Events and Seminars

09.19.25 Ric Duncanson – Marc-Antoni Racing

Hybrid-Electric Propulsion System For Commercial And Military Aircrafts

Date: September 19, 2025; Time: 2:30 PM Location: PWEB 175

Abstract: Decarbonizing long-haul air travel is essential to climate change mitigation but remains difficult because present alternatives to fossil fuels are constrained by energy density, mass, packaging, infrastructure, manufacturing readiness, and entrenched operating practices. Hybrid-electric propulsion offers a pragmatic near term pathway by pairing high-specific-energy liquid fuels with electric machines and power electronics to cut fuel burn and emissions while enabling novel engine airframe integration. Our discussions examines how the aerospace and defense start-up Marc-Antoni is developing a hybrid-electric propulsion system for single-aisle aircraft and evaluate its commercial viability across performance, weight, safety, certification, maintainability, and cost. The analysis focuses on core technologies, high efficiency generators and motors, propulsion system configurations, energy storage, and outlines a maturation roadmap. Our discussions also assesses defense applications. Confronted by growing threats from unmanned aerial systems and advanced missiles, the U.S. military is increasing its demand for non-kinetic, high-speed effects such as directed-energy weapons. These systems are limited by onboard power generation, power quality, and heat rejection. We will explore hybrid-electric architectures which could be adapted to combat aircrafts to provide higher continuous and pulsed electrical power with improved thermal margins, thereby enabling advanced electronic warfare and directed-energy capabilities.

Biographical Sketch: Ric Duncanson, a serial tech entrepreneur, is the founder of Marc-Antoni, an aerospace & defense start-up developing hybrid electric propulsion systems for civil and military aviation applications. Ric is an expert in technology transfer and the evaluation of intellectual property for commercialization, as evidenced by his acquisition of over 10 patents, ranging from lithium-ion cell components to a novel turbofan design. From 2017 to 2020, Ric was a member of the New York Institute of Technology (NYIT) Entrepreneurship and Technology Innovation Center (ETIC) program where he developed a robotic steering system for high-performance autonomous vehicles, as well as a four-motor full-torque vectoring all-wheel-drive system for high-performance electric race cars. Currently, Marc-Antoni is a member of the University of Connecticut (UConn) Technology Incubator Program (TIP), where the company is developing several research & development projects, including Titanium Niobium Oxide (TNO) lithium-ion cell, partially superconducting machines, and a superconducting turbofan. Although not a formally trained engineer or scientist, Ric considers himself an autodidact, having cultivated profound expertise in the engineering and scientific principles of the various subject matters associated with his innovations.

10.24.25 Dr. Jaime C. Grunlan – Texas A&M University

Protective Nanocoatings from Polyelectrolytes: Flame Retardancy, Gas Barrier, and High Voltage Insulation

Date: October 24, 2025; Time: 2:30 PM Location: PWEB 175

Abstract: Layer-by-layer (LbL) assembly is a conformal coating technology capable of imparting a multiplicity of functionalities on nearly any type of surface in a relatively environmentally friendly way. At its core, LbL is a solution deposition technique in which layers of cationic and anionic materials (e.g. nanoparticles, polymers and even biological molecules) are built up via electrostatic attractions in an alternating fashion, while controlling process variables such as pH, coating time, and concentration. Here we are producing nanocomposite multilayers (50 – 1000 nm thick), having 10 – 96 wt% clay, that can be completely transparent, stop gas permeation, and impart extreme heat shielding to carbon fiber reinforced polymer composites. Similar multilayer coatings exhibit very high dielectric breakdown strength and good thermal conductivity, for protection of high voltage electronics. In an effort to impart flame retardant behavior to fabric using fewer processing steps, a water-soluble polyelectrolyte complex (PEC) was developed. This nanocoating is comprised of polyethylenimine and poly(sodium phosphate) and imparts self-extinguishing behavior to cotton fabric in just a single coating step. Adding a melamine solution to the coating procedure as a second step renders nylon-cotton blends self-extinguishing. A PEC of PEI and polyacrylic acid is able to achieve an oxygen transmission rate below 0.005 cm3/m2/day at 100%RH and a thickness of just 2 m. This is an all-polymer foil replacement technology. Examples of bio-based polyelectrolytes (e.g., chitosan and phytic acid), being used for these same applications, will be shown. These coating techniques can be deposited using roll-to-roll processing (e.g., flexographic printing, dip-coating, or spray-coating). Opportunities and challenges will be discussed. Our work in these areas has been highlighted in C&EN, ScienceNews, Nature, Smithsonian Magazine, Chemistry World and various scientific news outlets worldwide.  For more information, please visit my website: https://grunlan-nanocomposites.com/

