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Improving the manufacturability and assessing the performance of rare earth zirconates for thermal barrier coatings
No full textKey aero-engine components are subject to gas stream temperatures above the melting point of their metal alloy, a demanding environment that requires the deployment of thermal barrier coatings (TBCs) for their operation. Electron-beam physical vapour deposition (EB-PVD) can produce TBCs with unique columnar microstructures, conferring them the strain compliance required to survive in the cyclic, high-strain, high-thermal load environment experienced by the rotating parts of the high-temperature turbine. Rare earth zirconates (REZs) are proposed as substitute materials of partially-yttria-stabilised zirconia (PYSZ) to operate at higher temperatures due to their ability to withstand CMAS (calcium‑magnesium-alumino-silicate) attack. On the other hand, the lower toughness of REZs makes them more susceptible to erosion damage during service, and some manufacturability issues have been noticed in previous studies. The current paper evaluates the manufacturability, CMAS resistance and erosion resistance of PYSZ and three REZ systems: gadolinium (GZ), neodymium (NZ) and lanthanum (LZ) zirconate. For the first time, successful NZ TBCs have been produced by EB-PVD, presenting a similar morphology, ease of manufacture and CMAS resistance to GZ, but inferior erosion resistance. LZ has compositional banding, lacks columnarity in La-rich regions, and has the lowest CMAS and erosion resistance of the REZs investigated. Co-evaporation of lanthana and PYSZ ingots increased compositional homogeneity of LZ, but the increased La content of the unoptimised process makes the coatings hygroscopic. The erosion resistance of the REZs is 6–10 times lower than PYSZ, but the study of their failure mechanisms indicates a potential improvement strategy by altering the coating morphology.The authors are thankful to Innovate UK for their Smart Award project #10020751, “High temperature tools for designing sustainable erosion resistant coatings”, which partially funded this work, and to Rolls-Royce Plc for providing additional funds.Surface and Coatings Technolog
Gas turbine equivalent operating hour estimation considering creep-LCF interactions
Get PDFGas turbine maintenance strategy relies heavily on accurate estimation of critical component life consumption of gas turbine engines during their operations. The equivalent operating hours (EOH) is a useful concept to measure the engine life consumption and support condition-based maintenance planning for gas turbine engines and their critical components. However, the current EOH calculation methods are mostly empirical and engine-specific, relying on vast operating data and experience. This paper introduces a novel physics-based method to estimate the EOH of the high-pressure turbine rotor blades of a gas turbine engine based on the damages caused by creep and low-cycle fatigue (creep-LCF) interactions. The method has been applied to a typical turbofan engine taking both 440-minute long-haul flight at one flight per day and 60-minute short-haul flight at two flights per day. A comparison of the predicted damages and life consumptions indicates that the creep EOH and also the creep damage of the engine of the short-haul aircraft is about 1.38 times that of the engine of the long-haul aircraft, the LCF equivalent operating cycles (EOC) and also the LCF damage of the engine of the short-haul aircraft is about 2.0 times that of the engine of the long-haul aircraft, and the total damages are more affected by the creep damage than the LCF damage with the creep damage being 6.78 times the LCF damage for the engine of the short-haul aircraft and 9.81 times for the engine of the long-haul aircraft. In addition, the total EOH or the total damage of the engine of the short-haul aircraft is about 1.44 times that of the engine of the long-haul aircraft. The proposed method shows a great potential to provide a quick estimate of the life consumption of gas turbine engines for condition monitoring, and it can be applied to other types of gas turbine engines.The Aeronautical Journa
Multi-agent deep reinforcement learning-based RIS-aided UAV communications
