Digital Experiment, Testing& Validation

Aims & Scope

Digital Experiment, Testing, and Verification has emerged as a transformative paradigm, effectively addressing the surging demands for efficiency, safety, reliability, and cost-effectiveness in the research and development of advanced equipment. This paradigm encompasses a wide range, from atomic-scale fabrication and micro/nano-manufacturing systems to large-scale industrial equipment and infrastructure. Fueled by the seamless integration of AI, IoT, advanced sensor, and cyber-physical technologies, Digital Experiment, Testing, and Verification offers a superior balance between rapid design iterations, stringent reliability requirements, and lifecycle budget constraints, when compared to traditional validation methods. We invite researchers, engineers, and industry professionals to submit their work on theoretical foundations, innovative methodologies, and practical applications of Digital Experiment, Testing and Verification.


The session will focus on the following points:

  • ▪Digital twin experimental validation

  • ▪Virtual ground & flight testing   

  • ▪Virtual assembly  

  • ▪Multi-source data fusion  

  • ▪Advanced sensor, remoting & IoT 

  • ▪Fault diagnosis & Expert System  

  • ▪AR, VR & MR for Digital Experiment, Testing and Verification 

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For session presentation applications, please contact the session chairs below:

Kuo Tian

Email: tiankuo#dlut.edu.cn (replace # with @)


