Search Results (158)

Ice formation on critical infrastructure such as wind turbine blades can lead to severe performance degradation and safety hazards. This study investigates the use of hyperspectral imaging (HSI) combined with machine learning to detect and classify ice on various coated and uncoated surfaces. Hyperspectral reflectance data were acquired using a push-broom HSI system under controlled laboratory conditions, with ice and rime ice generated using a thermoelectric cooling setup. Support Vector Machine (SVM) and Random Forest (RF) classifiers were trained on uncoated aluminum samples and evaluated on surfaces with different coatings to assess model generalization. Both models achieved high classification accuracy, though performance declined on black-coated surfaces due to increased absorbance by the coating. The study further examined the impact of spectral band reduction to simulate different sensor types (e.g., NIR vs. SWIR), revealing that model performance is sensitive to wavelength range, with SVM performing optimally in a reduced band set and RF benefiting from the full spectral range. A multiclass classification approach using RF successfully distinguished between glaze and rime ice, offering insights into more targeted mitigation strategies. The results confirm the potential of HSI and machine learning as robust tools for surface ice monitoring in safety-critical environments. Full article

Given the exceptional capabilities of femtosecond laser processing in achieving high-precision ablation for air-film cooling hole fabrication on turbine blades, it is imperative to develop an advanced monitoring methodology that enables real-time feedback control to automatically terminate the laser upon complete penetration detection, thereby effectively preventing backside damage. To tackle this issue, a spectrum-domain coherent imaging technique has been developed. This innovative approach adapts the fundamental principle of fiber-based Michelson interferometry by integrating the air-film hole into a sample arm configuration. A broadband super-luminescent diode with a 830 nm central wavelength and a 26 nm spectral bandwidth serves as the coherence-optimized illumination source. An optimal normalized reflectivity of 0.2 is established to maintain stable interference fringe visibility throughout the drilling process. The system achieves a depth resolution of 11.7 μm through Fourier transform analysis of dynamic interference patterns. With customized optical path design specifically engineered for through-hole-drilling applications, the technique demonstrates exceptional sensitivity, maintaining detection capability even under ultralow reflectivity conditions (0.001%) at the hole bottom. Plasma generation during laser processing is investigated, with plasma density measurements providing optical thickness data for real-time compensation of depth measurement deviations. The demonstrated system represents an advancement in non-destructive in-process monitoring for high-precision laser machining applications. Full article

To improve the convective heat transfer in internal cooling channels of heavy-duty gas turbine blades, this study experimentally and numerically investigates the thermal performance of rectangular channels with hybrid pin-fins and micro V-ribs turbulators. The transient thermochromic liquid crystal (TLC) technique and ANSYS 2019 R3 (ICEM CFD 2019 R3, Fluent 2019 R3, CFD-Post 2019 R3) were employed under Reynolds numbers ranging from 10,000 to 50,000, with the numerical model rigorously validated against experimental data (the maximum RMSE is 2.5%). It is found that hybrid pin-fins and continuous V-ribs configuration exhibits the maximum heat transfer enhancement of 27.6%, with an average friction factor increase of 13.3% and 21.9% improvement in thermal performance factor (TPF) compared to the baseline pin-fin channel. In addition, compared to the baseline pin-fin channel, hybrid pin-fins and broken V-ribs configuration exhibits average heat transfer enhancement (Nu/Nu0) of 24.4%, an average friction factor increase of 7.2% and 22.5% improvement across the investigated Reynolds number range (10,000~50,000) based on computational results. The synergistic effects of hybrid pin-fin and micro V-rib structures demonstrate superior coolant flow control, offering a promising solution for next-generation turbine blade cooling designs. This work provides actionable insights for high-efficiency gas turbine thermal management. Full article

With the enhancement of thermodynamic cycle parameters and heat dissipation constraints in aero-engines, effective thermal management has become a critical challenge to ensure safe and stable engine operation. This study developed a transient temperature evaluation model applicable to the entire flight envelope, considering fluid–solid coupling heat transfer on both the main flow path and fuel systems. Firstly, the impact of heat transfer on the acceleration and deceleration performance of a low-bypass-ratio turbofan engine was analyzed. The results indicate that, compared to the conventional adiabatic model, the improved model predicts metal components absorb 4.5% of the total combustor energy during cold-state acceleration, leading to a maximum reduction of 1.42 kN in net thrust and an increase in specific fuel consumption by 1.18 g/(kN·s). Subsequently, a systematic evaluation of engine thermal management performance throughout the complete flight mission was conducted, revealing the limitations of the existing thermal management design and proposing targeted optimization strategies, including employing Cooled Cooling Air technology to improve high-pressure turbine blade cooling efficiency, dynamically adjusting low-pressure turbine bleed air to minimize unnecessary losses, optimizing fuel heat sink utilization for enhanced cooling performance, and replacing mechanical pumps with motor pumps for precise fuel supply control. Full article

