dynamic software & engineering CAE-Software Model Calibration Sensitivity Analysis Optimization Robustness Evaluation Robust Design Optimization Metamodeling optiSLang Product Overview PRODUCT OVERVIEW Since its market launch in 2002, optiSLang has developed into one of the leading universal software platforms for CAE-based optimization in virtual prototyping. Based on design variations or measurement and observation points, effi cient variation analyses can be performed with minimal user input and few Typical workfl ow using sensitivity analysis, optimization, meta-models and validation of the best design solver calls. optiSLang supports the engineer with: Metamodeling with optiSLang • Calibration of virtual models to physical tests signs to the boundaries of tolerable stresses, deformations optiSLang’s expands parametric design studies to impact of input variability to the responses. For the identifi ca- • Analysis of parameter sensitivity and importance or other critical responses. As a result, the product behavior metamodeling based on a set of virtual design points, experi- tion process, optiSLang’s Coeffi cient of Prognosis (CoP) quanti- • Metamodeling may become sensitive to scatter with regard to material, mental design responses or fi eld observations. The software fi es the prognosis accuracy of the meta-model regarding the • Optimization of product performance geometric or environmental conditions. Subsequently, a automatically identifi es the corresponding input parameters response variation of a data set. The result is the Metamodel • Quantifi cation of product robustness and reliability also robustness evaluation has to be implemented as a Robust for every response variation. Furthermore, the best possible of Optimal Prognosis (MOP). This procedure fulfi lls the most referred as to Uncertainty Quantifi cation (UQ) Design Optimization (RDO) strategy consisting of: functional meta-model is identifi ed to adequately describe the important tasks in metamodeling: • Robust Design Optimization (RDO) also referred as to Design for Six Sigma (DFSS) • Sensitivity analyses to identify the most affecting opti- • Avoidance of overfi tting mization parameters regarding the optimization task • Identifi cation of the most corresponding meta-model In the past, one of the largest bottle necks for a success- • Multi-disciplinary and multi-objective optimizations to • Reduction of complexity in large dimensions of variability ful simulation driven product development was to create a determine the optimal design workfl ow including all necessary components for paramet- • Robustness evaluations to verify design robustness and By automatic reduction to important parameter spaces, ric design and system evaluation in one standardized pro- reliability the procedure ensures the creation of meta-models with a cess. Therefore, since version 4, optiSLang has been expand- minimum of virtual design points. Consequently, even tasks ing to a powerful simulation workfl ow environment. This optiSLang’s Best-Practice-Management supports this strat- involving a large number of optimization variables, scatter- makes the software the perfect tool for simulation driven egy by an automatic selection of the appropriate sensitivity, ing parameter as well as non-linear system behavior can be workfl ow generation using parametric models for sensitiv- optimization and robustness algorithms and their settings. effi ciently solved. ity analysis, optimization and robustness evaluation. The procedures are guided by intuitive drag & drop-work- fl ows and powerful postprocessing tools. Within optiS- Lang’s workfl ow building environment, any parametric CAD Robustness Evaluation and Reliability Analysis Simulation based product optimization in or CAE model can be easily integrated. Thus, the engineer optiSLang provides powerful sets of stochastic analysis al- virtual prototyping extensively benefi ts from the capabilities of parametric gorithms. It enables the user to conduct a reliable determi- The predominant aim of CAE-based optimization in virtual modeling and design studies in order to innovate and ac- nation of failure probabilities by evaluating the result value prototyping is often to achieve an optimal product perfor- celerate virtual product development. variation, which includes the identifi cation and consider- mance with a minimal usage of resources. This pushes de- 3D visualization of the Metamodel of Optimal Prognosis ation of relevant scatter input parameters. 