Get PDFHowever, traditional model-based phase-shift optimization is highly sensitive to imperfect CSI and becomes computationally prohibitive for large UPA-based RIS, while existing model-free solutions relying on single-agent DRL struggle with the exponentially growing action space. This paper presents a scalable multi-agent deep Q-network (MADQN)–based RIS controller designed for large-scale UAV–RIS systems under realistic channel dynamics. An end-to-end channel inference architecture is first introduced to mitigate CSI imperfection and reconstruct stable channel representations under UAV mobility. A multi-objective formulation is then developed to jointly optimize sum rate, energy consumption, and control latency, which is transformed into a multi-agent Markov decision process (MMDP) compatible with quantized RIS hardware. Building on this formulation, a dual-agent RIS controller is proposed, in which row and column agents cooperatively determine the quantized phase configuration of a large UPA RIS. Extensive simulations demonstrate that the proposed framework significantly outperforms benchmark schemes, showing acceptable robustness against varying Rician factor SNRs, UAV densities, and RIS sizes. These results confirm that the proposed MADQN-based controller is a promising and practical solution for scalable RIS control in large-scale multi-UAV communication systems.This work was supported by Engineering and Physical Sciences Research Council (EPSRC) Communications Hub for Empowering Distributed Cloud Computing Applications and Research (CHEDDAR) Project under Grant EP/X040518/1 and Grant EP/Y037421/1IEEE Acces
Permeability characterization of a biaxial stitched fabric: Insights from 2D flow experiments under unsteady and steady flow regimes
Get PDFThis study investigated in-plane permeability of a biaxial stitched E-glass fabric preform using 2D (radial) flow experiments under constant-injection pressure at fiber volume fractions of Vf = 41–54%. Unsteady permeability was determined through a repeated set of experiments on separate specimens and by tracking elliptical flow front propagation with time along x, y axes and θ = 45°. The fabric exhibited an anisotropic behavior with unsteady permeability along the production line (x-direction) being significantly higher than permeability along the transverse line (y-direction). The ratios of principal permeability components, Kuns, 1/Kuns, 2 were 5.67 ± 2.14, 3.72 ± 0.90 and 3.97 ± 0.87 at Vf = 0.41, 0.46 and 0.54, respectively. For steady permeability characterization, analytical relationship (driven from Darcy’s Law) between the permeability and process parameters (inlet hole diameter, resin viscosity, inlet and exit pressures) is usable only if the exit flow rate is measured at an elliptical mold edge, which is not practical as these characterization experiments are usually conducted with a non-elliptical mold (circular or square). In this study, steady permeability was calculated by using experimental steady flow rate, an assumption that the anisotropy ratio calculated in the unsteady regime remains constant at steady state, and a straightforward numerical iterative solution. The ratio of steady to unsteady permeabilities, Ks/Kuns was determined as 0.97 ± 0.33, 0.76 ± 0.09 and 0.54 ± 0.26 at Vf = 0.41, 0.46 and 0.54, respectively. This study presents a valuable methodology and key insights into the permeability of a biaxial fabric, extendable to other fabric types and contribute to advancing the understanding and modeling of mold filling in liquid composite molding processes.Journal of Composite Material
Composite material defect segmentation using deep learning models and infrared thermography
No full textFor non-destructive assessment, the segmentation of infrared thermographic images of carbon fiber composites is a critical task in material characterization and quality assessment. This study focuses on applying image processing techniques, particularly adaptive thresholding, alongside neural network models such as U-Net and DeepLabv3 for infrared image segmentation tasks. An experimental analysis was conducted on these networks to compare their performance in segmenting artificial defects from infrared images of a carbon-fibre reinforced polymer sample. The performance of these models was evaluated based on the F1-Score and Intersection over Union (IoU) metrics. The findings reveal that DeepLabv3 demonstrates superior results and efficiency in segmenting patterns of infrared images, achieving an F1-Score of 0.94 and an IoU of 0.74, showcasing its potential for advanced material analysis and quality control.This study was financed in part by the Coordenacao de Aper-feicoamento de Pessoal de Nivel Superior – Brasil (CAPES) –Finance Code 001 and by the National Council for Scientificand Technological Development - Brazil (CNPq) – Finance Codes 407140/2021-2 and 312530/2023-4.Revista de Informática Teórica e Aplicad
