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Material modeling using modern numerical methods accelerates the design process and reduces the costs of developing new products. However, for multiscale modeling of heterogeneous materials, the well-established homogenization techniques remain computationally expensive for high accuracy levels. In this contribution, a machine learning approach, convolutional neural networks (CNNs), is proposed as a computationally efficient solution method that is capable of providing a high level of accuracy. In this work, the data-set used for the training process, as well as the numerical tests, consists of artificial/real microstructural images (“input”). Whereas, the output is the homogenized stress of a given representative volume element $$\mathcal {RVE}$$ RVE . The model performance is demonstrated by means of examples and compared with traditional homogenization methods. As the examples illustrate, high accuracy in predicting the homogenized stresses, along with a significant reduction in the computation time, were achieved using the developed CNN model.

Advancements in additive manufacturing (3D printing) have enabled researchers to create complex structures, offering a new class of materials that can surpass their individual constituent properties. Selective laser sintering (SLS) is one of the most popular additive manufacturing techniques and uses laser power to bond powdered material into intricate structures. It is one of the fastest additive manufacturing processes for printing functional, durable prototypes, or end-user parts. It is also widely used in many industries, due to its ability to easily make complex geometries with little to no additional manufacturing effort. In the SLS process, tool path selection is important because it is directly related to the integrity of a 3D printed structure. In this research, we focus on how to obtain an optimal tool path for the SLS process from a numerical simulation. Also, we apply a deep learning technique to accelerate the simulation of the SLS processes, while obtaining accurate numerical results.

This work develops a computational Digital-Twin framework to track and optimize the flow of solar power through complex, multipurpose, solar farm facilities, such as Agrophotovoltaic (APV) systems. APV systems symbiotically cohabitate power-generation facilities and agricultural production systems. In this work, solar power flow is rapidly computed with a reduced order model of Maxwell’s equations, based on a high-frequency decomposition of the irradiance into multiple rays, which are propagated forward in time to ascertain multiple reflections and absorption for various source-system configurations, varying multi-panel inclination, panel refractive indices, sizes, shapes, heights, ground refractive properties, etc. The method allows for a solar installation to be tested from multiple source directions quickly and uses a genomic-based Machine-Learning Algorithm to optimize the system. This is particularly useful for planning of complex next-generation solar farm systems involving bifacial (double-sided) panelling, which are capable of capturing ground albedo reflection, exemplified by APV systems. Numerical examples are provided to illustrate the results, with the overall goal being to provide a computational framework to rapidly design and deploy complex APV systems.

Shotguns have increasingly used case-hardened steel pellets, as opposed to “classical” lead, owing to food safety and environmental concerns. Correspondingly, there is increasing concern about unintended ricochet of pellets and subsequent bodily harm, such as eye damage. Correspondingly, this paper focuses on the impact of pellets with a surface and the post-impact ricochet range. Equations for general three-dimensional inelastic impact with unilateral stick–slip conditions are derived for pellets and a post-impact trajectory analysis is performed. Qualitatively, with increasing friction, ranges of ricochet decrease because most of the pre-impact translational energy of the pellets is converted upon impact into rotational energy, which reduces the post-impact ricochet range. For slippery surfaces, wide ranging trajectory distributions occur owing to the mix of stick and slip conditions among the pellets. In the case of slip, the pellets retain much of their translational energy and the post-impact linear velocities are greater than high-friction cases, which leads to much larger post-impact trajectory ranges. The model enables analysts to make more quantitative decisions on the choices of parameters associated with shotgun use and can reduce trial-and-error procedures in testing.

The increase in readily available computational power raises the possibility that direct agent-based modeling can play a key role in the analysis of epidemiological population dynamics. Specifically, the objective of this work is to develop a robust agent-based computational framework to investigate the emergent structure of Susceptible-Infected-Removed/Recovered (SIR)-type populations and variants thereof, on a global planetary scale. To accomplish this objective, we develop a planet-wide model based on interaction between discrete entities (agents), where each agent on the surface of the planet is initially uninfected. Infections are then seeded on the planet in localized regions. Contracting an infection depends on the characteristics of each agent-i.e. their susceptibility and contact with the seeded, infected agents. Agent mobility on the planet is dictated by social policies, for example such as "shelter in place", "complete lockdown", etc. The global population is then allowed to evolve according to infected states of agents, over many time periods, leading to an SIR population. The work illustrates the construction of the computational framework and the relatively straightforward application with direct, non-phenomenological, input data. Numerical examples are provided to illustrate the model construction and the results of such an approach.

