One day, underwater robots may imitate fish like creatures very realistically. They can deceive not only real animals themselves, but also humans. This ability can obtain all kinds of information, from the health status of fish to the location of foreign ships.
Then, such a robot needs fast speed, high efficiency, strong mobility and secret sound. In other words, they must be very much like bottlenose dolphins or killer whales.
“We are very interested in developing the next generation of underwater vehicles, so we are trying to understand how dolphins and whales swim efficiently,” said Keith W. mored, an assistant professor of mechanical engineering and mechanics at the school of engineering and Applied Sciences at Lehigh University “We are studying how these animals are designed and what benefits this design has in terms of their swimming performance or the hydrodynamics of swimming. “
Mored is the lead researcher of a paper recently published in the Journal of interface of the Royal Society to study the hydrodynamics of whales by numerically simulating their swinging tail fins. Mored and his team developed a model for the first time, which can quantitatively predict how the motion of the fin should be adjusted to its shape to maximize efficiency.
The study is part of a larger project supported by the Naval Research Office’s multidisciplinary university research program. The project has received more than $7 million in funding over five years, of which $1 million will be donated to Lehigh University, including the University of Virginia, West Chester University, Princeton University and Harvard University.
The tail fins of cetaceans (whales and dolphins) have various shapes. The way these animals move their fins, or their kinematics, are also different. Some cetaceans have large flapping amplitude or large inclination angle of fins. Mored and his team hope to better understand the interaction between these two variables to determine whether the tail fin shape is suitable for a specific kinematic set.
Using the shape and motion data of five cetaceans (commonly known as bottlenose dolphins, spotted dolphins, killer whales, pseudokiller whales and beluga whales), they simulated each animal to determine their propulsion efficiency. Then they exchanged data, for example, to simulate the fin shape of a killer whale and the kinematics of a dolphin.
“We ran 25 such exchange simulations, and the results were really amazing,” mored said. “The shape of the pseudo killer whale fin is always the best, which means it is the most effective. It doesn’t matter what kind of kinematics we give it. The kinematics of the beluga whale is always the best, no matter what shape it is attached to. We didn’t expect this, so we began to study deeply and developed this relatively simple model, that is, how the efficiency depends on different kinematics and Shape variables are measured“
The model captures the data that mored and his team have already generated, so they extend the data set to check for any trends in the results. They found that the first mock exam not only predicted efficiency beyond the dataset, but also revealed that the specific shape was customized according to the specific kinematics.
An interesting finding is that the basic interaction between the cyclic force and the increased inertial force contributes to the movement of animals, mored said. Cyclic force is the force that produces lift, just like an aircraft.
“A tail that swings up and down will produce the same force as an aircraft, but it will also produce additional inertial force, which is related to the speed of fluid acceleration,” mored said. “In the past, people did not think that these increased forces had anything to do with the swimming of whales. This was completely unrecognized in the previous literature. However, we found that the acceleration of fins was indispensable to the trend of prediction efficiency, which fascinated us. It finally gave us an accurate prediction model. Without it, we can basically say that the shape of fins is not stable It changes efficiency, which is not true“
Having a model that can predict performance according to shape and kinematics provides a basic design equation for building an underwater robot like whales. The potential of these machines is enormous. Fast, efficient and highly maneuverable fish robots can help researchers test hypotheses about how animals swim and better understand the behavior of fish schools. They can be used to detect submarines and other submersibles. They can also be used to monitor the impact of climate change on fish stocks.
Mored and his team have moved on and extended the model to consider a wider range of variables, which are then verified with experimental data. Finally, they want to build a more predictive model that can capture the impact of these variables and then predict the performance of a range of applications.
“The question of fish swimming is really exciting because it’s too complicated,” he said. “It’s interesting to see order, see the structure, and understand what’s happening from these chaotic variables.”
Responsible editor; zl