FALL 2022 seminar

2 pm on 10/6

Steinman Hall Room 254

ABSTRACT

Point-of-Care (POC) disease diagnostic devices have attracted emerging attentions over the decade, and in the POC technologies, an on-chip blood plasma self-separation technology is desired for biomarker detection in the microfluidic platform to improve biosensing accuracy and enhance the signal-to-noise ratio. This talk will discuss the novel microfluidic platform that is implemented with both active and passive methods of self-separation with no external forces or devices, to achieve the on-chip blood plasma self-separation: The active approach for self-separation uses micro-filter structures in the microchannel, to filter and only get the clean plasma at the detection area of the microchannel. The passive approach for self-separation uses the mechanism of inertial migration of blood cells and Dean vortex in the microchannel, to separate the blood cells from the whole blood. In the passive approach, blood cells experience the net lift force and drag force induced by the secondary flow in the spiral microchannel and gradually migrate to the microchannel inner wall. The microfluidic platform is synergistically integrated with the nano biosensing technology by employing the nano electrodes in the microchannels. The microfluidic immuno-biosensing is implemented by immobilizing the antibodies using functionalized nanoparticles such as gold nanoparticles and carbon nanotubes. The target biomarkers are detected in the biofluid sample using the change in the electrical signals generated due to the interaction between antigens in the biofluid and immobilized antibodies on the nano electrodes. The ongoing results of this research have shown successful detection of cancer antigens-125 (CA-125) and human epididymis protein-4 (HE-4) antigens in pico and femto scale concentrations.

BIO

Dr. Eon Soo Lee is an Associate Professor with Tenure in Mechanical and Industrial Engineering and the Principal Investigator in Advanced Energy Systems and Microdevices Laboratory at New Jersey Institute of Technology (NJIT) since 2013. He is an elected Senior Member of the National Academy of Inventors (NAI) since 2020, and was enlisted as Vanguard Leaders in Higher Education in New Jersey by NJBIZ in 2018. He has served as an Editorial Board Member of Journal of Translational Engineering in Health and Medicine (JTEHM). Dr. Lee was also a founder of a startup company which was spun off from NJIT in 2018 for the development of the biochip technology for disease detection, diagnosis and monitoring. Dr. Lee has more than 20 years of professional research experiences including Stanford, Samsung and Hyundai along with NJIT, and has many (>100) publications of papers and patents in the areas of the mechanisms of carbon nanomaterials, microfluidics of complex fluids and micro-electrochemical biosensors with highly emerging recognitions in the research. He and his research has received many international recognitions and various innovation awards, including National Academy of Inventors Senior Member, NJBIZ 2018 Vanguard Higher Education Leaders, 2017 TechConnect National Innovation Award, 2017 IEEE NIH Best Design Award in Healthcare Innovations and Point-of-Care-Technologies (POCT) Conference, 2017 Defense Innovation Award, 2017 New Jersey Health Foundation Innovation Award and 2016 NSF National Innovation-Corps Award. He holds a Ph.D. and MS at Stanford University, and BS at Yonsei University, all in Mechanical Engineering.

3 pm on 10/20

Steinman Hall Room 254

ABSTRACT

This talk will discuss a dynamical approach for recovering unknown parameters of hydrodynamic equations such as damping coefficients or external forcing from partial observations of the underlying system. The method is based on a feedback-control scheme which incorporates observations as an exogenous term and drives the system to synchronize with the observations. This can be used to devise algorithms that systematically filter model errors resulting from the unknown parameters. Suitable conditions on the observational density and tuning parameters of the algorithm are identified under which convergence of the algorithm to the true value of the parameters can be guaranteed in the absence of observational errors.

BIO

Received PhD in Pure Mathematics from Indiana University under the supervision of Michael S. Jolly, studying the relation between analytic regularity of solutions to fluid equations and the dissipation length scale. Held visiting position at Institute of Pure and Applied Mathematics at UCLA and completed postdoctoral fellowship at Tulane University. Started at Hunter College in 2018 and joined the doctoral faculty at the Graduate Center in Fall 2021. Research interests are well-posedness issues for hydrodynamic equations, long-time behavior of dynamical systems, mathematics of turbulence, and data assimilation.