Phase field modeling of fracture has been in the focus of research for over a decade now. The field has gained attention properly due to its benefiting features for the numerical simulations even for complex crack problems. The framework was so far applied to quasi static and dynamic fracture for brittle as well as for ductile materials with isotropic and also with anisotropic fracture resistance. However, fracture due to cyclic mechanical fatigue, which is a very important phenomenon regarding a safe, durable and also economical design of structures, is considered only recently in terms of phase field modeling. While in first phase field models the material’s fracture toughness becomes degraded to simulate fatigue crack growth, we present an alternative method within this work, where the driving force for the fatigue mechanism increases due to cyclic loading. This new contribution is governed by the evolution of fatigue damage, which can be approximated by a linear law, namely the Miner’s rule, for damage accumulation. The proposed model is able to predict nucleation as well as growth of a fatigue crack. Furthermore, by an assessment of crack growth rates obtained from several numerical simulations by a conventional approach for the description of fatigue crack growth, it is shown that the presented model is able to predict realistic behavior., Deutsche Forschungsgemeinschaft, Projekt DEAL

The pandemic of 2020 has led to a huge interest of modeling and simulation of infectious diseases. One of the central questions is the potential infection zone produced by a cough. In this paper, mathematical models are developed to simulate the progressive time-evolution of the distribution of locations of particles produced by a cough. Analytical and numerical studies are undertaken. The models ascertain the range, distribution and settling time of the particles under the influence of gravity and drag from the surrounding air. Beyond qualitative trends that illustrate that large particles travel far and settle quickly, while small particles do not travel far and settle slowly, the models provide quantitative results for distances travelled and settling times, which are needed for constructing social distancing policies and workplace protocols.

In this work, a thermal Finite Element model is developed to simulate the performance of a blade-like tool for robotic work cells performing automated garment production using a novel thermoplastic stiffening layer. Uncertainty quantification and sensitivity analysis are applied to determine the most important design properties and optimize key performance metrics for swift and reliable garment assembly. Attention is focused on the geometric and thermal design properties that minimize sensitivity to environmental conditions while maximizing expected productivity. An example design is shown for illustrative purposes. This work may inform future design innovation for similar heating tools and reduce the need for physical experiments and long calibration times on the factory floor.

The goal of this article is to provide basic modeling and simulation techniques for systems of multiple interacting Unmanned Aerial Vehicles, so called “swarms”, for applications in mapping. Also, the paper illustrates the application of basic machine-learning algorithms to optimize their information gathering. Numerical examples are provided to illustrate the concepts.

Electrically induced tissue damage is a coupled phenomenon in multiphysics. Conducting electricity produces heat and this increases the temperature. The soft tissue like skin, organs, brain, or muscles is burnt under successive heating. This damage is modeled by using a damage parameter with a corresponding evolution law above a threshold temperature. Electromagnetism, thermomechanics, and damage modeling creates a set of coupled and nonlinear field equations, by solving them with the aid of the finite element method, we compute a realistic example where the tissue absorbs the electric energy, converts to heat, and gets burnt due to the excessive temperature increase.

The processing of powdered materials by laser processing has become quite widespread. One approach is to deposit a powder bed of material and to “flash” a pattern onto the bed in order to sufficiently heat up the material to sinter and/or fully melt the desired targeted pattern. This avoids rastering a single beam back and forth to produce the pattern. In order to adequately control the thermal fields that are induced by the laser pattern,simulation tools are needed to quickly ascertain the parameter setting needed, such as • powder porosity, • powder conductivity, • beam spatial distribution and • beam temporal duration. The focus of this work is to develop a rapid computational tool that can be used in conjunction with manufacturing systems. The approach is to develop precomputed solutions for point-source “beamlets” and then to create patterns by arranging the point-sources in the proper configuration. The entire thermal field is then created by superposing the solutions. A series of three-dimensional problems are solved to illustrate the approach, which is geared to rapidly solving the response to complex patterns.

The pandemic of 2019 has led to an enormous interest in all aspects of modeling and simulation of infectious diseases. One central issue is the redesign and deployment of ventilation systems to mitigate the transmission of infectious diseases, produced by respiratory emissions such as coughs. This work seeks to develop a combined Digital-Twin and Machine-Learning framework to optimize ventilation systems by building on rapidly computable respiratory emission models developed in Zohdi (Comput Mech 64:1025–1034, 2020). This framework ascertains the placement and flow rates of multiple ventilation units, in order to optimally sequester particles released from respiratory emissions such as coughs, sneezes, etc. Numerical examples are provided to illustrate the framework.

This paper focuses on viral decontamination by ultraviolet (UV) irradiation technologies. This work develops an efficient and rapid computational method to simulate a UV pulse in order to ascertain the decontamination efficacy of UV irradiation for a surface. It is based on decomposition of a pulse into a group of rays, which are then tracked as they progress towards the target contact surface. The algorithm computes the absorption at the point of contact and color codes it relative to the incoming irradiation. This allows one to quickly quantify the decontamination efficacy across the topology of a structure., Highlights • The pandemic of 2020 has led to a dramatic increase in research of infectious diseases. • This paper focuses on decontamination by ultraviolet irradiation technologies • Develops an efficient and rapid computational method to simulate a UV pulse to ascertain decontamination efficacy • Based on decomposition of a pulse into a groups of rays, which are then tracked and processed • The algorithm computes the absorption and color codes it relative to the incoming irradiation

