Quantum-inspired “tensor networks” can simulate the behaviour of turbulent fluids in just a few hours rather than the several days required for a classical algorithm. The new technique, developed by physicists in the UK, Germany and the US, could advance our understanding of turbulence, which has been called one of the greatest unsolved problems of classical physics.

Turbulence is all around us, found in weather patterns, water flowing from a tap or a river and in many astrophysical phenomena. It is also important for many industrial processes. However, the way in which turbulence arises and then sustains itself is still not understood, despite the seemingly simple and deterministic physical laws governing it.

The reason for this is that turbulence is characterized by large numbers of eddies and swirls of differing shapes and sizes that interact in chaotic and unpredictable ways across a wide range of spatial and temporal scales. Such fluctuations are difficult to simulate accurately, even using powerful supercomputers, because doing so requires solving sets of coupled partial differential equations on very fine grids.

An alternative is to treat turbulence in a probabilistic way. In this case, the properties of the flow are defined as random variables that are distributed according to mathematical relationships called joint Fokker-Planck probability density functions. These functions are neither chaotic nor multiscale, so they are straightforward to derive. However, they are nevertheless challenging to solve because of the high number of dimensions contained in turbulent flows.

For this reason, the probability density function approach was widely considered to be computationally infeasible. In response, researchers turned to indirect Monte Carlo algorithms to perform probabilistic turbulence simulations. However, while this approach has chalked up some notable successes, it can be slow to yield results.

Highly compressed “tensor networks”

To overcome this problem, a team led by Nikita Gourianov of the University of Oxford, UK, decided to encode turbulence probability density functions as highly compressed “tensor networks” rather than simulating the fluctuations themselves. Such networks have already been used to simulate otherwise intractable quantum systems like superconductors, ferromagnets and quantum computers, they say.

These quantum-inspired tensor networks represent the turbulence probability distributions in a hyper-compressed format, which then allows them to be simulated. By simulating the probability distributions directly, the researchers can then extract important parameters, such as lift and drag, that describe turbulent flow.

Importantly, the new technique allows an ordinary single CPU (central processing unit) core to compute a turbulent flow in just a few hours, compared to several days using a classical algorithm on a supercomputer.

This significantly improved way of simulating turbulence could be particularly useful in the area of chemically reactive flows in areas such as combustion, says Gourianov. “Our work also opens up the possibility of probabilistic simulations for all kinds of chaotic systems, including weather or perhaps even the stock markets,” he adds.

The researchers now plan to apply tensor networks to deep learning, a form of machine learning that uses artificial neural networks. “Neural networks are famously over-parameterized and there are several publications showing that they can be compressed by orders of magnitude in size simply by representing their layers as tensor networks,” Gourianov tells Physics World.

The study is detailed in Science Advances.

The post Quantum-inspired technique simulates turbulence with high speed appeared first on Physics World.

A new “sneeze simulator” could help scientists understand how respiratory illnesses such as COVID-19 and influenza spread. Built by researchers at the Universitat Rovira i Virgili (URV) in Spain, the simulator is a three-dimensional model that incorporates a representation of the nasal cavity as well as other parts of the human upper respiratory tract. According to the researchers, it should help scientists to improve predictive models for respiratory disease transmission in indoor environments, and could even inform the design of masks and ventilation systems that mitigate the effects of exposure to pathogens.

For many respiratory illnesses, pathogen-laden aerosols expelled when an infected person coughs, sneezes or even breathes are important ways of spreading disease. Our understanding of how these aerosols disperse has advanced in recent years, mainly through studies carried out during and after the COVID-19 pandemic. Some of these studies deployed techniques such as spirometry and particle imaging to characterize the distributions of particle sizes and airflow when we cough and sneeze. Others developed theoretical models that predict how clouds of particles will evolve after they are ejected and how droplet sizes change as a function of atmospheric humidity and composition.