2 pm on 10/27

Steinman Hall Room 254

ABSTRACT

Our research group is focused on the mechanical characterization of high strength adhesives subjected to high loading rate events under various environmental conditions such as cold and hot temperatures. Though high-strength and ductile structural adhesives are now available, their experimental characterization under high-strain rate loading is not well developed. The understanding of their mechanical behavior is further complicated under extreme temperatures. The seminar will also highlight some of the challenges faced in trying to computationally model these adhesives.

BIO

Niell Elvin is a professor of Mechanical Engineering at The City College of New York (CCNY). His research interests include smart materials and sensing with a focus on energy harvesting. Recently his research interest has also focused on the characterization of high strength adhesives in extreme environments. Niell Elvin completed his undergraduate degree at the University of Witwatersrand in South Africa and his graduate degrees (SM and PhD) at MIT. He was an assistant professor in the Department of Civil Engineering at Michigan State University from 2004 to 2007 and joined the Department of Mechanical Engineering at CCNY in 2008.

2 pm on 11/2 (Wed)

Steinman Hall Room 254

ABSTRACT

Lattice Boltzmann Methods are a relatively new approach to simulating many complex fluid systems. A key motivation behind the development of these methods was that they were derivable from simply underlying Statistical Mechanics models, i.e. hydrodynamic lattice gases. Lattice Boltzmann was originally derived as a Boltzmann limit of such lattice gases, but subsequent improvements of these Methods severed the link to such underlying methods. While the improvements were significant, they also came with drawbacks: while lattice gases are unconditionally stable, modern lattice Boltzmann Methods are not. Lattice gases obeyed an H-Theorem, but most modern lattice Boltzmann Methods do not. Fluctuations were included in lattice gases, but they turn out to be less trivial to re-introduce into lattice Boltzmann Methods. All those observations are reasons to examine what novel lattice gases might look like that include the advantages of modern lattice Boltzmann Methods and retain the advantages of lattice gases. In this talk we will give a brief overview of deriving lattice gases that have Boltzmann limits that are very close to current lattice Boltzmann Methods and a coarse-graining approach the maps Molecular Dynamics simulations onto lattice gas (and lattice Boltzmann) Methods and what we can learn about the underlying Physics from those approaches.

BIO

Alexander Wagner is a Professor in the Department of Physics at North Dakota State University. He received his D.Phil. in theoretical Physics from Oxford University working under Julia Yeomans on lattice Boltzmann Methods for multi-phase and multi-component mixtures. He then moved as a postdoc to the Department of Materials Science and Engineering at MIT where he worked with Chris Scott and developed lattice Boltzmann Methods for viscoelastic fluids. From there he moved as a postdoc in the Department of Physics at the University of Edinburgh where he worked with Mike Cates on sheared phase-separation and scaling issues in Phase ordering. He is involved in the Discrete Simulations of Fluid Dynamics conference, which he chaired from 2008-2012 and he remains active in the international committee. Since 2012 he has been a remote editor for Physical Review E, taking over the responsibility for the Computational Physics section. His current research interests focus on the fundamental underpinnings of discrete fluid simulation methods.

2 pm on 11/10

Steinman Hall Room 254

https://ccny.zoom.us/j/81357159148

ABSTRACT

Understanding transport of soft particles in porous media is important and imperative for many industries such as oil exploration, filtering, underground water pollution control and cell migration. In order to better regulate or control these processes, it is desirable to obtain a quantitative correlation between macroscale parameters such as total pressure drop and microscale parameters such as particle size, stiffness, concentration as well as flow properties and porous media properties. This talk will focus on building a quantitative relationship between these macro and microscale variables using Generalized Capillary Bundle Model. I will introduce two methods – direct method by analyzing the local pressure drop at a deformed particle in pore and energy method by analyzing total energy loss in the system. Both methods yield a pressure governing equation with identical mathematical structure and the same results. The results show that the total pressure drop is exponentially dependent on the particle concentration, the size ratio of particles to pore throat and porous media length. In addition, our model is also able to predict pressure-flow rate relation. Interestingly, different from single phase flow Darcy’s law where pressure increases linearly with flow rate, our model shows that the flow rate increasing rate is decreasing with flow rate. Comparing the model prediction with reported experimental data, with no more than two fitting parameters, our model captures a precise quantitative relationship between pressure drop, particle concentration, size ratio of particle to pore throat, porous media length, as well as flow rate. This work significantly enhances our understanding on transport of soft particles in porous media as well as benefits relevant industries.

