FLOWS (FLOws of Water and Solutes in soils) is a dynamic physically-based model to simulate water flow and solute transport in the soil–plant–atmosphere system. The numerical code was written in MATLAB. One-dimensional vertical transient water flow is simulated by numerically solving the 1D form of the Richards equation (RE) using an implicit, backward, finite differences scheme with explicit linearization. As for the solutes, the code solves the 1D Advection Dispersion equation (ADE) by an explicit, central difference scheme, allowing for a relatively easy inclusion of non-linear adsorption and other non-linear processes. The model produces information on the time evolution of (among many other outputs): soil water contents and pressure potentials in the soil profile; solute (tracers, adsorbed, volatile and reactive solutes) concentrations in the soil profile; water and solute uptake and actual evapotranspiration for simulated crops; deep percolation water and solute fluxes; root uptake of water and solutes, also in presence of water and osmotic stresses; water fluxes and related solute fluxes to runoff; drainage water fluxes and related solute fluxes; irrigation fluxes computed by the model; and temperature in the soil profile. Compared to other existing models, some specificities of FLOWS model are: (1) integrated simulation of carbon, nitrogen and phosphorus transformations and transport contextually to the simulation of water contents and fluxes; (2) macroscopic root uptake; (3) irrigation management, by allowing to optimize the time scheduling and volumes according to some criterion based on the average pressure head in the root zone; (4) the possibility of running multi-site simulations, which may be especially important for spatially distributed simulations; and (5) an interactive graphical user interface, allowing the user to easily handle even large input files and seeing the simulation results by graphical display of the main output variables.
Accurate simulation of crack propagation by the extended finite element method (XFEM) is often limited by the high computational cost associated with global mesh refinement, or the need for predefined crack path. This paper presents an efficient alternative, namely an XFEM-based moving refinement patch method (MRPM). The main contribution is a penalty-based coupling scheme that connects a locally refined mesh patch with a coarse global mesh. With this formulation, the refinement patch can dynamically follow the propagating crack tip without re-meshing or prescribing the crack path in advance. In this way, the proposed method combines the accuracy of local mesh refinement with the computational efficiency of coarse-mesh discretization. The performance of the proposed method is assessed through several benchmark problems, including crack propagation in a beam under four-point bending and crack coalescence in a plate containing two separated cracks. The results show that MRPM can accurately capture both local fracture evolution and global structural response, while significantly reducing the computational cost compared with the classical XFEM.
For the problems of scarce data, high cost and low efficiency in four-point bending property evaluation of the Laser Deposition Repaired TA15 short beam in aerospace, a Hybrid Constitutive Model Driven FEM - Small Sample Machine Learning framework is established to solve small sample scale issue in this study. Five Machine Learning model- ANN, SVR, GPR, RF and LSBoost were trained and compared by combining a novel K-Means Clustering-based data augmentation strategy (KMCE). the KMCE (Tangent core function) - GPR demonstrating superior accuracy and stability, achieving the lowest error (< 1.4
Structures composed of spatially-varying architected materials have great potential for targeted mechanical and/or multifunctional behavior, but lack of structural integrity at the interfaces between different architecture geometries can degrade the expected performance. While smooth transitions between functionally-defined architected materials can be designed easily using mathematical interpolation, a robust transitioning scheme is less apparent for architectures without a simple mathematical description. Here we focus on periodic, strut-based lattices exhibiting cubic symmetry of their nodes and propose an algorithm that automates the design of smoothly-varying transitions between any finite set of these lattices embedded in a macroscale structure. The algorithm takes in lattice existence and relative density fields indicating which lattice and associated relative density exists at each point in the macroscale structure, and an ordered set of m key lattices to be embedded. We define a transitional skeleton (i.e., nodes and connectivity) that encapsulates all m key lattices via a continuous morphing between different nodal configurations, and use it to spatially transition between the different lattice geometries. Material is added after manipulating the skeletons according to the given relative density distribution. We explain details of the algorithm in 2D using a multi-lattice design obtained from topology optimization and show the flexibility and robustness of the algorithm by generating a lattice-embedded rendering of The Starry Night by Vincent Van Gogh. In 3D, we explore the effect of the transition radius and demonstrate manufacturability of the spatially-varying lattice-embedded structures by 3D printing a multi-lattice canopy obtained from topology optimization.
We introduce a generative artificial intelligence (AI) algorithm trained to generate realistic three-dimensional microstructures of polymer-bonded explosives (PBX). We idealize PBX as a two-phase composite with anisotropic crystals as inclusions in a polymer matrix. To improve memory efficiency, we introduce a two-stage hierarchical algorithm in which a deep generative graph model captures the grain topology in the PBX, while a latent diffusion model generates the 3D geometry of individual grains. This approach enables us to idealize each grain as a simply connected body embedded in three-dimensional space. This divide-and-conquer strategy enables us to bypass the challenging task of generating manifolds of complex topology and to avoid wasted computations when applying convolutions to grids dominated by constant values. To generate PBX microstructures with realistic grain topology, we sample sub-domains of segmented micro-computed tomography (CT) images of a mock PBX with Idoxuridine (IDOX) grains to form a dataset. Each sub-domain is represented by a node-weighted graph with the position, size, and shape descriptors stored as node features. The topological dataset is then used to train a graph recurrent neural network (GraphRNN) that learns to add nodes and edges sequentially to form a weighted graph describing the IDOX granular network, consisting of nodes with features connected by edges. With the global topology prescribed, the individual grains are generated by a conditional denoising diffusion probabilistic model that takes node features as inputs and produces grains that match the prescribed node features. Numerical examples assess the similarity between the actual and synthetic microstructures.
