Material Point Research Articles

Parametric Optimization of Concentrated Photovoltaic-Phase Change Material as a Thermal Energy Source for Buildings

A concentrated photovoltaic system is evaluated as a thermal energy source employing phase change material to meet the domestic water heating demand. A paraffin wax-based phase change material is selected with a 58 °C melting point to store enough thermal energy to match the hot water demand in the buildings. The energy performance of the concentrated photovoltaics containing phase change materials is compared to that of the reference to determine the increased energy outputs due to the heat removal by the material. The concentrated photovoltaics-phase change material achieved 30% higher energy output compared to the reference concentrated photovoltaic, thus providing a strong justification for the improved thermal management design. An enthalpy-based thermal model is developed to compare the experimental results with model predictions, confirming a reasonable agreement between the results. The model is used determine the optimum melting point and container size for different phase change materials under different radiation concentrations for the hot climate of the United Arab Emirates.

Automated model discovery for tensional homeostasis: Constitutive machine learning in growth and remodeling.

We present a built-in physics neural network architecture, known as inelastic Constitutive Artificial Neural Network (iCANN), to discover the inelastic phenomenon of tensional homeostasis. In this course, identifying the optimal model and material parameters to accurately capture the macroscopic behavior of inelastic materials can only be accomplished with significant expertise, is often time-consuming, and prone to error, regardless of the specific inelastic phenomenon. To address this challenge, built-in physics machine learning algorithms offer significant potential. We introduce the incorporation of kinematic growth and homeostatic surfaces into the iCANN to discover the Helmholtz free energy and the pseudo potential. The latter describes the state of homeostasis in a smeared sense. To this end, we additionally propose a novel design of the corresponding feed-forward network in terms of principal stresses. We evaluate the ability of the proposed network to learn from experimentally obtained tissue equivalent data at the material point level, assess its predictive accuracy beyond the training regime, and discuss its current limitations when applied at the structural level. Our source code, data, examples, and an implementation of the corresponding material subroutine are made accessible to the public at https://doi.org/10.5281/zenodo.13946282.

Near-Field Clutter Mitigation in Speckle Tracking Echocardiography.

Near-field (NF) clutter filters are critical for unveiling true myocardial structure and dynamics. Randomized singular value decomposition (rSVD) stands out for its proven computational efficiency and robustness. This study investigates the effect of rSVD-based NF clutter filtering on myocardial motion estimation. In silico, material points and their displacements in a homogeneous medium under uniaxial compressions (0.5% - 9% axial strains at 0.5% increments) were simulated in finite-element models. They were exported to the k-Wave toolbox for simulations of pre- and post-deformed ultrasound images with/ without a realistic phase aberrating layer in a high-contrast diverging wave compounding scheme. In vivo, echocardiograms of 20 normal human hearts were acquired using a coded diverging wave compounding imaging method at 3200 frames/second in the transthoracic apical four-chamber view. Morphological component analysis (MCA), which is also a sparse representation method but computationally intensive, was used for comparison with rSVD. Both rSVD- and MCA-based filters were applied to beamformed ultrasound radio-frequency (RF) data before cross-correlation-based speckle tracking. Contrast-to-noise ratios (CNRs) and root-mean-square deviations (RMSDs) were computed from regions of interest to evaluate NF clutter filtering performance of rSVD and MCA. In silico, 2-D displacements estimated from rSVD-based clutter-reduced image data showed strong agreement with ground truth (R2 of 0.95). In vivo, CNR improvements ranged from 1.02 dB to 17.68 dB, consistently enhancing image quality across all subjects. An improvement of ∼4.9 dB in the apical segments was observed in 80% of cases. Mean RMSDs were below 5.0% for all rSVD-based NF clutter-reduced data. While both rSVD and MCA effectively filtered NF clutter, rSVD was significantly more practical. Our findings confirm the reliability, accuracy, and efficiency of rSVD-based clutter filtering in speckle tracking echocardiography. This underscores the feasibility of matrix decomposition-based methods, exemplified by rSVD, in NF clutter filtering for myocardial motion estimation.

