Machine learning is a powerful means for the rapid development of high-performance functional materials. In this study, we presented a machine learning workflow for predicting the corrosion resistance of a self-healing epoxy coating containing ZIF-8@Ca microfillers. The orthogonal Latin square method was used to investigate the effects of the molecular weight of the polyetheramine curing agent, molar ratio of polyetheramine to epoxy, molar content of the hydrogen bond unit (UPy-D400), and mass content of the solid microfillers (ZIF-8@Ca microfillers) on the low impedance modulus (lg|Z|_0.01Hz) values of the scratched coatings, generating 32 initial datasets. The machine learning workflow was divided into two stages: In stage I, five models were compared and the random forest (RF) model was selected for the active learning. After 5 cycles of active learning, the RF model achieved good prediction accuracy: coefficient of determination (R ²) = 0.709, mean absolute percentage error (MAPE) = 0.081, root mean square error (RMSE) = 0.685 (lg(Ω·cm²)). In stage II, the best coating formulation was identified by Bayesian optimization. Finally, the electrochemical impedance spectroscopy (EIS) results showed that compared with the intact coating ((4.63 ± 2.08) × 10¹¹ Ω·cm²), the |Z|_0.01Hz value of the repaired coating was as high as (4.40 ± 2.04) × 10¹¹ Ω·cm². Besides, the repaired coating showed minimal corrosion and 3.3% of adhesion loss after 60 days of neutral salt spray testing.

Point cloud data have rich semantic representations and can benefit various applications towards a digital twin. However, they are unordered and anisotropically distributed, thus being unsuitable for a typical Convolutional Neural Networks (CNN) to handle. With the advance of deep learning, several neural networks claim to have solved the point cloud semantic segmentation problem. This paper evaluates three different neural networks for semantic segmentation of point clouds, namely PointNet++, PointCNN and DGCNN. A public indoor scene of the Amersfoort railway station is used as the study area. Unlike the typical indoor scenes and even more from the ubiquitous outdoor ones in currently available datasets, the station consists of objects such as the entrance gates, ticket machines, couches, and garbage cans. For the experiment, we use subsets from the data, remove the noise, evaluate the performance of the selected neural networks. The results indicate an overall accuracy of more than 90% for all the networks but vary in terms of mean class accuracy and mean Intersection over Union (IoU). The misclassification mainly occurs in the classes of couch and garbage can. Several factors that may contribute to the errors are analyzed, such as the quality of the data and the proportion of the number of points per class. The adaptability of the networks is also heavily dependent on the training location: the overall characteristics of the train station make a trained network for one location less suitable for another.

Innovative drive trains targeted at 10-20 MW offshore turbines are investigated in the INNWIND.EU project in order to determine the impact on the Levelized Cost of Energy (LCoE) resulting when installed in the ,North sea at 50 m of water [1]. The two main technologies studied are superconducting direct drive (SCDD)[2] and the magnetic pseudo direct drive (PDD) [3] generators, which are both capable to providing compact drive trains with low weight and a small number of moving parts compared to a gearbox based drive train (see figure 1a). Superconducting field coils are used to provide the torque in the direct drive generators, where the armature windings are based on conventional copper wire and magnetic steel laminates operated at ambient temperature. Magnetic pseudo direct drive generators consist of a magnetic gearbox made of an inner free rotor (rotating at a geared up speed to the blade input) and an intermediate drive rotor inserted into an outer static armature winding, where the electricity is harvested.

A simple model based on a Perfectly Stirred Reactor (PSR) was proposed for moderate or intense low-oxygen dilution (MILD) combustion. The PSR calculation covers the entire flammability range and the tabulated chemistry approach was applied with a presumed joint probability density function (PDF). The jet in hot and diluted coflow experimental set-up under MILD conditions was simulated using PSR model for two oxygen dilution levels. The unheated fuel jet consists of 50% CH4 and 50% H2. The computed results for mean temperature major and minor species mass fractions were compared with the experimental data and simulation results through a multi-environment transported PDF approach. A good agreement was observed at three different axial locations for these comparisons despite peak value overprediction of CO formation suggesting that MILD combustion can be effectively modeled by the proposed PSR model with lower computational cost.

A significant number of naturally fractured reservoirs (NFRs) discovered in the world contain heavy and extra heavy oil. These reservoirs are important resources; however, the nature of naturally fractured reservoirs, especially those containing heavy and extra heavy oil, presents many unique and complex challenges for reservoir modeling and simulation. There have been a number of attempts over the last 50 years to develop methods to improve our understanding as to how the fracture systems impact oil recovery. For many decades, the dual-porosity approach has been the most popular and effective technique in modeling of NFRs. This approach separates the fracture and matrix systems into two different continua, each with its own set of properties. Fluid exchange between matrix and fractures is modeled through a Transfer Function (TF), while a shape factor describes the fracture-matrix surface area. However, the fracture-matrix fluid interaction is not yet fully understood for thermal processes, which represents a significant unknown in thermal reservoir simulation of NFRs containing (ultra) heavy oil. In this paper an extensive literature survey was initiated to establish a detailed understanding as to how shape factors are utilized for modeling non-isothermal, fracture-matrix fluid exchange in fractured reservoirs. The most appropriate way is to treat the shape factor as a time-dependent quantity to capture the pertinent features of non-isothermal fluid flow in fractured reservoirs. A series of numerical simulations have been conducted using the simulator STARS from Computer Modeling Group Ltd. in order to analyze the performance of existing transfer functions and shape factor formulations for dual-porosity, multiphase flow systems in thermal reservoir simulation. Based on this analysis, we introduce the concept of a new, transient shape factor for non-isothermal, dual-porosity models and compare our new concept with the existing shape factor models. The results from this study clearly confirm that a transient shape factor is required for an appropriate modeling of a thermal recovery process in NFRs when using dual-porosity formulations.

Latent heat of evaporation represents a large outgoing component of the energy balance established at a crop-stand surface. This explains why agriculture uses approximately 70% of all the freshwater withdrawn in the world. Increasing demand for water due to population growth, competition with industrial, domestic and environmental requirements, and the decreasing quality of water, limit the agricultural capacity for food production. The Water Resources, Development and Management Service (AGLW) of the Food and Agricultural Organization of the United Nations (FAO) is carrying out activities aimed at helping country members in supporting sustainable water management to securing food for a growing population. These activities cut across the various levels of the water domain, going from the (inter)national policy level down to local-level field applications. In this article, FAO's experiences in agricultural water management are used to provide lessons from the past and indicate directions for future challenges.