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Optislang Connects Title Story // Process Integration Process Integration ABAQUS | ADAMS | COMSOL | EXCEL | FLOEFD | GT POWER | MATLAB | LS DYNA MOTOR CAD | NASTRAN | PYTHON | ROCKY | SIEMENS NX | ZEMAX ... Product Data Product Statistical Connect PLM/SDM Connect PLM/SDM Analysis TITLE STORY // PROCESS INTEGRATION Parametric Variation Analysis Calibration Sensitivity Analysis Optimization OPTISLANG CONNECTS Robustness Evaluation Metamodeling Data-based ROMs optiSLang enables the building of transparent CAE tool chains and combines them with parametric algorithms for a broader usage of CAE techniques. Fig. 1: Simulation Workfl ow Management with optiSLang One of the major challenges of today’s CAE engineers is the gineers. Also, customers are aware of how many mistakes includes interfaces to all major software tools used in the maintain complex tool chains. The modular process inte- increasing complexity of processes while results have to might occur during this process. When different variants virtual product development. For example, there are inte- gration approach of building programmable nodes provides be delivered in shorter times. At the same time, within the have to be compared for fi nding an optimal design, it can grations of CAD parametric modeling with CATIA, Siemens a very economical way to standardize and automatize CAE engineering process, multiple disciplines like NVH, thermal- lead to an unsorted crowd of data. If simulations are done NX and ProE. CAE parametric modeling is supported by link- workfl ows. There are many examples of successful projects mechanical-electrical analyses, safety evaluation, tolerance by different engineers, it can be very hard to compare the ing ANSYS, ABAQUS, ADAMS, COMSOL, FLOEFD, GT POWER, with Dynardo’s customers like Bosch, Daimler, et al. management, cost etc., have to be considered. Improving results among each other. Thus, many teams or companies LS-DYNA, MOTOR CAD, NASTRAN, ROCKY, ZEMAX and many one discipline could require a compromise in others. Coop- started to standardize their working process, resulting in others. Programming environments like EXCEL, MATLAB, PY- eration in multiphysical simulation and multidisciplinary scripting environments with a mixture of Python, VBA, Perl, THON can also be applied (see Fig. 1). Dynardo established Example of connecting multiple disciplines optimization becomes essential for workfl ows to manage Bash, Matlab, etc. partnerships with many CAE/CAD/PLM vendors to develop With optiSLang’s GUI, a simulation workfl ow can be built different disciplines and teams. optiSLang users run multiple design variants for Robust and secure the support of these tools. graphically. Therein, any tool can be connected into se- Design Optimization (RDO) and face the same issues. In the The coupling with optiSLang can be automated, either quences or put into loops. Figure 2 (see next page) shows past, engineers had to write scripts to automatize the CAE in a single solver process chain or in very complex multi- such an optimization workfl ow. The following provides a Simulation Process Management process. Nowadays, because of multiphysical challenges, disciplinary and multi-domain workfl ows. The workfl ow more detailed insight. The daily work of an CAE engineer contains a high percent- CAD and PLM systems, tasks become even more complex. management allows users to combine several tools in se- age of repetitive tasks. Such as reports and result extraction New versions of tools are released every year. All parts of quences and iteration loops. Conditions, branches or nested Cost calculation via Excel sheet or many parts of model generation. It is also quite common the system need to be connected and maintained. Already loops can be set up graphically. Workfl ows can be stored as In most applications, cost has to be minimized while func- to manually transfer results of one discipline as input into several years ago, customers in cooperation with Dynardo a template project and used again for sub-workfl ows in a tional requirements have to be optimized. Therefore, the the next step of operation. For example, copying the geom- realized that a better support of process automation is collaborative project. Multiple disciplines can be handled in two disciplines have to be kept in mind and evaluated. Of- etry into a new directory where one starts to mesh it. This needed. In order to fulfi l this requirement, optiSLang has modular ways to be available in broader systems. This helps ten the cost calculation allows simulations only to be per- also applies for the results of the post processing. Maybe emerged from an RDO-tool with powerful algorithms to a connecting different experts and teams. formed if designs are economically valid and if the cost of there are Matlab or Python scripts to process the work, Process Integration and Design Optimization (PIDO) tool. With optiSLang the overall process is getting much the design variant is not too