Toward engineering lattice structures with the material point method (MPM)

Toward engineering lattice structures with the material point method (MPM)

Correlation between Material Properties, Crystalline Transitions, and Point Defects in RF Sputtered (N,Mg)-Doped Copper Oxide Thin Films

Correlation between Material Properties, Crystalline Transitions, and Point Defects in RF Sputtered (N,Mg)-Doped Copper Oxide Thin Films

The pinball contact algorithm for the material point method

The pinball contact algorithm for the material point method

A modified mixed-mode Timoshenko-based peridynamics model considering shear deformation

The original two-dimensional bond-based peridynamic (BBPD) framework, which only considers the pairwise forces (compression and tension) between two material points, is extended by incorporating the effect of shear deformation in the calculations and its influence on the failure of the bonds. To this end, each bond is considered as a short Timoshenko beam, and by doing so, the traditional BBPD is enhanced into a more comprehensive model known as multi-polar peridynamic (MPPD). The proposed novel approach explicitly considers the shear influence factor used in Timoshenko beams and introduces a strain-based shear deformation failure criterion. The model is then validated against two benchmark experimental tests (i.e., a standard pure mode I edge crack, and a Kalthoff-Winkler configuration) reported in the literature under in-plane dynamic loading and plane stress conditions. In most cases, the developed model is shown to be more accurate in predicting the crack paths obtained from the experimental results when compared to other theoretical methods delineated in the literature. Furthermore, a noticeable change in crack branching and crack path is observed in a study on the effects of Poisson's ratio and the loading rate. This investigation also demonstrated that the proposed MPPD model can accommodate materials with Poisson's ratios up to 1/3, expanding the range beyond the traditional BBPD limitations.

Analysis on the failure mechanism and entire evolution process of toppling bank slope under heavy rainfall utilizing material point method

Analysis on the failure mechanism and entire evolution process of toppling bank slope under heavy rainfall utilizing material point method

Explicit finite element material point method coupled with phase-field model for solid brittle fracture

Explicit finite element material point method coupled with phase-field model for solid brittle fracture

A material point finite element method for thermo-hydro-mechanical modeling in poro-elastic media with brittle fracturing

A material point finite element method for thermo-hydro-mechanical modeling in poro-elastic media with brittle fracturing

Modeling of Metal Cutting Processes Within the Material Point Method Using the Johnson–Cook Material Law

ABSTRACTThe material point method represents an alternative approach to simulations, differing from traditional methods like the finite element method (FEM). This technique involves discretizing bodies using material points while solving equations on a separate computational grid where each time step comprises three steps. Initially, the kinematic quantities of the material points are transferred to the grid nodes. Subsequently, computations are carried out on these nodes, with the results then reassigned to the material points, updating their position, velocity, stress as well as deformation state. After each time step, the previous grid is discarded as it does not rely on persistent information. As a result, a new grid is initialized in each time step, avoiding the risk of mesh distortion in the case of huge deformations, a common drawback arising in FEM analyses. This contribution presents simulations of three‐dimensional metal cutting processes utilizing the Johnson–Cook material law to accommodate for plastic strain rates and heat generated by plastic deformations. Additionally, the grid‐shift technique is employed to reduce oscillations and enhance robustness of the simulations.

Mathematical modeling of a perforated continuous steel-smelting unit

Relevance. The volume of steel production in Russia and in the world has doubled over the past 20 years, the cost of steel in Russia in the period from October 2018 to March 2020 increased from 45 thousand rubles to 105 thousand rubles. This determines the urgency of developing energy-efficient steel production technologies that will reduce the cost of production. The most common technology for the producing steel of the full metallurgical cycle involves iron reduction in blast furnaces and characterized by significant emissions of pollutants into the environment. One of the most promising areas of environmentally friendly and energy-efficient steel production is non-straw production. At the moment, there are about a hundred different iron recovery processes, some of them have been brought to industrial use. Aim. To develop a fuel supply system in a perforated hearth, eliminating heat losses in the steelmaking unit by organizing a perforated hearth, which allows heat to be returned to the working space of the furnace by heating the reducing agent. Methods. Numerical modeling by Volume of Fluid (VOF) and Euler-Euler (EE) methods. Results. The authors have determined the rate of supply of reducing gas, which ensures its conversion to carbon and hydrogen at the entrance to the working area of the furnace. It was found that the surface temperature of the perforated hearth on the gas side is 380°C, on the melt side does not exceed 1313°C, which is significantly lower than the melting point of the refractory material.

