Events

Laser and X-ray Diagnostics for a Physics-based Understanding of Aero-thermal Flows

Flows in high-speed boundary layers and modern propulsion devices are characterized by widely varying levels of turbulence, chemical reactions, temperatures, and pressures. These flows often exist within optically complex environments, including shock-wave interactions, intense near-wall gradients, ablative surfaces, plasmas, reactions of energetic materials, and other multi-phase phenomena. Understanding and predicting the performance of next-generation aero-propulsion devices and novel materials, therefore, requires advanced diagnostics that can resolve a broad range of spatiotemporal scales, interrogate multiple phases, and extract quantitative information about velocity and state variables within such challenging conditions. This talk discusses key innovations in ultrafast and ultrahigh-speed laser and X-ray diagnostic techniques that allow quantitative measurements of temperature, pressure, species, and liquid-solid-vapor mass distributions in highly dynamic environments. Recent efforts have focused on (i) achieving orders of magnitude improvements in spatio-temporal resolution and sensitivity of these techniques and (ii) the application of simultaneous multiparameter measurements in flows of practical interest. Finally, prospects for further innovations and coupled numerical simulations for the study of highly complex aero-thermal flows are discussed.

Dr. Terrence Meyer is a Professor of Mechanical Engineering and a Professor of Aeronautics and Astronautics (by courtesy) at  Purdue University, where he specializes in the development and application of novel laser and X-ray diagnostics for aero-thermal flows. After earning a PhD at the University of Illinois at Urbana-Champaign in 2001, Dr. Meyer worked as a Research Scientist at the Air Force Research Laboratory in Wright-Patterson AFB, Ohio. He then joined the faculty at Iowa State University in 2006 and Purdue University in 2015. He also served as a Visiting Assistant Professor at the Ecole Centrale Paris in France between 2001-2003 and as a Guest Professor at the Friedrich-Alexander University in Germany between 2010-2020. He has been very active in innovation, service, and outreach activities. He has chaired or co-chaired over a dozen technical symposia, published five book chapters/tutorials, and delivered five short courses on diagnostics for highly dynamic flow phenomena. His honors include the SAOT Young Researcher Award, the NSF Career Award, the AIAA Aerodynamic Measurement Technology Innovation Award, the Spira Award, and six semesters as an Outstanding Engineering Teacher at Purdue University. He is an Associate Fellow of the AIAA, and he is a Fellow of the Combustion Institute, ASME, and Optica, for which he also serves as an Associate Editor.
 

Come for updates and food, we hope to see you all there!

Gas-Surface Interactions for Hypersonics and Near-Space

Materials used for hypersonic vehicles and low-orbiting satellites must withstand harsh environments. Hypersonic flight generates a shock-heated, partially dissociated gas. Reactive atomic oxygen and nitrogen drive chemical reactions on material surfaces resulting in ablation or complex oxide layer formation. In low earth orbit, the outer region of Earth’s atmosphere is comprised mainly of oxygen atoms. Satellite materials must withstand collisions with reactive oxygen atoms at orbital velocity (7-8 km/s). For near-space altitudes, the flow transitions from continuum to free-molecular and gas-surface scattering dominates lift and drag.

This presentation describes new gas-surface interaction models that can be used in rarefied or continuum flow solvers to simulate low orbit satellites and hypersonic vehicles. The models were developed using recent molecular beam experimental data. The first type of model uses molecular beam scattering data for mixtures of dissociated air species reacting with high temperature carbon materials. This data is used to construct a 20-reaction air-carbon ablation model for use in large-scale CFD simulations of hypersonic flows. The second type of model is based on beam-surface scattering data for orbital velocity oxygen atoms impacting various satellite materials, including near-specular scattering materials that have the potential for low drag and high lift/drag. Instead of relying on conventional assumptions of either fully diffuse or specular reflection, this new model will provide quantitative predictions of satellite aerodynamics in low earth orbit.

AI-powered Automated Air Traffic Control

Both the U.S. national airspace system and the low-altitude airspaces call for innovations in automated air traffic control. For the national airspace system, the FAA's Brand New Air Traffic Control System (BNATCS) needs effective automation tools for air traffic monitoring and conflict resolution to support human air traffic controllers; for the low-altitude airspaces hosting small unmanned aerial systems (sUASs) and the emerging electric vertical take-off and landing (eVTOL) aircraft, automated air traffic control is the only feasible way to accommodate the envisioned high-density, fast-temp flight operations. In this talk, the speaker will present the models and algorithms of using reinforcement learning (RL) and large language models (LLM) for automating air traffic control, specifically aircraft separation assurance and conflict resolution. In addition, he will also discuss the readiness and challenges of testing, certifying and implementing these artificial intelligence (AI) tools in safety-critical aviation applications. 

