Professor Karen Willcox and Alumnus Michael Watkins Elected to National Academy of Engineering

The National Academy of Engineering (NAE) announced today that Karen Willcox, professor in the Department of Aerospace Engineering and Engineering Mechanics and director of the Oden Institute for Computational Engineering and Sciences and Al Bovik, professor in the Department of Electrical and Computer Engineering at The University of Texas of Austin, have been elected to the prestigious academy for 2022. In addition, alumni Michael Watkins and Ahmad Abdelrazaq have also been elected.

Election to the academy is among the highest professional distinctions bestowed upon an engineer. Membership honors those who have made outstanding contributions to engineering research and practice, including pioneering new and developing fields of technology and making major advancements in the engineering field and profession. In all, 111 new members and 22 foreign members joined the NAE in 2022.

“Throughout their careers, Al, Karen, Michael and Ahmad have been among the nation’s leading experts in their respective fields,” said Roger Bonnecaze, interim dean of the Cockrell School of Engineering. “From space exploration to streaming video quality, these four newly elected members have made significant contributions in a wide variety of engineering disciplines — a testament to the Cockrell School’s breadth, depth and overall excellence. We are extremely proud and honored to call them Texas Engineers.”

During the past decade, more than 15 UT Austin professors have been elected as new members to the NAE, and the university has almost 50 current and retired members.

About the four new members representing UT Austin:

Karen Willcox in front of monitors with rocket pictured

Karen Willcox is director of the Oden Institute for Computational Engineering and Sciences at UT Austin and holds the W.A. “Tex” Moncrief Jr. Endowment in Simulation-Based Engineering and Sciences Chair No. 5 and the Peter O’Donnell Jr. Centennial Chair in Computing Systems. She is also a professor in the Cockrell School’s Department of Aerospace Engineering and Engineering Mechanics. Willcox is being recognized for contributions to computational engineering methods for the design and optimal control of high-dimensional systems with uncertainties. Her research has produced scalable computational methods for design of next-generation engineered systems, with a particular focus on model reduction as a way to learn principled approximations from data and on multifidelity formulations to leverage multiple sources of uncertain information. Prior to joining UT Austin in 2018, Willcox spent 17 years on the faculty at the Massachusetts Institute of Technology, where she served as the founding co-director of the MIT Center for Computational Engineering. She is a fellow of the Society for Industrial and Applied Mathematics and the American Institute of Aeronautics and Astronautics as well as a member of the American Society for Engineering Education. Willcox received a bachelor’s degree in engineering science from the University of Auckland and master’s and doctoral degrees from MIT, both in aeronautics and astronautics.

Michael Watkins (B.S. Aerospace Engineering 1983, M.S. ASE 1985, Ph.D. ASE 1990) is a professor of aerospace and geophysics at the California Institute of Technology, following five years serving as director of NASA’s Jet Propulsion Laboratory. He is being recognized for leadership in the development of space geodesy and leading robotic missions for exploration of the Earth and planetary bodies. Prior to joining JPL as director, Watkins was a professor in the Cockrell School’s Department of Aerospace Engineering and Engineering Mechanics and director of UT’s Center for Space Research. Before that, he spent 22 years at JPL, where he led some of NASA’s most high-profile missions, including the Mars Curiosity Rover and the Cassini, Mars Odyssey and Deep Impact probes, in addition to leading the science development for the GRAIL moon-mapping satellites. Watkins, who is a pioneer in the development and use of gravity data in science applications, also originated the concept for the GRACE and GRACE Follow-On missions, which use twin satellites to measure Earth’s gravity field. Watkins received his bachelor’s, master’s and doctoral degrees from UT Austin, all in aerospace engineering.