Biographical Sketch: Dr. Jaime Grunlan is the Leland T. Jordan ’29 Chair of Mechanical Engineering at Texas A&M University, where he has worked for more than 20 years. He holds joint appointments in the Department of Materials Science and Engineering and the Department of Chemistry. He is a world leader in organic thermoelectric materials, super gas barrier layers, and environmentally-benign, flame retardant nanocoatings. He holds 17 issued U.S. patents and several EU patents. He has published more than 230 journal papers, with more than 29,000 citations. His work has been highlighted in Smithsonian Magazine, Nature, and the New York Times. He is an Editor of the Journal of Materials Science and Progress in Organic Coatings, and Associate Editor of Green Materials. In 2018, Prof. Grunlan became a Fellow of the American Society of Mechanical Engineers (FASME) and was awarded a doctorate honoris causa (i.e. honorary doctorate) from the University of South Brittany (Lorient, France). In 2023, he became a Fellow of the American Chemical Society (FACS). In 2024, he became Fellow of the Polymer Chemistry (POLY) and Polymeric Materials: Science and Engineering (PMSE) Divisions of ACS. He also became a Fellow of the National Academy of Inventors (FNAI) in 2024.

10.10.25 Dr. Sean Bradshaw – Pratt & Whitney

Powering the Future

Date: October 10, 2025; Time: 2:30 PM Location: PWEB 175

Abstract: Projected demand growth in the aviation sector over the next quarter century is driving the need for greater aircraft fuel efficiency and lower noise footprint.  This presentation will provide a brief overview of Pratt & Whitney’s approach to powering the future of flight, including geared turbofans, hybrid-electric propulsion, technical evaluations of synthetic aviation fuels, and supporting industry collaborations through ASTM on rigorous standards that would enable the commercial use of 100% synthetic aviation fuels.

Biographical Sketch: Dr. Sean Bradshaw is a senior technical fellow at Pratt & Whitney, where his primary focus is the development of advanced propulsion technologies that will power the future of flight. Pratt & Whitney is a world leader in the design, manufacture, and service of aircraft engines and auxiliary power units. He also provides strategic and technical leadership to the aviation industry by serving as: the chair of the ASME Committee on Sustainability, an associate editor of the ASME Journal of Engineering for Gas Turbines and Power, a member of the ASME Heat Transfer Committee, and a member of the Aeronautics & Space Engineering Board of the National Academies of Sciences, Engineering, and Medicine. Sean earned a B.S., an M.S., and a Ph.D. in Aeronautics & Astronautics from the Massachusetts Institute of Technology.

The Combined Use of Modeling and Large-scale Experiments in the Development of Fire Protection Solutions

Speaker: Dr. Francesco Tamanini – FM Global
Date: October 4, 2024; Time: 2:30 PM Location: PWEB 175

Abstract: Practical fire protection challenges are often not easily amenable to solutions that can be developed from a single approach.  The tools that are more frequently used include: engineering correlations, reduced-scale physical modeling, large-scale testing, computer simulations.  The last two find wide application in addressing loss prevention questions.  Large-scale testing, however, is very expensive and not always feasible.  CFD modeling, on the other hand, is not fully reliable in the absence of experimental validation.  These limitations can be overcome by combining the two approaches.  The seminar will discuss two cases where that was done and will highlight the challenges that were encountered.

Biographical Sketch: After doing initial work on the computer modeling of fires and coordinating for several years FM’s research activities in the area of explosions, Dr. Tamanini moved in 2004 to the Consulting Research Scientist position and eventually to Sr. Research Fellow.  In his current role, he provides support to the Manager of Research, and to the entire scientific and engineering staff, on issues spanning all research topics of interest to FM.  They include: fire testing, material flammability, CFD modeling of fires and explosions, impact of natural hazards (wind, flood, earthquake) on property, risk assessment, equipment reliability, and material damage. During April 2021-June 2023 he has been the Acting Director for the Equipment, Cyber and Materials Science Area.

