Abstract

Abstract

Abstract

Abstract

The dynamic characterization of an offshore wind turbine (OWT) and its foundation is an important task within the design stage of the support structure. The system fundamental frequency should not coincide with that of the loads that affect it, otherwise resonance phenomena can conclude with the collapse of the structure or its deterioration due to fatigue. Assuming a linear behaviour, the system fundamental frequency can be computed by solving the eingenvalue problem after defining the stiffness and mass global matrices. This procedure can become computationally expensive if soil-structure interaction (SSI) phenomena is taken into account. For this reason, a surrogate model based on Artificial Neural Networks (ANN) is proposed for estimating the fundamental frequency of the wind turbine assembly, jacket support structure and pile foundation considering SSI effects. A dataset is generated to train the ANN. This synthetic data collects the characteristics of the OWT-jacket-foundation and its fundamental frequency, which is obtained by a finite element substructuring model. The SSI is reproduced through impedance functions computed through a previously developed continuum model. Comparing the predictions of the ANN with the results obtained by the structural model, it is observed that this type of regression allows to reproduce in a sufficiently precise way the dependence of the fundamental frequency with respect to the variables that define the system. Thus the use of Machine Learning techniques, such as ANNs, makes it possible to take into account the system behaviour obtained by rigorous models in large-scale calculations that it would be unfeasible otherwise.

Abstract

Woody biomass energy is a kind of renewable energy that contributes to the reduction of greenhouse gas emissions, the creation of healthier forests, and the reduction of wildfire danger. Generally speaking, simulations of the motion of biomass particles are a time-consuming process due to a large number of particles and required simulation time. We used a physicsinformed neural network (PINN) model to predict the motion of particles by including their equations of motion to reconstruct the velocity fields and reduce the processing effort. compare to the discrete element methods, the PINNs methods have the advantage of predicting the velocity fields without the knowledge of the simulation's boundary and initial conditions as well as geometry. It has shown that the proposed model has reliable prediction results with a mean percentage error in time less than 1 percent.

Abstract

Forklift is a kind of material handling robot, which is widely used in short-distance handling in the industrial field. However, at present, the structural design of forklifts is generally based on designers' experience, and there are still many problems in domestic forklifts compared with foreign countries. In this paper, the forklift gantry is taken as the research object, and four typical surrogate modeling techniques, namely PRS, KRG, RBF, and SVR models, are used for optimal design and analysis. The study shows that the KRG model has the best performance and RBF model has the worst performance in terms of global and local accuracy. Multi-objective optimization design of the weight and total deformation of the gantry is carried out with the maximum stress of the gantry and I-beam geometry as constraints. Taking the KRG model as an example, the comparison of the results before and after optimization shows that the weight of the I-beam of the forklift gantry is reduced by 11.9% and the maximum total deformation is reduced by 23.2% while satisfying the constraints. Global sensitivity analysis (GSA) of the forklift gantry reveals that the height of the I-beam has the greatest impact on the gantry performance.

Abstract

Abstract

Approximately 70,000 Spanish off-stream reservoirs, many of them irrigation ponds, need to be evaluated in terms of their potential hazard to comply with the new national Regulation of the Hydraulic Public Domain. This requires a great engineering effort to evaluate different scenarios with two-dimensional hydraulic models, for which many owners lack the necessary resources. This work presents a simplified methodology based on machine learning to identify risk zones at any point in the vicinity of an off-stream reservoir without the need to elaborate and run full two-dimensional hydraulic models. A predictive model based on random forest was created from datasets including the results of synthetic cases computed with an automatic tool based on the two-dimensional numerical software Iber. Once fitted, the model provided an estimate on the potential hazard considering the physical characteristics of the structure, the surrounding terrain and the vulnerable locations. Two approaches were compared for balancing the dataset: the synthetic minority oversampling and the random undersampling. Results from the random forest model adjusted with the random undersampling technique showed to be useful for the estimation of risk zones. On a real application test the simplified method achieved 91% accuracy.