Traditional linear subspace reduced order models (LS-ROMs) are able to accelerate physical simulations in which the intrinsic solution space falls into a subspace with a small dimension, i.e., the solution space has a small Kolmogorov n-width. However, for physical phenomena not of this type, e.g., any advection-dominated flow phenomena such as in traffic flow, atmospheric flows, and air flow over vehicles, a low-dimensional linear subspace poorly approximates the solution. To address cases such as these, we have developed a fast and accurate physics-informed neural network ROM, namely nonlinear manifold ROM (NM-ROM), which can better approximate high-fidelity model solutions with a smaller latent space dimension than the LS-ROMs. Our method takes advantage of the existing numerical methods that are used to solve the corresponding full order models. The efficiency is achieved by developing a hyper-reduction technique in the context of the NM-ROM. Numerical results show that neural networks can learn a more efficient latent space representation on advection-dominated data from 1D and 2D Burgers' equations. A speedup of up to 2.6 for 1D Burgers' and a speedup of 11.7 for 2D Burgers' equations are achieved with an appropriate treatment of the nonlinear terms through a hyper-reduction technique. Finally, a posteriori error bounds for the NM-ROMs are derived that take account of the hyper-reduced operators.

In this work, the Smooth Particle Hydrodynamics (SPH) method, a Lagrangian mesh-free numerical scheme , is adapted for the first time to resolve thermal–mechanical–material fields in a range of Laser Fusion Additive Manufacturing processes. The method is capable of simulating large-deformation , free-surface melting, flow, and re-solidification of metallic materials with complex physics and material geometries. A novel SPH formulation for modeling isothermally-incompressible fluids, which allows for the accurate simulation of thermally-driven, liquid-phase metal expansion/contraction, is presented and verified. Fundamental validation of the methodology is performed via comparison with spot laser welding experiments. The methodology is then used to investigate the specific Additive Manufacturing process of the Selective Laser Melting of Metallic, micro-scale particle beds . The physics of a track deposition process is explored through numerical experiments, and the influence of processing parameters on the quality of the finished melt-track is investigated. The unique abilities of using a Lagrangian mesh-free method, as opposed to mesh-based numerical schemes, to model this process are highlighted. The SPH method is found to be a viable and promising numerical tool for simulating laser fusion driven Additive Manufacturing processes.

The goal of this article is to provide an introduction to basic modeling and simulation techniques for multiple interacting unmanned aerial vehicles (UAVs), called swarms, for applications in mapping. The target audience is senior students and young scientists. This review will serve to inform, orient, and direct someone already educated in environmental science but unaware of multiple-UAV interaction models.

Rapid free-form printing of particle-functionalized materials is an integral component of 3D printing and additive manufacturing. Such processes consist of attaching a dispenser to a robot arm which then releases droplets of specialized mixtures in free-space that are deposited onto a substrate. These approaches are popular because they utilize widely-available, highly-programmable, robots. However, often the release of a complex mixture in free-space is somewhat imprecise, thus electrodynamic field control has been proposed as a possible remedy to enhance the precision of such processes. Specifically, electrodynamic control of the material is achieved by electrifying the released material in the presence of a prescribed ambient electrical field generated from the substrate, in order to guide it to the desired position. There are many components that comprise such a system, thus motivating the construction of a simulation tool framework assembling submodels of the: The modular system structure allows for easy replacement of submodels within the overall framework. Numerical simulations are undertaken to illustrate the overall system model, which is comprised of an assembly of submodels. Afterwards, a Machine Learning Algorithm (MLA) is developed to identify and optimize the proper system parameters which deliver a desired printed pattern. Specifically, an MLA is developed to ascertain the appropriate combination of robotic motion and electrical fields needed to create structures which would be difficult or impossible by purely mechanical means alone. Afterwards, extensions involving more detailed models are then provided, based on the Discrete Element Method.

A mathematical model is developed to characterize the progressive time-evolution of a fragmenting incandescent object. The objective of these models is to provide a spatio-thermal footprint of the fragmentation field, which can be useful to guide fire safety rules in manufacturing workplaces, as well as to estimate fire hazards. Ascertaining the time-evolution of the temperature of the fragments is quite difficult to measure experimentally, which motivates the model development. Initially, analytical models based solely on ballistics, which provide qualitative trends, are developed to provide insight into the fundamental ratios that govern safe operating conditions. Thereafter, rapid numerical spatio-thermal models, which provide quantitative information, are then developed, based on particle methods. The model uses the released energy from the initial blast pulse to provide the starting kinetic energy of the system of particles and then numerically computes the trajectory and thermal state of the fragments under the influence of Numerical examples and provided and extensions to high-fidelity are discussed.

