Droplet shape representation using Fourier series and autoencoders
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866911810764931072 |
|---|---|
| author | Durve, Mihir Tucny, Jean-Michel Bhamre, Deepesh Tiribocchi, Adriano Lauricella, Marco Montessori, Andrea Succi, Sauro |
| author_facet | Durve, Mihir Tucny, Jean-Michel Bhamre, Deepesh Tiribocchi, Adriano Lauricella, Marco Montessori, Andrea Succi, Sauro |
| contents | The shape of liquid droplets in air plays an important role in aerodynamic behavior and combustion dynamics of miniaturized propulsion systems such as microsatellites and small drones. Their precise manipulation can yield optimal efficiency in such systems. It is desired to have a minimal representation of droplet shapes using as few parameters to automate shape manipulation using self-learning algorithms, such as reinforcement learning. In this paper, we use a neural compression algorithm to represent, with only two parameters, elliptical and bullet-shaped droplets initially represented with 200 points (400 real numbers) at the droplet boundary. The mapping of many to two points is achieved in two stages. Initially, a Fourier series is formulated to approximate the contour of the droplet. Subsequently, the coefficients of this Fourier series are condensed to lower dimensions utilizing a neural network with a bottleneck architecture. Finally, 5000 synthetically generated droplet shapes were used to train the neural network. With a two real numbers representation, the recovered droplet shapes had excellent overlap with the original ones, with a mean square error 10^-3. Hence, this method compresses the droplet contour to merely two numerical parameters via a fully reversible process, a crucial feature for rendering learning algorithms computationally tractable. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_15797 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Droplet shape representation using Fourier series and autoencoders Durve, Mihir Tucny, Jean-Michel Bhamre, Deepesh Tiribocchi, Adriano Lauricella, Marco Montessori, Andrea Succi, Sauro Fluid Dynamics Computational Physics The shape of liquid droplets in air plays an important role in aerodynamic behavior and combustion dynamics of miniaturized propulsion systems such as microsatellites and small drones. Their precise manipulation can yield optimal efficiency in such systems. It is desired to have a minimal representation of droplet shapes using as few parameters to automate shape manipulation using self-learning algorithms, such as reinforcement learning. In this paper, we use a neural compression algorithm to represent, with only two parameters, elliptical and bullet-shaped droplets initially represented with 200 points (400 real numbers) at the droplet boundary. The mapping of many to two points is achieved in two stages. Initially, a Fourier series is formulated to approximate the contour of the droplet. Subsequently, the coefficients of this Fourier series are condensed to lower dimensions utilizing a neural network with a bottleneck architecture. Finally, 5000 synthetically generated droplet shapes were used to train the neural network. With a two real numbers representation, the recovered droplet shapes had excellent overlap with the original ones, with a mean square error 10^-3. Hence, this method compresses the droplet contour to merely two numerical parameters via a fully reversible process, a crucial feature for rendering learning algorithms computationally tractable. |
| title | Droplet shape representation using Fourier series and autoencoders |
| topic | Fluid Dynamics Computational Physics |
| url | https://arxiv.org/abs/2403.15797 |