A Lagrangian-Eulerian framework for simulation of transient viscoelastic fluid flow

A novel framework for simulation of transient viscoelastic fluid flow is proposed. The viscoelastic stresses are calculated at Lagrangian nodes which are distributed in the computational domain and convected by the fluid. The coupling between the constitutive equation and the fluid momentum equations is established through robust interpolation with radial basis functions. The framework is implemented in a finite volume based flow solver that combines an octree background grid with immersed boundary techniques. Since the distribution of the Lagrangian node set is performed entirely based on spatial information from the fluid solver, the ability to simulate flows in complex geometries is therefore as general as for the fluid solver itself. In the Lagrangian formulation the discretization of the convective terms in the constitutive equations is avoided. No re-formulation of the constitutive equation is required for stable solutions. Numerical experiments are performed of UCM and Oldroyd-B fluids in a channel flow and of a four mode PTT fluid in a confined cylinder flow. The computed flow quantities consistently converge and agree excellently with analytical and numerical data for fully developed and transient flow

SelfPaint-A self-programming paint booth

In this paper we present a unique Fraunhofer approach that aims towards a vision to automate the product preparation in paint shops by automatically generating robot paths and process conditions that guarantee a certain wanted paint coverage. This will be accomplished through a combination of state-of-the-art simulation technology, inline quality control by novel terahertz thickness measurement technology, and surface treatment technology. The benefits of the approach are a shortened product preparation time, increased quality and reduced material and energy consumption. The painting of a tractor fender is used to demonstrate the approach

A numerical framework for simulation of swirled adhesive application

A numerical framework for simulation of swirled adhesive application along arbitrary robot motions and substrate geometries is pre- sented. The momentum and continuity equa- tions are solved on a Cartesian octree grid using a finite volume discretization. A viscoelastic constitutive model is used to describe the com- plex rheology of the adhesive and is solved us- ing a previously presented Lagrangian-Eulerian method. The flow from the nozzle to the target surface is modelled using experimental data, and a projected injection model is used to add adhesive material in the simulation close to the surface. The two-phase flow of adhesive and air is then simulated. Numerical results are com- pared with experimental data and good agree- ment is found

A Backwards-Tracking Lagrangian-Eulerian Method for Viscoelastic Two-Fluid Flows

A new Lagrangian–Eulerian method for the simulation of viscoelastic free surface flow is proposed. The approach is developed from a method in which the constitutive equation for viscoelastic stress is solved at Lagrangian nodes, which are convected by the flow, and interpolated to the Eulerian grid with radial basis functions. In the new method, a backwards-tracking methodology is employed, allowing for fixed locations for the Lagrangian nodes to be chosen a priori. The proposed method is also extended to the simulation of viscoelastic free surface flow with the volume of fluid method. No unstructured interpolation or node redistribution is required with the new approach. Furthermore, the total amount of Lagrangian nodes is significantly reduced when compared to the original Lagrangian–Eulerian method. Consequently, the method is more computationally efficient and robust. No additional stabilization technique, such as both-sides diffusion or reformulation of the constitutive equation, is necessary. A validation is performed with the analytic solution for transient and steady planar Poiseuille flow, with excellent results. Furthermore, the proposed method agrees well with numerical data from the literature for the viscoelastic die swell flow of an Oldroyd-B model. The capabilities to simulate viscoelastic free surface flow are also demonstrated through the simulation of a jet buckling case

A numerical multiscale method for fiber networks

Fiber network modeling can be used for studying mechanical properties of paper. The individual fibers and the bonds in-between constitute a detailed representation of the material. However, detailed microscale fiber network models must be resolved with efficient numerical methods. In this work, a numerical multiscale method for discrete network models is proposed that is based on the localized orthogonal decomposition method [arXiv:1810.05059]. The method is ideal for these network problems, because it reduces the maximum size of the problem, it is suitable for parallelization, and it can effectively solve fracture propagation. The problem analyzed in this work is the nodal displacement of a fiber network given an applied load. This problem is formulated as a linear system that is solved by using the aforementioned multiscale method. To solve the linear system, the multiscale method constructs a low-dimensional solution space with good approximation properties. The method is observed to work well for unstructured fiber networks, with optimal rates of convergence obtainable for highly localized configurations of the method.Comment: 10 pages, 8 figures, has been submitted to WCCM-ECCMAS 202