Many applications in additive manufacturing involve the dynamic deposition of powders and the in-flight heating of such material by a laser. In order to characterize such systems, the Discrete Element Method (DEM) is employed. Specifically, this paper focuses on the initial stages of this process by developing a modular discrete-element type multiphysics simulation method for the particle dynamics and heating by a laser and detailed thermal behavior. The objective is to provide researchers with a framework to construct computational tools for this growing industry. In order to achieve this, from a simulation standpoint, the overall particle-mixture system is constructed by coupling submodels for each primary physical process (dynamics and heating) together. An iterative staggering scheme is developed whereby, within every time step, each individual particle is solved “freezing” the state of the remaining multiparticle system. The state of the particle is then updated and the algorithm moves to the next particle in the system and the process is repeated. The overall process sweeps through the entire system repeatedly until convergence is achieved in an appropriate norm. As the system evolves, an error estimate dictates the time-step size that is needed to induce convergence to below an appropriate error level. Thus, the process can be considered as an implicit time-stepping scheme, which is combined with an (internal) iterative staggering process. In order to control rates of convergence within a time-step, the algorithm adjusts the time-step size. If the iterative process does not converge within a desired number of iterations, below an error tolerance, the time-step is reduced. The degree of time-step reduction is determined by utilizing an estimate of the spectral radius of the coupled system. Since the construction of model and solution process is modular, one can easily replace physical submodels with other choices, making it easy to numerically experiment with a variety of models. Qualitative and quantitative analyses are provided, as well as three-dimensional numerical examples. This paper addresses the first of two overall “macrostages” of a complex manufacturing process with DEM. The first stage, addressed in this paper, concentrates on dry-powder materials, which are of a discrete particulate character, using DEM to characterize the deposition and in-flight particle heating. Other papers of the author (Zohdi, 2010, 2013, 2014, 2015 [81,82], 2008) have addressed stage two, namely phase transformations, curing (cooling) and stress analysis, using more appropriate continuum and hybrid DEM-continuum approaches.

Rapid free-form printing of heated polymeric materials, whereby an extruder is attached to a robotic arm, is a growing field. In order to properly plan a complex tool path, one needs to determine the dynamic thermomechanical response of the extruded filament material as the robot arm is moving in free-space. Accordingly, the analysis in this work involves: • Kinematics of the robot arm and the extrusion-printer head, • Dynamics of the extruded polymer filament, • Thermal conduction along the length of the heated filament, • Convective and radiative cooling of the filament through the surroundings and • Curing and solidification of the filament. Numerical simulations are undertaken to illustrate the basic model and a Machine Learning Algorithm is developed to optimize the robotic system to deliver a prespecified structure. Detailed extensions are also discussed.

A modern circuit board consists of several thousand interacting electronic components. Reduced-order models are often used to rapidly simulate such systems for design purposes, which require the testing of large numbers of design configurations. Reduced-order simulators, such as SPICE, are often used, which are based on a lumped mass nodal analysis. While the intended use of such models is for rigid and isothermal systems, they are also used in order to acquire the temperature evolution in integrated circuits (IC) by computing the dissipation produced in components such as transistors. Generally, reduced-order circuit simulators are not as accurate as a detailed, computationally expensive, direct finite element method (FEM) simulation of an electromagnetic system. In this paper, we determine the inaccuracies introduced in a reduced-order model by simulating the same system by means of a detailed three-dimensional, coupled, and nonlinear FEM analysis for electromagneto-thermomechanical fields. Specifically, we compare results from a one-dimensional SPICE simulation to the three-dimensional transient FEM computation. Using SPICE, we calculate the electric potential with the known resistance providing Joule heating of a resistor on an IC. In an FEM computation, we solve coupled governing equations for electromagnetic potentials, determine electric current distribution, and integrate over the continuum body in order to calculate the dissipated power. We find out that there is an up to 30% of discrepancy between computations from SPICE and FEM in terms of the dissipation. After an intensive study explained in this manuscript, we obtain the root cause of the aforementioned significant difference between SPICE and FEM. The geometrical simplification from three-dimensional continuum to a one-dimensional model brings in an inadequate assumption on the boundary conditions. This assumption generates a significant error in the determined power from SPICE in the case of a resistor, concretely a standard micro-metal electrode leadless face (MELF) studied herein.

Unmanned Aerial Vehicles (UAVs), in particular quadcopters, have become extremely prevalent, driven by their low cost and ease of control. Unfortunately, privacy concerns related to the ubiquity of UAVs have prompted substantial citizen interest in the shooting down of UAVs that trespass onto their property, although the legality thereof is unclear. In this work, the dynamical response of a quadcopter to a series of randomly external impulses, which could be generated by objects launched toward the UAV, such shotgun pellets, etc., is formulated. A Discrete Element Method (DEM) is developed to describe the breakup of the quadcopter, in conjunction with a staggered implicit time-stepping scheme. The use of DEM allows for fragmentation of the quadcopter and also to compute the trajectories and distribution of the resulting debris field.

