Showing papers on "Modeling and simulation published in 2021"

Learning the solution operator of parametric partial differential equations with physics-informed DeepONets.

Abstract: Partial differential equations (PDEs) play a central role in the mathematical analysis and modeling of complex dynamic processes across all corners of science and engineering. Their solution often requires laborious analytical or computational tools, associated with a cost that is markedly amplified when different scenarios need to be investigated, for example, corresponding to different initial or boundary conditions, different inputs, etc. In this work, we introduce physics-informed DeepONets, a deep learning framework for learning the solution operator of arbitrary PDEs, even in the absence of any paired input-output training data. We illustrate the effectiveness of the proposed framework in rapidly predicting the solution of various types of parametric PDEs up to three orders of magnitude faster compared to conventional PDE solvers, setting a previously unexplored paradigm for modeling and simulation of nonlinear and nonequilibrium processes in science and engineering.

On the Modeling and Simulation of SLM and SLS for Metal and Polymer Powders: A Review

Abstract: Additive Manufacturing concentrates the attention, not only of the research and academic community, but of the industry as well. Selective Laser Melting (SLM) and Selective Laser Sintering (SLS) are among the broadest employed methods in AM, since they can treat almost all types of materials. Along with the extensive experimental research that is carried out regarding SLS and SLM, modeling and simulation are powerful tools allowing better and more in depth understanding of the processes. Nevertheless, there is no general framework in modeling, but mainly studies and proposed modeling approaches. The current paper reviews modeling methods and techniques that in literature are presented for the simulation of SLM and SLS. Besides the Finite Element Method, which is the most common method used, other numerical methods like Discrete Element Method, Smoothed Particles Hydrodynamics and Molecular Dynamics have been overviewed as well. The heat transfer and fluid dynamics models consist the main core of every simulation, while other sub-models are integrated to estimate parameters like residual stresses, part deformation, material microstructure, or crystallization. The main scope of the current paper is to provide a comprehensive and detailed review on the modeling and simulation of SLS/SLM and to inform the reader concerning the different modeling strategies.

Magttice: a lattice model for hard-magnetic soft materials.

Abstract: Magnetic actuation has emerged as a powerful and versatile mechanism for diverse applications, ranging from soft robotics, biomedical devices to functional metamaterials. This highly interdisciplinary research calls for an easy to use and efficient modeling/simulation platform that can be leveraged by researchers with different backgrounds. Here we present a lattice model for hard-magnetic soft materials by partitioning the elastic deformation energy into lattice stretching and volumetric change, so-called 'magttice'. Magnetic actuation is realized through prescribed nodal forces in magttice. We further implement the model into the framework of a large-scale atomic/molecular massively parallel simulator (LAMMPS) for highly efficient parallel simulations. The magttice is first validated by examining the deformation of ferromagnetic beam structures, and then applied to various smart structures, such as origami plates and magnetic robots. After investigating the static deformation and dynamic motion of a soft robot, the swimming of the magnetic robot in water, like jellyfish's locomotion, is further studied by coupling the magttice and lattice Boltzmann method (LBM). These examples indicate that the proposed magttice model can enable more efficient mechanical modeling and simulation for the rational design of magnetically driven smart structures.

A digital twin emulator of a modular production system using a data-driven hybrid modeling and simulation approach

Abstract: Virtual commissioning is a key technology in Industry 4.0 that can address issues faced by engineers during early design phases. The process of virtual commissioning involves the creation of a Digital Twin—a dynamic, virtual representation of a corresponding physical system. The digital twin model can be used for testing and verifying the control system in a simulated virtual environment to achieve rapid set-up and optimization prior to physical commissioning. Additionally, the modular production control systems, can be integrated and tested during or prior to the construction of the physical system. This paper describes the implementation of a digital twin emulator of an automated mechatronic modular production system that is linked with the running programmable logic controllers and allow for exchanging near real-time information with the physical system. The development and deployment of the digital twin emulator involves a novel hybrid simulation- and data-driven modeling approach that combines Discrete Event Simulation and Agent Based Modeling paradigms. The Digital Twin Emulator can support design decisions, test what-if system configurations, verify and validate the actual behavior of the complete system off-line, test realistic reactions, and provide statistics on the system’s performance.