Alan Bovik holds the Cockrell Family Regents Chair in Engineering #3 in the Cockrell School of Engineering’s Department of Electrical and Computer Engineering. He is being recognized for contributions to the development of tools for image and video quality assessment. Bovik’s work broadly focuses on creating new theories and algorithms that allow for the perceptually optimized streaming and sharing of visual media. He is the director of the UT Laboratory for Image and Video Engineering and a member of the Wireless Networking and Communication Group, the Institute for Neuroscience and the Machine Learning Laboratory. He has published over 1,000 technical articles and holds several U.S. patents. Bovik, who joined UT Austin in 1984, has received numerous recognitions for his work, including two Emmy Awards, the Progress Medal from the Royal Photographic Society, the Edison Medal from the Institute of Electrical and Electronics Engineers, the IEEE Fourier Award for Signal Processing and the Edwin H. Land Medal from The Optical Society of America. He is a fellow of IEEE, The Optical Society of America, the Society of Photo-Optical and Instrumentation Engineers and honorary fellow of the Royal Photographic Society. Bovik received his bachelor’s, master’s and doctoral degrees from the University of Illinois at Urbana-Champaign, where he has also been honored with a Distinguished Alumni Award.

Ahmad Abdelrazaq (B.S. Civil Engineering 1984, M.S. CE 1986) is senior executive vice president of the Highrise Building and Structural Engineering Divisions at Samsung C&T Corporation. He is being recognized for innovation in design, construction and health monitoring of the world’s tallest and most complex building structures. Since joining Samsung in 2004, Abdelrazaq has been involved in all aspects of construction planning, preconstruction services and structural design of many prominent building projects, including the Burj Khalifa, currently the tallest building in the world, and Jumeriah Gardens in Dubai, and the Samsung HQ, Inchon Tower, Korean World Trade Center and the Yongsan Landmark Tower in Seoul. As chief technical director for the Burj Khalifa project, he played a key role in the development of the structural and foundation systems for the tower from initial design concept through completion. Abdelrazaq also currently serves as a visiting professor at Seoul National University. Previously, he was associate partner and senior project structural engineer at Skidmore Owings & Merrill LLP in Chicago, as well as an adjunct professor at the Illinois Institute of Technology. Abdelrazaq received his bachelor’s and master’s degrees from UT Austin, both in civil engineering.


Noel Clemens Wins 2022 AIAA Aerodynamic Measurement Technology Award

Noel Clemens standing in front of windows in office

Professor Noel Clemens has been selected to receive the 2022 AIAA (American Institute of Aeronautics and Astronautics) Aerodynamic Measurement Technology Award. The award, established in 1995, is presented annually to a researcher who has exhibited continued contributions and achievements toward the advancement of advanced aerodynamics flowfield and surface measurement techniques for research in flight and ground test applications. Clemens was selected “for the development and application of innovative laser-based imaging techniques to bring new insight into the physics of complex turbulent flows.

Clemens, who has been a faculty member of the Department of Aerospace Engineering and Engineering Mechanics at UT Austin since 1993, focuses his research in the area of hypersonics, experimental gas dynamics, experimental methods and combustion. He specializes in measurement technology using laser imaging diagnostics to study mixing, combustion, ablation, shock/boundary layer interactions and other high-speed unsteady flows.

Throughout his career, Clemens has made many important contributions toward aerodynamic measurement technology, especially applications of planar imaging methods to challenging flow environments, such as hypersonic flows, mixing, combustion and plasmas. Achievements include multi-parameter measurements using a combination of techniques such as PIV (particle image velocimetry), PLIF (planar laser induced fluorescence), Rayleigh scattering, pressure sensitive paint, digital image correlation, and laser-induced incandescence. According to Google Scholar his published works have been cited over 6,500 times.

He is a pioneer in applying advanced diagnostics to supersonic and hypersonic flows. His group was the first to apply kilohertz PIV in supersonic flows, and the first to apply PIV to hypersonic flows. As they advanced the technology, pushing acquisition rates to higher and higher speeds, their work revealed new physical phenomena. His work in supersonic boundary layers revealed, for the first time, the presence of very large-scale coherent structures (“superstructures”) in the boundary layer, and their influence on shock-induced turbulent separation. His recent work involves using combined PIV, pressure sensitive paint and digital image correlation to understand the interaction between shock-induced turbulent separation unsteadiness and the vibration of compliant structures. 

