Structural Dynamics Laboratory for Verification and Validation (LVV) Across Scales and Environments

用于跨尺度和环境验证和确认 (LVV) 的结构动力学实验室

• 批准号: EP/N010884/1
• 负责人: Keith Worden
• 金额: $ 95.03万
• 依托单位: University of Sheffield
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FN010884%2F1
• 关键词: Structural; Dynamics; Laboratory; Verification; Validation

Engineering structures have three distinct phases of life: design/commission, operation and decommission; each with associated costs. Advances in structural dynamics hold the key to hugely reducing the costs of the first two phases. Structural dynamic Validation and Verification (V&V) promises increased trust in physical models to the extent that much first phase testing could be moved into a virtual/computational context. Currently, V&V is almost always conducted in standard laboratory conditions rather than in real operational environments; this is a critical barrier to building trust in models. In the operational phase of life, structural health monitoring (SHM) can allow movement from a safe life design philosophy (where the structure is retired after a fixed time) to a damage tolerant one (where the monitoring allows use as long as the structure is deemed to be healthy). This allows significant reductions in engineering reserve (or safety) factors, allowing lighter, greener, safer structures. SHM also allows the possibility of extending the lifetime of existing structures (e.g. extending the lifetime of 0.7% of EU bridge inventory by 10 years would save in the region of 3 billion Euros). A major technological barrier to SHM uptake is the fact that environmental variations can be confused with damage and can thus generate false alarms, reducing effectiveness of, and trust in, the systems. Furthermore, current V&V and SHM research on the effect of environmental variations is almost always limited to small-scale structures and components, requiring a leap of faith in the extrapolation to full-scale. The facility proposed here will aim to solve the two major problems discussed above by providing a capability to test at fullscale and across realistic environments. The LVV will offer the unique capapility of taking research from its most most fundamental concepts (Technology Readiness Level 0) to the point (Technology Readiness Level 7) where it is directly accessible to immediate industrial uptake.

工程结构有三个不同的生命阶段：设计/调试、运行和退役；每个阶段都有相关的费用。结构动力学方面的进步是大幅降低前两个阶段成本的关键。结构动态验证和验证(V&V)承诺增加对物理模型的信任，以至于许多第一阶段的测试可以转移到虚拟/计算环境中。目前，V&V几乎总是在标准的实验室条件下进行，而不是在真实的操作环境中进行；这是建立对模型的信任的关键障碍。在使用寿命阶段，结构健康监测(SHM)可以允许从安全寿命设计理念(结构在固定时间后退役)转向损伤容限设计理念(只要结构被认为是健康的，监测就允许使用)。这可以显著降低工程储备(或安全)系数，从而实现更轻、更环保、更安全的结构。SHM还允许延长现有结构的寿命(例如，将欧盟桥梁库存的0.7%的寿命延长10年，将节省约30亿欧元)。卫生与环境管理的一个主要技术障碍是，环境变化可能与损害混淆，从而可能产生错误警报，从而降低系统的有效性和信任度。此外，目前关于环境变化影响的V&V和SHM研究几乎总是局限于小范围的结构和部件，需要对从外推到全范围的信心飞跃。这里提议的设施旨在通过提供全面和跨现实环境的测试能力来解决上面讨论的两个主要问题。LVV将提供独特的能力，将研究从其最基本的概念(技术准备级别0)带到可立即被工业采用的点(技术准备级别7)。

项目成果

Investigating Experimental Repeatability and Feature Consistency in Vibration-Based SHM

研究基于振动的 SHM 中的实验重复性和特征一致性

• 发表时间: 2021
• 期刊: Enabling Next-Generation SHM for Cyber-Physical Systems - Proceedings of the 13th International Workshop on Structural Health Monitoring, IWSHM 2021
• 作者: Dardeno T.A.

Case study of connectivity of digital twins and experimental systems

数字孪生和实验系统连接的案例研究

• 发表时间: 2021
• 期刊: COMPDYN Proceedings
• 作者: Bonney M.

On the hierarchical Bayesian modelling of frequency response functions

频率响应函数的分层贝叶斯建模

• DOI: 10.48550/arxiv.2307.06263
• 发表时间: 2023
• 作者: Dardeno T

Investigating the Effects of Ambient Temperature on Feature Consistency in Vibration-Based SHM

研究环境温度对基于振动的 SHM 特征一致性的影响

• 发表时间: 2021
• 期刊: Enabling Next-Generation SHM for Cyber-Physical Systems - Proceedings of the 13th International Workshop on Structural Health Monitoring, IWSHM 2021
• 作者: Dardeno T.A.

On the application of population-based structural health monitoring in aerospace engineering.

• DOI: 10.3389/frobt.2022.840058
• 发表时间: 2022
• 期刊: FRONTIERS IN ROBOTICS AND AI
• 影响因子: 3.4
• 作者: Brennan, Daniel S;Gosliga, Julian;Gardner, Paul;Mills, Robin S;Worden, Keith
• 通讯作者: Worden, Keith