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

该提案所阐明的挑战是如何（1）准确评估城市地区的碳排放； （2）帮助设计和管理城市，以便减少碳足迹； （3）量化城市碳排放对全球气候变化的影响1.5度气候目标。减少绿屋气体排放是应对全球变暖的关键。 2020年，估计英国24％的温室气体排放量来自运输部门，能源供应量为21％，企业为18％，住宅区为16％，农业为11％[1]。对城市碳排放的准确评估将帮助决策者进行决策过程，以及公共和私人空间的经理，以优化能源使用，减少碳和经济利益。模型是了解碳生命周期和大气过程，进行预测，不确定性定量和最佳控制/设计的强大工具。但是，对环境和人类发展的综合评估可以说是面临最困难和最重要的“系统”问题[2]。复杂的碳循环和大气物理过程在广泛的空间（从米到程度）和时间（从小时到几天到几十年）上起作用。当前，跨社区，城市和全球量表尚无综合建模，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，我旨在开发混合AI（人工智能） - 多种物理信息的最佳管理框架，以准确评估和缓解城市地区的CO2。有效的碳评估和管理需要实施多尺度碳模型，以捕获城市碳排放和分散模式的足够空间和时间变异性。当前模型在计算上过于昂贵，要么无法捕获此类问题的详细可变性。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）来提高科学的地位，在该模型中，动态适应网格的使用使我们能够解决复杂的城市湍流和碳分散过程。城市基础设施对碳分散过程的影响在不同的范围内考虑。然后，基于AI的建模将用于城市基础设施的最佳设计和缓解碳排放的布局。根据所选城市伦敦现有基础设施的详细数据集测量城市中的能源效率和基于碳的能源。该建模框架将包括用于城市基础设施/布局的新碳参数化方案，从而更准确地评估城市碳排放及其对气候变化的影响。现有城市基础设施的潜在改进以及新的城市发展的最佳设计将通过此处提出的基于AI的最佳控制工具提供，以降低碳和能源效率。最后，将开发一个基于AI的GHG参数化模块，以将高分辨率网格的计算出的CO2通量与现有的Earth System建模耦合。然后，可以根据现有和改善的城市基础设施和布局来准确评估城市中碳排放对全球气候的影响。该创新框架将允许对脱碳化的现有和新政策选择进行批判性评估，从而改善本地和全球气候。该工具可能会改变我们未来的城市使用城市基础设施设计，GI和BI进行脱碳的方式，并为准确量化城市碳排放对全球变暖的影响铺平了道路。[1]贝斯，北。2020英国温室气体排放，最终数字。[2] Navarro等。 2018年。地球系统。 Dynam。，9，1045