Clemens has made major contributions in scalar imaging, especially the planar laser-induced fluorescence technique. Together with collaborators from Sandia, he invented the krypton PLIF method, which enables measurements of a conserved scalar in reacting flows, and can be used as a non-toxic flow marker in high-speed wind tunnels. He also was the first to use the sublimation of solid-phase naphthalene to investigate ablation-products transport in hypersonic boundary layers. His innovation was to measure the dispersion of the ablation products with PLIF of gas-phase naphthalene. This work led to the first demonstration of simultaneous scalar-velocity measurements in hypersonic boundary layers.

A key aspect of Clemens’ work over the years has been to carefully quantify the effect of the measurement system on the quality of the measurements. In a series of papers his group quantified the effect of imaging system resolution, noise, filtering and aliasing, on the measured data. Using these results, Clemens was able to design unique experiments that enabled him to investigate the structure of the finest-scales of turbulence in both non-reacting and reacting flows.

Currently, Clemens is the director of the ULI FAST for Hypersonics Aerodynamics Measurements (AFOSR/NASA) that focuses on developing a new measurement technology for hypersonic flight. This novel technique will re-define sensing and analysis of hypersonic vehicles, and could eventually be applied to lower-speed aircraft as well. Learn more about all of his group’s current research projects on his website.

Clemens is a Fellow of AIAA and the American Physical Society. He is the winner of three AIAA Best Paper Awards, served as associate editor of the AIAA Journal for two years, and served as the Editor-in-Chief of Experiments in Fluids for three years. He served as the chair of the ASE/EM Department at UT Austin from 2012-2020 and holds the Clare Cockrell Williams Centennial Chair in Engineering.

The award, which includes a personalized certificate and engraved medal, will be presented to Clemens during the AIAA SciTech Forum Awards Ceremony in January of 2022.


Jayant Sirohi Named Technical Fellow of Vertical Flight Society

Jayant Sirohi, a professor in the Department of Aerospace Engineering and Engineering Mechanics, has been named a Technical Fellow of the Vertical Flight Society (VFS) for 2021. The society, which was originally established as the American Helicopter Society in 1943, is the world’s oldest and largest technical society dedicated to the understanding and advancement of vertical flight technology.

Technical fellowships are granted to VHS members who are leading careers and developing technology that significantly advances the interests of the vertical flight community. Sirohi was selected for “pioneering contributions over 20 years on the theory and application of smart sensors and actuators in vertical lift, and experimental research of coaxial rotor systems, which have greatly expanded and enriched the body of knowledge.”

Sirohi’s work focuses on investigating the fundamental physics of different types of vertical lift aircraft, such as coaxial, counter-rotating helicopters and electric aircraft for urban air mobility, by means of various experimental and computational tools. His group’s goal is to expand the capabilities and understanding of the complex aeroelastic behavior of future vertical take-off aerial vehicles. He is the principal investigator of the Vertical Lift Research Center of Excellence funded by U. S. Army, Navy and NASA and is currently developing new rotor technology for the next generation of vertical lift aircraft. 

Before joining the Cockrell School of Engineering, Sirohi worked at Sikorsky Aircraft Corporation, where he was a staff engineer in the Advanced Concepts group. At Sikorsky, he was involved with the conceptual design of next-generation vertical take-off aircraft, as well as the performance enhancement of existing rotorcraft using advanced technologies.  

Sirohi earned a Ph.D. from the University of Maryland at College Park and joined the department in 2008. He holds the M.J. Thompson Regents Professorship in Aerospace Engineering and Engineering Mechanics. Learn more about Sirohi’s research.