high. Thus, expenses and ben- sometimes it would mean copy & paste or even retyping Thus, the software platform connects tools of customers to more transparent compared to scripting solutions. As a tool efi ts of simulation have to be fi gured in a balance. into Excel sheets. This procedure is well-known to CAE en- automatize their processes of design evaluation. optiSLang for Simulation Process Management (SPM), it can build and 2 RDO-JOURNAL // ISSUE 1/2019 3 Title Story // Process Integration Title Story // Process Integration Sensor Data Fig. 2: Optimization workfl ow in optiSLang considering structural costs and metric of performance map, running several solvers and using HPC Publish Workfl ow Process Execution Inhouse code evaluation How can CAE expertise be forwarded to other User & Project Management This part of the workfl ow (see upper part Fig.2) starts also team members? with the same parameters like in the excel sheet but, in this In the past, there was a relatively small group of CAE engineers case, the calculation takes longer to solve – so it is submit- with such a high level of expertise to perform key analysis and Fig. 3: Digital Twin - Calibration workfl ow ted to a Linux-cluster and runs multiple design points si- useful data extraction with tools which were diffi cult and high- multaneously. The results are automatically extracted and ly manual to handle. Democratization in CAE-based product de- forwarded to a successive Matlab node. velopment should mean to make analysis tools and results ac- cessible, not only to analysts and advanced simulation experts Start in web browser (no local installation needed) an upload measured curve Load case calculation for given geometry variation but also to all engineers involved in the development process. This part (see lower part of Fig. 2) consists of a nested Thus, collaborative work within CAD/CAE teams, which are re- chain, which briefly builds a performance grid for each sponsible for different “physics” or disciplines of the product, geometry variation proposed by the optimizer. Therefore, can share results and processes. optiSLang provides powerful a custom Design of Experiment (DOE) is used. The design interfaces to publish created RDO and CAE workfl ows. The pow- points are then forwarded to optiSLang’s MOP algorithm er of optiSLang’s RDO and Simulation Process Management can for metamodeling. Based on meta-models, the worst load- be easily integrated into customized platforms. ing case for the geometry is searched by optiSLang’s opti- mizer. The worst case scenario is forwarded to the Matlab Example of a collaborative workfl ow node. How such an integration works can be explained using the ex- ample of a digital twin. A machine has to be analyzed to fi gure Post processing out when to maintain or repair it. For this reason, sensors are The Matlab node takes the results of 3 prior parts of the installed. However, this measured data does not provide in- workfl ow and combines them for the optimization task. formation how the performance of the machine is effected in Additionally, some graphics are automatically produced. All the future. Therefore, sensor data has to be combined with 3D- the results are forwarded to the optimizer which can now simulation for a continuously updated calibration of important evaluate them and generate the next iterations until they machine parameters. As a result, necessary information can be converge. extracted to optimize maintenance cycles. Fig. 3 shows such a Get fi tted parameters automatically If the optimizer fi nds optimal designs, a report is gener- published workfl ow. The workfl ow receives the sensor data and Results are send via mail and automatically stored ated and send via mail. The whole process runs automati- calibrates the machine condition. Once this data is available, a cally. In this way, the Excel sheet and the CATIA model is up- condition check can be performed when maintenance will be Fig. 4: In-fi eld engineer use calibration workfl ow dated for each variation. Subsequently, ANSYS Workbench required. With such a workfl ow any engineer just has to: will be started. As additional benefi ts, optiSLang is capable of handling failed designs and economically controls the • download the sensor signal as a template with only the sensor data as a placeholder. Then, Authors // maximum runtime of a solver. • start the workfl ow the data is uploaded to a Process Execution & Data Manage- David Schneider, Henning Schwarz (Dynardo GmbH) Supporting collaborative, fl exible and standardized • forward the results to other team member ment System (see Fig. 3). Every team member can use this proce- workfl ows, optiSLang is the platform for effi cient, future- dure in a Process Execution System. In-fi eld engineers just need oriented CAE-teamwork. To further automate the process, the user defi nes the workfl ow a browser to have access to all data published in the workfl ow. 4 RDO-JOURNAL // ISSUE 1/2019 5.
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