Browse Topic: Thermodynamics
Quenching is the most critical step in the sequence of heat-treating operations, aiming to preserve the solid solution formed at the solution heat-treating temperature by rapidly cooling the material to near room temperature. Currently, there is no reliable, performance-informed quenching process that can consistently reduce the high scrap rate of airframe aluminum forging parts, which often suffer from significant residual stress and distortion. This limitation stems from the complex interactions between temperature, phase transformations, and stress/strain behavior—each influenced by the evolving temperature distribution and microstructural state of the workpiece. Conventional modeling techniques for quenching processes typically lump these multiscale, multi-physics phenomena into a simplified heat transfer coefficient (HTC). However, determining the spatial and temporal variations of HTC through experiments is both prohibitively time-consuming and costly. To address this challenge
Hybrid additive manufacturing (AM) and subtractive manufacturing (SM) processes utilize the combination of AM (e.g., LPBF and DED) and SM (e.g., milling and turning operations) to produce the final part. Due to the poor surface roughness resulting from the uneven melting of powders in AM, the subtractive process is a necessary finishing operation to improve the surface roughness of the AM part. The hybrid AM/SM technology combines the benefits of AM and SM processes to create complex geometry while introducing good surface finish and compressive stress to prevent crack initiation. However, the relationship between large process parameter space and the residual stress/distortion in the part is not well understood, which impedes the adoption of hybrid AM/SM to minimize the residual stress in the final product. To expedite the process optimization, we establish a pipeline for the sequential modeling of additive manufacturing (AM) and subtractive manufacturing (SM) processes. Key
There is a continued and growing need for better analysis and simulation of complex transmission systems with the rise of hybrid electric powerplants coming to future aviation vehicles. In this paper we discuss how reduced order modeling can help to efficiently predict the thermal behavior of gearboxes during operations smartly reusing data from SPH based oil flow simulations. To solve the thermal problem, a dynamic non-linear Reduced Order Model (ROM) is generated to estimate the Gear-Oil heat transfer coefficient (HTC) based on variable gearbox RPM and Oil fill level.
Quenching is a heat treatment process for the rapid cooling of a metallic workpiece in water, oil, or air to obtain certain desired material properties. It is the most critical step in the sequence of heat-treating operations to preserve the solid solution formed at the solution heat-treating temperature by rapidly cooling to near room temperature. Because of the complex interaction between temperature, phase-transformation, and stress/strain relation that depends on the temperature distribution and the microstructure of the workpiece, there is no performance-informed quenching process that can be applied reliably to reduce the high scrap rate of airframe aluminum forging parts with a significant amount of residual stress and distortion. Since large aluminum forging parts are increasingly used in aerospace structures to enable structural unitization, it is important to construct a digital twin modeling approach to mirror the physical quenching process for minimizing scrap rate
The Autoclave processing is commonly used in manufacturing high-performance fibre-reinforced thermoset composite components in the aerospace industry. Variations in the cure cycle, sometimes even apparently minor deviations from the prescribed cure cycle, can harm the laminate properties. Given the costly and time-consuming autoclave manufacturing process, there is a strong need to cure the maximum number of parts in the shortest possible time without compromising quality. In order to achieve high-rate automated manufacturing with the optimized autoclave process, it is important to construct a digital twin modelling approach to mirror the physical composite curing process in the virtual domain based on the integration of high-fidelity multi-physics models. The resulting digital twin includes a thermal CFD model, a thermo-chemo-mechanical module, and an efficient and accurate block coupling between these two modules. The customized Abaqus driven by local and spatial variation of the
This paper experimentally investigates direct effects of lightning strikes on flax fiber-reinforced polymers. Highcurrent artificial lightning strikes are conducted on coupon level to evaluate thermo-mechanical damage and to quantify the sufficiency of copper wire mesh as lightning strike protection (LSP). The dataset shall also serve for verification of prospected numerical simulation. The natural fiber flax, as a sustainable source of composite reinforcement, has been demonstrated to be suitable for semi-structural parts of rotorcraft. However, its low electrical and thermal conductivity requires a functional LSP layer for aviation applications. The test panels are investigated regarding their material combination, stacking sequence and level of LSP. Results show that two as well as three layers of 72 g/m2 copper mesh are not sufficient to withstand the standardized lightning current component A waveform of 200 kA. The high induced currents and low capability of energy dissipation
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