Advanced Design Technology to Showcase Breakthrough Physics-Enhanced Machine Learning - ASME 2026

LONDON - June 8, 2026 - PRLog -- Advanced Design Technology Ltd. (ADT), the global leader in advanced aerodynamic and hydrodynamic 3D inverse design software, is proud to announce its participation in the upcoming ASME Turbo Expo 2026 Turbomachinery Technical Conference & Exposition, taking place from June 15–19, 2026, in Milan, Italy. At Stand A33, ADT will unveil its revolutionary framework for Physics-Enhanced Machine Learning (PEML), directly addressing the industry's critical data bottleneck in the drive to more automated turbomachinery design.

Overcoming the AI Data Bottleneck via 3D Inverse Design

Traditional data-driven machine learning models require massive datasets—often spanning thousands of costly, high-fidelity Computational Fluid Dynamics (CFD) simulation runs—simply to "learn" basic physical laws and fluid constraints. This computational penalty is severely exacerbated by conventional CAD-based geometric parameters, which alter surfaces blindly and yield non-physical or unmanufacturable results.

ADT's TURBOdesign Suite resolves this bottleneck by deploying 3D Inverse Design as a foundational, physics-guaranteed filter for AI training. Rather than adjusting raw geometric coordinates, the framework uses aerodynamic loading parameters and circulation distributions to directly compute the 3D blade shapes. This methodology yields fundamental advantages:

• 20x Dimensionality Reduction: The number of parameters required to describe a fully 3D turbomachinery blade is up to 20 times less than when using traditional geometry-based direct design methods.
• Guaranteed Physical Consistency: Because the inverse design formulation inherently matches the specified work input and mass flow rate, the search space is limited entirely to valid, high-performing designs from the very first iteration.
• Lean-Data Machine Learning: By eliminating non-physical designs from the matrix, ADT's proprietary Reactive Response Surface (RRS) optimizer builds exceptionally high-accuracy surrogate models using fewer than 100 high-fidelity CFD training samples, rather than tens of thousands. Hence reducing the training time by two orders of magnitude.

Universal CAE and Multidisciplinary Integration

To further accelerate industrial deployment, the latest release introduces universal integration with leading commercial CAE suites, enabling automated synthetic data generation factories. TURBOdesign1 features direct, automated coupling for all turbomachinery applications into Ansys Fluent, alongside established workflows for Ansys CFX, Siemens Simcenter STAR-CCM+, and Cadence Fidelity/Fine Turbo. The system seamlessly handles meshing orchestration, execution, and automatic extraction of training data maps back into the design view.

Technical Paper Presentation: Machine Learning based Optimization of a LH2 Turbopump with Combined Inducer-Impeller Configuration

For more information, or to schedule an advance demonstration ahead of the exhibition, please visit: https://info.adtechnology.com/asme-turbo-expo-2026.