Inferring Aerodynamic Loads from Deformation Data


Karen Willcox – Professor and Director of the Oden Institute for Computational Engineering and Sciences

Patrick Blonigan, Senior Member of the Technical Staff, Sandia National Labs

Julie Pham – Graduate Research Assistant

Solving the inverse problem with Scientific Machine Learning (SciML)

The FAST methodology relies on Scientific Machine Learning (SciML) and a high-fidelity model of the vehicle structural response to produce an inverse map that can rapidly predict aerodynamic loads. The task of inferring aerodynamic loads from measurements of structural deformation is characteristically defined as an inverse problem. Inverse problems are typically computationally expensive and require a significant amount of time to solve. However, the FAST sensing technique requires a rapid evaluation of the inverse solution so that the inferred quantities of interest (QoIs) can inform the guidance and control system of the hypersonic vehicle in real-time. For real-time tractability, we aim to use scientific machine learning (SciML) to train an inverse map that can rapidly predict aerodynamic QoIs from on-board measurements of structural deformation.

As shown in the figure below, using SciML to develop the inverse map requires significant amounts of training data. To produce the necessary training data, we employ multi-physics and multi-fidelity models to produce forward simulation snapshots over a range of hypersonic flight conditions. The inputs to the forward model are parameterizations of distributed aerodynamic loads, and the outputs are the measurements of structural deformation. To increase robustness to the noisy nature of the measurements, the training dataset is augmented by adding synthetic noise realizations to the snapshots. With a large training set of physics-informed training samples, machine learning algorithms can be used to learn the inverse map from measurements to QoIs.

A physics-based forward model provides predictions of surface deformations, aerodynamic loads and thermal loads given vehicle state. We train a machine learning classifier to represent the inverse map: given surface deformation sensor data and thermal load sensor data, estimate the aerodynamic loads.

Many of the open questions involve sensor requirements, QoI parameterizations, aerothermoelastic modeling methods, and machine learning methods that can achieve a well-posed, robust, and computationally fast inverse mapping. The results will be validated using benchtop and wind tunnel experiments. Successful development of this strategy will make the FAST technique a novel advancement in the predictive capabilities for hypersonics. 

SciML framework using optimal classification trees (OCT) and/or optimal regression trees with linear models (ORT-L) to predict quantities of interest

Thermoelastic Framework for FAST Training Data Generation


Carlos E. S. Cesnik – Clarence L. (Kelly) Johnson Professor of Aerospace Engineering, University of Michigan

Michael Jones – Graduate Student Research Assistant, University of Michigan

Ana C. Meinicke – Graduate Student Research Assistant, University of Michigan

Solving the inverse problem with Scientific Machine Learning requires data to train its algorithms. Our goal is to determine the best way to obtain the required training data for the entire flight envelope in a timely manner and with the appropriate level of fidelity.

The training data for this problem will relate the temperature and structural deformation to the aerodynamic loading distribution, the inertial loading, and aeroheating. The aerothermoelastic solution used to calculate the temperature, deformation, and loading distributions will be developed here and will take advantage of the University of Michigan’s High-Speed Vehicle (UM/HSV) framework, a multi-physics, multi-fidelity simulation framework, and the finite element solver Abaqus. The UM/HSV (Klock & Cesnik, 2014) integrates aero-thermo-elastic-propulsive models to simulate a high-speed vehicle through its trajectory. With it, one can conduct stability analysis, flight trimming, dynamic simulation, and closed-loop control evaluation. It takes advantage of engineering and reduced-order models to capture the various effects impacting the flight of the vehicle as seen illustrated in Figure 1.

The aerothermoelastic modeling is conducted under the assumption that the highest fidelity model possible is not required to generate the appropriate excitation loads for the detailed thermal and elastic training data. Simplified loading distribution estimations are acceptable if they are representative of the solutions and spans the loading distribution space of interest. One of the major goals of our research is to determine the importance of loading model accuracy to the FAST methodology. The loading distribution space will be defined based on numerical and experimental test cases. The numerical  model is the IC3X vehicle developed by the AFRL Munitions Directorate (Witeof, Z. D., and Neergaard, 2014) and modified by the University of Michigan.  Also, derivatives of this model will be developed for the experimental efforts under FAST, and these will also be modeled and simulated in our new framework so SciML models can be created and validated with the experiments.

