Computer Science

Workflow support typically focuses on single simulation experiments. This is also the case for simulation based on finite element methods. If entire simulation studies shall be supported, flexible means for intertwining revising the model, collecting data, executing and analyzing experiments are required. Artifact-based workflows present one means to support entire simulation studies, as has been shown for stochastic discrete-event simulation. To adapt the approach to finite element methods, the set of artifacts, i.e., conceptual model, requirement, simulation model, and simulation experiment, and the constraints that apply are extended by new artifacts, such as geometrical model, input data, and simulation data. Artifacts, their life cycles, and constraints are revisited revealing features both types of simulation studies share and those they vary in. Also, the potential benefits of exploiting an artifact-based workflow approach are shown based on a concrete simulation study. To those benefits belong guidance to systematically conduct simulation studies, reduction of effort by automatically executing specific steps, e.g., generating and executing convergence tests, and support for the automatic reporting of provenance.

In this paper, we introduce a probabilistic statistics solution or artificial intelligence (AI) approach to identify and quantify permanent (non-zero strain) continuum/material deformation only based on the scanned material data in the spatial configuration and the shape of the initial design configuration or the material configuration. The challenge of this problem is that we only know the scanned material data in the spatial configuration and the shape of the design configuration of three-dimensional (3D) printed products, whereas for a specific scanned material point we do not know its corresponding material coordinates in the initial or designed referential configuration, provided that we do not know the detailed information on actual physical deformation process. Different from physics-based modeling, the method developed here is a data-driven artificial intelligence method, which solves the problem with incomplete deformation data or with missing information of actual physical deformation process. We coined the method is an AI-based material deformation finding algorithm. This method has practical significance and important applications in finding and designing thermal compensation configuration of a 3D printed product in additive manufacturing, which is at the heart of the cutting edge 3D printing technology. In this paper, we demonstrate that the proposed AI continuum/material deformation finding approach can accurately find permanent thermal deformation configuration for a complex 3D printed structure component, and hence to identify the thermal compensation design configuration in order to minimizing the impact of temperature fluctuations on 3D printed structure components that are sensitive to changes of temperature.

This paper presents an efficient gradient projection-based method for structural topological optimization problems characterized by a nonlinear objective function which is minimized over a feasible region defined by bilateral bounds and a single linear equality constraint. The specialty of the constraints type, as well as heuristic engineering experiences are exploited to improve the scaling scheme, projection, and searching step. In detail, gradient clipping and a modified projection of searching direction under certain condition are utilized to facilitate the efficiency of the proposed method. Besides, an analytical solution is proposed to approximate this projection with negligible computation and memory costs. Furthermore, the calculation of searching steps is largely simplified. Benchmark problems, including the MBB, the force inverter mechanism, and the 3D cantilever beam are used to validate the effectiveness of the method. The proposed method is implemented in MATLAB which is open-sourced for educational usage.

Probability density function (PDF) based turbulent combustion modelling is limited by the need to store multi-dimensional PDF tables that can take up large amounts of memory. A significant saving in storage can be achieved by using various machine-learning techniques that represent the thermo-chemical quantities of a PDF table using mathematical functions. These functions can be computationally more expensive than the existing interpolation methods used for thermo-chemical quantities. More importantly, the training time can amount to a considerable portion of the simulation time. In this work, we address these issues by introducing an adaptive training algorithm that relies on multi-layer perception (MLP) neural networks for regression and self-organizing maps (SOMs) for clustering data to tabulate using different networks. The algorithm is designed to address both the multi-dimensionality of the PDF table as well as the computational efficiency of the proposed algorithm. SOM clustering divides the PDF table into several parts based on similarities in data. Each cluster of data is trained using an MLP algorithm on simple network architectures to generate local functions for thermo-chemical quantities. The algorithm is validated for the so-called DLR-A turbulent jet diffusion flame using both RANS and LES simulations and the results of the PDF tabulation are compared to the standard linear interpolation method. The comparison yields a very good agreement between the two tabulation techniques and establishes the MLP-SOM approach as a viable method for PDF tabulation.

