J13
Simulation Governance

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15:30
conference time (CEST, Berlin)
TE Connectivity (TE) - Simulation Strategy
27/10/2021 15:30 conference time (CEST, Berlin)
Room: J
H. Brück, R. Gollee, (TE Connectivity GmbH, DEU)
H. Brück, R. Gollee, (TE Connectivity GmbH, DEU)
The presentation will discuss the vision and goals behind TE's global 5 year strategy for simulation and the elements we are considering including Headcount, Training, Cloud Simulation, Investments in Artificial Intelligence and other Software, Material Testing, Partnering with Universities, Software Vendors etc.. In general TE's strategy is based on 3 pillars: 1) Scale Agile Simulation/Democratization of Simulation 2) Advance Expert Product Perfomance as well as Process Simulation 3) Differentiate ourselves by providing online simulation tools for customers Related to Agile simulation, the presentation will show how the overall program that is touching hundreds of engineers is designed, whcih tools are used, how success is measured and the progress we have made so far. For Expert Simulation we will discuss the process on how our Roadmap was put together and we will introduce the Roadmap itself with a few selected examples. We will also discuss what measures are taken to improve prediction accuracy of our simulations and how Digital Twins are build to calibrate and align our models to reality. In terms of differentation we will introduce Simulation Apps that we provide to our customers. The first app that will be discussed helps our customers to better understand thermal behaviour of our products regarding load current and ambient temperature. The second app allows customers to check signal integrity of High Speed Data Links for Automotive Applications including geometrical tolerances, material variations and environmental effects. We will also touch on projects like generative design, expalin where we are using this technology and which influence this will have on simulation in the future. As our 5 year strategy is also an organizational change management effort we will discuss how we are adresing this as well. Last but not least we will talk about the Key Perfomormance Indicators that we will monitor as lead measure to improve Time to Market and reduce overall cost.
Simulation Strategy, Agile Simulation, Simulation Apps, Generative Design, Artificial Intelligence, Digital Twin, Cloud based Simulation
15:50
conference time (CEST, Berlin)
Mesh Convergence Study on a Notched Specimen Under Axial Load for the Evaluation of Maximum Stress and Stress Gradient on the Surface
27/10/2021 15:50 conference time (CEST, Berlin)
Room: J
E. De Tomaso (TU Darmstadt, DEU); J. Baumgartner (Technical University of Darmstadt, Mechanical Engineering Department, Research Group SAM -Fraunhofer LBF, Institute for Structural Durability and System Reliability, Darmstadt, DEU)
E. De Tomaso (TU Darmstadt, DEU); J. Baumgartner (Technical University of Darmstadt, Mechanical Engineering Department, Research Group SAM -Fraunhofer LBF, Institute for Structural Durability and System Reliability, Darmstadt, DEU)
In many applications in industrial field, bolted joints are often subjected to a combination of axial and bending loads, which cause stress concentrations in the thread root and in the transition from bolt shank to bolt head. The design of bolted joints is typically based on nominal stress, i.e. these local effects are not taken into account. However, in order to properly analyze the fatigue strength of a bolted joint, multi-axial stress state and support effects due to stress gradients cannot be neglected. Within numerical investigations, the evaluation of the local stress at the notch root requires a fine discretization of the finite element model in the notch area, which leads to a high amount of calculation time, especially in case of 3D simulations. To ensure that the results of the finite element analysis are not affected by size and shape function of the elements, the convergence behavior needs to be considered. The aim of the work is a study on the relationship between 1) mesh density and shape function used to model highly notched specimens and 2) the evaluated maximum stress and stress gradient on the surface. Numerous mesh configurations with different element sizes have been investigated in the notch area and compared with an extremely dense mesh. The convergence of maximal stress and stress gradient has been analyzed and discussed. The results show that the convergence rate for the maximum stress is much better than that for the stress gradient. However, in terms of computation time, it is usually not advantageous to increase the size of the finite element model of a bolted joint with a very dense mesh. Hence, a less dense mesh is proposed, which allows a good evaluation of the stress gradient, even if a deviation compared to the results from extremely dense mesh occurs.
