Browse Topic: Heat transfer
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.
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 accomplishments achieved under this study include (1) development of thermal abstraction technique for the AM process to speed up the macroscale level heat transfer analysis based on the manufacturing factors including scanning vector, laser power, dwelling time, etc.; (2) development of the sequentially coupled thermal-mechanical model to predict the residual stress and distortion after AM process by passing the temperature history obtained from heat transfer analysis to the mechanical analysis at each time point; (3) validation of the thermal-mechanical model for AM using thin-wall structure from literature and cantilever beam structure from UNT’s experiments data; (4) conduction of the parametric study on the chamber temperature and part design in the AM process to demonstrate how the temperature gradient and supporting structure affect the residual stress and distortion; (5) exploration of macro and micro scale models to predict the bulk and surface residual stress after cutting; (6) applying the developed modeling framework to tailoring the hybrid AM/SM process. To support model verification and demonstration, we print cantilever beam structure with different supporting structure designs and cutting strategies to study how these factors affect the final part residual stress and distortion. The data collected in the printing and cutting process is used to examine the applicability of the developed simulation tool.
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 and enable rapid process tailoring for reduced distortion, we have developed and validated a digital twin-based Quenching Laboratory Software (QLAB) tool. QLAB integrates a thermal multi-phase computational fluid dynamics (CFD) model, sequentially coupled with a Mechanical Threshold Stress (MTS) model and a precipitation model. The thermal CFD component captures turbulent flow, multi-phase transformations, and the complex heat transfer stages of quenching including vapor blanket formation, nucleate boiling, and convection—to accurately predict temperature evolution. The MTS-precipitation model quantifies the effects of microstructural precipitates on the material's mechanical response under thermal loading. QLAB has been thoroughly validated using representative aluminum airframe components, including aluminum bars with pockets and Lcorner parts. We demonstrate the tool's predictive accuracy by comparing its output against experimentally measured temperature and distortion fields. Finally, we apply the validated QLAB to conduct a virtual quenching test on a simplified aluminum airframe structure, showcasing its potential for performance-informed process optimization.
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 leads to electro-explosion of metal and transient mechanical forces from shock waves causing mechanical damage on the test panels. Back surface-velocities increase with higher peak currents and higher level of protection results in lower damage. It is shown that a stacking of copper wire mesh results in less arc root dispersion.
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 turbulence-induced heat transfer coefficient (HTC) imposed through one-way coupling determines the thermo-mechanical response in composite parts. Using the developed digital twin tool (SMARTCLAVE), HTC's spatial and temporal variation can be generated digitally without invoking an expensive and time-consuming experimental approach. The predicted local boundary conditions are used in SMARTCLAVE to determine the cure kinetics, temperature distribution, and thermal-mechanical response that drives the residual stress and distortion of composite parts after curing. The accuracy of the digital twin for autoclaving is demonstrated first using a benchmark problem followed by the capability demonstration with a single-part L-beam assembly. The benefits of using the digital twin tool are illustrated via the optimal placement of multiple parts in an autoclave to balance the throughput and quality.
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, increasing production efficiency, and engineers and machine operators' handling of variances in forging operations. A high-fidelity modeling of the coupling of thermal, metallurgical, and mechanical interactions is a key component to creating a digital twin of the physical quenching process. A high-fidelity thermal multi-phase computational fluid dynamics (CFD) model is applied to simulate fluid dynamics and temperature fields in the quenchant tank. The developed immersogeometric modeling approach is used next for an efficient model generation of a 3D workpiece with various dipping orientations. Given the temperature and pressure profiles predicted from the CFD-based heat transfer module, residual stress and distortion prediction modules are developed by including temperature and pressure fields mapping and temperature and strain rate dependent property evolution via Abaqus' user-defined subroutines. Verification and demonstration studies are performed using aluminum coupons dipped into a quenching tank with different orientations. Time histories of the temperature and residual stress fields were predicted to explore the relationship between the process and performance.
ABSTRACT
Abstract Transient numerical simulations are conducted over a NACA 0012 airfoil with triangular protrusions at a Reynolds number (Re) of 100000 using the γ-Reθ transition Shear Stress Transport (SST) turbulence model. Protrusions of heights 0.5%c, 1%c, and 2%c are placed at one of the three locations, viz, the leading edge (LE), 5%c on the suction surface, and 5%c on the pressure surface, while the angle of attack (AOA) is varied between 0° and 20°. Results obtained from the time-averaged solution of the unsteady Navier-Stokes equation indicate that the smaller protrusion placed at 5%c on the suction surface improves the post-stall lift coefficient by up to 59%, without altering the pre-stall characteristics. The improvement in time-averaged lift coefficients comes with enhanced flow unsteadiness due to vigorous vortex shedding. For a given protrusion height, the vortex shedding frequency decreases as the AOA is increased, while the amplitude of fluctuations in lift coefficient increases as the protrusion height is increased or as the AOA is increased. Nevertheless, mitigation of static stall phenomena is observed for most configurations investigated, and this finding can be beneficial for the design of Unmanned Aerial Vehicles (UAVs) and Micro Aerial Vehicles (MAVs). The enhancement in the vortex shedding frequencies due to triangular protrusion can be utilized for Vortex-Induced Vibrations (VIVs)-based energy generators.
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