Driving Engineering with autonomous
CAE and Self-Learning Intelligence
CDH/ACE provides a powerful, user-friendly and highly automated framework that leverages the strenghts of physical and data-driven modeling.
It significanly enhances CAE simulations by optimizing designs, drastically reducing manual effort, and providing deeper, faster insights into complex systems. It is a new, groundbreaking software to seamlessly integrate the precision of traditional Computer-Aided Engineering (CAE) with the speed and scalability of Data Analytics.
This is accomplished by performing Autonomous Computational Experiments in a self-learning and self-correcting process, that automatically combines complex CAE workflows with concepts from the field of machine learning and artificial intelligence.
While currently focused on Noise and Vibration Harshness (NVH) problems, its core principles are broadly applicable across various engineering disciplines.
Advantages
Reduced Time and Cost
Streamlines processes, making them faster than manual setups and dramatically accelerating response predictions compared to relying solely on physical models.
Improved Efficiency
Optimizes simulation results and facilitates the discovery of superior solutions at an accelerated pace while minimizing manual.
Enhanced Design Space Exploration
Enables the automatic exploration of a much wider range of design possibilities.
User-Friendly Approach
Offers a more intuitive approach, making advanced CAE accessible even to engineers without extensive expertise in numerical simulation.
CDH/ACE – The best of two worlds
CDH/ACE strategically combines the strengths of two distinct modeling approaches and reduces their limitations.
Direct physical Modelling
FEM-solvers like Nastran/Optran are used.
Advantages:
- Unrivaled in revealing underlying physical mechanisms
- Crucial for identifying root causes of problems
- Gaining deep insights into system behavior
Limitations:
- Requires significant engineering expertise for setup
- Computationally expensive
- Often taking hours per simulation runDemanding substantial resources
Data-Driven Models
Machine Learning/AI technologies are used.
Advantages:
- Offers extremely fast predictions (milliseconds)
- Excels in handling numerous input parameter variations
- Suitable for realtime applications
- Can predict the behavior of complex systems even without explicit underlying physical laws
Limitations:
- Highly reliant on large amounts of reliable input-output data for training and may exhibit limited generalizability to unforeseen scenarios.
Summary
The innovative approach of CDH/ACE lies in its ability to automate running physical simulations and efficiently modify parameters, while simultaneously training data models based on these simulation results for rapid predictions.
Automated cae and ai/ml Results
Not only the CAE solver interfaces to Nastran, Optran, and Optistruct FEM solvers are automated but in addition a wide-range of result plots as well as other useful results files is being generated automatically during the iterative learn-cycles.
Applications
Advanced CAE Processes
Manages easy usage of complex simulation workflows, including a plugin concept to account for customer specific functionality.
Automated Design of Experiments (DOE)
Systematizes the design of experiments to efficiently explore the impact of various features on system performance.
Parameter Optimization and Identification:
Accurately determines optimal parameter values and identifies key parameters influencing physical system responses.
Sensitivity Analysis and Robustness Studies
Conducts in-depth analyses of system sensitivity to parameter changes and assesses overall robustness, leveraging fast machine learning model evaluations.
Model Reduction
Creates simplified, real-time predictive data models using trained machine learning algorithms.
Python Integration and Automation
Provides a flexible, open Python based platform for integrating custom tools and automating workflows.
Performance Optimization
Fine-tunes simulation and solver settings for maximum efficiency.
Key Features
- Seamless integration of machine learning algorithms with robust CAE solvers
- Its autonomous nature, capable of generating its own CAE simulation data
- Continuous self-learning and self-correction through iterative cycles, further enhancing accuracy over time
- Specialized focus and efficiency for vibration and acoustics analysis
- Fully automated interface with Nastran, Optran, and Optistruct FEM solvers
- User-friendly design requiring minimal manual intervention
- High scalability across multiple CAE servers and compute nodes, supporting up to 64 CPU cores per machine
- Integration with existing CAE queuing systems for efficient job management
- Automated generation of all relevant data and post-processing visualizations
- An open, Python-based architecture for easy customization
- An ideal solution for optimization and robustness studies when combined with Optran, AMLS and FastFRS
ADVANTAGES
- Comfortable and easy usage
- Compatible to standard Nastran and RADIOSS software
- High Accuracy
- Drastic Reduction in computer and hardware cost
- Impressive Performance leads to shorter elapsed time and higher productivity
- Reduction of FE-modeling cost by using one master model for multiple analysis disciplines e.g. Crash-, Static- and NVH-Analysis
- Make higher frequency range analysis feasible
System Requirements
HARDWARE
Intel Xeon or AMD EPYC based CPU’s with at least 8 CPU-cores and 256Gb of ECC-enabled memory.
Note:
ARM is currently not supported. Suitable GPU hardware may be utilized for machine learning training acceleration.
OPERATING SYSTEM
Linux64 (x86_64), preferred RedHat 8 or higher
SUPPORTED SOLVERS / SOFTWARE
MSC.NASTRAN, Version 2019 or newer
NX.NASTRAN, Version 2020 or newer
Altair Optistruct, Version 2019 or newer
CDH/Optran, Version 5.3 or newer
Note:
For reasons of efficiency and stability in large-scale simulations, it is highly recommended to use CDH/AMLS and CDH/FastFRS in combination with the above listed FEM solvers.
TRIAL INSTALLATION
You can try this product free of charge for 30 days.
Have any questions about this product? Feel free to get in touch.
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