Sliding meshes are a powerful method to treat deformed domains in computational fluid dynamics, where different parts of the domain are in relative motion. In this paper, we present an efficient implementation of a sliding mesh method into a discontinuous Galerkin compressible Navier-Stokes solver and its application to a large eddy simulation of a 1-1/2 stage turbine. The method is based on the mortar method and is high-order accurate. It can handle three-dimensional sliding mesh interfaces with various interface shapes. For plane interfaces, which are the most common case, conservativity and free-stream preservation are ensured. We put an emphasis on efficient parallel implementation. Our implementation generates little computational and storage overhead. Inter-node communication via MPI in a dynamically changing mesh topology is reduced to a bare minimum by ensuring a priori information about communication partners and data sorting. We provide performance and scaling results showing the capability of the implementation strategy. Apart from analytical validation computations and convergence results, we present a wall-resolved implicit LES of the 1-1/2 stage Aachen turbine test case as a large scale practical application example.

Cable subsystems characterized by long, slender, and flexible structural elements are featured in numerous engineering systems. In each of them, interaction between an individual cable and the surrounding fluid is inevitable. Such a Fluid-Structure Interaction (FSI) has received little attention in the literature, possibly due to the inherent complexity associated with fluid and structural semi-discretizations of disparate spatial dimensions. This paper proposes an embedded boundary approach for filling this gap, where the dynamics of the cable are captured by a standard finite element representation C of its centerline, while its geometry is represented by a discrete surface Σ h that is embedded in the fluid mesh. The proposed approach is built on master-slave kinematics between C and Σ h , a simple algorithm for computing the motion/deformation of Σ h based on the dynamic state of C , and an energy-conserving method for transferring to C the loads computed on Σ h . Its effectiveness is demonstrated for two highly nonlinear applications featuring large deformations and/or motions of a cable subsystem and turbulent flows: an aerial refueling model problem, and a challenging supersonic parachute inflation problem. The proposed approach is verified using numerical data, and validated using real flight data.

This paper proposes an efficient FDTD technique for determining electromagnetic fields interacting with a finite-sized 2D and 3D periodic structures. The technique combines periodic boundary conditions---modelling fields away from the edges of the structure---with independent simulations of fields near the edges of the structure. It is shown that this algorithm efficiently determines the size of a periodic structure necessary for fields to converge to the infinitely-periodic case. Numerical validations of the technique illustrate the savings concomitant with the algorithm.

In this work, we have investigated a 2D model of band-to-band tunneling based on 2-band model and implemented it using 2D NEGF formalism. Being 2D in nature, this model better addresses the variation in the directionality of the tunneling process occurring in most practical TFET device structures. It also works as a compromise between semi-classical and multiband quantum simulation of TFETs. In this work, we have presented a sound step by step mathematical development of the numerical model. We have also discussed how this model can be implemented in simulators and pointed out a few optimizations that can be made to reduce complexity and to save time. Finally, we have performed elaborate simulations for a practical TFET design and compared the results with commercially available TCAD simulations, to point out the limitations of the simplistic models that are frequently used, and how our model overcomes these limitations.

During design optimization, a smooth description of the geometry is important, especially for problems that are sensitive to the way interfaces are resolved, e.g., wave propagation or fluid-structure interaction. A levelset description of the boundary, when combined with an enriched finite element formulation, offers a smoother description of the design than traditional density-based methods. However, existing enriched methods have drawbacks, including ill-conditioning and difficulties in prescribing essential boundary conditions. In this work we introduce a new enriched topology optimization methodology that overcomes the aforementioned drawbacks; boundaries are resolved accurately by means of the Interface-enriched Generalized Finite Element Method (IGFEM), coupled to a levelset function constructed by radial basis functions. The enriched method used in this new approach to topology optimization has the same level of accuracy in the analysis as standard the finite element method with matching meshes, but without the need for remeshing. We derive the analytical sensitivities and we discuss the behavior of the optimization process in detail. We establish that IGFEM-based levelset topology optimization generates correct topologies for well-known compliance minimization problems.

Metallic open cell foams have multiple applications in industry, e. g. as catalyst supports in chemical processes. Their regular or heterogeneous microscopic structure determines the macroscopic thermodynamic and chemical properties. We present an object-oriented python library that generates state space models for simulation and control from the microscopic foam data, which can be imported from the image processing tool iMorph. The foam topology and the 3D geometric data are the basis for discrete modeling of the balance laws using the cell method. While the material structure imposes a primal chain complex to define discrete thermodynamic driving forces, the internal energy balance is evaluated on a second chain complex, which is constructed by topological duality. The heat exchange between the solid and the fluid phase is described based on the available surface data. We illustrate in detail the construction of the dual chain complexes, and we show how the structured discrete model directly maps to the software objects of the python code. As a test case, we present simulation results for a foam with a Kelvin cell structure, and compare them to a surrogate finite element model with homogeneous parameters.