1 www.dynardo.de 2 Variation Analysis OPTISLANG – GENERAL PURPOSE TOOL FOR VARIATION ANALYSIS Sensitivity analysis, optimization and robustness evaluation based on multiple designs, measurement or observation points with a minimum of user input and solver runs for an effective virtual product development. A U T L T S O M A F A U T E • Identifi cation of the relevant input parameters and D E D L M response values based on sensitivity analysis A O • Stochastic sampling (LHS) for optimized scanning M D • Pre-optimization of the parameter sets with MOP T I U of multi-dimensional parameter spaces P L without additional solver runs O A • Quantifi cation of prognosis quality (CoP) of meta-models R • Further optimization of the parameter sets with the most • Generation of the Metamodel of Optimal Prognosis (MOP) appropriate algorithms (Best-Practice-Management) V A R I A SENSITIVITY T I R O OPTIMIZATION E N T E ANALYSIS M A R A P L CAE-Data A M I Optimal Design N I M L E S S S O L Measurement V Data E R - MODEL CALIBRATION R U N • Effi cient methods of stochastic analysis for the • Find the best fi t for simulation and measurement S determination of failure probabilities • Evaluation of result value variation • Identifi cation of the relevant scatter input parameter Coeffi cient of Prognosis (CoP) The CoP quantifi es the forecast quality of a meta-model (re- gression model) for the prognosis of a result value. ROBUSTNESS EVALUATION Metamodel of Optimal Prognosis (MOP) variables are evaluated with different meta-models. Thus, The MOP represents the meta-model with the best prog- a No Run Too Much-strategy is implemented with a maxi- nosis quality of the result value variation. For the determi- mum of prognosis quality regarding the given number of nation of the MOP, different subspaces of important input design evaluations. 3 www.dynardo.de 4 S Process Integration and Automation S E C O TIVIT R SI Y N A O P E N - S A P E L T Y I A S M C I I S Z A T IO N DoE Virtual Product manual HiL Calibration Simulation = Hardware ES S Connect PLM Connect PLM TN E S VA Digital Twin U L B U autom. complex CAx workfl ow O A R T Laboratory I Development O parametric studies, RDO, … N CAx 3rd party Product CAE-Data Production PROCESS INTEGRATION AND AUTOMATION Interactive process automation and integration as well as access to best possible parametric simulation Statistical Data-based ROM models are the key for successful CAE - based parametric studies. In optiSLang, this procedure is guided Analysis and supported by wizards and default settings. Process integration optiSLang supports the interfacing to any software tool used are displayed at the same time. This enables full access and custom applications is secured and optiSLang projects can be • Meta-models in virtual product development which can be externally traceability of the complete workfl ow. The user can connect any integrated into customized platforms. Repetitive and perva- • Tool integrations called. Interfacing is either offered by text fi le based or pre- complex simulation process of CAE solvers, pre- and postproces- sive tasks can be standardized and automatized. • Database connections defi ned tool integrations. Nowadays, more than 100 differ- sors in heterogeneous networks or clusters. They are automa- ent CAx/PLM software solutions are coupled with optiSLang. tized either in a single solver process chain or in very complex Current requirements for fl exibility and upcoming requests The new generation of optiSLang gives access to: multidisciplinary / multidomain fl ows. Even performance maps Extensibility for extensibility are satisfi ed by those interfaces. Therefore, and their appraisal can be part of standardized projects. The openness of optiSLang also enables users to plug: optiSLang is the platform to address future needs of para- • CAD (Catia, Nx, Creo, Solidworks …) • Algorithms for DOE, Optimization, Robustness etc. metric and simulation driven virtual product development. • CAE (ANSYS, Abaqus, AMESim …) • MS Excel, Matlab, Python … Integration of optiSLang into parametric model- • PLM (EKM, Teamcenter, Subversion ...) ing environments • In-house solver The modular structure of optiSLang supports the direct in- tegration of its modules into standard parametric modeling Different parametric environments can be collected and environments. This framework allows the seamless integra- combined to one automatized parametric workfl ow for tion of optiSLang into, e.g., ANSYS Workbench/AIM, Excel or simulation driven product development. SimulationX. Here, users do not have to leave their paramet- ric modeling environment and can access optiSLang mod- ules through a minimum of user input. Defi nition of CAx Workfl ows The graphical user interface supports the workfl ow approach Interfaces and automation visually by single building blocks and algorithms which are optiSLang provides Python, C++ and command line interfac- graphically coupled in order to show dependencies and schedul- es to allow the automatic creation, modifi cation, execution ing. The relationships can be determined