BIO

Shuaijun Li is a postdoctoral researcher in Department of Mechanical Engineering at City College of New York. He received his Ph.D. in mechanical engineering under Prof. Jing Fan and Prof. Charles Maldarelli in May 2022 from CCNY. Prior to his Ph.D., he obtained a master’s degree in geotechnical engineering from University of Chinese Academy of Sciences and a bachelor’s degree in mining engineering from China University of Mining and Technology. His research interests range from multiphase flow in porous media, nanoparticles-stabilized foams and emulsions to microfluidics and rock mechanics. He has been a principal participant in several national key research projects and has over 10 publications. His research results have been applied to various key engineering applications. He is also a reviewer for several peer-reviewed journals and served as a Youth Editorial Board member for journal Capillarity.

2 pm on 11/17

Steinman Hall Room 254

https://ccny.zoom.us/j/81357159148

ABSTRACT

The motion of a drop subject to an external force is ubiquitous in our everyday life, from rain splashing off a bird to a drop of ink printing onto a page. This talk will be broken into two parts. First we’ll focus on inviscid, inertial drop behavior. We’ll discuss how a drop inertially impacting a solid surface in the normal and oblique directions will vibrate in similar shapes as those predicted by sessile drop theory and how we can use the motion of a vibrating drop’s contact line to understand the constitutive law relating the drop’s apparent dynamic contact angle to its contact line velocity. We’ll find we are able to extract mobility parameters like those described by the Davis-Hocking model, and that mobility parameters extracted in this fashion can be used in simulations of drop-drop coalescence to accurately predict inertial post-coalescence dynamics. Second, we’ll briefly consider the case of two coalescing yield stress drops and show how the height of the bridge formed between them evolves similar to a Newtonian fluid, before arresting at long time due the fluid’s yield stress. We’ll find a model for the arrested interface shape based on the balance between capillary pressure and yield stress and solve numerically for the final arrested profile finding it to depend on the fluid’s yield stress, the drops’ surface tension and the coalescence angle, represented by a modified Bingham number. We also present a scaling argument for the bridge’s temporal evolution using the length scale found from this arrested shape analysis and present a similarity solution for the spatial evolution of the liquid bridge.

BIO

Vanessa Kern is currently a post-doctoral researcher working with Professor Andreas Carlson in the Mechanics section of the Math department at the University of Oslo in Oslo, Norway. She received her Bachelors in Chemical Engineering from Lehigh University, Pennsylvania and her Ph.D. from Cornell University, New York. She is an experimentalist whose focus is low-gravity/small length scale capillary fluid flows. Her current research focuses on the capture of fog from the atmosphere to relieve water scarcity in dry climates, as well as other problems in soft wetting, drop interactions with fibers and the motion of yield stress fluids. Her doctoral research focused on the mobility of a drop's contact line in inviscid inertial systems such as the impact of a drop with a solid surface, as well as the vibrations of an inviscid drop's interface.

2 pm on 12/8

Steinman Hall Room 254

ABSTRACT

From rain drops on a vehicle’s windshield to air bubbles sliding along our skin in a hot tub, we encounter motion of drops and bubbles on solid surfaces in our daily lives. Such motions exert a tangential shear stress on the surface that can be leveraged as a cleaning force for removing contaminants from the surface with applications in sustainable energy as well as food and biomedical industry. In this talk, I will first show how we can characterize the onset of surface depinning for partially wetting drops under forcing by wind and gravity to approach sustainable cleaning of solar panels and wind turbines. Next, I will explain how we can maximize surface sanitization with air bubbles in an aqueous medium by controlling the tilting angle of the target surface. Finally, I will reveal a new backflipping behavior associated with bubbles collision on tilted surfaces, and discuss how it can be leveraged to enhance sustainable cleaning with bubbles.

BIO

Alireza “Navid” Hooshanginejad is a Hibbitt Postdoctoral Fellow in the Center for Fluid Mechanics at Brown University. Navid received his B.Sc. (2014) in Mechanical Engineering from Sharif University of Technology in Iran, and his Ph.D. (2020) in Mechanical Engineering from the University of Minnesota. He has held a visiting Research Associate position at Flatiron Institute (2017), and a postdoctoral position in Biological and Environmental Engineering Department at Cornell University (2020-2022), before joining his current position at Brown University. Navid is an experimentalist and an applied mathematician working in the area of fluid mechanics and soft matter with focus on sustainable energy, and the environment. His research interests include drops and bubbles, interfacial instabilities, pattern formation, and self-assembly with a focus on table-top experiments and mathematical modeling. Navid has been the recipient of the Graduate Teaching Fellowship from the University of Minnesota (2020), and the Best Poster Award from the Gordon Research Conference on Granular Matter (2022). Navid is currently serving on the APS DFD Executive Committee as the Early Career Member.