Simulation of viscoelastic squeeze flows for adhesive joining applications

A backwards-tracking Lagrangian–Eulerian method is used to simulate planar viscoelastic squeeze flow. The momentum and continuity equations are discretized with the finite volume method and implicit immersed boundary conditions are used to describe objects in the domain. The viscoelastic squeeze flow, which involves moving solid geometry as well as free surface flow, is chosen for its relevance in industrial applications, such as adhesive parts assembly and hemming. The main objectives are to validate the numerical method for such flows and to outline the grid resolution dependence of important flow quantities. The main part of the study is performed with the Oldroyd-B model, for which the grid dependence is assessed over a wide range of Weissenberg numbers. An important conclusion is that the load exerted on the solids can be predicted with reasonable accuracy using a relatively coarse grid. Furthermore, the results are found to be in excellent agreement with theoretical predictions as well as in qualitative resemblance with numerical results from the literature. The effects of different viscoelastic properties are further investigated using the PTT model, revealing a strong influence of shear-thinning for moderate Weissenberg numbers. Finally, a reverse squeeze flow is simulated, highlighting important aspects in the context of adhesive joining applications

Computationally efficient viscoelastic flow simulation using a Lagrangian-Eulerian method and GPU-acceleration

A recently proposed Lagrangian-Eulerian method for viscoelastic flow simulation is extended to high performance calculations on the Graphics Processing Unit (GPU). The two most computationally intensive parts of the algorithm are implemented for GPU calculation, namely the integration of the viscoelastic constitutive equation at the Lagrangian nodes and the interpolation of the resulting stresses to the cell centers of the Eulerian grid. In the original CPU method, the constitutive equations are integrated with a second order backward differentiation formula, while with the proposed GPU method the implicit Euler method is used. To allow fair comparison, the latter is also implemented for the CPU. The methods are validated for two flows, a planar Poiseuille flow of an upper-convected Maxwell fluid and flow past a confined cylinder of a four-mode Phan Thien Tanner fluid, with identical results. The calculation times for the methods are compared for a range of grid resolutions and numbers of CPU threads, revealing a significant reduction of the calculation time for the proposed GPU method. As an example, the total simulation time is roughly halved compared to the original CPU method. The integration of the constitutive equation itself is reduced by a factor 50 to 250 and the unstructured stress interpolation by a factor 15 to 60, depending on the number of CPU threads used

A numerical multiscale method for fiber networks

Fiber network modeling can be used for studying mechanical properties of paper [1]. The individual fibers and the bonds in-between constitute a detailed representation of the material. However, detailed microscale fiber network models must be resolved with efficient numerical methods. In this work, a numerical multiscale method for discrete network models is proposed that is based on the localized orthogonal decomposition method [4]. The method is ideal for these network problems, because it reduces the maximum size of the problem, it is suitable for parallelization, and it can effectively solve fracture propagation. The problem analyzed in this work is the nodal displacement of a fiber network given an applied load. This problem is formulated as a linear system that is solved by using the aforementioned multiscale method. To solve the linear system, the multiscale method constructs a low-dimensional solution space with good approximation properties [5, 2]. The method is observed to work well for unstructured fiber networks, with optimal rates of convergence obtainable for highly localized configurations of the method

Numerical upscaling of discrete network models

In this paper a numerical multiscale method for discrete networks is presented. The method gives an accurate coarse scale representation of the full network by solving sub-network problems. The method is used to solve problems with highly varying connectivity or random network structure, showing optimal order convergence rates with respect to the mesh size of the coarse representation. Moreover, a network model for paper-based materials is presented. The numerical multiscale method is applied to solve problems governed by the presented network model