Abstract: Soot is a major air pollutant produced by incomplete combustion of hydrocarbon fuels. The contribution of soot to global warming is currently estimated with large uncertainty (partly) due to the fractal-like agglomerate structure of its constituent nanoparticles. Here, the dynamics of soot nanoparticles are investigated to advance our current understanding of particle formation during combustion. Discrete element modeling (DEM) enables the detailed description of the particle morphology (doi.org/10.1016/j.proci.2016.08.078) and optical properties (doi.org/10. 1016/j.proci.2018. 08.025) in population balance models and computational fluid dynamics (doi.org/10.1016/j.combustflame.2021.01.010). Power laws relating the optical properties of soot to its filamentary structure are derived by DEM (doi.org/10.1016/j.carbon.2017.06.004) to facilitate the accurate monitoring of soot emissions by aerosol (doi.org/10.1016/j.proci.2020. 07.055), laser (doi.org/10.1016/j.combustflame.2022.112025) diagnostics and fire detectors (doi.org/10.1016/j.powtec.2019.02.003). Most importantly, these relations enable the estimation of the soot direct radiative forcing accounting for its realistic agglomerate structure (doi.org/10.1021/acs.est.2c00428).

Biographical Sketch: Dr. Georgios Kelesidis is an Assistant Professor at Rutgers School of Public Health and Deputy Director of the Nanoscience and Advanced Materials Center of the Environmental and Occupational Health Sciences Institute at Rutgers University. Prior to this appointment, he was a Lecturer and Research Associate at the Department of Mechanical and Process Engineering of ETH Zürich, Switzerland. He received a Diploma in Chemical Engineering from the University of Patras, Greece with honors (top 3%), along with the Limmat Stiftung Award of Academic Excellence (2013). His subsequent MSc studies in Process Engineering at ETH Zürich were supported by a Particle Technology Laboratory Fellowship (2013-2015), while his MSc thesis earned the IBM research prize (2017) for computer modelling and simulations in chemistry, biology and material science. His 2019 PhD thesis on the morphology and optical properties of flame-made nanoparticles received the 2020 PhD Award from GAeF (German Association for Aerosol Research) and the ETH medal for Outstanding Doctoral Thesis (top 8 %). He received also the 1st Graduate Student Award on Carbon Nanomaterials at the 2019 AIChE Annual Meeting (Orlando, FL, USA), as well as Best Poster Awards at the European Aerosol Conference (EAC) in 2016 (Tours, France) and 2020 (Aachen, Germany), the 2019 ETH Conference on Combustion Generated Nanoparticles (Zürich, Switzerland) and the 2019 Fall Meeting of the Material Research Society (MRS). The societal impact of his PhD research was also highlighted by the Forbes Magazine by including him in the 2020 Forbes 30 under 30 Europe list for Science & Healthcare. He has co-authored 21 peer-reviewed articles so far, being the first author in 16 of them. He has organized technical sessions at MRS (2016), EAC (2019-2021), the 2020-2022 Annual Meetings of the American Association for Aerosol Research, the 11th International Aerosol Conference (2022) and the 9th World Congress on Particle Technology (2022). He has supervised so far 10 MSc and 7 BSc students. He is currently co-supervising 1 PhD student at ETH Zürich.

Abstract: Recent decades have seen rapid development in all manufacturing technologies, including additive manufacturing (AM). This has raised the need for design methods to leverage the new, increasingly complex fabrication possibilities. Topology optimization has the potential to generate new high-performing design solutions since it is a free-form design method that does not require a preconceived notion of the final layout. It uses computational mechanics and optimization tools to generate improved designs. For operating designs to perform as predicted, the used model must capture the material behavior. Additionally, the planned manufacturing process might induce material characteristics and design limitations that should be considered as the design is generated. This talk focuses on identifying and incorporating behavioral and manufacturing aspects within the design process. Different strategies for integration within topology optimization will be discussed. This includes consideration of manufacturing-induced material characteristics, which is illustrated through tailoring design to material extrusion-based AM. In material extrusion, a nozzle moves across a build plate and deposits a material bead on a 2D slice of the design. These processes typically induce some degree of anisotropy through weak(er) bonding between adjacent beads. To improve the manufacturability of large-scale designs, the application of a Mixed Integer Linear Programming formulation is discussed for highly restricted volume scenarios. Finally, a new design framework is introduced in which the interactive participation of the design engineer is enabled to resolve more complex mechanic phenomena.