Film cooling, a vital method for controlling surface temperatures in components subjected to intense heat, strives to enhance efficiency through innovative technological advancements. Over the last several decades, considerable advancements have been made in film cooling technologies for applications such as liquid rocket engines, combustion chambers, nozzle sections, gas turbine components, and hypersonic vehicles, all of which operate under extreme temperatures. This review presents an in-depth investigation of film cooling, its applications, and its key mechanisms and performance characteristics. The review also explores design optimization for combustion chamber components and examines the role of gaseous film cooling in nozzle systems, supported by experimental and numerical validation. Gas turbine cooling relies on integrated methods, including internal and external cooling, material selection, and coolant treatment to prevent overheating. Notably, the cross-flow jet in blade cooling improves heat transfer and reduces thermal fatigue. Film cooling is an indispensable technique for addressing the challenges of high-speed and hypersonic flight, aided by cutting-edge injection methods and advanced transpiration coolants. Special attention is given to factors influencing film cooling performance, as well as state-of-the-art developments in the field. The challenges related to film cooling are reviewed and presented, along with the difficulties in resolving them. Suggestions for addressing these problems in future research are also provided. Full article

Film cooling has been increasingly applied in turbine blade cooling design due to its excellent cooling performance. Although film-cooled blades demonstrate superior cooling effectiveness, the perforation design on blade surfaces compromises structural integrity, making fatigue failure prone to occur at cooling holes. Previous studies by domestic and international scholars have extensively investigated factors influencing film cooling effectiveness, including blowing ratio and hole geometry configurations. However, most research has overlooked the investigation of fatigue life in film-cooled blades. This paper systematically investigates blade fatigue life under various cooling air parameters by analyzing the relationships among cooling effectiveness, stress distribution, and fatigue life. Results indicate that maximum stress concentrations occur at cooling hole locations and near the blade root at trailing edge regions. While cooling holes effectively reduce blade surface temperature, they simultaneously create stress concentration zones around the apertures. Both excessive and insufficient cooling air pressure and temperature reduce thermal fatigue life, with optimal parameters identified as 600 K cooling temperature and 0.75 MPa pressure, achieving a maximum thermal fatigue life of 3400 cycles for this blade configuration. A thermal shock test platform was established to conduct fatigue experiments under selected cooling conditions. Initial fatigue damage traces emerged at cooling holes after 1000 cycles, with progressive damage expansion observed. By 3000 cycles, cooling holes near blade tip regions exhibited the most severe failure, demonstrating near-complete functional degradation. These findings provide critical references for cooling parameter selection in practical aeroengine applications of film-cooled blades. Full article

Aerospace engines ingest small particles when operating in a particulate-rich environment, such as sandstorms, atmospheric pollution, and volcanic ash clouds. These micron particles enter their cooling channels, leading to film-cooling hole blockage and thus thermal damage to turbine blades made of nickel-based single-crystal superalloy materials. This work studied the collision and deposition mechanisms between the micron particles and structure surface. A combined theoretical and numerical study was conducted to investigate the effect of deposits on particle collision and deposition. Finite element models of deposits with flat and rough surfaces were generated and analyzed for comparison. The results show that the normal restitution coefficient is much lower when a micron particle impacts a deposit compared to that of particle collisions with DD3 nickel-based single-crystal wall surfaces. The critical deposition velocity of a micron particle is much higher for particle–deposit collisions than for particle–wall collision. The critical deposition velocity decreases with the increase in particle size. When micron particles deposit on the wall surface of the structure, early-stage particle–wall collision becomes particle–deposit collision when the height of the deposits is greater than twice the particle diameter. For contact between particles and rough surface deposits, surfaces with a shorter correlation length, representing a higher density of asperities and a steeper surface, have a much longer contact time but a lower contact area. The coefficient of restitution of the particle reduces as the surface roughness of the deposits increase. The characteristic length of the roughness has little effect on the rebounding rotation velocity of the particle. Full article

In this study, a conceptual turbine blade model with internal cooling channels was designed and fabricated using the selective laser melting (SLM) process. The optimal manufacturing orientation was evaluated through simulations, and the results indicated that vertical orientation yielded the best outcomes, minimizing support material usage and distortion despite increased manufacturing time. Two configurations were produced, namely, an entire-turbine blade model and a cross-sectional model. Non-destructive analyses, including 3D laser scanning for dimensional accuracy, surface roughness measurements, and liquid penetrant testing, were conducted. Visual inspection revealed manufacturing limitations, particularly in the cooling channels at the leading and trailing edges. The trailing edge was too thin to accommodate the 0.5 mm channel diameter, and the channels in the leading edge were undersized and potentially clogged with unmelted powder. The dimensional deviations were within the acceptable limits for the SLM-fabricated metal parts. The surface roughness measurements were aligned with the literature values for metal additive manufacturing. Liquid penetrant testing confirmed the absence of cracks, pores, and lack-of-fusion defects. The SLM is a viable manufacturing process for turbine blades with internal cooling channels; however, significant attention should be paid to the design of additive manufacturing conditions to obtain the best results after manufacturing. Full article

