School of Mechanical Engineering Seminar
Monday, June 3, 2019 at 14:00
Wolfson Building of Mechanical Engineering, Room 206

Data-Driven Modeling of Pitching and Plunging Wings in Single and Tandem Configurations in Hovering Flight

Victor Troshin

Academic Advisor:

Prof. Avi Seifert

The presented work addresses the challenges related to the low order dynamic modeling of the fluid domain with immersed moving solid bodies in it. In this work mathematical tools were developed and tested on numerical and experimental data. The ultimate goal of this work was to develop a methodology which enables a real-time flow field estimation based on a small number of sensors. As an example of such a problem, modeling of pitching and plunging wings in single and tandem configurations was chosen. In order to achieve the defined goal, the presented research was carried out in the three following stages:

In the first stage of the research, a proper orthogonal decomposition (POD) methodology for a flow field in a domain with moving boundaries was developed. In the standard POD approach, the properties of the region of the domain which alternatingly occupied by fluid and solid are not defined. Thus, here, prior to the decomposition, the domain with moving or deforming boundaries was mapped to a stationary domain using volume preserving mapping. This mapping was created by combining a transfinite interpolation and volume adjustment algorithm. The algorithm is based on an iterative solution of the Laplace equation with respect to the displacement potential of the grid points. At this stage of the research, the method was validated on CFD simulation of pitching and plunging ellipse in a still fluid.

The main goal of the second stage of the research was to develop a low order model of a heaving airfoil in a still fluid using experimental measurements. This was achieved by modifying and applying the tools developed in the first stage of the study.  The modified POD approach together with a time delay neural network (TDNN) was used to model and predict the flow field evolution using only a couple of low profile load sensors. The neural network estimated the amplitudes of the most energetic modes using four sensory inputs. The modes were calculated using the proper orthogonal decomposition of the flow field data obtained experimentally by time-resolved, phase-locked particle imaging velocimetry (TRPIV). The model showed good estimation quality.

In the final stage of this study, a modeling methodology was implemented on measured flow field data of pitching and plunging wings in a tandem configuration. Here, the velocity field associated with the wings’ flapping motion was mapped and modeled using the previously developed POD approach. The flow field dynamics was approximated by a linear model based on only four POD modes. Since the state of the low order model (i.e. the amplitudes of the modes) is physically impossible to measure, a Kalman filter was implemented. The Kalman filter used the signals from two low-profile strain gauge sensors located at the root of the hindwing to evaluate the reduced-order state of the system. Then, the full state of the system was estimated using the POD approximation. Therefore, by using only two strain sensors’ signals, the complex vortex dynamics associated with the tandem wings motion was successfully modeled.

Speaker: Daniel Brodeski

M.Sc. student under the supervision of Dr. Raja Giryes

Wednesday, June 19^th, 2019 at 15:30

Room 011, Kitot Bldg., Faculty of Engineering

Deep Radar Detector

Abstract

While camera and LiDAR processing have been revolutionized since the introduction of deep learning, radar processing still relies on classical tools. In this work, we introduce a deep learning approach for radar processing, working directly with the radar complex data. To overcome the lack of radar labeled data, we rely in training only on the radar calibration data and introduce new radar augmentation techniques. We evaluate our method on the radar 4D detection task and demonstrate superior performance compared to the classical approaches while keeping real-time performance. Applying deep learning on radar data has several advantages such as eliminating the need for an expensive radar calibration process each time and enabling classification of the detected objects with almost zero-overhead.

(The talk will be given in English)

Speaker:     Dr. Yasmine Meroz
                     School of Plant Sciences and Food Security, Tel Aviv University

Monday, May 20^th, 2019
15:00 - 16:00

Room 011, Kitot Bldg., Faculty of Engineering

A Framework for Collective Behavior in Plant-Inspired Growth-Driven Systems

Abstract

A variety of biological systems are not motile, but sessile in nature, relying on growth as the main driver of their movement. Groups of such growing organisms can form complex structures, such as the functional architecture of growing axons, or the adaptive structure of plant root systems. These processes are not yet understood, however the decentralized growth dynamics bear similarities to the collective behavior observed in groups of motile organisms, such as flocks of birds or schools of fish. Equivalent growth mechanisms make these systems amenable to a theoretical framework inspired by tropic responses of plants, where growth is considered implicitly as the driver of the observed bending towards a stimulus. We introduce two new concepts related to plant tropisms: point tropism, the response of a plant to a nearby point signal source, and allotropism, the growth-driven response of plant organs to neighboring plants. We first analytically and numerically investigate the 2D dynamics of single organs responding to point signals fixed in space. Building on this we study pairs of organs interacting via allotropism, i.e. each organ senses signals emitted at the tip of their neighbor and responds accordingly. In the case of local sensing we find a rich phase space. This work sets the stage towards a theoretical framework for the investigation and understanding of systems of interacting growth-driven individuals.

https://www.biorxiv.org/content/10.1101/566364v1
 

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You are invited to attend a department seminar

VOLUMETRIC 3D-PRINTED ANTENNAS, MANUFACTURED VIA SELECTIVE POLYMER METTALIZATION

By:

Sergey Kolen
MSc student under the supervision of Prof. Pavel Ginzburg

Abstract

Additive manufacturing paves new ways to the efficient exploration of the third space dimension, providing advantages over conventional planar architectures. In particular, volumetric electromagnetic antennas can demonstrate superior characteristics, outperforming their planar counterparts. Here a new approach to the fabrication of electromagnetic devices is developed and applied to antennas, implemented on curved surfaces. Highly directive and broadband antennas are 3D‐printed on hemispherical supports. The antenna skeleton and the support are simultaneously printed with different polymer materials – PLA mixed with graphene flakes and pure PLA, respectively. Weakly DC‐conductive graphene PLA‐based skeleton is post‐processed and high‐quality conductive copper layer is selectively electrochemically deposited on it. The antenna devices are found to demonstrate radiation performance, similar to that achievable with conventional fabrication approaches. However, additive manufacturing of RF antennas provides superior capabilities of constructing tailor‐made devices with properties, pre‐defined by non‐standardized end users.

On Tuesday, May 28, 2019, 15:00
Room 011, EE-Class Building