Biographical Sketch: Josephine Carstensen is the Gilbert W. Winslow Career Development (Assistant) Professor in the Department of Civil and Environment Engineering (CEE) at MIT. She leads the Carstensen Group, conducting research that revolves around the engineering question of “how we design the structures of the future?” Her work spans from the development of computational design frameworks for various structural types and design scenarios to experimental investigations that are used to inform necessary algorithmic considerations.

Dr. Carstensen has received awards for both research and teaching, including the National Science Foundation CAREER award and CEE Maseeh Award for Excellence in Teaching. She joined the MIT CEE faculty in 2019 after two years as a lecturer at MIT, jointly appointed in CEE and Architecture.  She received her PhD from Johns Hopkins University in 2017 and holds a B.Sc. and a M.Sc. from the Technical University of Denmark.

Abstract: Current robots are primarily rigid machines that exist in highly constrained or open environments such as factory floors, warehouses, or fields. There is an increasing demand for more adaptable, mobile, and flexible robots that can manipulate or move through complex environments. This problem is currently being addressed in two complementary ways: (i) learning and control algorithms to enable the robot to better sense and adapt to the surrounding environment and (ii) embedded intelligence in mechanical structures. My vision is to create robots that can mechanically conform to the environment or objects that they interact with to alleviate the need for high-speed, high-accuracy, and high-precision controllers. In this talk, I will give an overview of our key challenges and contributions to developing mechanically conformable robots, including soft parallel mechanisms for dexterous manipulation, physically-coupled multi-agent systems, and dynamic origami.

Biographical Sketch: Zeynep Temel is an Assistant Professor with the Robotics Institute at Carnegie Mellon University. Her research focuses on developing robots that can mechanically conform to the environment or objects that they interact with. Prior to joining RI, she was a postdoctoral Fellow at the Microrobotics Lab in Harvard University. She received her Ph.D. from Sabanci University, Turkey, where her work is funded by Turkish Science Foundation. In 2020, she was selected as one of 25 members of the Young Scientists Community of World Economic Forum.

Abstract: Mixing-induced vibrational non-equilibrium was studied in the turbulent shear layer between a high-speed jet and a surrounding hot-air co-flow. The vibrational and rotational temperatures of N2 and O2 were determined by fitting measured spontaneous Raman scattering spectra to a model that allows for different vibrational and rotational temperatures. The mixing of the jet fluid with the co-flow gases occurs over microsecond time scales, which is sufficiently fast to induce vibrational non-equilibrium in the mixture of hot and cold gases. The effect of fluctuating temperatures on the time-averaged Raman measurement was quantified using single-shot Rayleigh thermometry. The Raman scattering results were found to be insensitive to fluctuations except where the flame is present intermittently. Vibrational non-equilibrium was detected in nitrogen but not in oxygen. This difference between species temperatures violates the two-temperature assumption often used in the modeling of high-temperature non-equilibrium flow. A multiple-pass cell was constructed to obtain single-shot Raman scattering measurements in the turbulent shear layer using a pulsed stretched laser. These measurements agreed with the previous time-average results and allowed us to make measurements near the fluctuating base of a lifted flame – a region where time-averaged measurements do not give meaningful results.

Biographical Sketch: Prof. Philip L. Varghese holds the Ernest H. Cockrell Centennial Chair in Engineering at The University of Texas at Austin and has an international reputation in the areas of rarefied and non-equilibrium flows and optical diagnostics for combustion and plasmas. He received his Bachelor of Technology degree from the Indian Institute of Technology in Madras in 1976, an MS from Syracuse University in 1977, and a PhD from Stanford University in 1983 all in Mechanical Engineering. He was a post-doctoral Scholar in the Molecular Physics Laboratory at SRI International and joined UT Austin in 1983 in the department of Mechanical Engineering. He was promoted to Associate Professor in 1988 and transferred to Aerospace Engineering in 1989. He was promoted to full Professor in 1995 and has been the Director of the Center for Aeromechanics Research since 1999. He served as Chair of the Department from 2009-2012.

