Digital Experiment, Testing& Validation

Aims & Scope

Digital Experiment, Testing, and Verification has emerged as a transformative paradigm, effectively addressing the increasing demands for efficiency, safety, reliability, and cost-effectiveness in the research and development of advanced equipment. This paradigm covers a wide range of applications, from atomic-scale fabrication and micro/nano-manufacturing systems to large-scale industrial equipment and infrastructure. Enabled by the seamless integration of AI, IoT, advanced sensors, and cyber-physical technologies, Digital Experiment, Testing, and Verification provides an effective balance between rapid design iterations, stringent reliability requirements, and lifecycle budget constraints compared with traditional validation methods. This session invites researchers, engineers, and industry professionals to share theoretical foundations, innovative methodologies, and practical applications of Digital Experiment, Testing, and Verification.

The session will focus on the following aspects:

  • • Digital twin experimental validation
  • • Virtual ground and flight testing
  • • Virtual assembly
  • • Multi-source data fusion
  • • Advanced sensors, remote sensing, and IoT
  • • Fault diagnosis and expert systems
  • • AR, VR, and MR for Digital Experiment, Testing, and Verification

Session Information

This session will be held in person. Online participation is unavailable.

For any questions regarding this session, please contact secretariat@idea-global.net.

Session Chairs

Presentations

  • Professor
    Dalian University of Technology
    Title: Digital Twin Method for High-Accuracy Structural Strength Assessment
    Abstract

    To be confirmed

  • Professor
    Harbin Institute of Technology
    Title: Digital Twin Enabled Intelligent Structures: A Perspective
    Abstract to be Announced
  • Postdoctor
    Dalian University of Technology
    Title: Intelligent Operation and Maintenance of Aircraft Structures based on Digital Twins
    Abstract
  • PhD Candidate
    Dalian University of Technology
    Title: Digital Twin for Full-field Deformation Monitoring under Dynamic Load
    Abstract
  • PhD Candidate
    Dalian University of Technology
    Digital Twin for Full-field Temperature Reconstruction based on Multi-Source Data Fusion
    Abstract
  • PhD Student
    Harbin Institute of Technology
    Title: Dynamic Data-driven Real-time Monitoring and Prediction of Thermal Protection Systems in Hypersonic Vehicles
    Abstract Hypersonic vehicles face extreme aerodynamic heating,making thermal protection system (TPS) reliability critical.This research introduces a real-time,data-driven framework for predicting and monitoring TPS backside temperatures.By combining physics-informed modeling,reduced-order techniques (POD),and deep learning (FC-LSTM, TCN),sparse sensor data are transformed into accurate,full-field temperature predictions.Dynamic data assimilation with Kalman filtering ensures continuous model updating,enabling early detection of potential overheating. Experiments using heat flux simulation validate the approach,demonstrating high accuracy,rapid response,and robustness under complex thermal loads.
  • PhD Student
    Harbin Institute of Technology
    Title: Dynamic Data-driven Test Verification of Intelligent Thermal Protection System
    Abstract This study designs a synergistic mechanism for passive thermal protection under low heat flux and timely active response through sweating cooling under medium to high heat flux. A dynamic sensor data-driven model for real-time perception, localization, and online prediction of local high-heat environments was established. The study focuses on researching and constructing a predictive control method for the amount of cooling fluid used. Comparative experiments verified that the active-passive synergistic scheme based on heat insulation tiles and water sweating cooling can achieve more efficient heat protection with less cooling fluid consumption compared to a single thermal protection structure. This has reference value for improving the thermal protection efficiency of key areas and the adaptability of aircraft to dynamic long-term flight environments.
  • PhD Student
    Harbin Institute of Technology
    Title: Rapid Prediction of Residual Mechanical Properties of C/SiC Composites based on Surrogate Model
    Abstract In the evaluation of residual mechanical properties of composite materials, traditional finite element simulation methods are time-consuming and inefficient, making it difficult to meet the requirements of real-time deduction and online assessment in digital twins. To address this issue, this study proposes a surrogate model-based rapid prediction method for the residual mechanical properties of C/SiC composites. First, a dataset of residual mechanical properties of C/SiC composites under various damage conditions is established through multi-scale simulation. Subsequently, an artificial neural network surrogate model is employed to train and fit the sample data, establishing the mapping relationship between environmental variables and residual mechanical properties. Finally, rapid structural-level damage state assessment is realized in combination with engineering criteria. The results demonstrate that the proposed method enables millisecond-level rapid prediction of the residual mechanical properties of thermal structural components and can be directly integrated into digital twin systems.
  • PhD Student
    Harbin Institute of Technology
    Title: Dynamic Data-driven In-situ Residual Performance Prediction of 2D-C/SiC under High-temperature Oxidation
    Abstract