Overview:

Engineering high-efficiency pumps requires a meticulous balance between pressure head requirements, power consumption. While Full-Fidelity CFD is the gold standard for validating these parameters, the sheer number of geometric variables—such as blade wrap angles, discharge widths, and volute curvatures—makes traditional "trial-and-error" simulation prohibitively slow.

In this webinar, we introduce the SHIFT Pump, a Physics-AI surrogate designed to predict complex internal flow characteristics instantly. Trained on high-fidelity simulation data, this model replaces hours of compute time with near-instantaneous inference.

By integrating an optimizer directly with the SHIFT Pump Model, engineers can automatically traverse thousands of impeller and volute configurations to find the global optimum for efficiency and total pressure head. This transition from singular simulations to AI-driven optimization allows for the rapid development of custom pump stages tailored to specific duty points.

What You'll Learn:

• Rapid Performance Mapping: Generating full pump performance curves in seconds rather than days.
• The SHIFT Framework: Understanding the training process for AI surrogates in internal rotating machinery.
• Automated Design Discovery: Using AI-coupled optimizers to minimize energy loss

Who Should Attend:

• Engineering and R&D leaders looking to leverage AI to reduce design cycle time and cost
• Product teams and technical executives evaluating the strategic value of foundational models in design
• Simulation, CFD engineers and Designers interested in leveraging AI/ML surrogate models for design exploration and optimization
• AI/ML researchers and practitioners working on physical simulations or model training
• Anyone curious about how agentic AI and Physics AI are reshaping engineering design