Among numerous awards he was Fulbright Senior Scholar in France in 1993 and was awarded the Boeing-A.D. Welliver Faculty Fellowship by the Boeing Company in 1998. He received the Lockheed Martin Aeronautics Company Award for Excellence in Engineering Teaching in Spring 2003, and was elected to the Academy of Distinguished Teachers at the University of Texas in 2005. In February 2012 he was selected Professor of the Year by the Senate of College Councils at UT Austin and was awarded The University of Texas System Regents’ Outstanding Teaching Award in August 2016.

Dr. Varghese’s research focuses on understanding the basic molecular processes occurring in high speed, high temperature, and non-equilibrium flows. This is an inter-disciplinary field, requiring a synthesis of physics and chemistry with the more traditional engineering disciplines of fluid mechanics, heat transfer, and thermodynamics. He applies his work to the study of hypersonic and rarefied flows, plasmas, and combustion. He has established a laser diagnostics laboratory for experimental studies in combustion and plasma discharges. He also has an active program in planetary scale simulations of rarefied flows and has developed a novel technique for accurate solutions of the Boltzmann equation using quasi-particle simulation. His research publications have been extensively referenced and a recent search showed over 3800 citations of his work on Google Scholar. He is co-inventor on six US patents related to applications of Raman spectroscopy.

Abstract: Idaho National Laboratory (INL) is at the forefront of the nation’s advanced reactor R&D effort. Advanced reactor are a promising form of baseload carbon free energy generation and several studies are expecting them to play a critical part in US decarbonization plans. The seminar presentation will be divided in two parts. The first will provide an overview of R&D activities at the lab in support of advanced reactor development efforts. The timeline for forthcoming reactor demonstrations efforts at INL and elsewhere will also be presented. The second part of the seminar will discuss the new ‘multiphysics modeling & simulation’ capabilities being developed at INL as part of the ‘MOOSE’ ecosystem. These tools are being developed primarily to model the complex physics in advanced reactors, but can also be employed to wider engineering applications. They represent a paradigm shit in multi-disciplinary engineering analysis, but enabling the tight coupling of various physical phenomena in nuclear reactors

Biographical Sketch: Dr. Abdalla Abou-Jaoude is an R&D Staff Scientist in the Advanced Reactor Technology Department of Idaho National Laboratory (INL). He is leading efforts in three main areas at INL: advanced modeling & simulation, molten salt irradiation, and nuclear technoeconomics. As the work package manager for the National Reactor Innovation Center’s (NRIC) Virtual Test Bed (VTB), and the Nuclear Energy Advanced Modeling and Simulation (NEAMS) campaign point of contact to the Nuclear Regulatory Commission (NRC), Dr. Abou-Jaoude engages with stakeholders and coordinates different efforts in support of advanced reactor multiphysics simulations capabilities. He is also leading work packages for the NEAMS and Molten Salt Reactor (MSR) campaign on developing multiphysics simulation capabilities for molten salt reactors.

Dr. Abou-Jaoude was awarded an internal lab project to demonstrate molten salt irradiation capability at INL. The project intends to conduct the first fueled chloride salt irradiation in history at the NRAD reactor. Abdalla also serves as Activity Lead for the Systems Analysis & Integration (SA&I) campaign on developing technoeconomic assessment of advanced reactors, notably microreactors. As part of this effort, he developed an “economics-by-design” framework to improve competitiveness of novel concepts and better align them with market needs.

Previously at INL, Abdalla has been involved in various aspect of advanced reactor designs, notably for molten salt reactors, sodium fast reactors (namely the Versatile Test Reactor), nuclear thermal propulsion, and heat-pipe based microreactors. He also previously supported a private-public partnership with a U.S. utility to evaluate hydrogen-cogeneration options at nuclear power plants. He graduated with a doctorate in Nuclear Engineering from Georgia Tech in 2017 and was the INL Deboisblanc Distinguished Postdoctoral Associate during 2018. He obtained a MEng in Mechanical with Nuclear Engineering from Imperial College London in 2013.