Session Chairs

Presentations

  • Professor
    Dalian University of Technology
    Title: Digital Twin Method for High-Accuracy Structural Strength Assessment
    Abstract The digital twin modelling method is proposed for the strength assessment and performance prediction of complex structures. Firstly, a multi-source data fusion approach is introduced to integrate simulation data with sensor readings, enabling the construction of a high-accuracy and robust digital twin. Secondly, an optimal sensor placement strategy is established for complex curved structures, enhancing the monitoring efficiency of sparse sensors. Thirdly, a dynamic update method for the digital twin is developed based on a reduced order model, ensuring the accuracy and reliability of the digital twin used in practical strength assessment scenarios accounting for loading deviations. Finally, the effectiveness of the proposed method is validated through structural strength tests on open-hole panels, hierarchical stiffened plates, and cargo spacecraft cabins. The results demonstrate that the method can achieve real-time, high-accuracy, and full-field strength assessment and performance prediction of complex structures.
  • Assistant Professor
    Harbin Institute of Technology
    Title: Optimal Sensor Placement for Predicting the Load-Carrying Capacity of Composite Laminates with Delamination
    Abstract A method of optimization of sensor placement (OSP) to predict the load-carrying capacity of composite laminates with delamination is proposed. Due to unknown delamination sizes, OSP becomes an NP-hard problem, solved here using a genetic algorithm (GA). The fitness function of GA incorporates parameter identification error and relative entropy. Numerical and experimental tests on laminates with embedded delamination show that OSP outperforms uniform sensor distributions, improving parameter identification and load prediction with limited data. Meanwhile, experiments confirm strain arrays effectively detect delamination-induced local buckling. This approach provides beneficial support for the implementation of digital twins in composite structures.
  • PhD Candidate
    Dalian University of Technology
    Title: Digital Twin for Full-field Deformation Monitoring Under Dynamic Load
    Abstract To ensure the safety of the large trusses under dynamic loads, full-field deformation monitoring in real-time is urgently needed. A digital twin for full-field deformation monitoring method under dynamic load (DT-SFDR-DL) is proposed. It mainly includes two aspects. Firstly, the sensor placement mechanism is obtained to guide rapid optimal sensor placement, improving the deformation monitoring efficiency by reducing sensor numbers. Secondly, a deformation reconstruction method incorporating adaptive regularization is established, which dynamically adjusts the L2 regularization coefficient using measured strain data to enhance the accuracy of the deformation monitoring. An 8-span truss is utilized for experiment validation, and the number of sensors is reduced by 92.21% with similar deformation monitoring accuracy. Meanwhile, the deformation field reconstruction accuracy is improved by 17.72% compared with the non-regularized method. Finally, the visualization of the full-field deformation digital twin is achieved with a time consumption of less than 0.007s/frame and the accuracy is 91.09%.
  • Doctoral Student
    Dalian University of Technology
    Title: Flight parameter-load-life digital twin modeling approach
    Abstract In order to achieve high-accuracy real-time life prediction in aircraft structural health monitoring, we propose a flight parameter-load-life digital twin modeling approach. First, the flight parameter-load digital twin model based on incremental learning eXtreme Gradient Boosting (XGBoost) is trained by the measured data of flight parameters and structural loads to realize high-accuracy dynamic prediction for key part loads. Then, a fatigue life estimation model is constructed based on the Detail Fatigue Rating (DFR) method to form a flight parameter-load-life digital twin model. The parameters of the fatigue life estimation model are calculated from the prediction results of the digital twin model, and the life consumption is estimated to realize the high-accuracy dynamic prediction of the remaining life. Finally, the effectiveness of the proposed method was verified by a typical flight-testing dataset.
  • Doctoral student
    Dalian University of Technology
    Title: Digital Twin-based Real-time Monitoring and Advanced Forecasting Method for Structural Strength Experiments
    Abstract This study proposes a digital twin framework for real-time monitoring and forecasting of full-field structural strength. It integrates simulation data with sparse sensor readings via a pre-trained model and an adaptive neuro-fuzzy network to predict high-load behavior from low-load data. Validated by a hydrostatic test, the method proves efficient and accurate for structural experiments.
  • Ph.D. candidate
    Harbin Institute of Technology
    Title: Development of an Experimental Validation System for Digital Twins of Thermal Protection Structures under Service-Relevant Conditions
    Abstract Carbon/carbon (C/C) composites are widely used in thermal protection systems of hypersonic vehicles, where accurate mechanical characterization under extreme environments is critical for reliability design and digital twin modeling. This study presents a novel experimental approach for in-situ strain measurement of needled C/C composites under rapid thermal-mechanical coupling. A stable high-temperature speckle pattern was fabricated via plasma spraying and laser etching, enabling full-field strain acquisition through DIC with adaptive camera exposure. Uniaxial tensile tests under repeated rapid heating were conducted to investigate the mechanical behavior and degradation mechanisms. Macro- and micro-scale analyses revealed key insights into the damage evolution of C/C under non-steady thermal and mechanical loads. The proposed method provides a valuable framework for characterizing structural performance in reentry-like environments and supports data acquisition strategies for building digital twins of thermal protection components.
  • Ph.D. candidate
    Harbin Institute of Technology
    Title: Research on Temperature Prediction for Thermal Protection Systems Based on Dynamic Data-Driven Approaches
    Abstract Thermal Protection Systems (TPS) constitute vital components guaranteeing the safe and reliable functioning of hypersonic vehicles. Consequently, precise real-time forecasting and control of their temperature fields is imperative. This paper applies a dynamic data-driven methodology to the temperature prediction model at the TPS substrate, constructing a DDDAS framework that integrates TPS, sensor data, online simulation and active water-cooling system. The online simulation utilizes a one-dimensional nonlinear numerical model for heat transfer in alumina-enhanced thermal barrier tiles, based on the finite difference method and Kirchhoff transformation. Validation via quartz lamp radiative heating experiments demonstrates that the proposed method achieves real-time predictive control of thermal protection system substrate temperatures.
  • Ph.D. candidate
    Harbin Institute of Technology
    Title: In-situ mechanical property identification and delamination growth prediction of laminates based on a digital twin-enabled framework
    Abstract Accurate characterization of composite properties and prediction of failure mechanisms are critical for structural design and reliability. In this study, we present a digital twin-enabled framework for the in-situ identification of elastic and interfacial properties of laminates, as well as for the prediction of delamination growth. To address the ill-posed nature of inverse identification, a stage-wise response modeling strategy is combined with sensitivity-guided parameter reduction. A multi-source data fusion approach—integrating force, strain, and out-of-plane displacement—is employed to enhance the fidelity and robustness of the virtual sensing environment. The proposed framework is experimentally validated through digital twin-assisted compression testing of open-hole laminates with initial delamination. Results demonstrate its effectiveness not only in parameter identification but also in accurately predicting delamination growth behavior. This work provides a reliable path for integrating data-driven digital twins into the response prediction of composite structures.