Real-Time Simulation for Control of Soft Robots With Self-Collisions Using Model Order Reduction for Contact Forces

Abstract: In rigid robotics, self-collision are usually avoided since it leads to a failure in the robot control and can also cause damage. In soft robotics, the situation is very different, and self-collisions may even be a desirable property, for example to gain artificial stiffness or to provide a natural limitation to the workspace. However, the modeling and simulation of self-collision is very costly as it requires first a collision detection algorithm to detect where collisions occur, and most importantly, it requires solving a constrained problem to avoid interpenetrations. When the number of contact points is large, this computation slows down the simulation dramatically. In this letter, we apply a numerical method to alleviate the contact response computation by reducing the contact space in a low-dimensional positive space obtained from experiments. We show good accuracy while speeding up dramatically the simulation. We apply the method in simulation on a cable-actuated finger and on a continuum manipulator performing exploration. We also show that the reduced contact method proposed can be used for inverse modeling. The method can therefore be used for control or design.

Simulation and Automated Modeling of Microwave Circuits: State-of-the-Art and Emerging Trends

Abstract: Microwave modeling and simulation are essential to designing microwave circuits and systems. Although fundamental concepts and approaches for modeling and simulation are mature, the drive to higher frequencies, tighter design margins, and more functionality/complexity of circuits continue to defy the capabilities of existing modeling and simulation methods. Newer algorithms are being developed to address the speed, accuracy and robustness of design algorithms. Coupled with the advent of more powerful computers and algorithms, microwave design automations are solving much more complex problems in much shorter time than previously imaginable. This paper describes the advances and state-of-the-art in automated modeling and simulation. Automated data-driven modeling approaches covering data sampling/generation, model structure adaptation, and model training/validation are described. Simulation of nonlinear microwave circuits is described covering formulations of simulation equations and advanced solution algorithms addressing problem size, convergence speed and solution accuracy. The descriptions highlight fundamental concepts, advanced methodologies, and future trends of development.

Modeling and simulation of energy-related human-building interaction: A systematic review

Abstract: Building energy use is highly sensitive to its occupants' energy-related behavior, including their presence and interaction with different building systems. The modeling and simulation of energy-related human-building interaction play an essential role in predicting the actual energy use of a building's operation. The present study provides a systematic review of the existing literature focusing on energy-related human-building interaction modeling and simulation tools and techniques to determine the significant findings, current limitations, and future research directions in this area. The main contribution of this study is to provide a state-of-art framework of the inputs and outputs used for modeling and simulation of occupants' energy-use behavior. A list of 95 articles, containing journal papers and conference proceedings published in the last 15 years, is collected and analyzed based on various parameters such as modeling purpose, building type application, occupant-related parameters, occupant-related data source, occupant related data duration, modeling method and techniques, simulation and programming software, simulation time-step, and modeling and simulation output. In addition, a word mining analysis has been employed to generate a bibliographical map of the reviewed articles based on the most repetitive keywords and their connections. Finally, the most efficient and practical techniques in the modeling and simulation of energy-related human-building interaction and future research direction are presented.