He has contributed original work in several technical areas:

  • extinguishment of fires by water sprays;
  • computer modeling of turbulent buoyancy controlled flames;
  • measurements of the flammability properties of materials;
  • large scale experiments on the combustion behavior of hydrogen releases into confined volumes;
  • definition of the reactivity characteristics of silane;
  • vent sizing requirements for explosions in layered vapor/air mixtures;
  • engineering tools for dust explosion protection vent sizing;
  • protection of storage of cellulose nitrate film;
  • interpretation of ceiling layer temperatures in large-scale fires; and
  • various other fire problems, as well as dust and gas explosions.

 

Franco started working at Factory Mutual Research in 1974 after receiving a Ph.D. in applied physics from Harvard University.  He also holds an MS degree in aeronautics from the California Institute of Technology and a Laurea in mechanical engineering from the Politecnico di Torino in Italy.  He has served as the Chairman of the Eastern States Section of the Combustion Institute, is the 1996 recipient of the Bill Doyle award of the AIChE, and has published numerous refereed papers and technical reports.

 

Recent Progress in Black-Box Function Optimization for Industry Problems

Abstract: One of the most common and important problems in the engineering industry is, arguably, to optimize a black-box expensive-to-evaluate function given a strict budget. The function can represent a real-world experiment or a costly simulation code. Specifically, given a set of potential power plant layouts, how do we find the best layout defined against a set of Quantities of Interest? Given a steam turbine, how do we configure its geometry to achieve the best efficiency? How can we optimize the life of a machine by knowing its design variables and how they connect to damage? Given a set of Computational Fluid Dynamics simulations, can we optimize a blade structure for cooling? The problem of course extends to a broad range of other industries and academia as well. As a different example, borrowed from materials discovery, consider a set of binary alloy lattice points: Which atoms should be placed on said points to discover the ground states? Surely, for all these cases, the faster we achieve high-quality optima (ideally global and robust) in terms of resources, the lower the overall cost.

Towards answering these questions, at General Electric Research our team has developed and maintain an industry-strength Efficient Global Optimization scheme called “Intelligent Design and Analysis of Computer Experiments” (IDACE) which builds on a time-tested Gaussian Process meta-model called Bayesian Hybrid Modeling (BHM) originally built from Kennedy O’Hagan’s work.

Having introduced the BHM/IDACE framework, we present in detail a set of successful BHM/IDACE industry case studies and compare to other optimization approaches such as Genetic Algorithms. Finally, we go on to discuss a range of recent modifications and enhancements to these tools all driven from real-world customer needs.

Short Bio: Jesper Kristensen works as a Lead Engineer in the Probabilistics and Optimization team at General Electric’s (GE) Research Center in upstate New York. The team is managed by Dr. Liping Wang. He joined the team in the fall of 2015 as a research engineer. Among other projects, he is currently in charge of a $1MM project leading six engineers to ensure GE stays ahead in Probabilistic capabilities including, but not limited to, meta-modeling, optimization, uncertainty quantification, and uncertainty propagation. He is also the project leader on multiple damage modeling efforts to create Digital Twins of steam turbines.

 

Jesper is a graduate of the Technical University of Denmark (DTU) and holds a Ph.D. from Cornell University in Applied and Engineering Physics advised by Prof. Nicholas Zabaras. His work has generally focused on surrogate modeling and on advanced optimization methods such as adaptive sequential Monte Carlo and Bayesian Global Optimization for improving materials discovery and test cost reduction.