Aerodynamic loadings over the vehicle surface, temperature distribution throughout the structure, and inertial loadings will be used to excite the corresponding structural deformation. Cruise altitude, Mach number, angle of attack, sideslip angle and fin deflections will be varied and sampled using Latin Hypercube Sampling to determine the loading distributions. These approximated loading cases will be fed into the high-fidelity thermoelastic model developed for Abaqus FEM as shown in Figure 2. The corresponding temperature and deformation outputs along with the input loading distributions will then be used to train the machine learning algorithms. Since it is impractical to assume that temperature and elastic deformation are known in many points of the vehicle, we will also investigate the optimal sensor locations to best observe the aerodynamic loads on the vehicle.

Figure 1. Training Load Distribution Generation

Figure 2. Structural Deformation Calculation using Abaqus

Klock, R. J., & Cesnik, C. E. S. (2014). Aerothermoelastic Simulation of Air-Breathing Hypersonic Vehicles. AIAA Science and Technology Forum and Exposition (SciTech2014), National Harbor, Maryland, 13—17 January.

Witeof, Z. D., and Neergaard, L. J. (2014). Initial Concept 3.0 Finite Element Model Definition. In Report AFRL-RWWV-TN-2014-0013 (Issue Eglin Air Force Base: Air Force Research Laboratory).


Experimental Validation: Benchtop and Mach 5 Windtunnel

The FAST methodology will be validated by measuring the structural response to known surface loads applied to 3D printed models of a generic hypersonic vehicle. Models will be tested in a no-flow “benchtop” configuration and in the UT Austin Mach 5 Wind Tunnel. Related wind tunnel measurements will be made in UTSA’s Mach 7 Ludwieg Tube. The structural response (deformation) will be determined using multi-point strain measurements. The FAST methodology will take as input the strain measurements and it will predict the loads, which can be compared to the actual loads.


Noel Clemens – Professor

Jayant Sirohi — Professor

Marc Eitner – Post-doctoral fellow

Brianna Blocher – Graduate research assistant

Ben Diaz – Graduate fellow

Aditya Panigrahi – Graduate research assistant

Brandon Cruz – Undergraduate research assistant

Experimental validation data will be acquired by testing two different models that are based on the outer mold line of the IC3X reference hypersonic vehicle. The models will be a benchtop configuration with no flow, and a wind tunnel model. The benchtop model is designated as IC3X-B to differentiate it from the actual IC3X. The benchtop model is 5 feet in length (40% scale), is made of a single material (plastic or metal), and has an internal structure that was designed specifically to meet the needs of this project. The benchtop model will be subjected to a variety of known structural and thermal loads in the absence of flow, and the resulting deformation will be measured. The wind tunnel model will be 1 ft long, 3D printed from a plastic or elastomer, and is designated as the IC3X-W. Both models will be fitted with a 6 degree-of-freedom force balance to enable measurements of three orthogonal forces and three moments. The models will also be instrumented with a large number of strain and thermal sensors as detailed below.

All experiments are performed at the J.J. Pickle Research Campus of The University of Texas at Austin. The benchtop model is attached to the rigid test stand as shown in the accompanying figure. The sting-based 6-DOF force-balance is fixed on one end to the test stand and the model is attached near its center of gravity to the other end. We impose known loads to the model and then measure its deformation with strain sensors. The loads are applied by using four linear actuators, each of which is connected to the model via a uniaxial load cell.

Benchtop model of IC3X.

The application of the loads is controlled via a LabView program. A load command is given within the program, which is then sent to a Microcontroller and two PWM motor controllers. The data from the four load cells is read through a National Instruments PXIe DAQ system and serves as the feedback variables.

A fiber optical system is used to obtain local strain and temperature measurements at discrete locations on the inside of the model. A set of four fibers containing eight discrete sensors is used to measure the strain, thus a total of 32 discrete strain measurements can be obtained during each loading case.