Mathematical Modeling and Simulations for Developing Nanoparticle-Based Cancer Drug Delivery Systems: A Review

Abstract: Nanoparticles are crucial for developing patient-/target-specific drug delivery systems. In recent days, mathematical modeling and simulation plays an important role in optimization of various parameters like nanoparticle-based drug dose, dissolution of drug particles, and adverse reaction from the nanoparticles. With the help of modeling and simulation, we can determine or optimize the type, shape, and size of the nanoparticles to be utilized as potential drug delivery system and its influence on the targeted cells/tissues. The main purpose of this review article is to discuss the latest modeling and simulation tools available for developing patient-specific nanoparticle-based drug delivery systems. In our current study, we are reporting different mathematical models used for cancer drug delivery systems. It also reports several numerical methods, and simulations models are available for representing nano-drug-bio interactions within the biological systems. This review highlights the applications of mathematical modeling and simulation software for developing a rational nano-carrier design and selecting accurate biomaterials for in vivo model.

A Bi-fidelity ensemble kalman method for PDE-constrained inverse problems in computational mechanics

Abstract: Mathematical modeling and simulation of complex physical systems based on partial differential equations (PDEs) have been widely used in engineering and industrial applications. To enable reliable predictions, it is crucial yet challenging to calibrate the model by inferring unknown parameters/fields (e.g., boundary conditions, mechanical properties, and operating parameters) from sparse and noisy measurements, which is known as a PDE-constrained inverse problem. In this work, we develop a novel bi-fidelity (BF) ensemble Kalman inversion method to tackle this challenge, leveraging the accuracy of high-fidelity models and the efficiency of low-fidelity models. The core concept is to build a BF model with a limited number of high-fidelity samples for efficient forward propagations in the iterative ensemble Kalman inversion. Compared to existing inversion techniques, salient features of the proposed methods can be summarized as follow: (1) achieving the accuracy of high-fidelity models but at the cost of low-fidelity models, (2) being robust and derivative-free, and (3) being code non-intrusive, enabling ease of deployment for different applications. The proposed method has been assessed by three inverse problems that are relevant to fluid dynamics, including both parameter estimation and field inversion. The numerical results demonstrate the excellent performance of the proposed BF ensemble Kalman inversion approach, which drastically outperforms the standard Kalman inversion in terms of efficiency and accuracy.

Hybrid Symbolic-Numeric Framework for Power System Modeling and Analysis

Abstract: With the recent proliferation of open-source packages for computing, power system differential-algebraic equation (DAE) modeling and simulation are being revisited to reduce the programming efforts. Existing open-source tools require manual efforts to develop code for numerical equations, sparse Jacobians, and discontinuous components. This paper proposes a hybrid symbolic-numeric framework, exemplified by an open-source Python-based library ANDES, which consists of a symbolic layer for descriptive modeling and a numeric layer for vector-based numerical computation. This method enables the implementation of DAE models by mixing and matching modeling components, through which models are described. In the framework, a rich set of discontinuous components and standard transfer function blocks are provided besides essential modeling elements for rapid modeling. ANDES can automatically generate robust and fast numerical simulation code, as well as and high-quality documentation. Case studies present a) two implementations of turbine governor model TGOV1, b) power flow computation time break down for MATPOWER systems, c) validation of time-domain simulation with commercial software using three test systems with a variety of models, and d) the full eigenvalue analysis for Kundur's system. Validation shows that ANDES closely matches the commercial tool DSATools for power flow, time-domain simulation, and eigenvalue analysis.

Modeling and Simulation as a Service infrastructure capabilities for discovery, composition and execution of simulation services:

Abstract: Modeling and Simulation as a Service (MSaaS) embodies the idea that simulations should be composed quickly for the task at hand from loosely coupled shared components, simulation services, in a cloud-based environment. These simulations are then offered, as composed simulation services, to human and technical consumers. Instrumental to this, is functionality that lets a simulation operator discover and compose simulation services and execute the composition. We describe this functionality in terms of what we call MSaaS infrastructure capabilities. Following the idea of stepwise refinement, the discovery and composition of simulation services can be done at design time using implementation-independent information about simulation services and at implementation time using implementation-specific information about simulation services. The execution environment can also be set up at design time and at implementation time. We therefore describe the MSaaS infrastructure capabilities in terms of how they are used on both implementation-independent and implementation-specific service information. By doing these elaborations, we intend to gain greater insight into how to perform simulation service discovery, composition, and execution. We conclude that although much of the required functionality for a MSaaS infrastructure is available through existing platforms and frameworks, it is necessary to offer this functionality as services, alongside (composed) simulation services, to fulfill the MSaaS vision.