There are many emerging manufacturing processes whereby surface structures are processed by spatially laser patterning of an entire feature at a time, as opposed to rastering a small beam. It is important to ascertain and ideally control the induced thermal fields underneath the pattern. This paper develops a computational framework to rapidly evaluate the induced thermal fields due to application of a laser on the surface. The aggregate thermal fields are efficiently computed by superposing individual “beamlet” heat-kernel solutions, based on Green’s functions, to form complex surface patterns. The utility of the approach is that laser-process designers can efficiently compute the results of selecting various system parameters, such as spatially-variable laser intensity within a pattern. This allows one to rapidly compute system parameter studies needed in the manufacturing of new products. Included are: Three-dimensional examples are provided to illustrate the technique. The utility of the approach is that an analyst can efficiently ascertain a large number of laser-input scenarios without resorting to computationally-intensive numerical procedures, such the Finite Element Method.

Electrical high voltage (HV) machines are prone to corona discharges leading to power losses as well as damage of the insulating layer. Many different techniques are applied as corona protection and computational methods aid to select the best design. In this paper we develop a reduced-order model in 1D estimating electric field and temperature distribution of a conductor wrapped with different layers, as usual for HV-machines. Many assumptions and simplifications are undertaken for this 1D model, therefore, we compare its results to a direct numerical simulation in 3D quantitatively. Both models are transient and nonlinear, giving a possibility to quickly estimate in 1D or fully compute in 3D by a computational cost. Such tools enable understanding, evaluation, and optimization of corona shielding systems for multilayered coils.

The trajectories and branching of electrical discharges through gaseous atmospheres, such as lightning and coronal emissions from high-voltage electromachinery, are of interest in a variety of applications. Multiple branches can evolve in an initially poor atmospheric conductor when a strong electrical discharge builds up, then propagates through the atmosphere by dielectric breakdown. Multiple branches can be generated in gases because of the disordered character of the media at the microscale, with an overall influence occurring from the ambient electric fields. In this paper, we develop a sufficiently flexible computational model to describe discharge trajectory and branching. The framework allows analysts to rapidly simulate thousands of electrical discharge scenarios, in order to statistically explore the dependency of the overall system behavior on the relevant physical parameters.

The increase in readily available computational power raises the possibility that robust agent-based modeling can play a productive role in the analysis of population dynamics. Accordingly, the objective of this work is to develop a robust agent-based computational framework to investigate the emergent structure of initially intermeshed hostile, competing, populations on a global scale. Specifically, we develop a model based on discrete entities (agents), each with their own interaction rules with their immediate neighbors, innate skill sets, reproductive rates, mobility and lifespans to represent a population. The global population is then allowed to evolve according to these local rules over many generations. The biological systems-level applications are numerous, stemming from human conflict on the macroscale to microbes on the microscale. Numerical examples are provided to illustrate the model construction and the results of such an approach.

The objective of this work is to model and simulate aerial drops of fire retardants in dangerous fire environments. Specifically, the work develops a computational framework for a model problem combining: • [1.] A meshless discrete element component that tracks the trajectory of released airborne materials from a controlled aircraft, ranging from retardant powders to encapsulated packets, subjected to prevailing wind velocities and fire-driven updrafts. • [2.] A Machine Learning Algorithm (MLA) to rapidly ascertain the optimal aircraft (unmanned or manned) dynamics to maximize the fire-retardant release effectiveness (released material usage and target impact). The framework is designed to enable Digital Twin type technologies, i.e. digital replicas that run in real time with the physical system. However, it is also designed to run at much faster rates, in order to enable MLA’s to optimize the planning, by running quickly on laptops and mobile systems. The overall guiding motivation is to provide a useful tool to enable rapid flight-path planning for aerial first-responders in real-time and to train pilots. Numerical examples are provided to illustrate the process.

In this paper, we investigate the explosion of a set of charged particles in an electromagnetic field and the progressive time-evolution of the blast envelope. It is shown that the initial spherical blast envelope evolves into the shape of an ellipsoid whose longitudinal axis is aligned with the magnetic field. Specifically, the individual particles rotate around the longitudinal axis, and rotate in decaying orbits until they become stationary, leaving a stationary overall ellipsoidal configuration of particles. In order to investigate this system, a direct multiparticle model is constructed, which balances the detonation energy released from the initial blast pulse with the subsequent particle-system kinetic energy and then computes the trajectories of the discrete interacting material under the influence of the electromagnetic field and drag from any surrounding medium. Under certain simplifying assumptions, the model can be solved for analytically, which provides a guide to identifying the key parameters that control the evolving blast envelope. Three dimensional examples are provided to illustrate the framework/method.