Layer-to-Layer Control in Laser Metal Direct Energy Deposition Additive Manufacturing

Abstract: ​Additive manufacturing (AM), or 3D printing, is beginning to deliver on its long-promised potential to transform industrial production.  Already, tooling and molds are making regular use of AM’s rapid CAD-to-part flexibility to deliver in days what previously took months.  In addition, AM facilitates much greater geometric complexity, which increases the value proposition for AM fabricated parts that are serving in increasingly critical roles.  However, the rate of industrial insertion remains slow due to stubborn problems in process variability arising from the spatial and dynamic complexity of AM, amplifying challenges in qualification.  In-process measurement and analysis, and the utilization of that data in closed-loop feedback control, are widely regarded as the remedy.   This talk will explore one such instance in a blown-powder, direct energy deposition (sometimes referred to as LENS) process.  Here, a laser scanner is used to detect and correct geometric anomalies.  The talk will consider how in-layer and layer-to-layer dynamics may couple to create multi-dimensional dynamic behavior not typically considered, and how novel control methods may stabilize these processes.

Biographical Sketch: Dr. Douglas A. Bristow is currently an Associate Professor in the Department of Mechanical and Aerospace Engineering at the Missouri University of Science and Technology (Missouri S&T).  He received his B.S. in Mechanical Engineering from Missouri S&T in 2001.  He received his M.S. and Ph.D., also in Mechanical Engineering, from the University of Illinois at Urbana-Champaign in 2003 and 2007, respectively.  Dr. Bristow is the Director of the Center for Aerospace Manufacturing Technologies, an industry consortium that currently includes eleven member companies.  He has more than 80 peer-reviewed publications and his research interests include precision motion control, repetitive and iterative process control, additive manufacturing process control, atomic force microscopy, and volumetric error compensation in machine tools and robotics.  Dr. Bristow’s research is currently funded by the National Science Foundation, the Department of Energy, the Digital Manufacturing and Design Innovation Institute, and multiple companies.  He is an Associate Editor at the ASME Journal of Dynamic Systems, Measurement and Control.

Mechanics under the Fold: How Origami Creates Sophisticated Mechanical Properties

Abstract: ​Origami, the ancient Japanese art of paper folding, is not only an inspiring technique to create sophisticated shapes, but also a surprisingly powerful method to induce nonlinear mechanical properties. Over the last decade, advances in crease design, mechanics modeling, and scalable fabrication have fostered the rapid emergence of architected origami structure and material systems. They typically consist of folded origami sheets or modules with intricate three-dimensional geometries, and feature many unique and desirable mechanical properties like auxetics, tunable nonlinear stiffness, multi-stability, and impact absorption. Rich designs in origami offer great freedom to prescribe the performance of such origami structures and materials. In addition, folding offers a unique opportunity of fabrication at vastly different sizes. This talk will highlight our recent studies on the different aspects of origami-based structures and materials–geometric design, mechanics analysis, and achieved properties–and discusses the challenges ahead.

Bio Sketch: Dr. Suyi Li is an assistant professor of mechanical engineering at the Clemson University. He received his Ph.D. at University of Michigan in 2014. After spending two additional years at Michigan as a postdoctoral research fellow, he moved to Clemson in 2016 and established a research group on dynamic matters. His technical interests are in origami-inspired adaptive structures, multi-functional mechanical metamaterials, and bio-inspired robotics. Within his first three years at Clemson, Dr. Li has secured more than one million dollars of research funding, including the prestigious NSF CAREER award. His paper on fluidic origami received the Best Paper Award by the ASME Branch of Adaptive Structures and Material Systems.

A Multiscale Moving Contact Line Theory and Simulation of Droplet Spreading and Cell Durotaxi

Abstract: In this talk, we present a novel multiscale moving contact line (MMCL) theory, which offers a powerful numerical simulation method for modeling and analysis of dynamic wetting, liquid droplet spreading on solid substrates, and various capillary motion phenomena. In the proposed multiscale moving contact line theory, we couple molecular scale adhesive interaction i.e. the van der Waals type interaction force and the macroscale fluid mechanics to solve droplet motions on solid substrates. In specific, we combine a coarse-grained adhesive contact model with a modified Gurtin-Murdoch surface hydroelasto-dynamics theory and the Navier-Stokes equation in the bulk fluids to formulate the multiscale moving contact line hydrodynamics theory in order to simulate a broader class of colloidal and soft matter physics phenomena, and related chemomechanical problems, such as cell motility, water spider walking, colloid suspension, and gas bubble in water, etc.

