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Browsing by Author "Tafti, Danesh"

Now showing 1 - 3 of 3
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    Deep Learning Methods for Predicting Fluid Forces in Dense Particle Suspensions
    Raj, Neil Ashwin (Virginia Tech, 2021-07-28)
    Modelling solid-fluid multiphase flows are crucial to many applications such as fluidized beds, pyrolysis and gasification, catalytic cracking etc. Accurate modelling of the fluid-particle forces is essential for lab-scale and industry-scale simulations. Fluid-particle system solutions can be obtained using various techniques including the macro-scale TFM (Two fluid model), the meso-scale CFD-DEM (CFD - Discrete Element Method) and the micro-scale PRS (Particle Resolved Simulation method). As the simulation scale decreases, accuracy increases but with an exponential increase in computational time. Since fluid forces have a large impact on the dynamics of the system, this study trains deep learning models using micro-scale PRS data to predict drag forces on ellipsoidal particle suspensions to be applied to meso-scale and macro-scale models. Two different deep learning methodologies are employed, multi-layer perceptrons (MLP) and 3D convolutional neural networks (CNNs). The former trains on the mean characteristics of the suspension including the Reynolds number of the mean flow, the solid fraction of the suspension, particle shape or aspect ratio and inclination to the mean flow direction, while the latter trains on the 3D spatial characterization of the immediate neighborhood of each particle in addition to the data provided to the MLP. The trained models are analyzed and compared on their ability to predict three different drag force values, the suspension mean drag which is the mean drag for all the particles in a given suspension, the mean orientation drag which is the mean drag of all particles at specific orientations to the mean flow, and finally the individual particle drag. Additionally, the trained models are also compared on their ability to test on data sets that are excluded/hidden during the training phase. For instance, the deep learning models are trained on drag force data at only a few values of Reynolds numbers and tested on an unseen value of Reynolds numbers. The ability of the trained models to perform extrapolations over Reynolds number, solid fraction, and particle shape to predict drag forces is presented. The results show that the CNN performs significantly better compared to the MLP in terms of predicting both suspensions mean drag force and also mean orientation drag force, except a particular case of extrapolation where the MLP does better. With regards to predicting drag force on individual particles in the suspension the CNN performs very well when extrapolated to unseen cases and experiments and performs reasonably well when extrapolating to unseen Reynolds numbers and solid fractions.
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    Effect of Kinematics and Caudal Fin Properties on Performance of a Freely-Swimming Fin
    Nayak, Anshul (Virginia Tech, 2020-12-23)
    Traditionally, underwater vehicles have been using propellers for locomotion but they are not only inefficient but generate large acoustic signature. Researchers have taken inspiration from efficient swimmers like fish to address the issue with alternate propulsion mechanism. Mostly, research on fish locomotion involved studying a foil tethered to a fixed point inside uniform flow. A major drawback of such study is that neither it resembles a freely swimming fish nor it takes into consideration the dynamics of moving fish on propulsive forces. Hence, in our current study, we focus on comparing the performance of a free swimming fin over tethered fin both experimentally and numerically. Experimentally, we focus on the oscillatory form of locomotion where the caudal fin pitches to generate necessary thrust as seen in boxfish. We intend to investigate the Caudal fin kinematics and its physical properties on locomotion performance. To better understand, we build an automated robo-physical model that swims in a circular path so as to carry extensive experiments. We focus on understanding the effect of flexibility, shape and thickness of caudal fin on performance. Currently, we have studied three different flexibility and for each flexibility, we studied three different shape. We found there must be an optimal flexibility for minimising the Cost of Transport (COT). We also propose that the steady forward speed linearly varies with tail tip velocity. Furthermore, we investigated the effect of thickness of fin and considered uniform and tapered fin with equal area moment of inertia. Numerically, we investigated the effect of phase offset between heave and pitch motion on the performance of a freely swimming fin and compared that to a tethered fin. A freely-swimming fin self propels and moves with steady speed while a tethered fin remains stationary and actuates under uniform flow. We model the fin as a rigid body undergoing prescribed motion in an inviscid fluid and solved for coupled interaction using panel method. We show the effect of phase offset for optimum performance and found a significant difference between tethered and freely swimming fin.
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    Turning-ascending flight of a Hipposideros pratti bat
    Rahman, Aevelina; Windes, Peter; Tafti, Danesh (Royal Society, 2022-06-08)
    Bats exhibit a high degree of agility and provide an excellent model system for bioinspired flight. The current study investigates an ascending right turn of a Hipposideros pratti bat and elucidates on the kinematic features and aerodynamic mechanisms used to effectuate the manoeuvre. The wing kinematics captured by a three-dimensional motion capture system is used as the boundary condition for the aerodynamic simulations featuring immersed boundary method. Results indicate that the bat uses roll and yaw rotations of the body to different extents synergistically to generate the centripetal force to initiate and sustain the turn. The turning moments are generated by drawing the wing inside the turn closer to the body, by introducing phase lags in force generation between the wings and redirecting force production to the outer part of the wing outside of the turn. Deceleration in flight speed, an increase in flapping frequency, shortening of the upstroke and thrust generation at the end of the upstroke were observed during the ascending manoeuvre. The bat consumes about 0.67 W power to execute the turning-ascending manoeuvre, which is approximately two times the power consumed by similar bats during level flight. Upon comparison with a similar manoeuvre by a Hipposideros armiger bat (Windes et al. 2020 Bioinspir. Biomim. 16, abb78d. (doi:10.1088/1748-3190/ abb78d)), some commonalities, as well as differences, were observed in the detailed wing kinematics and aerodynamics.