Abstract: Lithium-ion batteries are one of the critical momentums of our current mobile society. With the further development and application of increasingly high energy density batteries and large capacity battery packs in electric vehicles, cellphones, laptops, and large-scale energy storage systems, the consequences of battery safety issues now become significant threats. Internal short circuits (ISCs) and thermal runaways (TRs) are typical battery safety issues where mechanics, electrochemistry, and thermal are strongly coupled. Interdisciplinary endeavors are in pressing need to address these safety issues. In this talk, multiphysics modeling and characterization at both cell level (~102 mm) and active particle level (~1 μm) will be highlighted to provide a mechanistic understanding of the nature of triggering and evolution of ISCs as well as the responsible mechanical instabilities of the solid-solid interfaces. In the meantime, a machine-learning combined with physics-based modeling will be introduced to achieve faster computation with higher accuracy. Results provide new insights into multiphysics behaviors in battery safety issues and offer engineering-ready modeling methodologies for the next-generation battery design, evaluation, and monitoring.

Biographical Sketch: Dr. Jun Xu joined the Department of Mechanical Engineering at the University of Delaware as an Associate Professor in 2023 Fall. Dr. Xu served as the inaugural Director of NC Battery Complexity, Autonomous Vehicle and Electrification Research Center when he was an Associate Professor at the University of North Carolina at Charlotte. Dr. Xu’s research mainly focuses on multiphysics modeling and characterization of batteries, and impact dynamics. Dr. Xu now serves as an executive committee member of the Advanced Energy System Division, ASME. He is Associate Editor of ASME Journal of Electrochemical Energy Conversion and Storage, Scientific Reports and Batteries. Dr. Xu has published more than 130 peer-reviewed journal papers with citations of 5,400+, H-index 43. Dr. Xu was included in World’s Top 2% Scientist List (Stanford University, 2022) and awarded the prestigious James H. Woodward Faculty Research Award (2021, Chancellor’s Award) and Early-Career Faculty Awards for Excellence in Research (2022) at UNC Charlotte. Dr. Xu earned his Ph.D. degree from Columbia University in 2014.

Abstract: Weather Forecast and Climate Models, often referred to as General Circulation Models (GCMs), play pivotal roles in modern society, impacting various sectors, from everyday planning to aviation and national defense. This presentation explores the multifaceted significance of GCMs, both scientifically and economically.

Economically, accurate weather and climate predictions yield an annual economic benefit exceeding $160 billion. Moreover, recent economic assessments conducted across various countries consistently reveal robust cost-benefit ratios for investments in weather and climate services, typically ranging from 1:4 to 1:36. This remarkable potential 2,500% Return On Investment underscores their fundamental societal importance.

This presentation will provide an overview of the historical development of GCMs and shed light on the profound significance of weather and climate predictions in today’s world. It will also delve into the intricacies of GCMs, with an emphasis on their subgrid-scale processes and the methods used to account for them (i.e., parameterizations). Additionally, the application of Computational Fluid Dynamics (CFD) tools, such as large eddy simulations, to help develop and refine parameterizations will be explored.

Biographical Sketch: Dr. Maria Chinita is a researcher at the University of California Los Angeles and Jet Propulsion Laboratory. She earned her PhD in Meteorology from the University of Lisbon, Portugal, in 2018. Her research primarily focuses on atmospheric boundary layers, involving several modeling techniques and observational data to gain a better understanding of small-scale processes. She applies these insights to develop unified parameterizations for atmospheric convection.

