Gang-Won Jang

To develop a proof-of-concept digital twin framework for real-time prediction and visualization of lumbar spine biomechanics in both native and surgically treated models and to numerically verify its fidelity against coupled MBD-FEA simulations. A hybrid framework combining multibody dynamics (MBD) and finite element (FE) simulations with two sequential Kriging surrogate models was developed. Surrogate Model 1 predicted spinal loads from three-dimensional lumbar rotations; Surrogate Model 2 estimated von Mises stress and displacement from these loads for native and postoperative (TLIF + pedicle screw) spine models. Evaluation consisted of numerical verification against full-order MBD-FEA simulations and a plausibility check by benchmarking against literature-reported L4-L5 intradiscal pressure (IDP) ranges. For predefined compound 3-DOF (flexion/extension, lateral bending, and axial rotation) lumbar rotational motions, the mean absolute errors (MAEs) of nodal stress and displacement predicted by the digital twin relative to the coupled high-fidelity MBD-FEA outputs were ≤ 0.5% for the native spine and ≤ 0.2% for the postoperative spine. The L4-L5 IDP ranged from 108 to 513 kPa across all motions, indicating physiological levels overlapping with previously reported ranges (30-1530 kPa). However, the predicted values were lower than some reported maxima in flexion (approximately 1500 kPa). This framework demonstrated strong predictive performance in terms of numerical agreement, indicating how faithfully the surrogate model reproduces the high-fidelity coupled MBD-FEA reference solutions, and presented a proof-of-concept pipeline capable of real-time execution and visualization. However, experimental and clinical validation of the accuracy of IMU-derived segment kinematics and in vivo biomechanical outputs should be performed in future work.

The automated synthesis of planar linkage mechanisms has long been a challenge in mechanism design, requiring both geometric feasibility and motion accuracy. Recent advances in data-driven and neural network–based methods have shown promise in automating linkage synthesis, improving efficiency and scalability compared to traditional analytical or optimization-based techniques. Nevertheless, existing data-driven approaches remain limited in handling diverse joint configurations and often suffer from unstable optimization when refining linkage geometries. This study proposes a unified framework that combines deep learning with physics-based modeling for the automated synthesis of one-degree-of-freedom planar linkages. The method integrates (1) large-scale dataset generation using a spring-connected link model (SLM) capable of representing revolute, prismatic, and pin–slot joints; (2) contrastive graph learning for efficient retrieval of candidate linkages through embedding similarity between paths and graphs; and (3) a two-stage optimization process that refines linkage geometry via global–local search. The SLM enables feasibility evaluation based on strain energy, providing smooth design sensitivities and enhanced optimization stability. Experimental results demonstrate that the proposed method achieves superior accuracy across both seen and unseen target paths, outperforming conventional topology optimization approaches. Overall, the framework offers a promising direction for automating and accelerating the design of complex linkage systems by combining deep learning with advanced optimization techniques.

This study presents a structural analysis and optimization for the lightweight design of a buoyant rotor-type permanent magnet (BRPM) generator, which was first presented in Bang (2010), and compares its structural performance to that of a conventional generator with a spoke arm-type rotor and stator. The main benefit of a BRPM generator is that it can be constructed as a bearingless drive system, free from the mechanical failure of rotor bearings, by using a buoyant rotor. Additionally, the deformation of the generator by blade vibration can be effectively suppressed using joint couplings between the blades and the rotor. For design optimization, the objective is set as the mass of the rotor and the stator, and the maximum deformation of the airgap clearance between the rotor and the stator by external forces is constrained below 10% of the gap width. The commercial software OptiStruct is used for the analysis and optimization. In this investigation, the analysis and optimization are conducted for a 10 MW wind turbine generator. However, the proposed methods can be extended to larger generator designs without requiring considerable modification. The mass of the optimized 10 MW BRPM generator is 160.7 tons (19.3 tons for the rotor and 141.4 tons for the stator), while that of an optimized conventional spoke arm-type generator is 325.6 tons.

This study presents structural analysis and optimization for the lightweight design of a buoyant rotor-type permanent magnet (BRPM) generator, which was first presented in Bang (2010), and compares its structural performances to a conventional generator with a spoke arm-type rotor and stator. The main benefit of a BRPM is that it can be realized as a bearing-less drive system, free from the mechanical failure of rotor bearings, by using a buoyant rotor. Also, the deformation of a generator by blade vibration can be effectively suppressed using joint couplings between the blades and the rotor. For the design optimization, the objective is set as the mass of the rotor and the stator, and the maximum deformation of the airgap clearance between the rotor and the stator by external forces is constrained below 10% of the gap width. The commercial software Optistruct is used for the analysis and optimization. In this investigation, the analysis and optimization are conducted for a 10 MW wind turbine generator. However, the proposed methods can be extended to larger generator designs without requiring considerable modification. The mass of the opti-mized 10MW BRPM generator is 160.7 tons (19.3 tons for the rotor and 141.4 tons for the stator) while that of an optimized conventional spoke arm-type generator is obtained as 325.6 tons.