Bolted joints, fatigue, stress gradient, finite element analysis
16:10
conference time (CEST, Berlin)
Simulation Governance and Management
27/10/2021 16:10 conference time (CEST, Berlin)
Room: J
B. Szabo, R. Actis (ESRD, USA)
B. Szabo, R. Actis (ESRD, USA)
Advancements in predictive computational science make it possible to increase reliance of numerical simulation, necessitating fewer physical experiments for substantial savings in time and costs of product development projects. The first and perhaps the most challenging obstacle to full realization of the benefits of predictive computational science is a widespread misunderstanding of what numerical simulation is. Most managers and many individuals who present themselves as experts in numerical simulation confuse numerical simulation with “finite element modeling” or “numerical modeling“. Those are outdated concepts, responsible for much of the disappointing results that caused widespread loss of confidence in the usefulness and reliability of numerical simulation. Current simulation and data management practices will have to be revised in order to meet the technical requirements of predictive computational science. The presentation will focus on the central role of simulation governance and management in the coordination of experimental and analytical work necessary for proper use of the tools and techniques of predictive computational science with the objective to maximize the reliability of computed information. The presentation will outline the methodology of model development in the applied sciences, the essential constituents of which are the formulation, calibration and ranking of mathematical models, data and solution verification, validation and uncertainty quantification. It will be shown that consideration of the size of the domain of calibration is essential. Without such consideration just about any model, even pseudoscientific models, can be calibrated on a sufficiently small domain of calibration. The presentation will also highlight the differences between numerical simulation and finite element modeling. Understanding these concepts and procedures is an indispensable prerequisite to any successful implementation of a Simulation Governance plan. Recognizing that technology changes and the available information increases over time, planning must incorporate data management and systematic updates of simulation practices so as to take advantage of new information and advancements in technology.
verification, validation, uncertainty quantification, model development, calibration
16:30
conference time (CEST, Berlin)
Quality Enhancement by Automated Consideration of Modelling Guidelines
27/10/2021 16:30 conference time (CEST, Berlin)
Room: J
D. Friedemann, J. Rademann (Hochschule für Technik und Wirtschaft Berlin, DEU); B. Naeser (BMW Group, DEU)
D. Friedemann, J. Rademann (Hochschule für Technik und Wirtschaft Berlin, DEU); B. Naeser (BMW Group, DEU)
In todays simulation environments a number of framework conditions dominate the process of setting up a simulation model. For most FE solver codes “Best Practice” guidelines exist, that are recommended to observe in order to guarantee valid results. These guidelines differ between the solver codes, especially when it comes to numerical aspects. For complex und highly structured simulation models, which occur quite often in the automotive industry, additional modelling guidelines apply. Simulation experts from OEMs or suppliers are usually responsible to keep them in the state of the art. Within a company, these guidelines may vary, depending on the division of the company, the engineering discipline and the considered assembly group. The manual effort to meet all guidelines and standards, which apply for a specific simulation, is quite high. Therefore, this task is time consuming, complicated and prone to errors at times. Special effort is needed, when a simulation model is created for a specific task, and subsequently used for different problems. Usually the modellings guidelines will differ between different engineering tasks. Therefore, additional manual work is needed. The automated consideration of all applying modelling guidelines greatly enhances the current workflow of most development departments. In the planned presentation, a new approach is shown to store, access and consider modelling guidelines in simulation environments. The key feature of the process is a hierarchical database that contains different levels of modelling standards and guidelines which are readable for humans and machines just as well. A special benefit is drawn when it comes to multidisciplinary engineering environments. The automated consideration of modellings guidelines is an enabler for interoperability between subsequent simulation tasks. The methodology to set up and automatically consider the complete and correct set of modelling standards in SPDM environments is shown. The challange to keep a balance between strict quality assurance and avoiding limititations for simulation engineers is discussed and resolved. The corresponding workflow is illustrated with different examples. Advantages and constraints are going to be discussed.
Democratization of Simulation, Modelling Guidelines, Simulation Governance, Interoperabilty
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