Hybrid AI and multiscale physical modelling for optimal urban decarbonisation combating climate change

混合人工智能和多尺度物理建模，实现应对气候变化的最佳城市脱碳

• 批准号: EP/X029093/1
• 负责人: Fangxin Fang
• 金额: $ 304.64万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX029093%2F1
• 关键词: Hybrid; AI; multiscale; physical; modelling

The challenges articulated in this proposal are how to (1) accurately assess carbon emissions in urban areas; (2) help design and manage cities so that the carbon footprint is reduced; and (3) quantify the impact of urban carbon emissions on global climate change-towards the 1.5 degree climate goal.Greenhouse gas emission reduction is key to tackling global warming. In 2020, 24% of net greenhouse gas emissions in the UK were estimated to be from the transport sector, 21% from energy supply, 18% from business, 16% from the residential sector and 11% from agriculture [1]. Accurate assessment of urban carbon emissions will help policy makers in their decision-making processes and managers of public and private spaces to optimise energy use, carbon reduction and economic benefit. Models are powerful tools in understanding carbon life cycle and atmospheric processes, making predictions, uncertainty quantification and optimal control/design for decarbonisation. However, integrated assessment of the environment and human development is arguably the most difficult and important "systems" problem faced [2]. The complex carbon cycle and atmospheric physical processes act over a wide range of spatial (from meters to degrees) and temporal (from hours, days to decades) scales. Currently, there is no integrated modelling across neighbourhood, city and global scales which can be used for exploring the complex relationship between carbon emissions associated with human activities and global climate change. Here I aim to develop a hybrid AI (Artificial Intelligence)-multiscale physics-informed optimal management framework for accurate assessment and mitigation of CO2 in urban areas. Effective carbon assessment and management necessitate the implementation of multiscale carbon models that can capture adequate spatial and temporal variability of urban carbon emissions & dispersion patterns. Current models are either excessively computationally expensive, or fail to capture the detailed variability of such problems. The proposed work will advance the status of science by developing an advanced multiscale carbon model (based on our recently developed Fluidity-Urban model) where, the use of dynamically adapted meshes enables us to resolve complex urban turbulent flows and carbon dispersion processes. The effect of city infrastructures on carbon dispersion processes is considered at different scales. AI-based modelling will then be used for the optimal design of urban infrastructures and layout for mitigation of carbon emissions. Energy efficiency and carbon-based energy usage in cities are measured based on detailed datasets of existing infrastructures in the selected city-London. The modelling framework will include new carbon parameterisation schemes for urban infrastructures/layout, enabling more accurate assessment of urban carbon emissions, and their impact on climate change. Potential improvements to existing urban infrastructures, and optimal designs for new urban developments will be provided through the AI-based optimal control tool proposed here for carbon reduction and energy efficiency. Finally, an AI-based GHG parameterisation module will be developed for coupling the calculated CO2 fluxes at high resolution grids with existing Earth System modelling. The impact of carbon emissions in cities on global climate can then be evaluated accurately based on existing and improved city infrastructure and layouts.This innovative framework will allow the critical assessment of existing and new policy options on decarbonisation to be carried out, thus improving local and global climate. The tool could potentially change the way in which city infrastructure design, GI and BI for decarbonisation are used in our future cities and pave the way for accurate quantification of the impact of urban carbon emissions on global warming.[1] BEIS, N.. 2020 UK Greenhouse Gas Emissions, Final Figures.[2] Navarro et al. 2018. Earth Syst. Dynam., 9, 1045

该提案提出的挑战是如何（1）准确评估城市地区的碳排放； (2) 帮助设计和管理城市，以减少碳足迹； (3)量化城市碳排放对全球气候变化的影响——实现1.5度气候目标。温室气体减排是应对全球变暖的关键。 2020年，英国温室气体净排放量估计有24%来自交通部门，21%来自能源供应，18%来自商业，16%来自住宅部门，11%来自农业[1]。准确评估城市碳排放将有助于政策制定者的决策过程以及公共和私人空间的管理者优化能源使用、碳减排和经济效益。模型是了解碳生命周期和大气过程、进行预测、不确定性量化和脱碳最佳控制/设计的强大工具。然而，环境和人类发展的综合评估可以说是面临的最困难和最重要的“系统”问题[2]。复杂的碳循环和大气物理过程在广泛的空间（从米到度）和时间（从小时、天到几十年）尺度上发挥作用。目前，还没有跨社区、城市和全球尺度的综合模型可用于探索与人类活动相关的碳排放与全球气候变化之间的复杂关系。在这里，我的目标是开发一个混合人工智能（人工智能）-多尺度物理信息的最佳管理框架，用于准确评估和缓解城市地区的二氧化碳排放。有效的碳评估和管理需要实施多尺度碳模型，该模型可以捕获城市碳排放和扩散模式的充分空间和时间变化。当前的模型要么计算成本过高，要么无法捕捉此类问题的详细变化。拟议的工作将通过开发先进的多尺度碳模型（基于我们最近开发的流动性城市模型）来提高科学地位，其中使用动态适应的网格使我们能够解决复杂的城市湍流和碳扩散过程。城市基础设施对碳扩散过程的影响在不同尺度上被考虑。基于人工智能的建模将用于城市基础设施和布局的优化设计，以减少碳排放。城市的能源效率和碳基能源使用是根据所选城市伦敦现有基础设施的详细数据集进行测量的。该建模框架将包括针对城市基础设施/布局的新碳参数化方案，从而能够更准确地评估城市碳排放及其对气候变化的影响。通过这里提出的基于人工智能的优化控制工具，可以对现有城市基础设施进行潜在的改进，并为新的城市开发提供优化设计，以减少碳排放和提高能源效率。最后，将开发基于人工智能的温室气体参数化模块，用于将高分辨率网格下计算的二氧化碳通量与现有的地球系统模型耦合起来。然后，可以根据现有和改进的城市基础设施和布局，准确评估城市碳排放对全球气候的影响。这一创新框架将允许对现有和新的脱碳政策选项进行严格评估，从而改善当地和全球气候。该工具可能会改变未来城市中城市基础设施设计、脱碳的 GI 和 BI 的使用方式，并为准确量化城市碳排放对全球变暖的影响铺平道路。 [1] BEIS, N.. 2020 年英国温室气体排放量，最终数据。[2]纳瓦罗等人。 2018.地球系统。动态., 9, 1045