Investigation of the Impact Response of Bi‐Continuous Nanoporous Solids via the Material Point Method: Verification Against Molecular Dynamics Predictions

ABSTRACTMolecular dynamics (MD) and the material point method (MPM) are both particle methods in spatial discretization. Molecular dynamics is a discrete particle method that is widely applied to predict fundamental physical properties and dynamic materials behaviors at nanoscale. The MPM is a continuum‐based particle method that was proposed about three decades ago to simulate large‐deformation problems involving multiphase interaction and failure evolution beyond the nanoscale. However, it is still a challenging task to validate MD responses against the experimental data due to the spatial limitation in impact and/or shock tests. The objective of this investigation is therefore to compare the MPM and MD solutions for the impact responses of porous solids at nanoscale. Since the governing equations for MD and explicit MPM are similar in temporal domain with different spatial discretization schemes, the MPM solutions could be verified against the MD ones, and the MD solutions might then be indirectly validated against the MPM ones as validated beyond the nanoscale. Since both MD forcing functions and MPM constitutive modeling are well‐formulated for metallic solids, we report a comprehensive comparative study of porous and non‐porous gold cubic targets impacted by full density non‐porous gold cubic flyers using the MPM and MD, respectively. The overall deformation patterns and particle‐velocity histories are demonstrated and analyzed, as obtained with the two particle methods. It appears that the MD and MPM solutions are consistent in capturing the physical responses, which shows the potential of using the MPM for multiscale simulations of extreme events involving porous solids, such as underground penetration and space exploration. In addition, MD solutions might be indirectly validated against the MPM ones for evaluating geological responses to extreme loadings, which provides an alternative route for multiscale verification and validation.

Motion of a variable body in a time-dependent force field

The problem of translational-rotational motion of a variable body is considered under the assumption that the inertial properties of the body, as well as the external forces acting on it and the moments of forces clearly depend on time. The conditions under which the equations of motion are reduced to classical equations describing the motion of a solid body in a force field independent of time are indicated. There are cases when the equations of motion are reduced to completely integrable ones. The elements of the discussion of the 1920-1930 on the description of the motion of a material point of variable mass in a time-dependent field of attraction are reproduced.

Numerical Homogenization of Orthotropic Functionally Graded Periodic Cellular Materials: Method Development and Implementation.

This study advances the state of the art by computing the macroscopic elastic properties of 2D periodic functionally graded microcellular materials, incorporating both isotropic and orthotropic solid phases, as seen in additively manufactured components. This is achieved through numerical homogenization and several novel MATLAB implementations (known in this study as Cellular_Solid, Homogenize_test, homogenize_ortho, and Homogenize_test_ortho_principal). The developed codes in the current work treat each cell as a material point, compute the corresponding cell elasticity tensor using numerical homogenization, and assign it to that specific point. This is conducted based on the principle of scale separation, which is a fundamental concept in homogenization theory. Then, by deriving a fit function that maps the entire material domain, the homogenized material properties are predicted at any desired point. It is shown that this method is very capable of capturing the effects of orthotropy during the solid phase of the material and that it effectively accounts for the influence of void geometry on the macroscopic anisotropies, since the obtained elasticity tensor has different E1 and E2 values. Also, it is revealed that the complexity of the void patterns and the intensity of the void size changes from one cell to another can significantly affect the overall error in terms of the predicted material properties. As the stochasticity in the void sizes increases, the error also tends to increase, since it becomes more challenging to interpolate the data accurately. Therefore, utilizing advanced computational techniques, such as more sophisticated fitting methods like the Fourier series, and implementing machine learning algorithms can significantly improve the overall accuracy of the results. Furthermore, the developed codes can easily be extended to accommodate the homogenization of composite materials incorporating multiple orthotropic phases. This implementation is limited to periodic void distributions and currently supports circular, rectangular, square, and hexagonal void shapes.

