Researchers at the University of Oxford have unveiled a groundbreaking method for modeling turbulent systems, leveraging probabilities to transform computational hydrodynamics. This innovation promises to enhance the study of chaotic systems significantly.
For years, accurately predicting the dynamics of turbulent fluid flows has posed a challenge to scientists and engineers. Traditional computational methods have struggled to simulate anything beyond the simplest turbulent flows due to the chaotic and unpredictable nature of turbulence, characterized by swirling eddies of various shapes and sizes.
Collaborating with colleagues from Hamburg, Pittsburgh, and Cornell, the Oxford team redefined the problem to eliminate the need for direct resolution of these turbulent fluctuations. Instead of modeling the fluctuations directly, they represented them as random variables distributed according to a probability distribution function. This approach allowed them to extract significant flow characteristics, such as lift and drag, without delving into the chaos of turbulent oscillations.
Typically, modeling turbulence probability distributions requires solving complex Fokker-Planck equations, a task that classical methods cannot handle effectively. To tackle this, the team applied quantum-inspired computational technology developed at Oxford, utilizing tensor networks to represent turbulence probability distributions in a hyper-compressed format, enabling their simulation.
During the study, the quantum-inspired algorithm, running on a single processor core, completed computations in a few hours—tasks that would have taken an equivalent classical algorithm several days on a supercomputer. Future advancements are anticipated with the deployment of the quantum-inspired tensor network algorithm on specialized hardware, such as tensor processors and fault-tolerant quantum chips.
According to the researchers, this approach not only challenges the current limitations of turbulence modeling but also paves the way for simulating other chaotic systems that can be described probabilistically. Dr. Nikita Guryanov, the lead researcher from Oxford's physics department, stated, "The demonstrated and future computational advantages not only unveil new, previously inaccessible areas of turbulence physics for scientific exploration but also herald the emergence of next-generation hydrodynamic computational codes. This could enhance weather forecasting, improve vehicle aerodynamics, and increase the efficiency of the chemical industry, among other applications."