Simulation World.

Ansys Mechanical Performance on a 1500 cores Skylake Cluster Herbert Güttler, MicroConsult GmbH, Bernstadt, Germany The solder joints that connect the integrated circuit (IC) to the printed circuit board (PCB) are subject to failure due to mechanical stresses during thermal cycling caused primarily by thermal mismatch. When we started doing this class of simulations a decade ago, a typical simulation would take weeks to conclude. Over the years, with the help of software enhancements and new hardware capabilities, these runtimes could be reduced down to a few hours. The largest speedups could be achieved running models consisting only of solids and adding contacts would usually degrade performance and scaling. However, by adding features like contact small sliding or and contact splitting that became available during the latest ANSYS Mechanical releases those limitations could be largely overcome, especially at high core counts. We will report on results from actual real world problems that have been performed using up to 1500 Xeon Skylake cores on MicroConsult’s HPC cluster. MicroConsult is a high performance computing partner to ANSYS and works closely together with the ANSYS solver team in Canonsburg.

Focusing on mesh nonlinear adaptivity, this paper is also going to talk about how to change element types, dimension and other control parameters during solutions. The applications to some industrial problems are to be demonstrated.

For more than a decade Ansys adjoint solver technology in Fluent has grown in strength to emerge as a refined toolkit for shape optimization. The current adjoint solver, in combination with a suite of supporting tools, can optimize the performance of many types of fluid system of engineering interest for a wide range of user-selected objectives. Problems of compressible and incompressible fluid flow and heat transfer, including conjugate heat transfer, porous media and rotating flows can be addressed. The key to this success is a robust adjoint solver that can solve small and large problems and has a broad scope of supported physics, numerics and boundary conditions. In addition, custom-crafted mesh morphing tools and single and multi-objective constrained optimization algorithms are available. These tools are integrated into a flexible yet straightforward workflow.

Present paper present a recent development, ANSYS SMART (Separating, Morphing, Adaptive and Remeshing Technology) crack growth simulation framework, for automatic, robust and seamless simulation of complex crack growth. The framework is essential for large scale crack growth simulation. Validation with standard CCT specimen has shown very good agreement with analytic solution. Several examples are presented to demonstrate the automatic simulation of crack growth with mixed-mode fracture in focus for the specific application.

Mesh morphing enables engineers to explore similar variations of a problem quickly at a low cost by offering easy mesh shape changes. Progresses made in efficient and accurate computing mesh morphing field, improving user experience in setting up mesh morphing controls and applying the developed technologies will be presented.

Size of CAE problems have steadily increased over time. Mesh generation has increasingly become a bottleneck due to largely serial software. We will present a distributed parallel solution to unstructured volume meshing.

This presentation highlights two recent methods to automatically convert faceted simulation results into CAD bodies. The first one shows how to automatically generate a CAD body from topology optimization and preserving boundary condition faces. The second one applies the shape change of a displacement result to the original CAD body without making topology changes.

On how the parameter modeler in SpaceClaim can be used as a object-oriented, visual programming environment.

The presentation will provide an insight into the challenges posed by the resolution of turbulence in CFD. It will show that turbulence is the central driving factor for CFD and will detail the level of computing power which will be required with different strategies for handling turbulence. Strategies for achieving optimal performance on highly massive CPU/GPU hardware will be highlighted.

This presentation will cover Ansys' approach to a single, integrated user experience and set of services for running simulations The approach spans workstation, on-premise HPC and public/private hybrid cloud. The presentation will cover the architecture, functionality and integration with Ansys products.

Many engineering professors recognize the benefits of integrating simulations into the undergraduate curriculum such as connecting physics to real-life applications, supporting project-based learning and helping students acquire skills sought by industry. However, they struggle with how to incorporate simulations into their courses without taking away from the teaching of physics. At Cornell, I have developed an approach that blends the teaching of physics and simulations using just-in-time, problem-based learning. This approach seamlessly integrates the teaching of theory with the hands-on use of ANSYS Mechanical and Fluent. It has become clear from these efforts that ANSYS can serve not only as a problem-solving platform but also as a visual learning platform that helps students master physics better than through a pure textbook approach. My approach has been implemented in over 10 Cornell engineering courses as well as in a free “massive open online course” or MOOC at edx.org which has an enrollment exceeding 160,000 people from universities and industry. My MOOC has helped thousands of people to not only learn practical simulations in a hands-on manner but also physics through simulations. It has opened a pathway towards the democratization of simulation whereby all engineering graduates would be able to deploy simulations reliably. The MOOC demonstrates how to shift the paradigm in engineering education by embracing two disruptive technologies: simulation and online learning. Considering that now most faculty have experience with online learning due to Covid-19, this is the moment for industry to push universities to leave behind a dated, theory-heavy education model and move towards the next-generation engineering curriculum in which simulation becomes the standard to teach physics on-demand. This would benefit a large number of undergraduate programs and ensure students graduate with real-world skills, empowered and excited to solve practical problems.