The internal structure of the IC3X-B was designed specifically for this project. To access the interior of the model (to attach and maintain sensors), it is made from four separate pieces, which attach to each other using a tapered twist-lock mechanism, as shown in the figure below. In addition, multiple sections of the wall are reduced in thickness to produce points of flexure. These local flexures lead to large strain values during static loading and offer excellent locations for attachment of the strain sensors, due to the expected high signal to noise ratio.

IC3X-B in separate pieces and assembled.

The benchtop model is fabricated in house using additive manufacturing (3D printing). In-house manufacturing allows rapid design iterations and enables the use of materials with sufficiently low Young’s modulus.

The IC3X-W is a 1 ft long (8% scale) model that is designed to be tested in the Mach 5 wind tunnel. Its internal structure will be mostly identical to that of the larger benchtop model. The fabrication of this model is challenging, as the small dimensions approach the limitations of the additive manufacturing method.

IC3X-W mounted on the sting/strut.

The stiffness of the wind tunnel model (material choice and wall thickness) is based on numerical simulations, which model the structural deformation resulting from flow-induced pressure. The simulations are performed in ANSYS Fluent and ANSYS Mechanical and account for viscous flow effects and structural non-linearities. This model will also be instrumented with fiber optical sensors to obtain strain and temperature measurements on the inside wall. In addition to these discrete sensors, the outside of the model will be measured using pressure sensitive paint and stereoscopic digital image correlation, which obtains distributed surface pressure and strain information.

CFD solution of the flow around the IC3X-W in the Mach 5 wind tunnel.

Experimental Validation: Mach 7 Ludwieg Tube

UT San Antonio, under the direction of Prof. Chris Combs, will conduct experimental validation studies in their newly constructed Mach 7 Ludwieg tube. The Ludwieg tube is an intermittent-flow facility that produces Mach 7 flow for a duration of 50 to 100 milliseconds. Aeroelastic models will be mounted in the test section and simultaneous measurements of internal strain and surface pressure will be made. In some cases, the aeroelastic model will be mounted on a sting balance to measure integrated aerodynamic forces and moments. The transducer measurements will be supplemented with field measurements such as pressure sensitive paint and digital image correlation. The UTSA Ludwieg tube is described in detail below. The Ludwieg tube will complement related studies that will be made in UT Austin’s Mach 5 wind tunnel.     

UTSA Hypersonic Ludwieg Tube

Figure 1. Schematic of the UTSA Mach 7 Ludwieg Tube.

UTSA has recently completed construction of a new Mach 7.2 hypersonic Ludwieg tube facility. After roughly three years in development the facility—designed by PI Combs—is now fully operational. The UTSA Hypersonic Ludwieg tube has a constant test section cross-section of 203 mm × 203 mm (8” × 8”). The driver tube can be pressurized using either compressed gas bottles or a four-stage compressor—depending on the desired gas composition—providing stagnation pressures up to approximately 14 MPa. While it is anticipated that the primary test gas will be air for the duration of this program, it is possible to test with nitrogen or other more exotic test gases depending on the needs of a given experimental campaign. With an 18-m-long folded driver tube, individual test runs have a steady-state duration of roughly 50-100 ms depending on initial conditions. The insulated driver tube pipe can be pre-heated up to 700 K for wind tunnel tests. The freestream velocity is on average 1130 m/s, resulting in freestream Reynolds numbers between 0.5-200 × 106 m-1, making the UTSA facility one of the few at a U.S. university capable of accessing this Reynolds and Mach number range. The flow is exhausted into a roughly 6 m3 vacuum dump tank, enabling multiple steady-state passes of hypersonic flow for total test times up to 500 ms. Optical access for potential experiments can be provided by modular glass windows on the wind tunnel sidewalls, floor, and ceiling. While the facility run duration is relatively short compared to blowdown facilities, the steady test time correlates to over 1500 flow lengths for an appropriately scaled test model.

Figure 2.  UTSA Mach 7 Ludwieg tube facility.