To build on this work, the UVR researchers sought to understand how the shape of the nasal cavity affects these processes. They argue that neglecting this factor leads to an incomplete understanding of airflow dynamics and particle dispersion patterns, which in turn affects the accuracy of transmission modelling. As evidence, they point out that studies focused on sneezing (which occurs via the nose) and coughing (which occurs primarily via the mouth) detected differences in how far droplets travelled, the amount of time they stayed in the air and their pathogen-carrying potential – all parameters that feed into transmission models. The nasal cavity also affects the shape of the particle cloud ejected, which has previously been found to influence how pathogens spread.

The challenge they face is that the anatomy of the naval cavity varies greatly from person to person, making it difficult to model. However, the UVR researchers say that their new simulator, which is based on realistic 3D printed models of the upper respiratory tract and nasal cavity, overcomes this limitation, precisely reproducing the way particles are produced when people cough and sneeze.

Reproducing human coughs and sneezes

One of the features that allows the simulator to do this is a variable nostril opening. This enables the researchers to control air flow through the nasal cavity, and thus to replicate different sneeze intensities. The simulator also controls the strength of exhalations, meaning that the team could investigate how this and the size of nasal airways affects aerosol cloud dispersion.

During their experiments, which are detailed in Physics of Fluids, the UVR researchers used high-speed cameras and a laser beam to observe how particles disperse following a sneeze. They studied three airflow rates typical of coughs and sneezes and monitored what happened with and without nasal cavity flow. Based on these measurements, they used a well-established model to predict the range of the aerosol cloud produced.

“We found that nasal exhalation disperses aerosols more vertically and less horizontally, unlike mouth exhalation, which projects them toward nearby individuals,” explains team member Salvatore Cito. “While this reduces direct transmission, the weaker, more dispersed plume allows particles to remain suspended longer and become more uniformly distributed, increasing overall exposure risk.”

These findings have several applications, Cito says. For one, the insights gained could be used to improve models used in epidemiology and indoor air quality management.

“Understanding how nasal exhalation influences aerosol dispersion can also inform the design of ventilation systems in public spaces, such as hospitals, classrooms and transportation systems to minimize airborne transmission risks,” he tells Physics World.

The results also suggest that protective measures such as masks should be designed to block both nasal and oral exhalations, he says, adding that full-face coverage is especially important in high-risk settings.

The researchers’ next goal is to study the impact of environmental factors such as humidity and temperature on aerosol dispersion. Until now, such experiments have only been carried out under controlled isothermal conditions, which does not reflect real-world situations. “We also plan to integrate our experimental findings with computational fluid dynamics simulations to further refine protective models for respiratory aerosol dispersion,” Cito reveals.

The post ‘Sneeze simulator’ could improve predictions of pathogen spread appeared first on Physics World.

Researchers led by Denis Bartolo, a physicist at the École Normale Supérieure (ENS) of Lyon, France, have constructed a theoretical model that forecasts the movements of confined, densely packed crowds. The study could help predict potentially life-threatening crowd behaviour in confined environments. 

To investigate what makes some confined crowds safe and others dangerous, Bartolo and colleagues – also from the Université Claude Bernard Lyon 1 in France and the Universidad de Navarra in Pamplona, Spain – studied the Chupinazo opening ceremony of the San Fermín Festival in Pamplona in four different years (2019, 2022, 2023 and 2024).

The team analysed high-resolution video captured from two locations above the gathering of around 5000 people as the crowd grew in the 50 x 20 m city plaza: swelling from two to six people per square metre, and ultimately peaking at local densities of nine per square metre. A machine-learning algorithm enabled automated detection of the position of each person’s head; from which localized crowd density was then calculated.

“The Chupinazo is an ideal experimental platform to study the spontaneous motion of crowds, as it repeats from one year to the next with approximately the same amount of people, and the geometry of the plaza remains the same,” says theoretical physicist Benjamin Guiselin, a study co-author formerly from ENS Lyon and now at the Université de Montpellier.

