Exactly How Can We Put AI to Work in the Field of Aerodynamics Flow Measurement?

Visualising a vehicle that can alter its aerodynamic characteristics in real time is a remarkable concept that could revolutionise the automotive industry. Artificial Intelligence (AI) has the potential to completely transform the way in which automobiles are designed and manufactured, by allowing for the optimisation of a vehicle’s aerodynamics in accordance with various flow conditions. This could lead to improved performance, greater fuel efficiency, and a more dynamic driving experience.

The effectiveness of road vehicles is heavily dependent upon their aerodynamic properties. Therefore, the implementation of an aerodynamic package can substantially enhance the range of upcoming electric cars, bringing us closer to our objective of becoming independent from fossil fuels.

The use of wind tunnel testing and computational fluid dynamics (CFD) can be used to increase the efficiency of a car in the wind. By creating a flow condition and attempting a set of configurations, the most effective solution can be chosen. However, this method of selection may not always be the best, as it may not take into account all parameters or the optimum configuration for a variety of flow situations.

By combining Artificial Intelligence (AI) with wind tunnel experimentation, it is possible to develop an adaptive, feedback-based system that is able to autonomously modify vehicle aerodynamics for any condition of external airflow (Wind Tunnel Experiments, or WTEs). This integration of AI and wind tunnel research provides a powerful tool for improving the aerodynamic performance of vehicles.

Technology development for observing turbulent flows; NEXTFLOW

The Universidad Carlos III de Madrid (UC3M) is the recipient of an European Research Council (ERC) Starting Grant to support the NEXTFLOW project. The primary objective of this project is to develop innovative technologies with improved efficiency and reliability for the measurement of turbulent flows.

These methods, which make use of AI and data mining, may improve cars’ aerodynamics and reduce their negative effects on the environment.

One of the key obstacles in the field of aerodynamics is the progress of more reliable techniques for characterising and managing turbulent flows (such as the liquid motion around an aircraft wing). Exploring the complexity of turbulent flows is an ongoing task, as the exact behaviours of these flows remain difficult to predict and comprehend. As such, advancements in the field of aerodynamics hinge on the development of more effective methods of understanding and controlling turbulent flows.

It is difficult to completely understand their behaviour using the current methods since “they are chaotic, with intricate dynamics,” UC3Department M’s of Bioengineering and Aerospace Engineering coordinator Stefano Discetti comments on the NEXTFLOW project.

The quantification of turbulent flows has become an essential component of contemporary industrial operations, due to its prevalence in various applications. A greater knowledge of the turbulence dynamics can be leveraged to increase performance in industries such as transportation. By refining our methods for measuring and managing turbulent flows, we can create new opportunities for improving efficiency in the transportation sector.

Experts in the field of aerodynamics and hydrodynamics are in agreement that turbulent flows have a significant effect on the amount of resistance experienced by a variety of vehicles, such as cars, aircraft, and boats, when in motion. A more thorough comprehension and exploration of these turbulent flows could potentially lead to the improvement of their efficiency, as well as a reduction of the environmental effects caused by their movement.

The current methods employed to identify turbulent flow in experiments are limited to providing a partial description of its velocity, temperature, or pressure, as noted by Discetti. This ERC project aims to develop advanced measuring equipment, incorporating Artificial Intelligence (AI) and data mining techniques, to gain a more comprehensive understanding of turbulence’s dynamic behaviour and to improve control over it.

Particle image velocimetry is improved by using artificial intelligence (PIV)

Volumetric Particle Image Velocimetry (PIV) is a method that is being used to measure and analyse the three-dimensional flow of fluids. By tracking the path of particles that are illuminated by a laser light, it is possible to precisely reconstruct the motion of the fluid. This method provides a clear visualisation of the flow, allowing for a better understanding of the dynamics and characteristics of the fluid.

In order to more accurately capture temporal dynamics in three-dimensional (3D) spaces, scientists are seeking to utilise data from high-frequency point probes. To further refine the precision of particle image velocimetry (PIV) techniques, researchers from the Universidad Carlos III de Madrid (UC3M) have proposed a novel data mining approach in a recent paper published in the journal Experimental Thermal and Fluid Science. This approach has the potential to drastically improve the accuracy of PIV techniques.

Researchers will be utilising equations from fluid mechanics to derive pressure fields from high-precision, time-resolved data. The intention is to create manageable models that are capable of characterising flow behaviour and developing effective control algorithms.

The findings of these studies could potentially lay the groundwork for the creation of novel methods for accurately assessing and controlling flows in actual utilizations. According to Discetti, this has the potential to be beneficial for more than one industry, including aviation, as it could potentially improve their operations and reduce their environmental impact.

Different uses of AI in the field of aerodynamics

  • Computational aerodynamics may benefit from the use of AI.
  • It may help in the research and development of aerodynamic designs.
  • In the bicycle industry, it may be utilised to create more wind-resistant frames.
  • It’s a tool for learning new things.
  • Using the data available, it may be possible to accelerate the process of obtaining numerical solutions to feasible aeronautical design and development challenges.

The design procedures might be influenced by CNNs

Rather than relying on the conventional methods for calculating functions in high-dimensional spaces, deep learning can offer an expedited and productive outcome. Furthermore, deep learning architectures such as deep neural networks (DNNs) are commonly employed in data mining and are particularly suitable for uncovering multi-scale characteristics from massive, high-dimensional datasets.

Recent literature (Lecun et al., 1998; Taylor et al., 2010; Zuo et al., 2015) has demonstrated the efficacy of Deep Convolutional Neural Networks (CNNs) when it comes to analysing photographs. Such networks have shown to be capable of learning high-level features even when there are strong spatial and temporal correlations present in the input.

The potential benefits of Convolutional Neural Networks (CNNs) for the study of fluid mechanics have been garnering the attention of researchers due to their ability to adapt to three-dimensional and transient conditions, as well as their flexibility to represent complex forms.

This study has demonstrated that Convolutional Neural Networks (CNNs) can be utilised to facilitate simulation-based design and optimisation in near real-time, thus providing an effective means of designing. However, it is important to bear in mind that the general prediction behaviour for unknown airfoil geometries from different families is limited due to the fact that only three airfoil shapes were used for the purpose of training.

The aim of future research is to train on a large dataset which encompasses numerous airfoil families, and to augment the training datasets with operations such as translation and rotation so as to convert a limited amount of input data into a significantly larger array of substantially altered data (Shijie et al., 2017). This advancement will not only drastically improve speed, but also safeguard the network from learning insignificant patterns. Implementing physical restrictions on networks, including mass and momentum conservation, may be accomplished through the exploration of physical loss functions.

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