Performance based modifications of random forest to perform automated defect detection for fluorescent penetrant inspection

The established Machine Learning algorithm Random Forest (RF) has previously been shown to be effective at performing automated defect detection for test pieces which have been processed using fluorescent penetrant inspection (FPI). The work presented here investigates three methods (two previously proposed in other fields, one novel method) of modifying the FPI RF based on the individual performance of decision trees within the RF. Evaluating based on the 2 Score, which is the harmonic mean of precision and recall which places a larger weighting on recall, it is possible to reduce the RF in size by up to 50%, improving speed and memory requirements, whilst still gain equivalent results to a full RF. Introducing a performance based weighting or retraining decision trees which fall below a certain performance level however, offers no improvement on results for the increased computation time required to implement

HVOF and laser cladded Fe-Cr-B coating in simulated biomass combustion: microstructure and fireside corrosion

Biomass is often considered as a low carbon alternative to fossil fuels in the power industry. However the heat exchangers in biomass plants can suffer from chloride based aggressive fireside corrosion. A commercially available amorphous Fe-Cr-B alloy was deposited onto a stainless steel substrate by HVOF thermal spray and laser cladding. The controlled environment corrosion tests were conducted in a HCl rich environment at 700°C for 250 h with and without KCl deposits. The samples were examined with XRD, SEM and EDX mapping to understand the corrosion mechanisms. In the absence of any deposits, the amorphous HVOF coating performed very well with a thin oxide growth whereas the crystalline laser cladding suffered from ~350 μm metal loss. The scales were composed of MnWO₄, Fe₂O₃, Fe₃O₄ and Cr₂O₃. When a KCl deposit was present, the HVOF sprayed coating delaminated from the substrate and MnCl₂ was found in the scale

Using ResNets to perform automated defect detection for Fluorescent Penetrant Inspection

Fluorescent Penetrant Inspection (FPI) is a popular Non-Destructive Testing (NDT) method which is used extensively in the aerospace industry. However, the nature of FPI means results are susceptible to the effects of human factors and this can lead to variable results, making automation desirable. Previous work has investigated the use of established machine learning method Random Forest to perform automated defect detection for FPI. Whilst good results were obtained, there was still a significant number of false positives being identified as defective. This paper presents work done to investigate the potential of using deep learning methods to perform automated defect detection. A dataset was obtained from a set of 99 titanium alloy test pieces with cracks induced using thermal fatigue loading. These test pieces were repeatedly processed and using data augmentation a large dataset was obtained. This data was used to train a ResNet34 and ResNet50 architecture as well as a Random Forest. Two significant results were obtained. Firstly, the ResNet50 is able to create a network capable of detecting 95% of defects with a false call rate of 0.07. This result far exceeded that obtained using the Random Forest method despite both methods only having access to a small dataset. This demonstrated the strong capability of deep learning architectures. The second result was that increasing the amount of data obtained from non defective regions significantly increases performance. This result is encouraging as this data, obtained from non-cracked parts, can be quickly and cheaply obtained by reprocessing test pieces

The impact of the explicit representation of convection on the climate of a tidally locked planet in global stretched-mesh simulations

This is the author accepted manuscript.Software: The LFRic-Atmosphere source code and configuration files are freely available from the Met Office Science Repository Service (https://code.metoffice.gov.uk) upon registration and completion of a software license. The UM and JULES code used in the publication has been committed to the UM and JULES code trunks, having passed both science and code reviews according to the UM and JULES working practices; in the UM/JULES versions stated in the paper (vn13.3). Scripts to post-process and visualize model output are available at https://github.com/dennissergeev/stretched_mesh_code and depend on the following open-source Python libraries: aeolus (Sergeev & Zamyatina 2024), geovista (Little 2023), iris-esmf-regrid (Worsley 2023), iris (Met Office 2023), matplotlib (Hunter 2007), and numpy (Harris et al. 2020).Convective processes are crucial in shaping exoplanetary atmospheres but are computationally expensive to simulate directly. A novel technique of simulating moist convection on tidally locked exoplanets is to use a global 3D model with a stretched mesh. This allows us to locally refine the model resolution to 4.7 km and resolve fine-scale convective processes without relying on parameterizations. We explore the impact of mesh stretching on the climate of a slowly rotating TRAPPIST-1e-like planet, assuming it is 1:1 tidally locked. In the stretched-mesh simulation with explicit convection, the climate is 5 K colder and 25% drier than that in the simulations with parameterized convection (with both stretched and quasi-uniform meshes). This is due to the increased cloud reflectivity — because of an increase of low-level cloudiness — and exacerbated by the diminished greenhouse effect due to less water vapor. At the same time, our stretched-mesh simulations reproduce the key characteristics of the global climate of tidally locked rocky exoplanets, without any noticeable numerical artifacts. Our methodology opens an exciting and computationally feasible avenue for improving our understanding of 3D mixing in exoplanetary atmospheres. Our study also demonstrates the feasibility of a global stretched mesh configuration for LFRic-Atmosphere, the next-generation Met Office climate and weather model.UKRILeverhulme Trus

Simulations of idealised 3D atmospheric flows on terrestrial planets using LFRic-Atmosphere

This is the author accepted manuscript.Data availability. A post-processed dataset is provided in a Zenodo archive: https://doi.org/10.5281/zenodo.7818107. Along with visualisation scripts, it contains LFRic-Atmosphere output, averaged in time and interpolated to a common lat-lon grid. It also contains time mean UM data shown in the Appendix A.We demonstrate that LFRic-Atmosphere, a model built using the Met Office’s GungHo dynamical core, is able to reproduce idealised large-scale atmospheric circulation patterns specified by several widely-used benchmark recipes. This is motivated by the rapid rate of exoplanet discovery and the ever-growing need for numerical modelling and characterisation of their atmospheres. Here we present LFRic-Atmosphere’s results for the idealised tests imitating circulation regimes commonly used in the exoplanet modelling community. The benchmarks include three analytic forcing cases: the standard Held-Suarez test, the Menou-Rauscher Earth-like test, and the Merlis-Schneider Tidally Locked Earth test. Qualitatively, LFRic-Atmosphere agrees well with other numerical models and shows excellent conservation properties in terms of total mass, angular momentum and kinetic energy. We then use LFRic-Atmosphere with a more realistic representation of physical processes (radiation, subgrid-scale mixing, convection, clouds) by configuring it for the four TRAPPIST-1 Habitable Atmosphere Intercomparison (THAI) scenarios. This is the first application of LFRic-Atmosphere to a possible climate of a confirmed terrestrial exoplanet. LFRic-Atmosphere reproduces the THAI scenarios within the spread of the existing models across a range of key climatic variables. Our work shows that LFRic-Atmosphere performs well in the seven benchmark tests for terrestrial atmospheres, justifying its use in future exoplanet climate studiesScience and Technology Facilities Council (STFC)UKRILeverhulme Trus