Ansys Twin Builder is a multi-technology platform that allows engineers to create, validate, and deploy high-fidelity, simulation-based digital twins—connected, virtual replicas of in-service physical assets. The platform is an open solution that supports Hybrid Digital Twins, combining physics-based Reduced Order Models (ROMs) derived from detailed 3D simulations (from Ansys Fluent, Mechanical, etc.) with data-driven analytics (like machine learning) to provide unparalleled accuracy and predictive insights.
Key Capabilities and Benefits:
- Hybrid Analytics: Combines the power of physics-based models with real-world sensor data and machine learning to improve prediction accuracy and asset management.
- Multidomain System Modeling: Allows engineers to create hierarchical schematics of complex systems using built-in libraries (e.g., Modelica, Fluid Power) and standard languages (VHDL-AMS, C/C++, Python, SPICE).
- Reduced Order Models (ROMs): Compiles complex 3D physics simulations into compact, high-performing ROMs that can run in a fraction of the time, enabling real-time or near-real-time performance.
- IIoT Deployment: The Twin Deployer exports the digital twin model as an executable (FMU, C/C++ code) that can be deployed to the cloud, the edge, or offline, and easily integrates with major Industrial IoT (IIoT) platforms.
- Predictive Maintenance: Improves predictive maintenance outcomes, leading to maintenance cost savings of up to 20% and a potential 25% increase in product performance over the asset's lifetime.
Target Users and Use Cases:
Target users are typically engineers, R&D professionals, and asset managers in large industrial and technology companies. While widely used in aerospace, automotive, and energy, it has specific applications in the biotech/healthcare sector:
- Pharma/Biopharma Manufacturing: Optimizing drug production processes, managing complexities of scaling, and using in-line checks to monitor critical quality attributes.
- Personalized Medicine: Designing and optimizing custom bioreactors using digital twins to grow and modify cells for therapeutic purposes.
- Clinical Trials: Tracking patients in real-time to predict clinical trajectories, which can help reduce the scope and cost of trials.
- Medical Devices: Simulating medical equipment for real-time monitoring and performance optimization.