Abstract: Metal additive manufacturing (AM), e.g., laser-based powder bed fusion (L-PBF), offers an enabling opportunity for making complex metal parts or customized alloys with design freedom. The unique thermal cycle of rapid heating, fast solidification, and melt-back during metal AM may cause very complex metal pool dynamics, such as steep temperature gradient and high cooling rate, intense Marangoni flow, and intrinsic cyclic heat treatment. The complex very complex kinetic process and thermal history may lead to various quality issues of the printed parts. Therefore, the understanding and prognosis of metal pool dynamics remain the central intractable problem for printing high-quality metal parts or new alloys. Computational fluid dynamics (CFD) models may help to understand the complex thermo-mechanical process physics, but require the calibration of model parameters and are computationally expensive for real-time prognosis. On the other hand, machine learning has the potential to handle high-dimensional and massive process data for efficient surrogate modeling and decision-making. However, pure data-driven machine learning models suffer from black-box or explainability, are inherently computation-intensive and storage-intensive, and need a large amount of high-quality labeled training data to achieve a good performance. A deep knowledge gap exists between machine learning modeling and computational modeling in the prediction of melt pool dynamics. To take full advantage of ML methods while leveraging the physical laws underpinning melt pool dynamics, this talk presents a physics-informed machine learning (PIML) approach to integrate deep learning with the governing equations of the melt pool for forward prediction of the temperature and velocity fields in the melt pool. The PIML approach may also inverse learning of unknown model constants (e.g., Reynolds number and Peclet number) of the governing equations. The robust PIML algorithm also shows fast convergence by enforcing physics via soft penalty constraints.

Biographical Sketch: Dr. Yuebin Guo is Henry Rutgers Professor of Advanced Manufacturing and Leads the New Jersey Advanced Manufacturing Institute at Rutgers University-New Brunswick, USA. Prior to Rutgers, he served as the Assistant Director for Research Partnerships at the U.S. Advanced Manufacturing National Program Office (AMNPO). He was also an Alexander von Humboldt Fellow at RWTH Aachen and Fraunhofer IPT, Aachen, Germany. His research focuses on manufacturing processes, digital twins, physics-informed machine learning, and materials informatics. He is the author of more than 300 peer-refereed technical publications in these areas. He is a recipient of numerous awards, including the SME Sargent Progress Award, ASME Federal Government Swanson Fellow, Tau Beta Pi Outstanding Faculty, NSF CAREER, SAE Teetor Educational Award, and SME Outstanding Young Manufacturing Engineer. He is an elected fellow of ASME, SME, and CIRP.

Abstract: Thermoelectric coolers (TECs), also known as Peltier coolers, are solid-state cooling devices powered by a direct (dc) current. They offer high reliability, silent operation, and do not require the use of refrigerant chemicals that can be harmful to the environment. However, TEC performance is generally limited as compared to traditional vapor-compression refrigeration systems and this has limited their relevance and applicability, especially when other necessary system components, such as heat exchangers, are considered. RTX Technology Research Center (RTRC) has developed an advanced heat exchanger / heat sink technology on a DARPA program that provides >50% enhancement in heat removal at equivalent operating conditions. Such an improvement means that an air-cooling system can deliver heat-removal performance that approaches that of liquid cooling. When these DARPA heat exchangers are combined with TECs, the resulting cooling system can, for the first time, perform to the level required for galley refrigeration on commercial aircraft. The resulting system, known as SpaceChiller, comes at an opportune time and fills a technology need that is arising in the aerospace industry as airlines start to fly increasingly long distances using small, single-aisle aircraft.

Biographical Sketch: Dr. Pearson has been with RTX Technology Research Center (RTRC, previously known as UTRC) in January 2011. Since then, he has worked on a wide range of projects spanning most of United Technologies’ and RTX’s diverse business units including Pratt & Whitney, Collins Aerospace, Raytheon, and Carrier. Major research areas have included advanced heat exchangers, eco-friendly refrigeration systems, thermally engineered metamaterials, and thermoelectric power generation and cooling. He became a Team Leader of the Heat Transfer team in November 2020. Since October 2022, he has been leading Thermofluid Science Discipline, a team of 15 staff that conduct high-risk and low-TRL research across RTX’s businesses, focused on the company’s unique challenges in heat transfer, fluid dynamics, large-scale thermodynamic systems, and interfacial physics. Dr. Pearson holds a Ph.D., M.S., and B.S. degree in Mechanical & Aerospace Engineering from the Illinois Institute of Technology in Chicago, IL, where he worked on NASA-sponsored work in electrodynamics and was an NSF Graduate Research Fellowship recipient. He has over 23 granted and pending patents and 10 peer-reviewed journal publications.