Corruption and default risk: global evidence
Get PDFThe extant literature explores the consequences of corruption on firms’ growth and survival. However, its impact on default risk remains unexplored. On the basis of a sample of 189,109 firm‐years from 2004 to 2021 across 47 countries, our study reveals that a one standard deviation increase in corruption is associated with an 11.3% increase in default risk. Our channel analysis identifies information asymmetry and managerial risk‐taking as key mechanisms through which corruption influences default risk. This adverse effect is particularly pronounced in countries with opaque information environments, weak governance frameworks and inadequate external monitoring of firms. We further highlight the detrimental impact of corruption on firms’ borrowing costs and banks’ loan performance. Our study emphasizes the importance of enhancing information transparency and implementing stringent control mechanisms as a basis of mitigating corruption's detrimental effects across a range of different socio‐political contexts.Journal of Business Finance & Accountin
Tailoring mesoporous Y-Zeolite molecular sieve for effective removal of micropollutants from water
Get PDFOveruse and misuse of antibiotics have led to persistent antibiotic residues in water, challenging conventional remediation approaches. This study developed a tailored molecular sieve material, i.e., mesoporous Y-zeolite (M-Y zeolite), through hydrothermal synthesis for tetracycline (TC) removal from simulated and real water matrices. The average pore size of M-Y zeolite was 3.16 nm, more than 1.7 times the second-widest dimension of the targeted TC molecule (0.81 nm), allowing for effective adsorption. With a specific surface area of 516 m2 g–1, M-Y zeolite achieved a maximum adsorption capacity of 88 mg g–1. The pseudo-second-order and Freundlich models indicated that adsorption occurred on a multilayer heterogeneous surface through chemisorption. The intraparticle diffusion model indicated that the adsorption process was governed by both liquid film diffusion and intraparticle diffusion. Mechanistic studies identified pore filling, complexation, and electrostatic interactions as the main adsorption mechanisms. After four regeneration cycles, the M-Y zeolite retained 66% of its initial adsorption capacity. In real water tests, removal efficiency slightly declined (4–14%) at 10 mg L–1 TC due to competing ions and organic matter but remained >99% at 0.1 and 1 mg L–1 TC. These findings offer a promising mesoporous material for antibiotic removal, marking a significant advancement in water treatment.This work was supported by Fundamental Research Funds for the Central Universities (No. E3E40504X 2), the National Key R&D Program of China (No. 2022YFE0209500), and the Air Pollution Status Assessment and Refined Management Supporting Project of Qinghai Province (No. E341970201,E34050701).ACS ES&T Wate
Minimum Lap Time Simulation Framework: Active Aerodynamics and Torque Distribution
No full textThe Minimum Lap Time Problem (MLTP) simulation framework is an optimisation tool designed to simultaneously optimise a vehicle’s trajectory, control inputs, and selected design parameters to achieve the fastest possible lap time for a given track. It employs an optimal control approach with direct collocation, leveraging CasADi and IPOPT to define and solve the optimisation problem.
The MLTP framework builds on the original work by Zarzuelo and Siampis [1] and the extensions by Jiménez et al. [2], expanding its capabilities with a more advanced vehicle model and enhanced optimisation functionalities, including active aerodynamics and active torque distribution configurations.
The optimisation framework is applied to the DrivAer hp-F high-performance vehicle model and has been tuned for the Circuit de Barcelona-Catalunya. It includes design optimisation parameters, such as the distribution of drive torque, brake torque, and roll stiffness, as well as a nominal tyre load shift parameter to analyse tyre characteristics.
The MLTP framework has a modular structure, making it a versatile tool for broader applications in vehicle performance optimisation. It enables adaptation to different vehicles and tracks while allowing extensions such as alternative vehicle models, aerodynamic concepts, and design parameters. Users applying it to new scenarios should establish new initialisation cases and re-tune optimisation parameters to ensure viable solutions.