The advantage of adopting the coarse grain adhesive contact model in the moving contact line theory is that it can levitate and separate the liquid droplet with the solid substrate, so that the proposed multiscale moving contact line theory avoids imposing the non-slip condition, and then it removes the subsequent shear stress singularity problem, which allows the surface energy difference and surface stress propelling droplet spreading naturally.

We have also developed a soft matter model for biological cells that can model actin polymerization and ATP hydrolysis, and retrograde flow in cellular lamellipodia. By employing the MMCL method, we have successfully simulated cell durotaxi over the soft elastic substrates with non-uniform elastic stiffness.  By employing the proposed method, we have successfully simulated droplet spreading over various elastic substrates and cell durotaxi over the substrates with non-uniform elastic stiffness. The obtained numerical simulation results compare well with the experimental and molecular dynamics results reported in the literature.

Biographical Sketch:  Dr. Shaofan Li is currently a full professor of applied and computational mechanics at the University of California-Berkeley. Dr. Li graduated from the East China University of Science and Technology (Shanghai, China) with a BS degree in 1982; he also holds MS Degrees from both the Huazhong University of Science and Technology (Wuhan, China) and the University of Florida (Gainesville, FL, USA) in 1989 and 1993 respectively. In 1997, Dr. Li received a PhD degree from the Northwestern University (Evanston, IL, USA), and he was also a post-doctoral researcher at the Northwestern University during 1997-2000. In 2000, Dr. Li joined the faculty of the Department of Civil and Environmental Engineering at the University of California-Berkeley. Dr. Shaofan Li is the recipient of IACM Fellow Award [2017]; Distinguished Fellow Award of ICCES [2014]; ICACM Computational Mechanics Award [2013], USACM Fellow Award (2013), A. Richard Newton Research Breakthrough Award [2008], and NSF Career Award [2003]. Dr. Li has published more than140 articles in peer-reviewed scientific journals (SCI) with h-index 43 (Google Scholar), and he is also the author of two research monographs/graduate textbooks.

Predicting Fuel Properties of Potential Biofuels Using an Improved Artificial Neural Network Based on Molecular Structure

Abstract: The next generation of alternative fuels is being investigated through advanced chemical and biological production techniques for the purpose of finding suitable replacements to diesel and gasoline while lowering production costs and increasing process yields. Chemical conversion of biomass to fuels provides a plethora of pathways with a variety of fuel molecules, both novel and traditional, which may be targeted. In the search for new fuels, an initial, intuition-driven prediction of fuel compounds with desired properties is required. Due to the high cost and significant production time needed to synthesize these materials for testing, a predictive model would allow chemists to screen fuel properties of potentially desirable fuel candidates at the ideation stage. Recent work has shown that predictive models, in this case artificial neural networks (ANN’s) analyzing quantitative structure property relationships (QSPR’s), can predict the cetane number (CN) of a proposed fuel molecule with relatively small error. A fuel’s CN is a measure of its ignition quality, typically defined using prescribed ASTM standards and a cetane testing engine. Alternatively, the analogous derived cetane number (DCN), obtained using an Ignition Quality Tester (IQT), is a direct measurement alternative to the CN that uses an empirical inverse relationship to the ignition delay found in the constant volume combustion chamber apparatus. Model validation and expansion of the experimental database used in this study implemented DCN data acquired using an IQT. The present work improves on an existing model by optimizing the model architecture along with the key learning variables of the ANN and by making the model more generalizable to a wider variety of fuel candidate types. The approach enables researchers to focus on promising molecules by eliminating less favorable candidates in relation to their ignition quality.

Biographical Sketch: Hunter Mack is an Assistant Professor in the Department of Mechanical Engineering at the University of Massachusetts Lowell.  His research focuses on combustion, biofuels, and energy efficiency.  Prior to joining UML, he was a Project Scientist & Lecturer at the University of California at Berkeley, a Senior Engineer at solar concentrator start-up Banyan Energy, and a Postdoctoral Researcher in the Combustion Analysis Laboratory at UC Berkeley.  He received his M.S. (2005) and Ph.D. (2007) from UC Berkeley with an emphasis on multi-component fuels in Homogeneous Charge Compression Ignition (HCCI) engines. He also holds a B.S. in Mechanical Engineering from Washington University in St. Louis and a B.A. in Physics from Hendrix College (Conway, Arkansas).