Connect with us
We’re experimenting with AI-generated content to help deliver information faster and more efficiently.
While we try to keep things accurate, this content is part of an ongoing experiment and may not always be reliable.
Please double-check important details — we’re not responsible for how the information is used.

Computer Modeling

Unveiling New Disturbances in Hypersonic Flows

At hypersonic speeds, complexities occur when the gases interact with the surface of the vehicle such as boundary layers and shock waves. Researchers were able to observe new disturbances in simulations conducted for the first time in 3D.

Avatar photo

Published

on

As researchers delve into the complex world of hypersonic speeds, they encounter unexpected phenomena when gases interact with the surface of vehicles. A team from the University of Illinois Urbana-Champaign’s Department of Aerospace Engineering has made a groundbreaking discovery by conducting fully 3D simulations for the first time. This achievement was possible due to access to Frontera, the National Science Foundation-funded leadership-class computer system, and software developed by previous graduate students.

The researchers, Deborah Levin and Irmak Taylan Karpuzcu, observed new disturbances in the flow around a cone-shaped model at hypersonic speeds. Normally, one would expect concentric ribbons of gas around the cone, but they noticed breaks in the flow within shock layers both in single and double cone shapes. These breaks were particularly noticeable near the tip of the cone, where air molecules are closer together, making them more viscous.

The team’s findings indicate that as the Mach number increases, the shock wave gets closer to the surface, promoting these instabilities. However, when they ran simulations at lower speeds (Mach 6), they did not see the break in the flow. This suggests that the cone geometry, which represents a simplified version of many hypersonic vehicles, plays a crucial role in understanding how the flow affects surface properties.

The researchers’ software allowed them to run the simulation efficiently on parallel processors, making it much faster than previous methods. They were able to compare their results with data from experiments under high-speed conditions and found breaks that they didn’t expect to see. The most challenging part of the work was analyzing why these breaks in the flow were happening.

The team developed a code based on triple-deck theory to numerically simulate the problem again. Running the 3D direct simulation Monte Carlo simulation is hard, but when they set up a second computer program to make sure everything works and is within the limits for their flow conditions, they saw the break in two big chunks in 180-degree periodicity around the cone.

The beauty of the direct simulation Monte Carlo lies in its ability to track each air molecule in the flow and capture shocks. This method introduces randomness and repetition to calculate fluid dynamics, making it more extensive than classical computational fluid dynamics methods.

This research has significant implications for designing hypersonic vehicles, as understanding how the flow affects surface properties can lead to better design considerations. The team’s findings also demonstrate the importance of conducting fully 3D simulations in researching complex phenomena at high speeds.

Artificial Intelligence

Scientists Uncover the Secret to AI’s Language Understanding: A Phase Transition in Neural Networks

Neural networks first treat sentences like puzzles solved by word order, but once they read enough, a tipping point sends them diving into word meaning instead—an abrupt “phase transition” reminiscent of water flashing into steam. By revealing this hidden switch, researchers open a window into how transformer models such as ChatGPT grow smarter and hint at new ways to make them leaner, safer, and more predictable.

Avatar photo

Published

on

By

The ability of artificial intelligence systems to engage in natural conversations is a remarkable feat. However, despite this progress, the internal processes that lead to such results remain largely unknown. A recent study published in the Journal of Statistical Mechanics: Theory and Experiment (JSTAT) has shed light on this mystery. The research reveals that when small amounts of data are used for training, neural networks initially rely on the position of words in a sentence. However, as the system is exposed to enough data, it transitions to a new strategy based on the meaning of the words.

This transition occurs abruptly, once a critical data threshold is crossed – much like a phase transition in physical systems. The findings offer valuable insights into understanding the workings of these models. Just as a child learning to read starts by understanding sentences based on the positions of words, a neural network begins its journey by relying on word positions. However, as it continues to learn and train, the network “keeps going to school” and develops a deeper understanding of word meanings.

This shift is a critical discovery in the field of artificial intelligence. The researchers used a simplified model of self-attention mechanism – a core building block of transformer language models. These models are designed to process sequences of data, such as text, and form the backbone of many modern language systems.

The study’s lead author, Hugo Cui, explains that the network can use two strategies: one based on word positions and another on word meanings. Initially, the network relies on word positions, but once a certain threshold is crossed, it abruptly shifts to relying on meaning-based strategies. This transition is likened to a phase transition in physical systems, where the system undergoes a sudden, drastic change.