High Performance Electric Vehicle Powertrain Modeling, Simulation and Validation

Abstract: Accurate electric vehicle (EV) powertrain modeling, simulation and validation is paramount for critical design and control decisions in high performance vehicle designs. Described in this paper is a methodology for the design and development of EV powertrain through modeling, simulation and validation on a real-world vehicle system with detailed analysis of the results. Although simulation of EV powertrains in software simulation environments plays a significant role in the design and development of EVs, validating these models on the real-world vehicle systems plays an equally important role in improving the overall vehicle reliability, safety and performance. This modeling approach leverages the use of MATLAB/Simulink software for the modeling and simulation of an EV powertrain, augmented by simultaneously validating the modeling results on a real-world vehicle which is performance tested on a chassis dynamometer. The combination of these modeling techniques and real-world validation demonstrates a methodology for a cost effective means of rapidly developing and validating high performance EV powertrains, filling the literature gaps in how these modeling methodologies can be carried out in a research framework.

Contact and friction simulation for computer graphics

Abstract: Efficient simulation of contact is of interest for numerous physics-based animation applications. For instance, virtual reality training, video games, rapid digital prototyping, and robotics simulation are all examples of applications that involve contact modeling and simulation. However, despite its extensive use in modern computer graphics, contact simulation remains one of the most challenging problems in physics-based animation. This course covers fundamental topics on the nature of contact modeling and simulation for computer graphics. Specifically, we provide mathematical details about formulating contact as a complementarity problem in rigid body and soft body animations. We briefly cover several approaches for contact generation using discrete collision detection. Then, we present a range of numerical techniques for solving the associated LCPs and NCPs. The advantages and disadvantages of each technique are further discussed in a practical manner, and best practices for implementation are discussed. Finally, we conclude the course with several advanced topics, such as anisotropic friction modeling and proximal operators. Programming examples are provided on the course website to accompany the course notes.

Transient Thermal Analysis of the Circuit Breaker Current Path with the Use of FEA Simulation

Abstract: The finite element analysis (FEA) is an essential and powerful numerical method that can explicitly optimize the design process of electrical devices. In this paper, the employment of the finite element method (FEM) as ANSYS is proposed in order to aid electrical apparatus engineering and modeling of low voltage modular circuit breakers. The procured detailed model of a miniature circuit breaker (MCB) was undergoing transient thermal simulations of the current path. Acquired data were juxtapositioned with experimental data procured in the laboratory. The reflection of the simulation approach was clearly noted in the experimental results. Mutual areas of the modeled element expressed similar physical properties and robustness errors while tested in the specific conditions—faithfully reflecting those that were experimented with. Moreover, the physical phenomena essential for electrical engineering could be determined on the model stage. These types of 3D models can be used to analyze the thermal behavior of the current path during the current flowing condition.

Analytical Modeling and Simulation of Gate-All-Around Junctionless Mosfet for Biosensing Applications

Abstract: A new analytical model for a Junctionless Field Effect Transistor that can be used in biosensor applications is proposed in this research work. The Semiconductor device analyzed here employs a Gate-All-Around structure made of two dissimilar materials. The main objective of the surrounding gate is to reduce the Short Channel Effects owing to its scalability. This model introduces a novel dual material structure embedded with a nanocavity to make it suitable for biosensing applications. 2-D Poisson’s equation is solved using the Finite Differentiation Method to obtain the surface potential, which in turn is employed to determine the electric field and threshold voltage of the proposed structure. Finally, the biosensor sensitivity of the device is analyzed and the obtained results are verified using 2D TCAD simulations.