Research Field
Addressing the critical needs for independent R&D of advanced power and energy equipment, the Digital Twin Research Center aims to:
■ Break through key technologies in digital intelligence-enabled simulation, design, manufacturing, virtual-real interaction testing via digital twins, and intelligent operation & maintenance.
■ Independently develop intelligent digital simulation/design software and digital twin testing/intelligent O&M platforms.
■ Establish an intelligent R&D system for advanced power and energy equipment based on digital intelligence empowerment.
These advancements will:
■ Drive paradigm-shifting innovation across the full lifecycle R&D of advanced power equipment.
■ Provide comprehensive support for the design, manufacturing, testing, operation, and maintenance of power and energy systems.
Research directions:
■ Digital Intelligence-Enabled Power and Energy Equipment
Addressing our country’s critical need for an independent innovation capability system in power and energy equipment, the center aims to develop a proprietary software suite for R&D of digital intelligence-enabled equipment, including high-fidelity numerical simulation, AI-aided simulation, AI algorithms, generative AI models, digital intelligence-enabled design, digital twin-based virtual-real interaction testing, intelligent operation & maintenance
■ Aerodynamic Stability Mechanisms and Control of Gas Turbine Engines
Targeting the imperative for wide-range stable and efficient operation under extreme conditions, the center investigates aerodynamic instability mechanisms and control strategies, encompassing compressor instability mechanisms and early warning methods under extreme conditions, stability enhancement mechanisms and intelligent stability extension design, intelligent flow-field feature identification and control for variable operating conditions, instability prediction and intelligent control systems.
Team Building
The center comprises 10 staff members, including 3 senior research scientists (full professorship equivalent), 1 associate research scientist, 3 postdoctoral researchers and 19 graduate researchers.
Project and Major Achievement
There are 16 ongoing projects, including 8 new projects.
2024 Research Focus:
Aligned with institutional R&D priorities for power and energy equipment, we advanced seven core technical domains: (1) Turbomachinery Internal Flow Field Simulation, (2) AI-Driven Performance & Flow Field Prediction, (3) Generative Aerodynamic Design for Turbomachinery, (4) Intelligent Stability Enhancement Design for Compressors, (5) Parametric Modeling and Rapid Cooling Effectiveness Prediction for Turbine Double-Wall Cooling, (6) Cross-Medium Aerial Propulsion System Design, (7) Digital Twin-Enabled Interactive Visualization Methodologies.
Digital Intelligence-Enabled Power & Energy Equipment R&D
■ Turbomachinery simulation platform development: completed software architecture refinement, SRS documentation, and UI prototype; established digital twin models for aero-engine performance/flow field simulation; developed integrated turbomachinery aerodynamic design framework.
■ Generative design breakthroughs: proposed agent-based collaborative design paradigm; built reinforcement learning-enabled generative architecture; implemented generative design for naval gas turbine compressor rotor.
■ Academic leadership: Chaired CASFIS: A Youth-Led Vanguard International Symposium, 1st Artificial Intelligence for Fluid Dynamics and Turbomachinery Conference: Design, Modeling, and Analysis.
Digital Twin-Enabled Virtual-Physical Testing
■ Lightweight flow prediction technology: developed deep symmetric attention networks for rapid flow field prediction; created “Row-by-Row Modeling & Slice Processing” database strategy; reduced 3D complexity via 2D-to-3D conversion.
■ Multi-scale validation: achieved rapid prediction for single blade row, full compressor stage, three-stage compressor.
■ Interactive visualization:implemented real-time performance/flow field visualization; demonstrated aircraft-engine integrated virtual testing.
Gas Turbine Aerodynamic Stability
■ Instability mitigation methods: tip leakage flow-driven stall warning system; body force model for distortion stability prediction.
■ Experimental validation: casing treatment stability enhancement (1.5-stage naval compressor); non-axisymmetric endwall design verification (heavy-duty gas turbine).
■ AI-driven design: intelligent casing treatment optimization for naval compressor.
Patents and Paper
In 2024, the center published 13 academic papers, among which 8 were indexed in SCI and 5 in EI databases. Additionally, 12 invention patent applications were filed, with 4 patents granted and 4 software copyrights registered.
International Cooperation and Exchanges
2024 Key Academic Initiative:
In collaboration with the Bureau of International Cooperation, Chinese Academy of Sciences (CAS) and the CAS Hong Kong Institute of Innovation, we convened the CAS Early-Career International Academic Forum. This assembly united 43 scholars from 22 institutions across 10 countries/regions, including China, UK, France, Germany, Japan, South Korea, Australia, and New Zealand.
The forum featured:
■ 26 plenary and keynote lectures;
■ In-depth scholarly discourse on 4 strategic topics;
■ Delivery of cutting-edge international expertise and technical insights supporting CAS major scientific missions.
Dean: DU Juan 86-10-82543272 dujuan@iet.cn
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Laboratory