Understanding this phenomenon from a theoretical viewpoint is essential. The researchers emphasize that their findings can provide valuable insights into making neural networks more efficient and safer to use. The study’s results are published in JSTAT as part of the Machine Learning 2025 special issue and included in the proceedings of the NeurIPS 2024 conference.

The research by Cui, Behrens, Krzakala, and Zdeborová, titled “A Phase Transition between Positional and Semantic Learning in a Solvable Model of Dot-Product Attention,” offers new knowledge that can be used to improve the performance and safety of artificial intelligence systems. The study’s findings have significant implications for the development of more efficient and effective language models, ultimately leading to advancements in natural language processing and understanding.

Continue Reading

Artificial Intelligence

The Quantum Drumhead Revolution: A Breakthrough in Signal Transmission with Near-Perfect Efficiency

Researchers have developed an ultra-thin drumhead-like membrane that lets sound signals, or phonons, travel through it with astonishingly low loss, better than even electronic circuits. These near-lossless vibrations open the door to new ways of transferring information in systems like quantum computers or ultra-sensitive biological sensors.

Avatar photo

Published

on

By

The Niels Bohr Institute at the University of Copenhagen has made a groundbreaking discovery that could revolutionize the way we transmit information. Researchers, in collaboration with the University of Konstanz and ETH Zurich, have successfully sent vibrations through an ultra-thin drumhead, measuring only 10 mm wide, with astonishingly low loss – just one phonon out of a million. This achievement is even more impressive than electronic circuit signal handling.

The drumhead, perforated with many triangular holes, utilizes the concept of phonons to transmit signals. Phonons are essentially sound waves that travel through solid materials by vibrating atoms and pushing each other. This phenomenon is not unlike encoding a message and sending it through a material, where signal loss can occur due to various factors like heat or incorrect vibrations.

The researchers’ success lies in achieving almost lossless transmission of signals through the membrane. The reliability of this platform for sending information is incredibly high, making it a promising candidate for future applications. To measure the loss, researchers directed the signal through the material and around the holes, observing that the amplitude decreased by only about one phonon out of a million.

This achievement has significant implications for quantum research. Building a quantum computer requires super-precise transfer of signals between its different parts. The development of sensors capable of measuring the smallest biological fluctuations in our own body also relies heavily on signal transfer. As Assistant Professor Xiang Xi and Professor Albert Schliesser explain, their current focus is on exploring further possibilities with this method.

“We want to experiment with more complex structures and see how phonons move around them or collide like cars at an intersection,” says Albert Schliesser. “This will give us a better understanding of what’s ultimately possible and what new applications there are.” The pursuit of basic research is about producing new knowledge, and this discovery is a testament to the power of scientific inquiry.

In conclusion, the quantum drumhead revolution has brought us one step closer to achieving near-perfect signal transmission. As researchers continue to explore the possibilities of this method, we can expect exciting breakthroughs in various fields, ultimately leading to innovative applications that will transform our understanding of the world.

Continue Reading

Computer Modeling

Scientists Crack Code to Simulate Quantum Computations, Paving Way for Robust Quantum Computers

A multinational team has cracked a long-standing barrier to reliable quantum computing by inventing an algorithm that lets ordinary computers faithfully mimic a fault-tolerant quantum circuit built on the notoriously tricky GKP bosonic code, promising a crucial test-bed for future quantum hardware.

Avatar photo

Published

on

By

The researchers have successfully simulated quantum computations with an error correction code known as the Gottesman-Kitaev-Preskill (GKP) code. This code is commonly used in leading implementations of quantum computers and allows for the correction of errors without destroying the quantum information.

The method developed by the researchers consists of an algorithm capable of simulating quantum computations using a bosonic code, specifically the GKP code. This achievement has been deemed impossible until now due to the immense complexity of quantum computations.

“We have discovered a way to simulate a specific type of quantum computation where previous methods have not been effective,” says Cameron Calcluth, PhD in Applied Quantum Physics at Chalmers and first author of the study published in Physical Review Letters. “This means that we can now simulate quantum computations with an error correction code used for fault tolerance, which is crucial for being able to build better and more robust quantum computers in the future.”

The researchers’ breakthrough has far-reaching implications for the development of stable and scalable quantum computers, which are essential for solving complex problems in various fields. The new method will enable researchers to test and validate a quantum computer’s calculations more reliably, paving the way for the creation of truly reliable quantum computers.

The article Classical simulation of circuits with realistic odd-dimensional Gottesman-Kitaev-Preskill states has been published in Physical Review Letters. The authors are Cameron Calcluth, Giulia Ferrini, Oliver Hahn, Juani Bermejo-Vega, and Alessandro Ferraro.

Continue Reading

Trending