Optimal Numerical/Experimental Assessment on GFRP for Wind Turbine Blades in Repairing Process utilizing Photo Polymerizable Resin

Fiber reinforced plastics are drawing significant attention to the renewable energy sectors in terms of their excellent specific strength, modulus and stiffness properties as well as outstanding processability, corrosion, and chemical resistance. Wind turbine blades, which inherent GFRP main bodies, are the representative practice for generating renewable energy. While the wind turbine blades may inevitably be damaged due to the harsh service environments like low temperature, acidic moisture, and intense UV irradiation. Remediations on a damaged wind turbine are indispensable to preserve the integrity, guarantee the strength, and expanse the service life. The photo polymerizable resins are featured by less energy consumption, equipment requirement and curing time compared to conventional thermal polymerizable resins. During this dissertation, the photo polymerizable resins were employed to conduct patch remediation on pre-damaged GFRP specimens. The stress and damage distributions of repaired GFRP specimens were obtained through numerical/experimental assessments to validate the remediation feasibility; properties in low temperature were investigated, which was in consideration of the relative cold service environment; durability of repaired GFRP specimens in long-term UV and acid ageing were evaluated to represent the typical service environments of wind turbine blades.List of Tables iv List of Figures v Abstract x 1. Introduction 1 1.1 Research background 1 1.2 Research objectives 6 1.3 Chapter overview 6 2. Literature Review 8 2.1 Fiber reinforced composite materials 8 2.2 Fabrication techniques of composites 9 2.3 Repair methods of composites 16 3. Experimental and Numerical Analysis of UV repaired GFRP 22 3.1 Introduction 22 3.2 Experimental works 24 3.2.1 Materials 24 3.2.2 Fundamental specimens fabrication and remediation 24 3.2.3 Characterizations 28 3.2.4 Experimental results and discussion 28 3.3 Numerical works 32 3.3.1 Material properties determination 32 3.3.2 Geometry and material properties 36 3.3.3 Intralaminar damage model 40 3.3.4 Interlaminar damage model 42 3.3.5 Simulation results of tensile test 46 3.3.6 Simulation results of bending test 55 3.4 Conclusions 64 4. Effect of Low Temperature Environment on UV repaired GFRP 66 4.1 Introduction 66 4.2 Experimental works 68 4.3 Results and discussion 69 4.3.1 Tensile and bending strength 69 4.3.2 Mold II fracture toughness 74 4.3.3 Interlaminar shear strength 76 4.3.4 Microscopic observations 77 4.4 Conclusion 81 5. Durability of UV repaired GFRP among Acidic Atmosphere 82 5.1 Introduction 82 5.2 Experimental works 84 5.2.1 Materials 84 5.2.2 Fundamental specimens fabrication 85 5.2.3 Specimens remediation and ageing conditioning 85 5.2.4 Characteristics 88 5.3 Results and discussion 89 5.3.1 Tensile and bending strength 89 5.3.2 Mode I fracture toughness 91 5.3.3 Interlaminar shear strength 95 5.3.4 Thermal analysis 96 5.3.5 SEM observations 98 5.4 Conclusions 101 6. Performance of UV repaired GFRP during UV ageing Process 103 6.1 Introduction 103 6.2 Experimental works 105 6.2.1 Materials 105 6.2.2 Specimens remediation and ageing conditioning 105 6.2.3 Characteristics 107 6.3 Results and discussion 108 6.3.1 Tensile and bending strength 108 6.3.2 Mode I fracture toughness 111 6.3.3 Interlaminar shear strength 113 6.3.4 Thermal analysis 114 6.3.5 SEM observations 117 6.4 Conclusions 120 7. Summary and Conclusions 121 Acknowledgements 125 References 126Docto

Stylized Table Tennis Robots Skill Learning with Incomplete Human Demonstrations

In recent years, Reinforcement Learning (RL) is becoming a popular technique for training controllers for robots. However, for complex dynamic robot control tasks, RL-based method often produces controllers with unrealistic styles. In contrast, humans can learn well-stylized skills under supervisions. For example, people learn table tennis skills by imitating the motions of coaches. Such reference motions are often incomplete, e.g. without the presence of an actual ball. Inspired by this, we propose an RL-based algorithm to train a robot that can learn the playing style from such incomplete human demonstrations. We collect data through the teaching-and-dragging method. We also propose data augmentation techniques to enable our robot to adapt to balls of different velocities. We finally evaluate our policy in different simulators with varying dynamics.Comment: Submitted to ICRA 202

Gradient-based Bi-level Optimization for Deep Learning: A Survey

Bi-level optimization, especially the gradient-based category, has been widely used in the deep learning community including hyperparameter optimization and meta-knowledge extraction. Bi-level optimization embeds one problem within another and the gradient-based category solves the outer-level task by computing the hypergradient, which is much more efficient than classical methods such as the evolutionary algorithm. In this survey, we first give a formal definition of the gradient-based bi-level optimization. Next, we delineate criteria to determine if a research problem is apt for bi-level optimization and provide a practical guide on structuring such problems into a bi-level optimization framework, a feature particularly beneficial for those new to this domain. More specifically, there are two formulations: the single-task formulation to optimize hyperparameters such as regularization parameters and the distilled data, and the multi-task formulation to extract meta-knowledge such as the model initialization. With a bi-level formulation, we then discuss four bi-level optimization solvers to update the outer variable including explicit gradient update, proxy update, implicit function update, and closed-form update. Finally, we wrap up the survey by highlighting two prospective future directions: (1) Effective Data Optimization for Science examined through the lens of task formulation. (2) Accurate Explicit Proxy Update analyzed from an optimization standpoint.Comment: AI4Science; Bi-level Optimization; Hyperparameter Optimization; Meta Learning; Implicit Functio

CuNeRF: Cube-Based Neural Radiance Field for Zero-Shot Medical Image Arbitrary-Scale Super Resolution

Medical image arbitrary-scale super-resolution (MIASSR) has recently gained widespread attention, aiming to super sample medical volumes at arbitrary scales via a single model. However, existing MIASSR methods face two major limitations: (i) reliance on high-resolution (HR) volumes and (ii) limited generalization ability, which restricts their application in various scenarios. To overcome these limitations, we propose Cube-based Neural Radiance Field (CuNeRF), a zero-shot MIASSR framework that can yield medical images at arbitrary scales and viewpoints in a continuous domain. Unlike existing MIASSR methods that fit the mapping between low-resolution (LR) and HR volumes, CuNeRF focuses on building a coordinate-intensity continuous representation from LR volumes without the need for HR references. This is achieved by the proposed differentiable modules: including cube-based sampling, isotropic volume rendering, and cube-based hierarchical rendering. Through extensive experiments on magnetic resource imaging (MRI) and computed tomography (CT) modalities, we demonstrate that CuNeRF outperforms state-of-the-art MIASSR methods. CuNeRF yields better visual verisimilitude and reduces aliasing artifacts at various upsampling factors. Moreover, our CuNeRF does not need any LR-HR training pairs, which is more flexible and easier to be used than others. Our code will be publicly available soon