This dataset is associated with the following publications:
Rijns, S., Teschner, T. R., Blackburn, K., Siampis, E., & Brighton, J. (2024). Optimising Vehicle Performance with Advanced Active Aerodynamic Systems [In Review]. Advanced Vehicle Engineering Centre, Cranfield University.
Rijns, S., Teschner, T. R., Blackburn, K., Siampis, E., & Brighton, J. (2024). Performance Optimisation of High-Performance Vehicles with Active Aerodynamics and Active Torque Distribution Capabilities [In Review]. Advanced Vehicle Engineering Centre, Cranfield University.
References:
[1] Adrián Zarzuelo, Efstathios Siampis (2022). MLTP (https://www.mathworks.com/matlabcentral/fileexchange/119398-mltp),
MATLAB Central File Exchange.
[2] Jiménez Elbal, A., Zarzuelo Conde, A., & Siampis, E. (2024). Simultaneous Optimisation of Vehicle Design and Control for Improving Vehicle Performance and Energy Efficiency Using an Open Source Minimum Lap Time Simulation Framework. World Electric Vehicle Journal, 15(8), 366. https://doi.org/10.3390/wevj1508036
A review of solar absorption chillers and thermal storage by phase change materials
No full textThe emergence of advanced absorption chillers designed for the effective utilisation of low-grade thermal energy indicates a notable advancement in the discipline of cooling technology. These chillers, which range from small air-cooled systems to larger solar-gas-fired units, are specifically designed to address the escalating requisites for environmentally sustainable cooling alternatives. This paper presents a comprehensive review of solar absorption chillers and their integration with thermal energy storage systems, with a focus on the application of phase change materials (PCMs). It analyses the performance and configurations of single, double, and triple-effect chillers, along with the role of various solar thermal collectors in delivering the required input temperatures for cooling applications. Sensible, thermochemical, and latent heat storage methods are explored, emphasising cascade PCM systems for improved thermal efficiency and load flexibility. While the review highlights the significant potential of solar-powered absorption chillers in advancing sustainable cooling, particularly in hot climates such as those found in Africa, it also identifies key research gaps. These include the limited analysis of medium-temperature cooling demand (2–12°C), the need for integrated thermal storage systems using PCMs for multi-level cooling demands, and the lack of region-specific feasibility studies in diverse African conditions. Overall, the paper offers valuable insights into optimising solar absorption cooling technologies through effective storage integration and system design, supporting their broader adoption in energy efficient, low carbon applications.Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energ
On real-time semantic segmentation with comprehensive off-road datasets for enhanced terrain classification
No full textThis paper introduces a novel approach to dataset annotation across 13 diverse classes for terrain classification. The method is applied to an established set of terrain-related datasets, and a new dataset by the authors’ team referred to as CranfieldTerra. These datasets were trained using 18 distinct neural network (NN) architectures, and based on overall test accuracy, precision, recall, and Intersection over Union (IoU) scores, the accuracy of each label, and training time, the effectiveness of these models was evaluated. Furthermore, the methodology has been systematically validated and tested in real-time using a platform called the Husky-A200. This comprehensive evaluation ensures the reliability and accuracy of the methodology under practical conditions.
An innovative real-time switch model is introduced that dynamically selects the most appropriate neural network from the set of pre-trained models based on the calculated environmental density rate and the presence of specific features like ’Person, House, Vegetation Area, and Mud Area’ class counts. This approach significantly enhances the adaptability and performance of real-time semantic segmentation in varied environmental conditions, leading to more efficient terrain classification.The first author acknowledges the Republic of Turkey, Ministry of National Education (1416-YLSY), for supporting the studies under the PhD scholarship ref. U9BYTAB2LDGA7LK.Engineering Applications of Artificial Intelligenc