The US will deliver its Exascale systems (capable of a billion-billion operations per second) over the next couple of years, enabling a new generation of application codes to deliver fundamentally new simulation and analysis results. At the same time, the underlying tools and libraries that enable these application codes to achieve success will have a much broader impact, enabling improved performance for countless other applications, even those targeted toward workstations and clusters that contain accelerator devices. In this presentation we give a brief overview of the Exascale Computing Project (ECP), then some details about the ECP Software Technology portfolio. We discuss the challenges of preparing for Exascale platforms, which include preparations for accelerated architectures, and highlight opportunities for interaction with US industry. While GPU accelerators have been available for some years, the emergence of new accelerated devices brings new opportunities and challenges. We will discuss ECP efforts to realize performance and portability across devices from Nvidia, AMD and Intel.

As Additive Manufacturing (AM) technology evolves, better understanding of the data generated during AM projects becomes vital to realizing its potential. This applies both to empirical data, from production and testing, and to data generated by the various simulation packages that help users to understand process parameters and reduce “trial & error”. Simulation results can help to optimize and calibrate your process. Testing validates simulation and ensures that final parts meet design requirements. You need to capture and use data from both in order to speed your AM development process and certify products at lower cost. GRANTA MI for Additive Manufacturing is focussed on these 3 things; 1)Traceability and capture of data across AM Value Chain 2)Efficient data analytics to enable the technical decision-making process 3)Integration between AM technology and simulation solutions

Integrated Computational Materials Engineering (ICME) avails a set of computational tools based on fundamental understanding of Materials Science and Engineering principles. This further enables engineers to obtain and optimize the process-structure-property-performance relationships for further exploitation in design, processing and assuring performance of functional parts and products for the industry. Recently, Ansys became active in the area of ICME by leveraging its existing tools as well as building new tools and engaging in research projects within and outside the company. Ansys is developing two variants of tools, the first being a set of tools by which material information could be streamlined into the performance simulation ecosystem at Ansys and the second being another set by which existing simulation tools at Ansys could be used for generating materials information. In order to realize the above-mentioned possibilities, several action items have been pursued. First, the existing CALculation of PHAse Diagram (CALPHAD) techniques have been extended as a function of cooling rates to account for high thermal gradient and cooling rate processes ranging from Casting to Additive Manufacturing (AM) technologies and hybrid technologies such as Castforging. Second, the existing Ansys tools have been shown to simulate existing materials characterization scenarios such as conventional hardness testing, crystal plasticity based mechanical property estimations based on experimental and simulated microstructural evolutions for a variety of cooling rates, AM powder bed heat transfer models and the effect of such heat transfer on delineating design methodologies for optimal processing, performance and combinations of those. Third, a real time simulation methodology would be showcased where instead of combining and collecting data from sensors for validation of simulations, an AM based case study would be shown where the rhs weak form in Finite Element has been computed using sensor data leading to real-time simulation outcomes in the volume which further leads to better computation on evolution of nonlinear material properties as well as paves the way for simulation assisted feedback control systems in AM in the future. The same methodology has been also extended to NISAR earth interior displacement simulations based on surface data from simulated NASA inputs. Fourth, our current ongoing research partnerships in the area of ICME will be explored in greater details alongwith our current ongoing efforts in ICME based understanding of disease causing species and their interactions with nanomedicine.

Material characterization leads to a significant effort in many companies. This is particularly true if the material can be varied in some way, be it on purpose or not. A classic example are composite materials. There the user can combine different fibers and matrix systems leading to different composite materials. In addition, also the fiber volume fraction plays a key role with respect to stiffness and strength of the resulting material. However, we are not limited to classical composites. For instance, the infill structures in additive manufacturing can also be treated as (meta-)material to ensure an efficient simulation. Also in this case, you have some choices that influence the macroscopic material behavior: you can choose between different (lattice) structures and you can vary the relative density. Experimental testing of all combinations would be inefficient. This is where ANSYS Material Designer aims to come into play. The goal is to provide you with an environment where you can easily investigate a certain microstructure, obtain corresponding macroscopic properties, use them in a macroscopic simulation, and identify how the macroscopic loading impacts the microstructure. In addition, you could also try to optimize the design of the microstructure or the way key parameters of the microstructure vary over the macroscopic part. This all allows to reduce the amount of experimental testing and replace it by virtual material testing, thus obtaining results quicker and cheaper.

This presentation details how accurate and consistent material definitions and properties are important for multiphysics simulations, and how Ansys Materials solutions can provide them.

ANSYS Additive Science enables users to optimize their machine process parameters via prediction of meltpool dimensions, porosity and microstructure. This can dramatically reduce the time and cost needed to develop new process parameters for metal additive manufacturing machines.