Author(s): Queiruga, A; Zohdi, T | Abstract: A computational framework for assisting in the development of novel textiles is presented. Electronic textiles are key in the rapidly growing field of wearable electronics for both consumer and military uses. There are two main challenges to the modeling of electronic textiles: the discretization of the textile microstructure and the interaction between electromagnetic and mechanical fields. A director-based beam formulation with an assumed electrical current is used to discretize the fabric at the level of individual fibrils. The open-source package FEniCS was used to implement the finite element model. Contact integrals were added into the FEniCS framework so that multiphysics contact laws can be incorporated in the same framework, leveraging the code generation and automated differentiation capabilities of FEniCS to produce the tangents needed by the implicit solution method. The computational model is used to construct and determine the mechanical, thermal, and electrical properties of a representative volume elements of a plain woven textile. Dynamic relaxation to solve the mechanical fields and the electrical and thermal fields is solved statically for a given mechanical state. The simulated electrical responses are fit to a simplified Kirchhoff network model to determine effective resistances of the textile. Copyright © 2016 John Wiley a Sons, Ltd.

Most modern electromagnetic devices consist of dielectric and magnetic particulate composites. Predicting the effective electric permittivity and effective magnetic permeability of the envisioned composite is of great importance in validating the design for such applications. In this work, we propose a numerical method based on Yee's scheme and statistically generated representative volume element to estimate these effective electromagnetic properties for linear isotropic composites made with ellipsoidal particles. By considering particle geometry and composite microstructure precisely, it provides a more accurate tool for their design than available analytical bounds. Several numerical examples of composite microstructures are presented to demonstrate the capability of the proposed method. Comparison with analytical bounds and experimental results from literature is conducted to show validity. Keywords: Electrical properties, Magnetic properties, Computational modeling, Composites design

In industry, particle-laden fluids, such as particle-functionalized inks, are constructed by adding fine-scale particles to a liquid solution, in order to achieve desired overall properties in both liquid and (cured) solid states. However, oftentimes undesirable particulate agglomerations arise due to some form of mutual-attraction stemming from near-field forces, stray electrostatic charges, process ionization and mechanical adhesion. For proper operation of industrial processes involving particle-laden fluids, it is important to carefully breakup and disperse these agglomerations. One approach is to target high-frequency acoustical pressure-pulses to breakup such agglomerations. The objective of this paper is to develop a computational model and corresponding solution algorithm to enable rapid simulation of the effect of acoustical pulses on an agglomeration composed of a collection of discrete particles. Because of the complex agglomeration microstructure, containing gaps and interfaces, this type of system is extremely difficult to mesh and simulate using continuum-based methods, such as the finite difference time domain or the finite element method. Accordingly, a computationally-amenable discrete element/discrete ray model is developed which captures the primary physical events in this process, such as the reflection and absorption of acoustical energy, and the induced forces on the particulate microstructure. The approach utilizes a staggered, iterative solution scheme to calculate the power transfer from the acoustical pulse to the particles and the subsequent changes (breakup) of the pulse due to the particles. Three-dimensional examples are provided to illustrate the approach.

Across many modern industries, as technologies have matured, the use of more complex processes involving multiphase materials has increased. In the food industry, multiphase fluids are now relatively wide-spread, in particular, because of the desire to have faster throughput for large-scale food production. In many cases involving transport, such materials consist of a fluidized binder material with embedded particles. As one increases the volume fraction of particles, a corresponding increase in effective overall viscosity occurs. Often, during the process, the material must be heated, for example, to ensure food safety, induce pasteurization, sterilization, etc. For real-time control, this requires rapidly computable models to guide thermal processing, for example by applied electrical induction. In the present analysis, models are developed for the required heating field (electrically induced) and pressure gradient needed in a pipe to heat a multiphase material to a target temperature and to transport the material with a prescribed flow rate.

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The objective of this work is to illustrate how to algorithmically integrate Machine-Learning Algorithms (MLA’s) with multistage/multicomponent fire spread models. In order to tangibly illustrate this process, this work develops a framework for a specific model problem combining: (I) a meshless discrete element “submodel” that tracks the trajectory of airborne hot particles/embers, subject to prevailing wind velocities and updrafts, (II) a topographical “submodel” of the ambient combustible material whereby airborne embers that make contact are allowed to start secondary fires (if conditions are appropriate), combined with ground-based surface spread and burn rates for generating new embers, new updrafts (due to hot air), etc., and (III) a Machine-Learning Algorithm to rapidly ascertain the multi-submodel system parameters that force the overall model to match observations. The submodels compute both ground and airborne hot-ember driven fire propagation, as well as subsequent distribution of debris/soot, which is important for air-quality assessment. The overall framework is designed for use in digital twin technology, which refers to an adaptive digital replica of a physical system, whereby model updates are continuously in near real-time. This necessitates a rapid simulation paradigm that can easily interface with telecommunications, cameras and sensors. The presented framework is designed to run quickly on laptops and hand held devices, with the guiding principle being to make it potentially useful for first-responders in real-time.

Lidar and other time-of-flight technologies have recently received significant attention in both academic and industrial biomechanics communities, driven by technological advances in human body scanners . This paper develops an efficient and rapid computational method to simulate fast flash-type single-pulse Lidar, based on decomposition of a Lidar pulse into a group of rays, which are then tracked and processed. This allows one to quickly quantify the uncertainty in the response by identifying regions where the return signal will be erroneous due to multiple reflections.

