SimuTech Skills Center

ANSYS DesignXplorer is a design optimization application that operates under the ANSYS Workbench environment and incorporates both traditional and nontraditional optimization through a goal-driven approach. ANSYS DesignXplorer software provides instantaneous feedback on all proposed design modifications, which dramatically decreases the number of design iterations and improves the overall design process. The easy-to-use GUI within the ANSYS DesignXplorer program is based upon the Workbench technology. The GUI allows users to consider multiple designs so they can more quickly and efficiently create new products within their existing product lines or optimize parts for new operating conditions. ANSYS DesignXplorer software interacts with all ANSYS Workbench platform components and offers bi-directional associativity with leading CAD packages such as SolidWorks, Solid Edge, Mechanical Desktop, Inventor, Unigraphics and Pro/ENGINEER so that design changes can be quickly made to your design database. Students will learn about ANSYS DesignXplorer through lectures and relevant tutorials. After completing the course, attendees will be able to use ANSYS DesignXplorer software to study, quantify, and graph various structural, thermal and flow responses on parts and assemblies.

The Getting Started course offers you an introduction to the practical application of CAE-based sensitivity analysis, multidisciplinary optimization and robustness evaluation with optiSLang. During the training you will be informed about the possibilities of process integration and variation analysis. By means of illustrative examples, sensitivity analyses and different optimization steps as well as their results will be explained and evaluated. Here, the focus is on the user-friendly application and operation of the software.

This course page will help you to automatize your FEA workflow with optiSLang by providing different examples for e.g. Ansys Workbench, APDL or LS-DYNA. Moreover, it will show how to enlarge your analyzes methods by using HPC, Signal Processing and Pictures in the post processing of optiSLang.

This course will help you to automate your CFD workflow with optiSLang by providing different examples for e.g. CFX standalone, Fluent standalone, or inside Ansys Workbench. Moreover, it will show how to enlarge your analyzes methods by using signal processing and pictures in the post processing of optiSLang.

This course gives information about the mathematical basis of model calibration. Furthermore, the issue of relevance and quality of the identified parameters will be discussed. These methods can be applied easily for any RDO task with the help of optiSLang. A key role is the definition of signals and signal functions as well as the sensitivity analysis using the Metamodel of Optimal Prognosis (MOP). Model calibration means to adapt the results of simulation models to actual measurement data. Here, a measured response curve, e.g. a load displacement curve, is taken as a reference and parameters of the simulation model will be modified until the best correlation between reference and simulation is obtained. This method is also known as "reverse engineering". Using this methodology, parameters that cannot be measured directly, such as material parameters, are identified. Therefore, this method is called parameter identification.

This course page will help you to automatize your simulation workflow with optiSLang by providing different examples for e.g. Maxwell or HFSS inside Ansys Electronic Desktop (AEDT) or Motor-CAD and with a self-learning content demonstrating the combination of AEDT and optiSLang. Moreover, it will show how to enlarge your analyzes methods by using signal processing and pictures in the post processing of optiSLang.

The course introduces robustness and reliability assessments by probabilistic methods and the relation to sigma level or design for six sigma approaches. The application of robustness and reliability assessments will be demonstrated by using an application example of an optimized design. Virtual product development is conducted using deterministic parameters, hence under idealized conditions. Particularly in the context of product optimization, the product performance under random influences (tolerances, external influences) must be warranted. For a robustness or reliability analysis, input parameters are modelled as random variables. The result of a robustness assessment is a prognosis of the variation of the product performance. Moreover, a root cause analysis is possible by identifying the most relevant inputs influencing the scatter of the system response. Strategies for finding optimal yet robust designs are discussed. For the proof of safety requirements, optiSLang offers methods of reliability analysis, which compute the rare event of violating a ΓÇôpossibly non-linearΓÇô limit state.