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An improved flux vector splitting method for characteristic-wise WENO schemes of the Euler equations
Get PDFSteger-Warming (SW) [1] and Lax-Friedrich-type (LF) [2] flux vector splitting methods are used extensively by shock capturing WENO schemes in varieties of compressible flow simulations. Due to the less dissipation, the SW method is preferred in flow calculations that require fine scale structures such as direct numerical simulation of turbulence. However, this paper shows that, even if the characteristic-wise WENO scheme is used, the SW method may still exhibit some oscillations near contact discontinuities, while the LF method does not. Analysis similar to the reference [3] shows that, using the SW method may make the characteristic-wise WENO scheme become close the component-wise WENO scheme near subsonic contact discontinuities. Based on that, an improved flux vector splitting method, which adjusts the eigenvalues of the flux vector splitting in the characteristic-wise WENO procedure, is proposed to obtain the low-dissipation property and prevent contact discontinuity oscillations at the same time. Numerical experiments are performed to validate and evaluate the new method. Numerical results show that the proposed method keeps the non-oscillatory flow field near discontinuities as LF method and also avoids smearing out other flow regions, similar to the SW method
Landslide run-out simulations with depth-averaged models and integration with 3D impact analysis using the Material Point Method
Get PDFLandslides pose a significant threat to human safety and the well-being of communities, making them one of the most challenging natural phenomena. Their potential for catastrophic consequences, both in terms of human lives and economic impact, is a major concern. Additionally, their inherent unpredictability adds to the complexity of managing the risks associated with landslides. It is crucial to continuously monitor areas susceptible to landslides. In situ detection systems like piezometers and strain gauges play a vital role in accurately monitoring internal pressures and surface movements in the targeted areas. Simultaneously, satellite surveys contribute by offering detailed topographic and elevation data for the study area. However, relying solely on empirical monitoring is insufficient for ensuring effective management of hazardous situations, especially in terms of preventive measures. This study provides advanced simulations of mudflows and fast landslides using particle depth-averaged methods, specifically employing the Material Point Method adapted for shallow water (Depth Averaged Material Point Method). The numerical method has been parallelized and validated through benchmark tests and real-world cases. Furthermore, the investigation extends to coupling the depth-averaged formulation with a three-dimensional one in order to have a detailed description of the impact phase of the sliding material on barriers and membranes. The multidimensional approach and its validation on real cases provide a robust foundation for a more profound and accurate understanding of the behavior of mudflows and fast landslide
A performance analysis procedure based on corrected displacements to evaluate the seismic response of steel 2D frames
Get PDFIn the proposed methodology, a load pattern is applied in a non-adaptive fashion to obtain the seismic response of two-dimensional steel moment resisting frames. The proposed methodology is based on the structural dynamics theory and consists of a single run nonlinear analysis. This invariant load pattern is formulated by considering higher mode effects with the use of an effective modal mass contribution factor. Also, part of the proposed procedure, a corrective factor is employed to adjust the displacements obtained from the nonlinear analysis ensuring that the drift values obtained from the corrected displacements are adequate. The procedure allows the analysis of the structural response, i.e, story displacement and story drifts. To evaluate the methodology a nine-story steel moment frame is analyzed. Material and geometric non linearities are considered for all the cases. The results are compared with the ones obtained by the Nonlinear time history analysis
The role of Beclin1 in astrocyte autophagy in the dentate gyrus
No full textThe dentate gyrus is a unique part of the brain because it is known for housing neurogenesis in the adult brain, a process which normally stops early in development. This makes the dentate gyrus an area of great interest, especially in combating neurodegenerative diseases. This area, as well as the entire nervous system, is composed of both neurons, which send and receive signals, and glial cells, which are responsible for supporting neurons. Glial cells including astrocytes, oligodendrocytes, and ependymal cells are present in both the central nervous system and peripheral nervous system, while Schwann Cells are present only in the peripheral nervous system, and microglia are present only in the central nervous system. Apoptosis and autophagy are both processes which degrade and recycle materials. Autophagy degrades materials inside the cell, such as organelles and proteins, and uses lysosomes to carry out the process. Apoptosis consists of degrading old or damaged cells, and is known as programmed cell death. Many proteins affect these processes including Beclin-1, UC3, p62, and the BCL-2 family of proteins. Some studies have used machine learning algorithms in conjunction with simulation and statistical software to study these proteins and the processes they are involved in. Several areas of research remain unfilled, especially in the area of glial cells and the proteins involved in autophagy and apoptosis, as not many machine learning studies have examined this
Fast prediction of rain erosion in wind turbine blades using a data-based computational tool