One key aspect of many additive manufacturing processes is the deposition of heated mixtures of particulate materials onto surfaces, which then bond and cool, leading to complex microstructures and possible residual stresses. The overall objective of this work is to construct a straightforward computational approach that researchers in the field can easily implement and use as a numerically-efficient simulation and design tool. Specifically because multifield coupling is present, a recursive, staggered, temporally-adaptive, finite difference time domain scheme is developed to resolve the internal microstructural thermal and mechanical fields, accounting for the simultaneous elasto-plasticity and damage. The time step adaptation allows the numerical scheme to iteratively resolve the changing physical fields by refining the time-steps during phases of the process when the system is undergoing large changes on a relatively small time-scale and can also enlarge the time-steps when the processes are relatively slow. The spatial discretization grids are uniform and dense. The deposited microstructure is embedded into spatial discretization. The regular grid allows one to generate a matrix-free iterative formulation which is amenable to rapid computation and minimal memory requirements, making it ideal for laptop computation. Numerical examples are provided to illustrate the approach. This formulation is useful for material scientists who seek ways to deposit such materials while simultaneously avoiding inadvertent excessive residual stresses.

The general problem of a loosely flowing erosive granular jet undergoing impact with a compliant surface is common in many manufacturing processes, and also in the operating environment of a variety of machine parts. This paper presents a three-dimensional, collision-driven discrete particle simulation framework for investigating the dynamics of a jet of erosive particles impacting a surface with a specified porosity and compliance. The framework is capable of handling repeated collisions between incoming particles and rebounding particles, and between particles and surfaces. It is also capable of performing a coupled simultaneous calculation of sub-surface stresses in the material, assuming a certain porosity. Well illustrated numerical examples are presented with detailed analysis for investigations on the mechanics and energetics of the interfering collisions in eroding jets close to the target surface, on the effect of such interference on the material erosion, and on the evolving stress levels and potential damage zones under the action of impact. Particularly, the assumption of considering first-order collisions between oncoming and rebounding jet particles is re-examined. The influence of repeated collisions on energy transferred to the surface was found to be significant under conditions which involves high particle numbers or fluxes, and also high degrees of inelasticity. The overall trends for parametric variations were found to be in accordance with reported trends in the literature.

Boxing and related sports activities have become a standard workout regime at many fitness studios worldwide. Oftentimes, people are interested in the calories expended during these workouts. This note focuses on determining the calories in a boxer's jab, using kinematic vector-loop relations and basic work–energy principles. Numerical simulations are undertaken to illustrate the basic model. Multi-limb extensions of the model are also discussed.

The final publication is available at Springer via http://dx.doi.org/10.1007/s40571-015-0089-9 A modular discrete element framework is presented for large-scale simulations of industrial grain-handling systems. Our framework enables us to simulate a markedly larger number of particles than previous studies, thereby allowing for efficient and more realistic process simulations. This is achieved by partitioning the particle dynamics into distinct regimes based on their contact interactions, and integrating them using different time-steps, while exchanging phase-space data between them. The framework is illustrated using numerical experiments based on fertilizer spreader applications. The model predictions show very good qualitative and quantitative agreement with available experimental data. Valuable insights are developed regarding the role of lift vs drag forces on the particle trajectories in-flight, and on the role of geometric discretization errors for surface meshing in governing the emergent behavior of a system of particles.

The primary objective of this paper is to characterize the motion of small electromagnetically sensitive particles which are embedded in a flowing neutral fluid. There are a variety of industrial applications for electromagnetic particle-laden fluids, such as fluid-based actuators, coatings and functionalized inks, to name a few. This work compares the relative strengths of the forces induced by electromagnetic fields and fluid drag, and their composite effects on particle motion. Both analytical and numerical investigations are undertaken. After an analysis of a single isolated particle, a three-dimensional model problem comprised of a Representative Volume Element of flowing particle-laden fluid, under the action of external electromagnetic fields, is studied. A computational staggering scheme is developed to solve the coupled system utilizing a fully implicit Finite-Difference discretization of the Navier–Stokes equations for the fluid and a direct particle-dynamics description is used for the particles. For large numbers of embedded particles, because of the extreme computational difficulty of interface-conforming fine-mesh spatial discretizations for the fluid, simplifying assumptions for the coupling are made based on semi-analytical computation of drag-coefficients, allowing for the use of coarser meshes. Even after these simplifications, the particle-fluid system is strongly-coupled. The strongly-coupled system is implicitly solved, iteratively, within each time-step using a recursive staggering scheme, which employs temporal adaptivity to control the coupling error. The approach allows researchers to rapidly compute such systems with moderate laptop/desktop resources. Numerical examples are provided to illustrate the model and the numerical solution scheme, and limitations and extensions of the model are discussed.

