Aerodynamics and Fluid Mechanics Commons^™

A Convolutional Neural Network Model Based On Multiscale Structural Similarity For The Prediction Of Flow Fields, Yifu An, Xiaosong Du, Joaquim R.R.A. Martins

Mechanical and Aerospace Engineering Faculty Research & Creative Works

We have seen the emerging applications of deep neural networks for flow field predictions in the past few years. Most of the efforts rely on the increased complexity of the model itself or take advantage of novel network architectures, such as convolutional neural networks (CNN). However, reaching low prediction error cannot guarantee the quality of the predicted flow fields in terms of the perceived visual quality. This work introduces the multi-scale structural similarity (MS-SSIM) index method for flow field prediction. First, we train CNN models using the commonly used root mean squared error (RMSE) loss function as the reference. Then …

Novel Adaptive Sampling Algorithm For Pod-Based Non-Intrusive Reduced Order Model, Jiachen Wang, Xiaosong Du, Joaquim R.R.A. Martins

Mechanical and Aerospace Engineering Faculty Research & Creative Works

The proper orthogonal decomposition (POD) based reduced-order model (ROM) has been an effective tool for flow field prediction in the engineering industry. The sample selection in the design space for POD basis construction affects the ROM performance sensitively. Adaptive sampling can significantly reduce the number of samples to achieve the required model accuracy. In this work, we propose a novel adaptive sampling algorithm, called conjunction sampling strategy, which is based on proven strategies. The conjunction sampling strategy is demonstrated on airfoil flow field prediction within the transonic regime. We demonstrate the performance of the proposed strategy by running 10 trials …

Theoretical Study Of Magnetic Particles In A Shear Flow Subjected To A Uniform Magnetic Field, Christopher A. Sobecki

Doctoral Dissertations

"Magnetic manipulation of non-spherical magnetic microparticles is important for applications in shape-based and magnetic-based separations such as waste management, disease diagnostics, drug delivery, and mining. Manipulations of magnetic microparticles also include chain formation to assemble compositions for electronics, drug loading designs, and magnetorheological fluids for smart armor, hydraulic brakes, and dampers. In microfluidic devices, separation-formation-effectiveness depends on the shape of the channel, the shear rate, and the magnetic field strength and direction.

Particle separation and chain formation involved highly complex and computational expense-demanding studies in microfluidic devices, magnetic fields, and particle- particle/wall interactions. This research took complex experimental studies and …

Dynamic Behavior And Interactions Of Ferrofluid Droplets Under Magnetic Fields In Low Reynolds Number Flows, Md Rifat Hassan

Doctoral Dissertations

Digital microfluidics in combination with emulsion microfluidics are crucial building blocks of droplet-based microfluidics, which are prevalent in a wide variety of industrial and biomedical applications, including polymer processing, food production, drug delivery, inkjet printing, and cell-based assays. Therefore, understanding the dynamics and interactions of droplets as well as the interactions between the droplets and solid surfaces are of great importance in order to improve the performance or product in these applications.

Recently, several studies in the literature have demonstrated the potential of magnetic fields in controlling the behavior of droplets in microscale; however, the fundamental mechanism behind the interesting …