No full textWind turbines (WTs) face a high risk of failure due to environmental factors like erosion, particularly in high-precipitation areas and offshore scenarios. In this paper we introduce a novel computational tool for the fast prediction of rain erosion damage on WT blades that is useful in operation and maintenance decision making tasks. The approach is as follows: Pseudo-Direct Numerical Simulation (P-DNS) simulations of the droplet-laden flow around the blade section profile are employed to build a high-fidelity data set of impact statistics for potential operating conditions. Using this database as training data, a machine learning-based surrogate model provides the feature of the impact pattern over the 2-D section for given wind and rain conditions. With this information, a fatigue-based model estimates the remaining lifetime and erosion damage for both homogeneous and coating-substrate blade materials. This prediction is done by quantifying the accumulated droplet impact energy and evaluating operative conditions over time periods for which the weather at the installation site is known. In this work, we describe the modules that compose the prediction method, namely the database creation, the training of the surrogate model and their coupling to build the prediction tool. Then, the method is applied to predict the remaining lifetime and erosion damage to the blade sections of a reference WT. To evaluate the reliability of the tool, several site locations (offshore, coastal, and inland), the coating material and the coating thickness of the blade are investigated. In few minutes we are able to estimate erosion after many years of operation. The results are in good agreement with field observations, showing the promise of the new rain erosion prediction approach
Multiscale data-driven modeling of the thermomechanical behavior of granular media with thermal expansion effects
Get PDFA multiscale data-driven (MSDD) methodology is proposed for simulating the thermomechanical behavior of granular materials subjected to thermal expansion. The macroscale is handled using a continuous model based on the Finite Volume Method (FVM), while the microscale response is captured at Representative Volume Elements (RVEs) with the Discrete Element Method (DEM). To significantly reduce the computational cost of the analyses, the microscale DEM computations are not performed online, �.�., simultaneously with the macroscale FVM ones, as generally done in standard multiscale approaches. Instead, they are performed in advance to create a comprehensive database of RVE solutions under different initial conditions and thermal strains. This dataset is then used to train an Artificial Neural Network (ANN), which serves as a surrogate model for the macroscale solver. The MSDD approach is validated against pure DEM solutions of problems with distinct thermal conditions. Remarkably, we demonstrate that with only three input parameters, namely porosity, fabric, and thermal strain, the surrogate model can predict the microstructure evolution, as well as the updated conductivity and Cauchy stress tensors of the granular assembly. This allows for a generally accurate simulation of transient thermomechanical analyses at a drastically lower computational cost than the pure DEM approach
Computational analysis of non-proportional biaxial shear reverse experiments superimposed by different cyclic loads
No full textThis paper deals with anisotropic ductile damage and fracture behavior under low positive stress triaxialities. Novel tension–shear biaxial low–cycle experiments with different numbers of loading cycles (up to twenty) have been performed using a cruciform biaxially loaded specimen. During the experiment, a tensile preload is first imposed on the horizontal axis until it reaches 3kN; then, it is kept constant while different shear cyclic loading sequences are superimposed on the vertical axis until failure. All cyclic loadings are driven to a large strain range to investigate ductile damage, and the same amplitude is maintained for each repeated reverse loading cycle within a single loading pattern. In addition, numerical simulations are performed with an anisotropic stress-state-dependent plastic-damage continuum model, also considering the Bauschinger effect. The experimental and numerical analysis of the evolution of the first principal total strain and damage strains highlights the influence of the cyclic loading history on the material behavior. Moreover, fracture surfaces are examined by scanning electron microscopy to analyze the different mechanical performances at the micro-leve
Machine learning model for correlating microstructural features and macroscopic properties of heterogeneous composites
Get PDFThis study explores the use of machine learning (ML) models in predicting the macroscopic properties of heterogeneous composites. Traditional micromechanics parameters have limitations, thus ML models with and without feature engineering are utilized. For artificial neural network (ANN) models with feature engineering, microstructural descriptors from SEM images of nickel-based superalloys are used to predict hardness. 10 descriptors are selected to reduce the computational cost of the deep neural network (DNN) with the support of the shallow neural network (SNN), and accuracy is enhanced by incorporating two additional descriptors. The result surpasses existing physics-based models. Models without feature engineering employ a convolutional neural network (CNN) to predict the effective thermal conductivity of thermal insulation composite materials. The CNN model demonstrates accurate predictions for novel microstructures. ML models can achieve more efficient predictions than traditional methods, indicating their potential in advancing materials science. In summary, harnessing artificial intelligence to capture the scattering characteristics of heterogeneous materials enables both DNN and CNN models to achieve more efficient predictions compared to traditional methods. This highlights the potential of machine learning in advancing materials science and expediting the development of materials with desired properties
Data-driven Permeability Prediction of 3D Fibrous Microstructures
Get PDFFor the manufacturing process simulation of fiber-reinforced polymer composites, f low simulations have to be performed at multiple spatial scales which govern the flow through the fiber structures. Repetitive multiscale flow simulations are computationally expensive and time-consuming. In order to speed up the multiscale simulation workflow, fast machine learning surrogate models or emulators could be used to replace one or more of the flow simulations. In this work, feature-based emulators and geometry-based emulators are developed using neural networks for predicting the permeability of 3D fibrous microstructures based on a reference dataset (doi:10.5281/zenodo.10047095). The best model achieved a mean relative error of 8.33% on the test set with a significantly faster inference time compared to a conventional simulator