PREdictive Modelling with QuantIfication of UncERtainty for MultiphasE Systems (PREMIERE)

多相系统不确定性量化的预测建模 (PREMIERE)

• 批准号: EP/T000414/1
• 负责人: Omar Matar
• 金额: $ 835.94万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FT000414%2F1
• 关键词: PREdictive; Modelling; QuantIfication; UncERtainty; MultiphasE

PREMIERE will integrate challenges identified by the EPSRC Prosperity Outcomes and the Industrial Strategy Challenge Fund (ISCF) in healthcare (Healthy Nation), energy (Resilient Nation), manufacturing and digital technologies (Resilient Nation, Productive Nation) as areas to drive economic growth. The programme will bring together a multi-disciplinary team of researchers to create unprecedented impact in these sectors through the creation of a next-generation predictive framework for complex multiphase systems. Importantly, the framework methodology will span purely physics-driven, CFD-mediated solutions at one extreme, and data-centric solutions at the other where the complexity of the phenomena masks the underlying physics. The framework will advance the current state-of-the-art in uncertainty quantification, adjoint sensitivity, data-assimilation, ensemble methods, CFD, and design of experiments to 'blend' the two extremes in order to create ultra-fast multi-fidelity, predictive models, supported by cutting-edge experimental investigations. This transformative technology will be sufficiently generic so as to address a wide spectrum of challenges across the ISCF areas, and will empower the user with optimal compromises between off-line (modelling) and on-line (simulation) efforts so as to meet an a priori 'error bar' on the model outputs. The investigators' synergy, and their long-standing industrial collaborations, will ensure that PREMIERE will result in a paradigm-shift in multiphase flow research worldwide. We will demonstrate our capabilities using exemplar challenges, of central importance to their respective sectors in close collaboration with our industrial and healthcare partners. Our PREMIERE framework will provide novel and more efficient manufacturing processes, reliable design tools for the oil-and-gas industry, which remove conservatism in design, improve safety management, and reduce emissions and carbon footprint. This framework will also provide enabling technology for the design, operation, and optimisation of the next-generation nuclear reactors, and associated reprocessing, as well as patient-specific therapies for diseases such as acute compartment syndrome.

Premiere将整合EPSRC繁荣成果与医疗保健（健康国家），能源（韧性国家），制造业和数字技术（富有弹性的国家，生产性国家）作为推动经济增长的领域的挑战。该计划将通过为复杂的多相系统创建下一代预测框架，将一个多学科研究人员团队汇总为这些部门的前所未有的影响。重要的是，框架方法将纯粹由物理驱动的，一个极端的CFD介导的解决方案，而以数据为中心的解决方案在另一个中以数据为中心的解决方案，该现象的复杂性掩盖了基本的物理学。该框架将在不确定性量化，伴随灵敏度，数据唤起，集合方法，CFD和设计方面推进当前的最新技术，并设计两个极端，以创建尖端实验研究支持的超快速多预性，预测模型。这种变革性技术将足够通用，以应对整个ISCF领域的各种挑战，并将在离线（建模）和在线（模拟）（模拟）工作之间以最佳折衷为用户，以满足模型输出的先验“错误栏”。调查人员的协同作用及其长期的工业合作将确保首映会导致全球多相流研究进行范式变化。我们将使用示例挑战来证明我们的能力，这对各自部门与我们的工业和医疗保健伙伴密切合作至关重要。我们的首映框架将为石油和天然气行业提供新颖，更高效的制造工艺，可靠的设计工具，从而消除了设计的保守主义，改善安全管理，减少排放和碳足迹。该框架还将为下一代核反应堆的设计，操作和优化以及相关的重新处理以及针对急性隔室综合征等疾病的患者特定疗法提供促进技术。

项目成果

Interaction of two non-coalescing bubbles rising in a non-isothermal self-rewetting fluid

• DOI: 10.1016/j.euromechflu.2021.01.009
• 发表时间: 2021-05
• 期刊: European Journal of Mechanics - B/Fluids
• 作者: Mounika Balla;M. Tripathi;O. Matar;K. Sahu

Linear stability analysis of Taylor bubble motion in downward flowing liquids in vertical tubes

垂直管内向下流动液体泰勒气泡运动的线性稳定性分析

• DOI: 10.1017/jfm.2022.261
• 发表时间: 2022
• 期刊: Journal of Fluid Mechanics
• 影响因子: 3.7
• 作者: Abubakar H

Discovery of mixing characteristics for enhancing coiled reactor performance through a Bayesian optimisation-CFD approach

• DOI: 10.1016/j.cej.2023.145217
• 发表时间: 2023-08-16
• 期刊: CHEMICAL ENGINEERING JOURNAL
• 影响因子: 15.1
• 作者: Basha，Nausheen;Savage，Thomas;Matar，Omar K.
• 通讯作者: Matar，Omar K.

Effect of surfactant on elongated bubbles in capillary tubes at high Reynolds number

表面活性剂对高雷诺数毛细管内拉长气泡的影响

• DOI: 10.1103/physrevfluids.5.093605
• 发表时间: 2020
• 期刊: Physical Review Fluids
• 影响因子: 2.7
• 作者: Batchvarov A

Three-dimensional dynamics of falling films in the presence of insoluble surfactants

• DOI: 10.1017/jfm.2020.796
• 发表时间: 2020-05
• 期刊: Journal of Fluid Mechanics
• 影响因子: 3.7
• 作者: A. Batchvarov;L. Kahouadji;C. Constante-Amores;Gabriel Farah Norões Gonçalves;Seungwon Shin;J. Chergui;D. Juric;R. Craster;O. Matar