Have you ever been trapped in an unexpected torrential downpour? Weather forecasting systems have always tried to anticipate adverse weather events. These systems, however, are heavily dependent on bulky, stationary, expensive equipment such as weather radar, impeding timely updates on local weather conditions for personal use. Tackling this gap in knowledge and practicality, a research team from Osaka Metropolitan University and the University of Tokyo developed an attachable and lightweight sensor sheet that features a flexible resistive sensor and a reservoir computing analysis. This single device allows simultaneous real-time measurement of raindrop volume and wind speed, reporting weather information when attached to umbrellas, cars, or houses. Research lead Professor Kuniharu Takei of Osaka Metropolitan University noted, “The findings open up a promising economical approach to weather reporting, contributing to disaster preparedness and greater community safety.” 

To determine rain volume, the sensor measures the electrical resistance generated when a raindrop hits its surface. It is protected by a superhydrophobic silicone sheet of polydimethylsiloxane (PDMS), which is infused with graphene and further processed with a laser. The superhydrophobic silicone repels water droplets, ensuring the durability and stability of the sensor. Laser texturing allows constant control and measurement of the behavior of water droplets, be they staying, sliding, bouncing, or splitting on the sensor surface. The sensor can be easily fixed to a wide range of surfaces and remains functional when flat or bent. Testing changes in rain volume estimations with the sensor mounted at various angles showed no significant differences, suggesting that the sensor can be attached to hand-carried items such as umbrellas. If widely adopted, it would be possible to obtain mass data that enables the development of real-time local weather maps.

Wind speed has a significant effect on water droplet behavior, indicating the need to measure wind speed at the same time as raindrop volume. Conventionally, measuring multiple pieces of weather data requires multiple sensors, increasing power consumption. Going beyond this traditional practice, the researchers made use of a machine learning algorithm called reservoir computing (RC) to analyze the output data. Changes in rain and wind conditions resulted in resistance changes, which were detected by the sensor and then recorded as time-series data. Such data was used to train the machine, which predicted the pattern and reported rain volume and wind speed as output information.

Even though there is still more work to be done to further improve its accuracy, the sensor is expected to be a mainstay of next-generation weather sensing. The study progresses the United Nations Sustainable Development Goals on resilient infrastructure, sustainable cities, and climate action. “We believe this device can contribute to realizing the ultimate Internet-of-Things society, which is safe, secure, comfortable, and disaster-free,” concluded Professor Takei, “and we would like to engage actively in industry-government-academia collaboration that promotes such practical applications.”

The complex aerodynamics around a moving car and its tires are hard to see, but not for some mechanical engineers.

Specialists in fluid dynamics at Rice University and Waseda University in Tokyo have developed their supercomputer simulation methods to the point where it’s possible to accurately model moving cars, right down to the flow around rolling tires.

The results are there for all to see in a video produced by Takashi Kuraishi, a research associate in the George R. Brown School of Engineering lab of Tayfun Tezduyar, the James F. Barbour Professor of Mechanical Engineering, and a student of alumnus Kenji Takizawa, a professor at Waseda and an adjunct professor at Rice.

“He has been escalating the complexity of his calculations, starting with a stand-alone tire and now having the rest of the car,” Tezduyar said of Kuraishi, who joined the Rice lab in 2020 and is co-supervised by Tezduyar and Takizawa.

The video also demonstrates the efficacy of the NURBS Surface-to-Volume Guided Mesh Generation method, a technique developed by the Team for Advanced Flow Simulation and Modeling co-led by Tezduyar and Takizawa to model flow dynamics around and through complex-geometry objects. NURBS stands for Non-Uniform Rational Basis Splines, a mathematical technique to describe 3D shapes and provide computational analysis of fluid and structural mechanics problems involving such shapes.

An earlier video of fluid flow in a beating heart showed the “through.” The new simulation shows what’s happening around a moving object, in this case, the extraordinary activity around a common subject. Complicating the model is the fact that the tires are in contact with the road and deform as they roll.

“We’re dealing with near-actual car and tire geometries,” Tezduyar said. 

A detailed description of the methods and the car simulation was published last month in the journal Computational Mechanics. Since then, the Rice-Waseda team made the video to bring the illustrations to life.

“Knowing the airflow behavior around the car and its tires will lead to a better understanding of their aerodynamic performance,” said Kuraishi, who earned undergraduate, master’s, and Ph.D. degrees at Waseda and spent a year as a postdoctoral researcher there with Takizawa before coming to Houston. “Simulations this sophisticated are important to provide realistic solutions and reliable answers in design and performance evaluation.”

Tezduyar, whose lab has also modeled recovery parachutes for NASA’s Orion capsules, said NURBS use in computational analysis has grown dramatically in recent years, combining efficiency and accuracy by lowering the number of “mesh” points necessary to model a system. Think of the mesh as a net of fluid (like air) around an object, with the mesh points living in 3D “elements.” The points and elements move when the object moves. 

In one model of a moving car, the computational flow analysis with NURBS was achieved with about 1.1 million points, a fraction of the number used in customary methods, while retaining its accuracy. That lowers the computational cost as well, Tezduyar said.

“We have a 3D mesh around the car and the tires, with more points near the tire surfaces for higher accuracy where it matters more,” he said. “As the tire rotates, the points and elements rotate with it, but the problem is that as the tire rotates, the elements moving under the tire collapse -- and this is what other methods cannot handle. Our method does, and it is key to getting an accurate simulation.”

As with the heart study, Tezduyar said their team is eager to collaborate with scientists and industry to model complex systems, including tires and vehicles.

“As time passes, naturally, new tire designs or improvements will be considered,” he said. “It would be very beneficial for tire manufacturers to do this type of simulation before they invest in generating a prototype because it would give them comprehensive and detailed numerical data about the aerodynamics around the tire that would be difficult to get in any other way.”

Co-authors of the paper are Satoshi Yamasaki, Zhaojing Xu, and Ryutaro Kaneko, all of Waseda University.