In a first for crowd studies, the researchers treated the densely packed crowd as a continuum like water, and “constructed a mechanics theory for the crowd movement without making any behavioural assumptions on the motion of individuals,” Guiselin tells Physics World.

Their studies, recently described in Nature, revealed a change in behaviour akin to a phase change when the crowd density passed a critical threshold of four individuals per square metre. Below this density the crowd remained relatively inactive. But above that threshold it started moving, exhibiting localized oscillations that were periodic over about 18 s, and occurred without any external guiding such as corralling.

Unlike a back-and-forth oscillation, this motion – which involves hundreds of people moving over several metres – has an almost circular trajectory that shows chirality (or handedness) and a 50:50 chance of turning to either the right or left. “Our model captures the fact that the chirality is not fixed. Instead it emerges in the dynamics: the crowd spontaneously decides between clockwise or counter-clockwise circular motion,” explains Guiselin, who worked on the mathematical modelling.

“The dynamics is complicated because if the crowd is pushed, then it will react by creating a propulsion force in the direction in which it is pushed: we’ve called this the windsock effect. But the crowd also has a resistance mechanism, a counter-reactive effect, which is a propulsive force opposite to the direction of motion: what we have called the weathercock effect,” continues Guiselin, adding that it is these two competing mechanisms in conjunction with the confined situation that gives rise to the circular oscillations.

The team observed similar oscillations in footage of the 2010 tragedy at the Love Parade music festival in Duisburg, Germany, in which 21 people died and several hundred were injured during a crush.

Early results suggest that the oscillation period for such crowds is proportional to the size of the space they are confined in. But the team want to test their theory at other events, and learn more about both the circular oscillations and the compression waves they observed when people started pushing their way into the already crowded square at the Chupinazo.

If their model is proven to work for all densely packed, confined crowds, it could in principle form the basis for a crowd management protocol. “You could monitor crowd motion with a camera, and as soon as you detect these oscillations emerging try to evacuate the space, because we see these oscillations well before larger amplitude motions set in,” Guiselin explains.

The post Modelling the motion of confined crowds could help prevent crushing incidents appeared first on Physics World.

The anomalous and ultra-low thermal expansion of cordierite results from the interplay between lattice vibrations and the elastic properties of the material. That is the conclusion of Martin Dove at China’s Sichuan University and Queen Mary University of London in the UK and Li Li at the Civil Aviation Flight University of China. They showed that the material’s unusual behaviour stems from direction-varying elastic forces in its lattice, which act to vary cordierite’s thermal expansion along different directions.

Cordierite is a naturally-occurring mineral that can also be synthesized. Thanks to its remarkable thermal properties, it is used in products ranging from pizza stones to catalytic converters. When heated to high temperatures, it undergoes ultra-low thermal expansion along two directions, and it shrinks a tiny amount along the third direction. This makes it incredibly useful as a material that can be heated and cooled without changing size or suffering damage.

Despite its widespread use, scientists lack a fundamental understanding of how cordierite’s anomalous thermal expansion arises from the properties of its crystal lattice. Normally, thermal expansion (positive or negative) is understood in terms of Grüneisen parameters. These describe how vibrational modes (phonons) in the lattice cause it to expand or contract along each axis as the temperature changes.

Negative Grüneisen parameters describe a lattice that shrinks when heated, and are seen as key to understanding thermal contraction of cordierite. However, the material’s thermal response is not isotropic (it only contracts only along one axis when heated at high temperatures) so understanding cordierite in terms of its Grüneisen parameters alone is difficult.

Advanced molecular dynamics

In their study, Dove and Li used advanced molecular dynamics simulations to accurately model the behaviour of atoms in the cordierite lattice. Their closely matched experimental observations of the material’s thermal expansion, providing them with key insights into why the material has a negative thermal expansion in just one direction.

