Vacancies
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We offer a PhD position in TU Delft on developing smart filtration membranes that clean themselves - make an impact towards cleaner industry and environment!
Job description
Currently fouling is the major obstacle that hinders the long-term operation and wide-spread utilisation of membranes in various applications. A promising solution to fouling is introduction of stimuli-responsive membranes, whose pore sizes and/or surface properties can be altered via external stimuli. While first stimuli-responsive membranes have been proposed and demonstrated in the literature, their means to control the gating state are infeasible for countering fouling in an economically and ecologically feasible way. In this PhD project you will develop a novel type of smart membrane that counteracts fouling via mechanical and electromechanical agitation. You will: (1) study the state-of-the-art of the (gating) membranes and mechanical metamaterials to understand their underlying working mechanisms; (2) design and model novel separation membranes that incorporate mechanical metamaterial structures and smart material actuators (Smart Meta Membranes) in order to attain mechanical and/or electrical control over their pore size/shape and thus permeability; and (3) manufacture prototypes of these membrane designs and experimentally validate their functioning. You will work under the supervision of dr. Hanieh Bazyar and dr. Andres Hunt in a joint project between the departments of Process and Energy (P&E) and Precision Microsystems Engineering (PME), at the faculty of Mechanical, Maritime and Materials Engineering (3mE). This position will be embedded in the Engineering Thermodynamic section of the P&E department (ETh/3mE). This research will be enabled and supported by the facilities of both departments: the state-of-the-art membrane research facilities of the PE department and the well-equipped multi-scale manufacturing facilities of the PME department.
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Experimental measurements and machine learning for active control strategies in bubble drag reduction techniques for liquid flows.
Job description
Mechanical systems involving fluids are inextricably connected to virtually all transport systems and, by extension, carbon emissions. Any effort to limit the latter lies chiefly in our ability to represent the underlying physics and, possibly, control them. The associated fluid dynamics, however, introduce remarkably complex behavior. In recent years, machine learning and artificial intelligence are revolutionizing the field by developing low-dimensional models that can significantly facilitate flow control. However, control strategies remain limited to simple scenarios that involve single-phase flows. Our primary goal is to tackle the substantially more complex challenge of data-driven low-dimensional modeling and control of multiphase flows. The focus will be on a specific scenario of friction drag reduction in liquid flows by gas injection.
In this Ph.D. project, you will work on developing state-of-the-art data-driven models for reduced-order modeling of such flows, which will enable successful closed-loop control of your design. To achieve this ambitious goal, you will exploit various flow measurement techniques (including particle image velocimetry and pressure and drag measurements) to ensure optimal data collection and representation of different flow states.
As a PhD student, you will be part of two vibrant teams of researchers within the Multiphase Systems (MS) and Learning for Autonomous Control (LAC) sections, with diverse backgrounds and expertise. Your supervisors will be Dr. Angeliki Laskari and Dr. Cosimo Della Santina within the Process & Energy and Cognitive Robotics Departments.
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Future challenges in sustainability increasingly require floating structures to operate in proximity to one another. This project will shed light on their complex interaction and control capabilities.
Job description
Offshore wind farms, wave-energy converter arrays, and maneuvering ship convoys all involve hydrodynamically-coupled systems of multiple floating structures. The flow around each structure produces an unsteady wake that can impact neighboring structures, and cause force and kinematic fluctuations. These interactions may reduce operational efficiency, induce fatigue loading and thus maintenance costs, and can even lead to system failure.
This four-year PhD project will develop methods for the identification of such dynamic interactions and the design of controllers that mitigate the force fluctuations on hydrodynamically interacting structures. The ultimate goal is to leverage these results to develop harm-minimization strategies that prevent the potential deleterious effects of such wake-structure interactions.
The PhD student will work at the Faculty of Mechanical, Maritime, and Materials Engineering (3mE) of TU Delft, within both the Fluid Mechanics group of the Process & Energy department and the Data-Driven Control section of the Delft Center for Systems and Control.
The identification of the hydrodynamically coupled system will be based on the generation and analysis of experimental data, using a unique unsteady flows facility which combines advanced flow diagnostic methods with industrial robotics hardware. The project will leverage data-reduction tools such as the dynamic mode decomposition to build a system model from the experimental data. The considerable nonlinearities inherent to the unsteady flow-structure interactions between floating bodies present a challenge for these analysis tools which will be overcome by exploring the use of recently developed nonlinear extensions to these methods. In this way the student will identify system models which are both physically interpretable and generalizable and are appropriate for the design of controllers that mitigate the unsteady loads on the hydrodynamically coupled structures. The approach will build on optimization-based control methods like model predictive control.
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PhD position Wall bounded turbulence: from the instantaneous to the statistical
Job description
One of the still open questions in wall turbulence is to link instantaneous flow features with the currently available wealth of statistical insights: how are these structures "put together" in time in order to create the well-established statistical picture? This is especially crucial in high-Reynolds number flows, which are still unattainable by direct numerical simulations and for which time-resolved experimental data are relatively scarce.
The goal of this PhD project is to fill this gap in available data but also in the subsequent analysis.The focus will be coherent structures in the logarithmic and wake regions and specifically their time footprint and evolution. The results will be pivotal both from a fundamental standpoint but more importantly as input for fine-tuning low-order models and optimizing drag reduction strategies, many of which still rely on statistical information.
To achieve this goal, you will design and perform high-quality experiments in moderate-to high-Reynolds number turbulent boundary layers, by employing state-of-the-art high-speed systems in water flows, covering hundreds of boundary layer turnover times. You will also develop novel analytical tools based on both numerical and experimental data that will reveal the temporal characteristics of structures in the log and wake regions and their scalings.
As a PhD student, you will be part of a vibrant team of researchers in the Fluid Dynamics and Multiphase Systems groups, with diverse backgrounds and expertise, and you will have the chance to travel to international conferences to present your work. This PhD position is funded by AFOSR and will be supervised by Assistant prof. Angeliki Laskari and Prof. Christian Poelma from the Multiphase systems group. The group is part of the Process & Energy Department, which aspires to conduct world-class research & education focusing on sustainable process & energy technologies, to enable the energy transition. The research is conducted from a deep understanding of the underlying physics and is oriented towards industrial applications and societal needs.
PhD Students Andrea Mangel Raventos and Allesanro Cavalli in 2 minutes about working at Process and Energy.
Assistant Professor Daniel Tam in 2 minutes about working at Process and Energy.