Eliminate endless simulation iterations with optimization-driven design approach.
When integrating multiple CAD, CAE and in-house tools into a unified simulation process, modeFRONTIER simplifies the complexity. It combines trusted data-driven algorithms to optimize workflows, helping you streamline processes, enhance efficiency and reduce development time.
By removing barriers to workflow automation, modeFRONTIER allows you to configure multiple design optimization strategies with pre-configured scenarios, enabling you to find the best design solution faster.
Capabilities
Eliminate endless simulation iterations with optimization-driven design approach.
When integrating multiple CAD, CAE and in-house (alternative: custom) tools into a unified simulation process, modeFRONTIER simplifies the complexity. It combines trusted data-driven algorithms to optimize workflows, helping you streamline processes, enhance efficiency and reduce development time.
By removing barriers to workflow automation, modeFRONTIER allows you to configure multiple design optimization strategies with pre-configured scenarios, enabling you to find the best design solution faster.
Capabilities
Simulation workflow automation
Leverage the interaction between the engineering disciplines and determine the global optimum solution, instead of optimizing each discipline sequentially.
Distributed execution
Use available computational resources to explore the design space efficiently.
Planner
With the modeFRONTIER Planner environment, you can define multiple design exploration and optimization strategies for the same simulation workflow in a single project file.
AI-data driven modeling
Enabling the development of computationally efficient surrogate models that expedite the exploration of complex designs spaces.
Design optimization
With the self-initializing or autonomous algorithms mode, you can expect to balance the time needed to reach an optimal design with the quality of that solution. Try pilOPT, our proprietary algorithm, for one-click self-adaptive design optimization.
Data analytics
Benefit from multi-dimensional design charts and advanced data analysis tools to perform statistical assessment of complex datasets.
The foundation for effective multidisciplinary design optimization
- Boost efficiency: Direct integration with all parametric engineering solvers.
- Maximum flexibility: Custom-made connectors with any third-party software.
- Maximize the investment: Make the most of your modeling and simulation software.
Maximize your CPU power
- Remote resources: Execute workflows on HPC or clouds, supporting fluctuating workloads.
- Parallel execution: Shorten turnaround time with task partition and simultaneous design execution.
- Grid computing: Leverage software licenses and computational resources installed on other local machines.
Decouple workflow creation from evaluation strategy
- Evaluation strategy: Choose among single run, DOE, optimization or robust and reliability analysis.
- Objectives: Define one or more quantities you want to minimize or maximize.
- Constraints: Add the limits you want to respect.
Make fast, accurate design predictions
- Response surface models (RSM): Use small amounts of data efficiently to build meta-models.
- Machine learning (ML) algorithms for RSM training: Exploit your simulation and experimental datasets to build and train an effective metamodel.
- Explainable AI with reduced order models (ROM): Obtain a surrogate of the whole 3D CAE solution, while significantly reducing computational costs.
Configure optimization algorithms with one-click
- Hybrid strategy integration: Integrate multiple numerical strategies for efficient and thorough design space exploration.
- Minimal prior knowledge needed: Ideal when you have limited knowledge of variable behavior or problem characteristics.
- Computational-efficient: Minimize the number of interactions needed, ensuring optimal use of computational resources.
Make informed data-driven design decisions
- Multi-dimensional design charts: Visualize optimization trends and distributions to identify the best design candidates and interpret data more effectively.
- Sensitivity analysis: Rank design parameters to understand their influence on the outcome.
- Multi-variate analysis: Use tools like Clustering and Multi-criteria decision making to group similar data and rank design alternatives for better decision-making.
Connect to ESTECO VOLTA platform
Democratize multidisciplinary simulations across teams
Streamline expert simulation workflows and deliver automated, pre-configured processes that can be reused by a broader team of engineers for collaborative multidisciplinary design optimization (MDO) studies.
VOLTA provides modeFRONTIER users with features related to collaboration, distributed execution of designs, and traceability of optimization results.