Portable, massively parallel implementation of a material point method for compressible flows

Portable, massively parallel implementation of a material point method for compressible flows

Machine Learning based Analysis of RVE in Concrete Structures

In the numerical modeling of structural elements, particularly utilizing the finite element method (FEM), multiscale models represent the most robust approach for analyzing heterogeneous materials such as concrete. These models incorporate information from multiple scales—typically, macroscopic and mesoscopic—to more accurately capture the material's response and its impact on structural behavior. In this context, each material point in the numerical model is associated with a Representative Volume Element (RVE), which is the smallest statistically representative portion of the material. During the analysis of a multiscale model via FEM, each integration point in the model requires the execution of an independent computational model that simulates the RVE and is processed in parallel to the main model (on the fly process). Deformations originating from the macroscale are applied to the mesoscale models, and the responses obtained are then reintegrated into the macroscale. This approach results in significant demand on processing resources and time, which can limit its practical application in large-scale projects or situations requiring rapid responses. This article propose the representation of the RVE through a Machine Learning model (ML) capable of simulating the mechanical behavior of the material. This approach eliminates the on the fly processing. Throughout the text, examples of RVEs represented by ML models are presented, and their specificities are discussed. Finally, the advantages and disadvantages of this methodology are examined.

Streamlined workflow for the simulation of submarine landslides with the material point method

Industry demand for numerical simulations is growing every year due to the advances in computer hardware that make increasingly complex and detailed simulations possible. Numerical tools are particularly useful for situations in which the available analytical and experimental tools are limited or unavailable. For that reason, they are a powerful tool for the analysis of natural phenomena, such as submarine landslides. Submarine landslides are commonly accompanied by large deformations and large displacements, which makes them challenging for traditional numerical tools, such as the finite element method. Thus, a numerical tool which is equipped to handle this behavior is necessary. In this paper, we describe a streamlined workflow to simulate submarine landslides that encompass problem modeling, simulation and post-processing. The simulator, developed in-house, uses the Generalized Interpolation Material Point (GIMP) to solve momentum conservation equations and is able to handle large deformations, contact and physical nonlinearities. The proposed workflow acts as a wizard that creates templates for multiple problem definition strategies, automates simulation execution, and provides a range of postprocessing variables to better describe the results. Furthermore, practical examples are presented, detailing the process of mesh and particle definition, simulation execution and analysis of run out distances, deposition height and impact pressure assessment.

Influence of velocity on conductor casing driving via Material Point Method

The conductor is the first casing settled in an oil well and it has important functions regarding the initial phase of well construction. Some of its functions are to bear loads from the wellhead, bear the surface casing during its cementation, and insulate unconsolidated formations. The soil response to the conductor settlement is an important factor, for a poor settlement may cause excessive wear on the wellhead equipment or even the loss of the well. Therefore, a simulation of the self-weight phase of a conductor driving was conducted to enhance knowledge about the soils state during this process. The soil was modeled as soft clay, and the mechanical behavior was described by the Mohr-Coulomb model. As the conductor driving results in large displacements in a short period, the traditional Finite Element Method (FEM) is unable to model this system. Therefore, the Material Point Method (MPM), which is a modification of FEM, was used to model the conductor-soil system. Three conductor velocities were simulated and the resultant force in the casing outer surface was analyzed. The soil’s stress state, displacement, and velocities were studied.

Parametric Study of Pile Installation Using a Numerical Approach with Material Point Method

In engineering, problems dealing with rigid structures that penetrate the ground are common. A better understanding of the installation process of these bodies can be obtained through numerical studies. Each technique has its own advantages and challenges, and the choice of the appropriate method depends on the specific geotechnical conditions. The main objective of this work is to conduct a parametric study of pile installation by impact driving using a prototype numerical model in order to analyze the behavior of these structures when penetrating the soil. This will enable the construction of more complex numerical models that simulate impact problems, such as driving a conductor casing in oil well projects or installing monopiles in the construction of wind turbines. To achieve the established objectives, the Anura3D is employed, an open source simulator based on Material Point Method (MPM). The parametric study is carried out considering a non-cohesive soil, modeled by Mohr-Coulomb. Additionally, the pile is treated as a rigid body. The parametric study conducted in this work provides valuable insights into optimizing engineering projects, enhancing its efficiency and effectiveness .