dynamic software & engineering CAE-Software Sensitivity analysis Multiobjective optimization Multidisciplinary optimization Robustness evaluation Reliability analysis Robust Design Optimization (RDO) optiSLang optiSLang multiPlas ETK SoS ANSYS optiSLang ANSYS optiSLang Combining powerful parametric model capabilities with Robust Design Optimization ANSYS Workbench provides leading technology for parametric and persistent CAD and CAE modelling for simulation driven product development. optiSLang focuses on effi ciency and automation of RDO methods for complex non-linear analysis models with many parameters, including stochastic variables. ANSYS Workbench project screen with the three optiSLang drag and drop modules sensitivity analysis, optimization and robustness evaluation to defi ne an RDO analysis. This includes robust handling of design failures and solver noise. ANSYS optiSLang offers several modes of interoperability • using parametric ANSYS Workbench models with the In addition ANSYS optiSLang provides eral cores with ANSYS Remote Solve Manager and the use • using the optiSLang Workbench plugin to expose optiSLang GUI with the help of the optiSLang ANSYS • Flexible text-based interfacing tools that can connect of ANSYS HPC Pack Parametric Licenses for simultaneous optiSLang technology within the ANSYS Workbench GUI Workbench integration node ANSYS products, or any scriptable products, that may be computing of different designs are fully integrated. With • using text-based interfacing between ANSYS and optiS- part of your engineering process the continue crashed session option, further processing of Lang The optiSLang Workbench plugin toolbox includes the • Interfacing between optiSLang GUI and parametric aborted analyses is secured using all previously computed modules for sensitivity analysis, optimization and robust- ANSYS Workbench models via optiSLang’s ANSYS Work- data. All successful designs are stored in optiSLang’s data- ness evaluation which can easily be dragged and dropped bench integration node base and can be used independently from the ANSYS Work- onto the desktop to form an interactive process chain. • Easy to switch from Workbench integrated to interfacing bench design table. Furthermore, adding designs or recal- mode at any time culation is possible at any time. • Full functionality, including support for parameters and optiSLang Workbench plugin - General Features responses not extractable or integrated in ANSYS Work- • Automatic identifi cation of important parameters dur- bench, e.g. non-scalable responses such as load displace- ing sensitivity analysis ment curves • Automatic buildup of best possible regression functions • Flexilbity to include 3rd party solvers, along with ANSYS (meta models) having best possible forecast quality to technology, in the process chain response variation in a given sample set • Multidisciplinary and multiobjective optimization • Robustness evaluation Application • optiSLang’s minimalist philosophy reduces the number From ANSYS Workbench version 14.0, the optiSLang inte- of CAE solver runs gration can be used easily with drag and drop functional- • Designed for large numbers of parameters and non- ity. The user only needs to set up the variation space and linear RDO tasks the objectives. Then, optiSLang automatically identifi es • Predefi ned insightful and effi cient result post-processing the Metamodel of Optimal Prognosis. Afterwards, a Best- module Practice-Management generates the appropriate methods for optimization. The options for parallel computing at sev- Wizard to select the most appropriate optimization algorithms Extensive GUI for MOP-Postprocessing 1 www.dynardo.de 2 ANSYS optiSLang PRODUCT OVERVIEW ANSYS OPTISLANG Since market launch in 2002, optiSLang has evolved into one of the leading software solutions for CAE- based sensitivity analysis, optimization and robustness evaluation. Due to user-friendly automated workfl ows, effi cient methods and a powerful post-processing, optiSLang provides the basis for your Typical workfl ow using sensitivity analysis, optimization using meta-models and validation of the best design effi cient CAE-based Robust Design Optimization (RDO). RDO in virtual prototyping The goal of CAE-based optimization in virtual prototyping tify the important scattering variables and quantifi es and making while only requiring a minimum of solver runs. appropriate functional model is chosen to result in the best is often to achieve an optimal product performance with explains result variations of product behavior. The distinc- Consequently, even RDO tasks involving a large number possible prognosis quality of variation based on a given set a minimal usage of resources (e.g. material, energy). This tive features of optiSLang provide you with a maximum of of optimization variables, scattering parameter as well as of designs. Here, the MOP represents the most important pushes designs to the boundaries of tolerable stresses, de- variation prognosis quality and result reliability for decision non-linear system behavior can be effi ciently solved. Dur- correlations between parameter input and result variation. formations or other critical responses. As a result, the prod- ing this process, optiSLang’s Best-Practice-Management If the prognosis quality of variation is high, MOPs can be uct behavior may become sensitive to scatter with regard feature automatically selects the appropriate optimization used to replace the CAE-calculations in optimization proce- to material, geometric or environmental conditions. Subse- algorithms and their settings. The procedures are guided dures or robustness evaluations. quently, a robustness evaluation has to be implemented in by intuitive drag & drop-workfl ows and powerful post-pro- the optimization task leading to a Robust Design Optimiza- cessing tools. Within a controlling workfl ow, any CAE simu- tion (RDO) strategy that consists of: lation data can easily be integrated and again made acces- Robustness Evaluation and Reliability Analysis sible for external solvers as well as pre and post processors. When optimized designs are sensitive toward scattering ge- 1. Sensitivity analyses to identify the most affecting pa- Thus, optiSLang gives you the opportunity to benefi t from ometry, material parameters, boundary conditions or loads, rameters regarding the optimization task the full capabilities of parametric studies in order to inno- a verifi cation of product robustness as early as possible in 2. Multi-disciplinary and multi-objective optimizations to vate and accelerate your virtual product development. the development process becomes a core requirement of determine the optimal design CAE-based virtual product development. The implementa- 3. Robustness evaluations to verify robustness values and tion of robustness evaluation procedures has always been failure probabilities Coeffi cient of Prognosis (CoP) and the a key feature in Dynardo’s software development. Today, Metamodel of Optimal Prognosis (MOP) optiSLang provides one of the most powerful sets of algo- Variable reduction and the application of reliable quanti- rithms available for commercial application. It enables the RDO with optiSLang tative measures of variable importance are the main chal- user to conduct a reliable determination of failure probabil- optiSLang expands the capabilities of parametric optimi- lenges in parametric sensitivity analysis. optiSLang’s sensi- ities by evaluating the result value variation including the zation studies to RDO. For example, the software includes tivity module generates the CoP which enables you to fi lter identifi cation and consideration of relevant scatter input the infl uence of scattering inputs, uses statistics to iden- the relevant input parameters. This ensures that the most parameters. 3D visualization of the Metamodel of Optimal Prognosis 3 www.dynardo.de 4 RDO–Methodology MASTER OF DESIGN – CAE-BASED ROBUST DESIGN OPTIMIZATION WITH OPTISLANG Sensitivity analysis, optimization and robustness evaluation with a minimum amount of user input and solver runs for your 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 (sensitivity analysis + CoP/MOP) 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) A R I A SENSITIVITY V T I O E R N OPTIMIZATION E T 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 • Effi cient methods of stochastic analysis for the U N determination of failure probabilities • Find the best fi t for simulation and measurement S • Evaluation of result value variation • Identifi cation of the relevant scatter input parameter (CoP + MOP) 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) The MoP represents the meta-model with the best progno- with the help of meta-models. Thus, a No Run Too Much- sis quality of the result value. For the determination of the strategy will be implemented with a maximum of progno- MOP, subspaces of important