Peng Wei is an associate professor in the Department of Mechanical and Aerospace Engineering at the George Washington University, with courtesy appointments at Electrical and Computer Engineering Department and Computer Science Department. By contributing to the intersection of control, optimization, machine learning, and artificial intelligence, he and his team develop autonomous functions and decision support tools for aviation, avionics and aerial robotic systems. His current focuses are (1) safety, efficiency, and scalability of aircraft autonomy, multi-agent autonomy and human-autonomy teaming; (2) aviation applications including air traffic management/control (ATM/C), airline operations, UAS traffic management (UTM), advanced air mobility (AAM), and aviation electrification; and (3) AI safety, security and certification for safety-critical systems. Prof. Wei is an AIAA Associate Fellow. He serves as an associate editor for AIAA Journal of Aerospace Information Systems (JAIS), AIAA Journal of Guidance, Control, and Dynamics (JGCD), and Journal of Open Aviation Science. He received his Ph.D. degree in Aerospace Engineering from Purdue University in 2013 and bachelor’s degree in Control Theory from Tsinghua University in 2007.

Stochastic Planning, Control, and Optimization for Spacecraft Autonomy

Fluid Mechanics of Riblets Drag Reduction

Riblets are a surface texture composed of tiny ribs applied on aircraft skin to reduce drag, which saves on fuel, increases the payload and extends the range. To the fast-moving turbulent air that flows over it, riblets turn out to be smoother, generating less skin friction, than a perfectly flat surface. However, riblet performance is highly sensitive to their cross-sectional shape and features, which is bad news because the micron-sized ribs, imperceptible to the naked eye and challenging to measure even with precision instruments, are impossible to manufacture and maintain perfectly. Thus, accurate tolerancing, not only for manufacture but also for lifetime wear planning and monitoring, is key to this technology, requiring predictive capability of the kind that derives from advances in basic understanding. In this regard, I will present some of the progress we have made in the last few years, building on decades of research, on the fluid mechanics of turbulence over riblet surfaces. The support of the Australian Research Council, Cooperative Research Australia and the U.S. Air Force Office of Scientific Research FA2386-23-1-4071 is gratefully acknowledged.

Daniel Chung is a professor in the Department of Mechanical Engineering at the University of Melbourne. He obtained his bachelor's degree in engineering and computer science from the University of Melbourne in 2003, and his PhD in aeronautics from Caltech in 2009. He was a postdoc at the Jet Propulsion Laboratory before joining the University of Melbourne in 2012. Daniel's research uses computational fluid dynamics, where he tries to distil turbulent flows into simplified problems and to build physics-based models for prediction. Recently, he has been interested in understanding and controlling turbulent flow and thermal convection over rough surfaces, riblets and moving wavy surfaces.

Smart Decision-Making for Autonomous Systems in Space Exploration Missions

Many autonomous systems benefit from efficient operations and advanced autonomy levels in space exploration missions, such as human missions to Mars and on-orbit servicing, assembly, and manufacturing (OSAM) missions. Due to dynamic operating environments, complex system behaviors, and strict mission constraints, it is challenging to realize full autonomy with capabilities of fuel or energy-efficient operations. Without human intervention, real-time decision-making, including both motion planning and logic/reasoning decisions, plays a critical role in assuring the reliability and performance of such a system toward mission success. This talk will present our work on developing sophisticated modeling approaches, scalable optimization algorithms, and machine learning based optimal control methods that collectively contribute to advanced decision-making strategies for efficient autonomous systems in space exploration missions. The discussion will highlight applications in two distinct types of autonomous systems. This first concerns space vehicle real-time guidance for Mars entry, powered descent, and landing mission, where onboard propellant is limited and high precision landing is required. The second focuses on origami-inspired deployable systems for OSAM, where systems automatically adapt their shape/functionality to mission needs. The seminar will articulate our overarching goal: to achieve a high level of autonomy for these systems, enabling them to navigate dynamic environments, complex operational scenarios, and stringent mission constraints effectively.

Dr. Ran Dai is a professor in the School of Aeronautics and Astronautics at Purdue University. Before joining Purdue, she was the Netjets Assistant Professor at The Ohio State University. She received her B.S. degree in Automation Science from Beihang University and her M.S. and Ph.D. degrees in Aerospace Engineering from Auburn University. After graduation, she worked as an engineer in an automotive technology company, Dynamic Research, Inc., and then joined the University of Washington as a postdoctoral fellow. Dr. Dai’s research focuses on control of autonomous systems, numerical optimization, networked dynamical systems, and space robotics. She is an associate fellow of AIAA and a recipient of the NSF Career Award and NASA Early Faculty Career Award. Dr. Dai is serving as an associate editor of the Journal of Guidance, Navigation, and Control and IEEE Transactions on Aerospace and Electronic Systems.