Beneficial properties achieved by nanostructuring effects in materials have generated tremendous interests in applications in surface engineering, especially in thermal barrier coatings (TBC). Limitations in conventional TBC processing for gas turbines and aero-propulsion systems have been exposed during past decades when rapid progress was made in nano-structuring coating research and developments. The present work is a comprehensive review of the current state of progress in nanostructured TBC (Ntbc) in reference to its microstructure, damage progression, failure mechanisms and a wide range of properties. The review aims to address the comparative performance analysis between the nanostructured and conventional (microstructured) 6–8 wt.% yttrium stabilized zirconia (YSZ) TBC systems. Oxidation resistance and sintering behavior in two TBCs are considered as the central focus of discussion. A few schematics are used to represent major microstructural features and failure progression. A performance analysis is performed for standard 2-layer, as well as functionally graded multilayer, TBC systems. A comparison of TBC characteristics processed by plasma spray and vapor deposition techniques is also made as reference. Compared to the sea of R&D efforts made for conventional TBC (Ctbc), limited experimental studies on Ntbc offers conflicting data, and prediction modeling and computational research are scarce.

Detecting and identifying drones is of great interest due to the proliferation of highly manoeuverable drones with on-board sensors of increasing sensing capabilities. In this paper, we investigate the use of radars for tackling this problem. In particular, we focus on the problem of detecting rotary drones and distinguishing between single-propeller and multi-propeller drones using a micro-Doppler analysis. Two different radars were used, an ultra wideband (UWB) continuous wave (CW) C-band radar and an automotive frequency modulated continuous wave (FMCW) W-band radar, to collect micro-Doppler signatures of the drones. By taking a closer look at HElicopter Rotor Modulation (HERM) lines, the spool and chopping lines are identified for the first time in the context of drones to determine the number of propeller blades. Furthermore, a new multi-frequency analysis method using HERM lines is developed, which allows the detection of propeller rotation rates (spool and chopping frequencies) of single and multi-propeller drones. Therefore, the presented method is a promising technique to aid in the classification of drones.

To better support the transition to more industrial uses of additive manufacturing, this study examined the use of an Arcam Q20+ industrial 3D printer for producing heavily nested Ti-6Al-4V parts with both in-specification (IS) and out of specification (OS) oxygen content in reused grade 5 powder chemistries. Both the OS and IS powder chemistries were evaluated to understand their impact on build integrity and on static and fatigue performance. The results from our evaluations showed that controlling the bed preheat temperature in the Q20+ to relatively low values (326–556 °C) was effective in limiting microstructural coarsening during the long build time and enabled adequate/balanced performance vis à vis the tensile strength and ductility. Overall, the tensile properties of the IS Ti-6Al-4V material in the as-built and machined states fully met the requirements of ASTM F2924-14. By contrast, the ductility was compromised at oxygen levels above 0.2 wt.% (OS) in Ti-6Al-4V produced by EBM. Removal of the surface layer by machining increased the consistency and performance of the IS and OS Ti-6Al-4V materials. The fatigue behaviour of the EBM Ti-6Al-4V material was in the range of properties produced by casting. Due to the strong influence of both the surface finish and oxygen content on the fatigue strength, the IS Ti-6Al-4V material exhibited the highest performance, with results that were in the range of parts that had been cast plus hot isostatically pressed.

Artificial intelligence (AI), machine learning (ML) and deep learning (DL) along with big data (BD) management are currently viable approaches that can significantly help gas turbine components’ design and development. Optimizing microstructures of hot section components such as thermal barrier coatings (TBCs) to improve their durability has long been a challenging task in the gas turbine industry. In this paper, a literature review on ML principles and its various associated algorithms was presented first and then followed by its application to investigate thermal conductivity of TBCs. This combined approach can help better understand the physics behind thermal conductivity, and on the other hand, can also boost the design of low thermal conductivity of the TBCs system in terms of microstructure–property relationships. Several ML models and algorithms such as support vector regression (SVR), Gaussian process regression (GPR) and convolution neural network and regression algorithms were used via Python. A large volume of thermal conductivity data was compiled and extracted from the literature for TBCs using PlotDigitizer software and then used to test and validate ML models. It was found that the test data were strongly associated with five key factors as identifiers. The prediction of thermal conductivity was performed using three approaches: polynomial regression, neural network (NN) and gradient boosting regression (GBR). The results suggest that NN using the BR model and GBR have better prediction capability.

This research study investigated the hybrid processing of 316L stainless steel using laser powder bed (LPB) processing with high-speed machining in the same build envelope. Benchmarking at four laser powers (160 W, 240 W, 320 W, and 380 W) was undertaken by building additively with machining passes integrated sequentially after every ten deposited layers, followed by the final finishing of select surfaces. The final geometry was inspected against the computer-aided design (CAD) model and showed deviations smaller than 280 µm for the as-built and machined surfaces, which demonstrate the good efficacy of hybrid processing for the net-shape manufacturing of stainless steel products. The arithmetic average roughness values for the printed surfaces, Ra (linear) and Sa (surface), were 11.4 um and 14.9 um, respectively. On the other hand, the vertical and horizontal machined surfaces had considerably lower roughness, with Ra and Sa values ranging between 0.33 µm and 0.70 µm. The 160 W coupon contained layered, interconnected lack of fusion defects which affected the density (7.84 g·cm⁻³), yield strength (494 MPa), ultimate tensile strength (604 MPa), Young’s modulus (175 GPa), and elongation at break (17.3%). By contrast, at higher laser powers, near-full density was obtained for the 240 W (7.96 g·cm⁻³), 320 W (7.94 g·cm⁻³), and 380 W (7.92 g·cm⁻³) conditions. This, combined with the isolated nature of the small pores, led to the tensile properties surpassing the requirements stipulated in ASTM F3184—16 for 316L stainless steel.

To understand the physics of volcanic ash impact on gas turbine hot-components and develop much-needed tools for engine design and fleet management, the behaviors of volcanic ash in a gas turbine combustor and nozzle guide vanes (NGV) have been numerically investigated. High-fidelity numerical models are generated, and volcanic ash sample, physical, and thermal properties are identified. A simple critical particle viscosity—critical wall temperature model is proposed and implemented in all simulations to account for ash particles bouncing off or sticking on metal walls. The results indicate that due to the particle inertia and combustor geometry, the volcanic ash concentration in the NGV cooling passage generally increases with ash size and density, and is less sensitive to inlet velocity. It can reach three times as high as that at the air inlet for the engine conditions and ash properties investigated. More importantly, a large number of the ash particles entering the NGV cooling chamber are trapped in the cooling flow passage for all four turbine inlet temperature conditions. This may reveal another volcanic ash damage mechanism originated from engine cooling flow passage. Finally, some suggestions are recommended for further research and development in this challenging field. To the best of our knowledge, it is the first study on detailed ash behaviors inside practical gas turbine hot-components in the open literature.