Mesh adaptation techniques can significantly impact Computational Fluid Dynamics by improving solution accuracy and reducing computational costs. In this review, we begin by defining the concept of mesh adaptation, its core components and the terminology commonly used in the community. We then categorise and evaluate the main adaptation strategies, focusing both on error estimation and mesh modification techniques. In particular, we analyse the two most prominent families of error estimation: feature-based techniques, which target regions of high physical gradients and goal-oriented adjoint methods, which aim to reduce the error in a specific integral quantity of interest. Feature-based methods are advantageous due to their reduced computational cost: they do not require adjoint solvers, and they have a natural ability to introduce anisotropy. A substantial portion of the literature relies on second-order derivatives of scalar flow quantities to construct sensors that can be equidistributed to minimise discretisation error. However, when used carelessly, these methods can lead to over-refinement, and they are generally outperformed by adjoint-based techniques when improving specific target quantities. Goal-oriented methods typically achieve higher accuracy in fewer adaptation steps with coarser meshes. It will be seen that various approaches have been developed to incorporate anisotropy into adjoint-based adaptation, including hybrid error sensors that combine feature-based and goal-oriented indicators, sequential strategies and adjoint weighting of fluxes. After years of limited progress, recent work has demonstrated promising results, including certifiable solutions and applications to increasingly complex cases such as transonic compressor blades and film-cooled turbines. Despite these advances, several critical challenges remain: efficient parallelisation, robust geometry integration, application to unsteady flows and deployment in high-order discretisation frameworks. Finally, examples of the potential role of artificial intelligence in guiding or accelerating mesh adaptation are also discussed. Full article

To meet the increasingly stringent performance indicators of gas turbines, the turbine inlet temperature has increased, and variable geometry turbine technology is widely applied. Therefore, this study developed a quasi-two-dimensional (quasi-2D) method for variable geometry turbine performance considering cooling air mixing based on the elementary blade method and the cooling airflow mixing model. To address the high-dimensional, multi-variable data fitting problem of variable geometry turbines considering the effects of cooling air, this study adopted a BP neural network to further establish a surrogate model for variable geometry turbine performance. A sensitivity analysis of a single-stage turbine was conducted. The variable geometry cooling performance of a single-stage turbine and an E³ five-stage low-pressure air turbine were calculated, and the corresponding surrogate models were established. The relative errors between the calculated mass flow rate and efficiency of the single-stage turbine and the experimental values were no more than 0.70% and 4.44%, respectively; for the five-stage air turbine, the maximum relative errors in mass flow rate and efficiency were no more than 1.67% and 1.385%, respectively. When the throat area of the single-stage turbine nozzle changed by ±30%, the maximum relative errors between the calculated mass flow rate and efficiency and their experimental values were 3.602% and 4.228%, respectively; thus, the determination coefficients of the constructed BP neural network model for the training samples were all greater than 0.999, indicating that the surrogate model has high prediction accuracy and strong generalization ability and can quickly predict variable geometry turbine cooling performance. Full article

This research work details the main factors affecting the orifice and profile morphology of micro-holes processed by the one-step femtosecond laser helical drilling method. Cylindrical holes or even inverted cone holes can be obtained with the appropriate deflection angle and translation distance. The orifice morphology of the micro-hole is mainly influenced by the rotation speed of the Dove prism installed inside the hollow motor, laser output power, and laser repetition frequency. A higher instantaneous power density can improve the outlet morphology and produce sharper cutting edges and thinner recast layers, although it may increase the splashing around the inlet to some extent. Subsequent to the experiment, it was determined that in order to enhance the quality of the holes, it was necessary to select a higher laser power and a lower repetition frequency, such as 10 W and 100 kHz, according to the experiments. A recast layer thickness of less than 5 µm and a surface roughness value of less than 0.8 µm were obtained within 3–5 s processing time, which can satisfy the requirements for aircraft application of efficiency and quality. Full article

