and the material properties resulting in better stability and higher load carrying capacity. A detailed study is required to assess the performance of high-speed rotor systems supported on GFBs, therefore in this study a bump type GFB is analyzed for its static and dynamic characteristics. The static characteristics are obtained by solving the non-linear Reynolds equation through an iterative procedure.

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.

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.

This specification covers a titanium alloy in the form of welding wire (see 8.5).

This method outlines a standard procedure for performing conformity tests of bearings utilizing liners of bonded polytetrafluoroethylene (PTFE). The data from these tests shall be used to determine if the product meets the conformity requirements of the applicable specification.

Abstract AISI H13 hot work tool steel is commonly used for applications such as hot forging and hot extrusion in mechanical working operations that face thermal and mechanical stress fluctuations, leading to premature failures. Cryogenic treatment was applied for AISI H13 steel to improve the surface hardness and thereby fatigue resistance. This work involves failure analysis of H13 steel specimens subjected to cryogenic treatment and gas nitriding. The specimens were heated to 1020°C, oil quenched followed by double tempering at 550°C for 2 h, and subsequently, deep cryogenically treated at −185°C in the cryochamber. Gas nitriding was carried out for 24 h at 500°C for 200 μm case depth in NH3 surroundings. The specimens were subjected to rotating bending fatigue at constant amplitude loading at room temperature.

This procurement specification covers solid rivets and hollow end rivets made from a corrosion and heat resistant steel of the type identified under the Unified Numbering System as UNS S66286 and of 80 ksi single shear strength at room temperature.

This SAE Aerospace Recommended Practice (ARP) establishes methods for testing airframe plain bearings. The purpose of ARP5448 and its associated slash sheets is to document test methods commonly used to evaluate airframe bearings. These test methods may be referenced in specifications, part standards, purchase orders, etc., when the test is deemed appropriate to the intended use of the bearing by the end user of the bearing. These test methods are not intended to encompass every conceivable requirement for an airframe bearing. The end user of the bearing must exercise engineering judgment to determine the most appropriate standard and/or nonstandard tests for the application.

This procurement specification covers retaining rings of the spiral wound type with uniform rectangular cross-section, made of a corrosion and heat resistant age hardenable iron base alloy of the type identified under the Unified Numbering System as UNS S66286, work strengthened and heat treated to a tensile strength of 185 to 240 ksi at room temperature.