“Our research demonstrates that the anomalous thermal expansion of cordierite originates from a surprising interplay between atomic vibrations and elasticity,” Dove explains. The elasticity is described in the form of an elastic compliance tensor, which predicts how a material will distort in response to a force applied along a specific direction.

At lower temperatures, lattice vibrations occur at lower frequencies. In this case, the simulations predicted negative thermal expansion in all directions – which is in line with observations of the material.

At higher temperatures, the lattice becomes dominated by high-frequency vibrations. In principle, this should result in positive thermal expansion in all three directions. Crucially, however, Dove and Li discovered that this expansion is cancelled out by the material’s elastic properties, as described by its elastic compliance tensor.

What is more, the unique arrangement of crystal lattice meant that this tensor varied depending on the direction of the applied force, creating an imbalance that amplifies differences between the material’s expansion along each axis.

Cancellation mechanism

“This cancellation mechanism explains why cordierite exhibits small positive expansion in two directions and small negative expansion in the third,” Dove explains. “Initially, I was sceptical of the results. The initial data suggested uniform expansion behaviour at both high and low temperatures, but the final results revealed a delicate balance of forces. It was a moment of scientific serendipity.”

Altogether, Dove and Li’s result clearly shows that cordierite’s anomalous behaviour cannot be understood by focusing solely on the Grüneisen parameters of its three axes. It is crucial to take its elastic compliance tensor into account.

In solving this long-standing mystery, the duo now hope their results could help researchers to better predict how cordierite’s thermal expansion will vary at different temperatures. In turn, they could help to extend the useful applications of the material even further.

“Anisotropic materials like cordierite hold immense potential for developing high-performance materials with unique thermal behaviours,” Dove says. “Our approach can rapidly predict these properties, significantly reducing the reliance on expensive and time-consuming experimental procedures.”

The research is described in Matter.

The post Elastic response explains why cordierite has ultra-low thermal expansion appeared first on Physics World.

Tu seras accueilli(e) au sein du campus UFPI de Saclay constitué d’une trentaine de personnes dont une équipe de formateurs passionnés par la pédagogie et la technique. Tu y découvriras un univers stimulant et un large éventail d’outils pédagogiques et…

Read more →

L’article [Stage] Travail sur un Outil Pédagogique en Réalité Virtuelle Couplé à un Simulateur de Conduite Nucléaire – EDF – Palaiseau est apparu en premier sur Réalité Augmentée - Augmented Reality.

Dans ce stage, vous aurez l’opportunité de travailler sur l’intégration de la réalité virtuelle (VR) dans le processus de simulation en contexte d’usine. En utilisant le composant Buildup, vous permettrez à l’utilisateur de naviguer virtuellement entre les différentes stations de…

Read more →

L’article [Stage] Ingénieur Développement App Delmia Vr (c++) – Vélizy-Villacoublay (78) – DASSAULT SYSTEMES est apparu en premier sur Réalité Augmentée - Augmented Reality.

Pour rendre les simulations de véhicule en Réalité virtuelle plus immersive et réaliste, Renault explore le développement de plateformes de simulations « Phygitales ». Ces plateformes sont composées de quelques éléments physiques du véhicule avec lesquels les utilisateurs interagissent, et complétées par…

Read more →

L’article [Stage] BAC+4 – Ingénieure/Ingénieur Généraliste spécialité Informatique/Robotique (F/H) – Renault –  Guyancourt est apparu en premier sur Réalité Augmentée - Augmented Reality.

Le stagiaire aura en charge de prendre en main une des pistes d’amélioration identifiées (restitution vidéo, réalité augmentée). Il sera en charge de :– Réaliser un état des lieux de la solution existante et de ses limitations,– Préparer et présenter…

Read more →

L’article [stage] Développement solution réalité augmentée de conduite à distance de train  – ALSTOM – Villeurbanne est apparu en premier sur Réalité Augmentée - Augmented Reality.