分类： 机械工程 >> 机械工程其他学科 分类： 计算机科学 >> 计算机软件 提交时间： 2022-02-16
摘要： 近年来，数字孪生受到了广泛关注，数字孪生也正在变得越来越复杂。目前的数字孪生案例大多集中在一个特定的场景上，面对多层次多场景的工作环境，甚至模型的交互与耦合，仍缺乏构建复杂数字孪生的方法。本文提出了一种标准化的基于模型分割和组装的复杂数字孪生模型的建模方法。首先，将数字孪生的复杂模型按照4C架构中的层次(Composition)、场景(Context) 、组件(Component)和代码(Code)划分为若干简单模型。层次和场景使数字孪生专注于特定尺度和场景中的有效元素。组件和代码用于开发简单数字孪生模型。其次，通过信息融合、多尺度关联、多场景交互，将数字孪生的简单模型组装成复杂模型。本体模型构建了不同数字孪生中实体的完整信息库。知识图谱在不同尺度的数字孪生之间架起了关系的桥梁。场景迭代实现行为交互和计算结果精度的提高。本文提供了一种可实现的方法来构建复杂的数字孪生模型，并支持组件和代码的复用促进数字孪生的快速开发。
分类： 水利工程 >> 水利工程其他学科 提交时间： 2024-04-01
摘要： A new digital twin (DT) framework with optimal sensor placement (OSP) is proposed to accurately calculate the modal responses and identify the damage ratios of the offshore jacket platforms. The proposed damage identification framework consists of two models (namely one OSP model and one damage identification model). The OSP model adopts the multi-objective Lichtenberg algorithm (MOLA) to perform the sensor number/location optimization to make a good balance between the sensor cost and the modal calculation accuracy. In the damage identification model, the Markov Chain Monte Carlo (MCMC)-Bayesian method is developed to calculate the structural damage ratios based on the modal information obtained from the sensory measurements, where the uncertainties of the structural parameters are quantified. The proposed method is validated using an offshore jacket platform, and the analysis results demonstrate efficient identification of the structural damage location and severity.
分类： 核科学技术 >> 辐射物理与技术 提交时间： 2024-05-08
摘要： Accurate and efficient online parameter identification and state estimation are crucial for leveraging Digital Twin simulations to optimize the operation of near-carbon-free nuclear energy systems. In previous studies, we developed a reactor operation digital twin (RODT). However, non-differentiabilities and discontinuities arise when employing machine-learning-based surrogate forward models, challenging traditional gradient-based in verse methods and their variants. This study investigated deterministic and metaheuristic algorithms and developed hybrid algorithms to address these issues. An efficient modular RODT software framework that incorpo rates these methods into its post-evaluation module is presented for comprehensive comparison. The methods were rigorously assessed based on convergence profiles, stability with respect to noise, and computational performance. The numerical results show that the hybrid KNNLHS algorithm excels in real-time online applications, balancing accuracy and efficiency with a prediction error rate of only 1% and processing times of less than 0.1 s. Contrastingly, algorithms such as FSA, DE, and ADE, although slightly slower (approximately 1 s), demonstrated higher accuracy with a 0.3% relative L2 error, which advances RODT methodologies to harness machine learning and system modeling for improved reactor monitoring, systematic diagnosis of off-normal events, and lifetime management strategies. The developed modular software and novel optimization methods presented offer pathways to realize the full potential of RODT for transforming energy engineering practices.
点击量
待评议
点击量
下载量
评论
通过
