Can quantum algorithms revolutionise the simulation of turbulent flows?
量子算法能否彻底改变湍流模拟?
基本信息
- 批准号:EP/X017249/1
- 负责人:
- 金额:$ 25.72万
- 依托单位:
- 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:
- 关键词:
项目摘要
Our research vision is to create a framework and toolbox to marry over 60 years of high-performance computing with quantum computing to revolutionise understanding, modelling, and simulation of fluid mechanics. The efficient conversion of energy in wind farms, the explosions of supernovas, and the air resistance around airplanes have a common factor: a fluid. Fluid mechanics is a major UK industrial and research strength, which is an enabling technology from transport, healthcare, marine and energy. According to the 2021 UK white paper, fluid mechanics is a sector that employs 45,000 people in 2,200 companies, which generates a £14-billion output to the UK. Fluids of practical interest can be turbulent. Both in fundamental and applied research, numerical simulation is key to understanding, predicting and controlling turbulent flows. In fundamental research, the goal is to unveil the physical mechanisms, scales and dynamics of turbulence. In industry, the goal is to embed accurate numerical simulations of turbulence with a fast turnaround into the engineering design cycle. We are far from achieving this.Although we know an excellent model for turbulent flows (the Navier-Stokes equations), the chaotic nature of turbulence makes accurate computer simulations exceedingly expensive. For example, a state-of-the-art simulation of turbulence of a simple channel flow needs 350 billion grid points and takes 260 million computing hours. To analyse fundamental and engineering configurations, large supercomputing resources are deployed. Although the flop operations of computers roughly double every two years, we will need to wait for decades before being able to tackle a fundamental flow, such as a channel flow, at realistic flow velocities. The next generation of large exascale computers, however, will only allow for a three- to five-fold increase in the flow velocities with respect to the state-of-the-art. The question is "how can we accurately simulate turbulent flows of practical interest with affordable computations?" Classical algorithms are reaching their limits.Key to this proposal is the observation that the numerical solution of the nonlinear equations of turbulence revolves around solving linear systems. Linear systems can be solved formidably fast by quantum algorithms. Quantum computing offers a repository of algorithms that can revolutionise computational science and turbulence simulations. This is because classical computers require computational resources that scale exponentially with the system's degrees of freedom, whereas quantum algorithms scale only polynomially. This is also known as the quantum advantage. If the conjectures published in 2021 by Google, Microsoft, IBM, MIT, Harvard, among others, on the quantum advantage are correct, the simulation of a turbulent system can be accelerated by ten to thousand orders of magnitudes. This project will pioneer this research field. In this project, we will develop and test quantum-enhanced computational fluid dynamics (q-CFD) by exploiting the untested, but plausible, quantum advantage. This will blaze the trail for computing turbulence with a synergistic combination of classical and quantum algorithms.
我们的研究愿景是创建一个框架和工具箱,将 60 多年的高性能计算与量子计算结合起来,彻底改变流体力学、风电场能量转换、超新星爆炸和量子计算的理解、建模和模拟。飞机周围的空气阻力有一个共同因素:流体力学是英国工业和研究的主要优势,根据 2021 年英国白皮书,流体力学是一项来自交通、医疗、海洋和能源的支持技术。部门拥有 2,200 家公司的 45,000 名员工,为英国带来了 140 亿英镑的产出。无论是在基础研究还是应用研究中,数值模拟都是理解、预测和控制湍流的关键。 ,目标是揭示湍流的物理机制、规模和动力学。在工业中,目标是快速嵌入湍流的准确数值模拟。我们还远未实现这一目标。尽管我们知道湍流的优秀模型(纳维-斯托克斯方程),但湍流的混沌性质使得精确的计算机模拟变得极其昂贵。 - 简单通道流湍流的最先进模拟需要 3500 亿个网格点并需要 2.6 亿计算小时来分析基本和工程配置,但需要部署大量的超级计算资源。计算机的翻转操作大约每两年翻一番,我们需要等待几十年才能以实际的流速处理基本的流动,例如通道流动,然而,下一代大型计算机将只允许这样的速度。相对于最先进的技术,流速提高了三到五倍。问题是“我们如何通过可承受的计算来准确地模拟实际感兴趣的湍流?” 。钥匙该提议的观察结果是,湍流非线性方程的数值求解围绕着线性系统的求解展开。量子计算提供了一个可以彻底改变计算科学和湍流模拟的算法库。是因为经典计算机需要随系统自由度呈指数级扩展的计算资源,而量子算法仅按多项式扩展,这也称为“如果”中发表的猜想。 2021年,谷歌、微软、IBM、麻省理工学院、哈佛大学等机构关于量子优势的观点是正确的,湍流系统的模拟可以加速十到千个数量级,该项目将开创这一研究领域。 ,我们将利用未经测试但合理的量子优势来开发和测试量子增强计算流体动力学(q-CFD),这将为经典算法和量子算法的协同组合开辟计算湍流的道路。
项目成果
期刊论文数量(0)
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Luca Magri其他文献
Event Detection in Optical Signals via Domain Adaptation
通过域适应进行光信号事件检测
- DOI:
10.23919/eusipco58844.2023.10289940 - 发表时间:
2023-09-04 - 期刊:
- 影响因子:0
- 作者:
Antonino Maria Rizzo;Luca Magri;Pietro Invernizzi;Enrico Sozio;Gabriele Aquaro;Stefano Binetti;C. Alippi;G. Boracchi - 通讯作者:
G. Boracchi
Computing distances and means on manifolds with a metric-constrained Eikonal approach
使用度量约束 Eikonal 方法计算流形上的距离和均值
- DOI:
10.48550/arxiv.2404.08754 - 发表时间:
2024-04-12 - 期刊:
- 影响因子:0
- 作者:
Daniel Kelshaw;Luca Magri - 通讯作者:
Luca Magri
Data-driven computation of adjoint sensitivities without adjoint solvers: An application to thermoacoustics
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- DOI:
10.1136/bmjopen-2016-014707 - 发表时间:
2024-04-17 - 期刊:
- 影响因子:2.9
- 作者:
D. E. Ozan;Luca Magri - 通讯作者:
Luca Magri
An expert-driven data generation pipeline for histological images
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- DOI:
- 发表时间:
2024-06-03 - 期刊:
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Roberto Basla;Loris Giulivi;Luca Magri;Giacomo Boracchi - 通讯作者:
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Physics-constrained convolutional neural networks for inverse problems in spatiotemporal partial differential equations
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- DOI:
10.48550/arxiv.2401.10306 - 发表时间:
2024-01-18 - 期刊:
- 影响因子:0
- 作者:
Daniel Kelshaw;Luca Magri - 通讯作者:
Luca Magri
Luca Magri的其他文献
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