2 pm on 12/13

Steinman Hall Room 254

ABSTRACT

Energy Efficiency is one of the four pillars of decarbonization of the manufacturing industry as published in the U.S. Department of Energy’s Industrial Decarbonization Roadmap in September 2022. Industrial energy efficiency improvement not only reduces energy costs and use for manufacturers, but also reduces the greenhouse gases emission, and increases the plant resiliency. In this seminar, Dr. Languri will discuss details of several energy efficiency projects that he has been leading as the principal investigator. Transformers are widely used in the power industry as critical components in transmission and distribution systems for voltage changing devices. As a result, transformers constantly generate heat that requires management. Efficient transformer cooling is an important factor in increasing the lifetime of transformers and reducing the associated maintenance costs. It has been reported that the lifetime of the transformer has increased by 10% with 1 °C decrease in the core temperature. The transformer oil which is typically a mineral oil, used commonly in cooling of transformers has an inherent disadvantage of low thermal conductivity. This limits the amount of heat transfer from the core of the transformer to the surroundings. This research discusses the improvement of thermal conductivity of transformer oil by using functionalized nanodiamond particles in transformer oil. In this study, enhancement in natural convection heat transfer is studied by functionalizing 5 nm-size diamond particles to transformer oil’s molecules. The rate of heat transfer improved by 82.5% as 0.4 wt.% functionalized nanodiamond were added to the baseline fluid.

BIO

Ethan Languri is an Associate Professor of Mechanical Engineering with tenure, a licensed Professional Engineer (PE) in the State of Tennessee, and Director of the U.S. Department of Energy sponsored Industrial Assessment Center at Tennessee Tech University. Prior to joining Tennessee Tech as a tenure-track Assistant Professor in 2014, he was a Senior Mechanical Engineer at Applied Research Associates in Panama City, Florida, where he was involved with energy design and analysis for buildings and shelters for the U.S. Department of Defense contracts. Dr. Languri earned his Mechanical Engineering Ph.D. in 2011 from University of Wisconsin-Milwaukee, Milwaukee, Wisconsin followed by Postdoctoral Fellow appointments at the University of Wisconsin-Milwaukee and Texas A&M University, College Station, Texas. He received his Mechanical Engineering B.Sc. and M.Sc. in 2005 and 2007 from Babol Noshirvani University of Technology in Iran, respectively.

2 pm on 12/21

Steinman Hall Room 254

ABSTRACT

Buildings account for 40% of total energy consumption, 74% of electricity use, and 37% of greenhouse gas emissions in the U.S. The environmental challenges of climate change—rising surface temperature, frequent and intense floods, droughts, and storms—result from and increase the energy usage and peak demands of buildings. Proactive and integrated building technologies are required to ensure a resilient and sustainable future environment. This talk will review Dr. Wang’s research efforts in building performance modeling as well as novel building technologies. She will describe an evolving machine learning-based method and its application in fault detection and diagnostics technology for building systems. She will present a hybrid approach integrating physics-based simulations with machine learning methods for simulating indoor greenery systems in the built environment. In addition, she will discuss her current research efforts on urban building energy modeling and future research in the context of the remaining scientific and technical challenges.

BIO

Dr. Liping Wang is an Associate Professor in Civil and Architectural Engineering and Construction Management at the University of Wyoming. She has 20 years of building performance modeling experience. Her research interests are in fault detection and diagnostics, energy efficiency for indoor agriculture, and resource-efficient and resilient community. She is an Associate Editor for the Journal of Sustainable Energy Technologies and Assessments and the Guest Editor for Energy and Buildings. She is a licensed professional engineer and a member of the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) and the International Building Performance Simulation Association. She is the Subcommittee Chair for Fault Detection and Diagnosis of ASHRAE TC 7.5 Smart Building Systems and a voting member for ASHRAE TC4.7 Energy Calculations. In 2019, she was honored to receive the NSF CAREER award for quantifying the effects of indoor greenery systems on building energy use and thermal comfort.