Restoring components in the hot gas path of turbine engines after service-induced degradation is crucial for economic efficiency. This study investigates the printability of Rene 65 powder on a degraded first-stage turbine blade using two additive manufacturing techniques: Laser Powder Bed Fusion (L-PBF) and Laser Powder Directed Energy Deposition (L-DED). Deposited material was evaluated using optical microscopy (OM), scanning electron microscopy (SEM), and Electron Backscatter Diffraction (EBSD) to characterize its crystallographic texture, while microhardness testing provided insight into its mechanical properties. Our results show that L-PBF excels at replicating intricate features, such as small cooling holes, and produces a highly texturized microstructure oriented parallel to <001> under optimal parameters (80 W, 400 mm/s, unidirectional scanning), although at a slower pace. In contrast, L-DED offers a versatile, rapid, and cost-effective method for repairing medium to large parts, yielding an equiaxed microstructure and higher as-printed hardness—approaching GTD 111 values due to an aging effect from high heat input. Both processes effectively restored the dimensional integrity of degraded blade tips, paving the way for more sustainable and economical maintenance strategies in the aerospace industry. Full article

Integrating machine learning into additive manufacturing offers transformative opportunities to optimize material properties and design high-performance, fatigue-resistant structures for critical applications in aerospace, biomedical, and structural engineering. This study explores mechanistic machine learning techniques to tailor microstructural features, leveraging data from ultrasonic fatigue tests where very high cycle fatigue properties were assessed up to $1\times {10}^{10}$ cycles. Machine learning models predicted critical fatigue thresholds, optimized process parameters, and reduced design iteration cycles by over 50%, leading to faster production of safer, more durable components. By refining grain orientation and phase uniformity, fatigue crack propagation resistance improved by 20–30%, significantly enhancing fatigue life and reliability for mission-critical aerospace components, such as turbine blades and structural airframe parts, in an industry where failure is not an option. Additionally, the machine learning-driven design of metamaterials enabled structures with a 15% weight reduction and improved yield strength, demonstrating the feasibility of bioinspired geometries for lightweight applications in space exploration, medical implants, and high-performance automotive components. In the area of titanium and aluminum alloys, machine learning identified key process parameters such as temperature gradients and cooling rates, which govern microstructural evolution and enable fatigue-resistant designs tailored for high-stress environments in aircraft, biomedical prosthetics, and high-speed transportation. Combining theoretical insights and experimental validations, this research highlights the potential of machine learning to refine microstructural properties and establish intelligent, adaptive manufacturing systems, ensuring enhanced reliability, performance, and efficiency in cutting-edge engineering applications. Full article

The trailing edge of gas turbine blades encounters concentrated heat loads, necessitating cooling techniques distinct from those used in mid-chord regions. Narrow cooling channels in these areas typically incorporate pin fins or dimples for internal cooling. In contrast, external cooling relies on cutback film cooling configurations, which differ significantly from mid-chord designs. Large eddy simulation (LES) has emerged as a powerful tool for investigating heat transfer in these challenging environments, capturing intricate flow phenomena and turbulence effects that Reynolds-Averaged Navier–Stokes (RANS) simulations often cannot resolve. This review synthesizes findings from 54 LES-based studies on trailing edge cooling, focusing on three key configurations: pin fin arrays, dimpled surfaces, and cutback film cooling. LES consistently demonstrated higher accuracy in predicting heat transfer and cooling effectiveness, outperforming RANS by resolving complex flow structures such as horseshoe vortices, shear layer vortices, and unique flow interactions inherent to these geometries. Furthermore, LES provided detailed turbulence statistics and local heat transfer distributions, offering critical insights for optimizing and improving predictive models. Beyond its demonstrated capabilities, this review underscores the future potential of LES in advancing shape optimization, transient flow analysis, and multi-physics simulations, including conjugate heat transfer and flow-structure interactions. Full article

Power generation is an important part of air vehicle energy management when developing long-endurance and reusable hypersonic aircraft. In order to utilize an air turbine power generation system on board, fuel-based rotating cooling has been researched to cool the turbine’s rotor blades. For fuel-cooling air turbines, each blade corresponds to a separate cooling channel. All the separate cooling channels cross together and form a distributary cavity and a confluence cavity in the center of the disk. In order to determine the flow characteristics in the distributary and confluence cavities, computational fluid dynamics (CFD) simulations using the shear–stress–transport turbulence model were carried out under the conditions of different rotating speeds and different mass flow rates. The results showed great differences between non-rotating flow and rotating flow conditions in the distributary and confluence cavities. The flow in the distributary and confluence cavities has rotational velocity, with obvious layering distribution regularity. Moreover, a high-speed rotational flow surface is formed in the confluence cavity of the original structure, due to the combined functions of centrifugal force, inertia, and the Coriolis force. Great pressure loss occurs when fluid passes through the high-speed rotational flow surface. This pressure loss increases with the increase in rotating speed and mass flow rate. Finally, four structures were compared, and an optimal structure with a separated outlet channel was identified as the best structure to eliminate this great pressure loss. Full article