Many additive manufacturing technologies involve the deposition of particles onto a surface followed by selective, targeted, laser heating. This paper develops a modular computational framework which combines the various steps within this overall process. Specifically, the framework synthesizes the following: Numerical examples are provided and extensions are also addressed for two advanced processing scenarios involving solid-liquid-gas phase transformations.

Recent efforts in the construction sector to develop panels made of agricultural waste and polymer mixtures are becoming more frequent. These panels are usually developed in pursuit of low embodied energy, biodegradability and energy efficiency. PMMA's transparency and structural advantages over other thermoplastics is making this material progressively more common in facades. While advances in PMMA's recyclability are under development, the process still presents multiple challenges regarding environmental efficiency and resulting structural integrity. It is important, therefore, to establish mixtures where PMMA's structural advantages are balanced with low carbon emission substrates, such as agricultural wastes. Typically, agricultural waste in architectural panels is used for structural reinforcement (i.e. fibers). However, agro-derived materials such as sucrose have additional and/or alternative potentials in construction applications. Sucrose's unique optical properties can be implemented for light and thermal control. Additionally, since the energy losses of buildings concentrate in the envelopes another major challenge of cladding substrates pertains to how the facade material can improve energy efficiency, i.e. acting as thermal mass while providing light transmission control avoiding conditions such as glare. Facade materials that can minimize environmental impact while supporting energy savings and appropriate natural light conditions bear strong potential for advancing sustainable building technologies. This analysis provides an expression for the overall reflectivity of combinations of sucrose and PMMA, as well as, estimates of the thermal heating rate, as a function of the relative volume fractions and material properties.

As a method for bonding powdered materials, sintering has distinct advantages, such as the production of a near final-shape of the desired product, without the need for significant post-processing. However, sintering has certain deficiencies, such as incomplete or weak bonding. Research is ongoing to improve the process. One approach to improve sintering processes of powdered materials is via electrically enhanced bonding, whereby electricity is pumped through the material, while it is compressed in a press, in order to induce Joule-heating. This paper develops a computationally based model for the direct simulation of electrically enhanced sintering of powdered materials using particle-based methods. The overall approach is to construct three coupled sub-models which primarily involve: (a) particle-to-particle mechanical contact, (b) particle-to-particle thermal exchange and (c) particle-to-particle electrical current flow. These physical processes are strongly coupled, since the dynamics dictates which particles are in contact and the contacts determine the electrical flow. The flow of electricity controls the Joule-heating and the induced thermal fields, which soften the material, leading to enhanced particle binding. The strong multiphysics-coupled sub-models are solved iteratively within each time-step using a recursive staggering scheme, which employs temporal adaptivity to control the error. If the process does not converge (to within an error tolerance) within a preset number of iterations, the time-step is adapted (reduced) by utilizing an estimate of the spectral radius of the coupled system. The modular approach allows for easy replacement of submodels, if needed. Numerical examples are provided to illustrate the model and numerical solution scheme.

This work develops a rigorous variational upper bound for the difference between thermal fields generated in uniform media and thermal fields generated in heterogeneous media, for the same external loading. The bound can be calculated in a simple manner, with knowledge only of the heterogeneous material properties and a relatively easy-to-compute thermal field associated with a uniform medium. In order to evaluate the bound, the difficult-to-compute thermal field, associated with the heterogeneous material, does not need to be calculated. Three-dimensional numerical examples are provided to illustrate the results.

© 2016 Elsevier B.V. An electromagnetic beam model is developed for the simulation of actuated electronic textiles. The beam is solved using a nonlinear director-based kinematic description with additional temperature and electric potential fields along its length. The three fields are fully coupled by mutual dependences on the deformation, Lorenz force, back electromotive force, temperature dependent constitutive responses, and the Seebeck effect. Instead of solving Maxwell's equations in full detail, a quasistatic approximation is used to solve the electric potential in the presence of a moving material medium. The current-carrying beam approximation is used to further simplify the solution space for the potential. While this formulation alleviates the spatial and temporal discretization restrictions, the coupled problem is an index-1 semi-explicit Differential Algebraic Equation requiring special treatment. The time dependent problem is solved using different Runge-Kutta methods. Diagonally implicit Runge-Kutta methods and explicit Runge-Kutta methods using implicit solution of the electric potential problem are explored. The finite element model is implemented using the open source package FEniCS, which is able to automatically generate the linearizations of the multiphysics equations required for the implicit solutions. A model problem is constructed with which to test and analyze the physical formulation and numerical solution techniques. The time stepping methods are verified using the convergence orders of the higher-order Runge-Kutta methods. Runtime comparisons show that the explicit methods are generally more computationally efficient than the implicit schemes used for this problem. For the implicit schemes, a staggered solution is significantly faster than a monolithic solution at most time step sizes. However, at very large time steps, such as those that would be used for dynamic relaxation, the monolithic solution can be more efficient than the staggered solution.