Uncertainty quantification and sensitivity analysis for resilient infrastructure systems

弹性基础设施系统的不确定性量化和敏感性分析

• 批准号: ST/Y003713/1
• 负责人: Francesca Pianosi
• 金额: $ 17.77万
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=ST%2FY003713%2F1
• 关键词: Uncertainty; quantification; sensitivity; analysis; resilient

Computational modelling provides a vital tool to support infrastructure decisions, allowing to evaluate risks and benefits of different infrastructure options on a virtual system (or "digital twin") before committing to a particular design. Model outputs though are conditional on a range of uncertain assumptions and input data, due to our incomplete or imperfect knowledge of the drivers and the properties of the system being modelled. When models are used for long-term planning, the uncertainty about the current properties and drivers of the system is compounded with further uncertainty about how these will evolve in the future. For example, when planning water infrastructure for drought resilience, we need to make a set of uncertain assumptions about the way that future climate will affect water sources and how changes in the economy, society and lifestyle will affect future water demand. Another example is the energy sector, where the growing contribution of intermittent sources such as wind and solar introduces unprecedented levels of uncertainty to the quantification of energy production potential, both in the short and long term. Overconfidence in model results and insufficient consideration of the breath of possible futures is a key obstacle to infrastructure resilience. If models are used to inform large investment decisions, they must be trustworthy and defensible. Decision-makers need to be made aware of the uncertainties affecting model output(s) and the critical assumptions that define the scope of validity of the model. Also, as uncertainty about the future is irreducible, modelling for resilience should aim at identifying designs that achieve an acceptable performance across a wide range of future scenarios, rather than designs that are optimal under any particular scenario.Uncertainty Quantification and Sensitivity Analysis (UQ&SA) is a set of generic methods that can be used to analyse the propagation of uncertainties in model and thus improve the model's construction, validation, and use for decision-making under uncertainty. UQ&SA are "model-agnostic" methodologies, meaning that they are applicable to any mathematical model regardless of the specific application domain. The goal of this project is to set the foundations for integrating UQ&SA functionalities in the DAFNI platform. We believe this is very important for DAFNI to become a platform that not only enables users to share, combine and execute models, but also enables and promotes best practices for responsible modelling.To achieve the project goal, we will develop DAFNI Workflows that use functionalities already existing in DAFNI for uncertainty propagation (the "Loop" functionality, which allows repeated executions of the same model against different input set-ups) and integrate them with existing open-source software packages for UQ&SA. In these Workflows, we will use two simple "proof-of-principle" models from the water and energy sector, so to ensure feasibility of the project within the limited timeframe, but also to produce training materials for current and future DAFNI users to learn "by example". Indeed throughout the project we will run a series of seminars and tutorial sessions on UQ&SA for early-career researchers, and develop recommendations for the DAFNI technical team on future developments needed in order to scale-up the applicability of UQ&SA to more complex models.

计算建模提供了支持基础设施决策的重要工具，允许在承诺特定设计之前评估虚拟系统（或“数字孪生”）上不同基础设施选项的风险和收益。然而，模型输出取决于一系列不确定的假设和输入数据，因为我们对驱动因素和所建模系统的属性的了解不完整或不完善。当模型用于长期规划时，系统当前属性和驱动因素的不确定性与未来如何演变的进一步不确定性交织在一起。例如，在为抗旱能力规划水基础设施时，我们需要对未来气候将如何影响水源以及经济、社会和生活方式的变化将如何影响未来的水需求做出一系列不确定的假设。另一个例子是能源部门，风能和太阳能等间歇性能源的贡献越来越大，给能源生产潜力的量化带来了前所未有的不确定性，无论是短期还是长期。对模型结果的过度自信和对未来可能的呼吸考虑不足是基础设施恢复力的一个关键障碍。如果模型被用来为大型投资决策提供信息，它们必须是可信和可辩护的。决策者需要了解影响模型输出的不确定性以及界定模型有效性范围的关键假设。此外，由于未来的不确定性是不可减少的，复原力建模的目标应该是确定在广泛的未来情景中实现可接受性能的设计，而不是在任何特定情况下都是最优的设计。不确定性量化和敏感性分析（UQ&SA）是一套通用的方法，可用于分析模型中不确定性的传播，从而改善模型的构建，验证，并用于不确定性下的决策。UQ&SA是“模型无关”的方法，这意味着它们适用于任何数学模型，而不管特定的应用领域。该项目的目标是为在DAFNI平台中集成UQ&SA功能奠定基础。我们相信这对于DAFNI成为一个平台非常重要，不仅使用户能够共享，联合收割机和执行模型，而且还能够实现和促进负责任建模的最佳实践。为了实现项目目标，我们将开发DAFNI工作流，使用DAFNI现有的功能进行不确定性传播（“循环”功能，允许针对不同的输入设置重复执行相同的模型），并将其与UQ&SA现有的开源软件包集成。在这些工作流程中，我们将使用来自水和能源部门的两个简单的“原理证明”模型，以确保项目在有限的时间内的可行性，同时也为当前和未来的DAFNI用户制作培训材料，以“通过示例”学习。事实上，在整个项目中，我们将为早期职业研究人员举办一系列关于UQ&SA的研讨会和辅导课程，并为DAFNI技术团队制定未来发展所需的建议，以便将UQ&SA的适用性扩展到更复杂的模型。