Julie Pham Recognized with Aviation Week Network’s 20 Twenties Program Award

Aerospace engineering graduate student Julie Vi Pham is one of only 20 students across the globe named to Aviation Week Network’s 20 Twenties Class of 2021. Winners are selected not only for their academic performance, but also for their ability to communicate the value of their research and to contribute to a broader community. According to Aviation Week Network’s press release, the program “brings together technology hiring managers, students and faculty worldwide to recognize what’s needed for business and academic growth and success.”

Pham, who received her B.S.E. in mechanical engineering at Arizona State University, is pursuing a master’s degree and ultimately a Ph.D. in aerospace engineering at UT Austin under the advisement of Karen Willcox, a professor in the Department of Aerospace Engineering and Engineering Mechanics (ASE/EM), and the director of the Oden Institute for Computational Engineering and Sciences.

“My research uses scientific machine learning and physics-based reduced-order models to provide a mathematical foundation for predictive digital twins in the aerospace field,” said Pham. “This work advances the robustness of real-time control and decision-making in autonomous systems, including unmanned aerial vehicles (UAVs) and hypersonic vehicles.”

CFD simulation of IC3X hypersonic vehicle

Pham is currently developing a novel sensing strategy for hypersonic environments, called the Full-Airframe Sensing Technology (FAST).

“The strategy employs inverse methods with machine learning to infer quantities of interest, such as distributed pressure loads, from indirect measurements of structural deformation. The FAST methodology can enable advanced hypersonic vehicles with predictive sensing and control capabilities.”

Pham said she is also passionate about contributing to the broader STEM community through teaching, mentorship and outreach. During her undergraduate years, she played an active role in tutoring and research mentoring to help students from all backgrounds succeed in technical fields. At UT, Pham is serving as the president of the Graduate Ladies of Aerospace and Mechanics (GLAM) organization which was established to create a welcoming environment for all ASE/EM graduate students, and to promote diversity and inclusion in these fields.

Pham plans to work as a research engineer in either industry or at a national laboratory after graduating from UT Austin.


New NASA-UT Hypersonics Project Aims to Transform Sensing for High-Speed Vehicles

NASA and the Air Force Office of Scientific Research are backing a team of four universities, led by The University of Texas at Austin, in a project to redefine sensing and analysis of hypersonic vehicles, which can travel at least five times the speed of sound and potentially revolutionize space and air travel.

The three-year, $3.3 million project is funded by NASA’s University Leadership Initiative, and the team’s goal is to create a new paradigm in sensing for hypersonic vehicles, which could also be applied to lower-speed craft. The project — Full Airframe Sensing Technology (FAST) — will treat the vehicles themselves as sensors, analyzing aerodynamic changes during flight tests, and use that information to infer where force is being applied so they can better protect and control the vehicles.

“We are taking conventional sensors and distributing them across the vehicle, allowing them to make measurements they weren’t meant to make,” said Noel Clemens, professor in the Cockrell School of Engineering’s Department of Aerospace Engineering and Engineering Mechanics and leader of the project. “By getting information from all the sensors simultaneously, we will be able to analyze the shape of the vehicle and infer the distribution of forces acting on the vehicle.”

Typical sensors are tiny and only measure a very narrow scope of information. And it’s been challenging to deploy them on hypersonic vehicles because the extreme heat caused by high-speed travel causes them to burn up. By changing where sensors are placed — inside the vehicle instead of outside — and using them to track how the vehicle’s physical shape changes, the team can get insights into pressure and force put on the vehicles in real time.

This process — getting a global picture from a variety of sensors and using measurements to infer what is happening — is getting increasing attention across technology industries, said Karen Willcox, director of the Oden Institute for Computational Engineering and Sciences and a professor of aerospace engineering. But, Willcox said, using the entire vehicle as a sensor takes this to a new level and represents a new way of thinking, applicable to all flight regimes, including hypersonic flight.

“When it comes to things like dynamic sensing onboard a hypersonic vehicle, that way of thinking just hasn’t been explored yet,” Willcox said. “It’s all been focused on the local measurements and how you get a better sensor that can stand the heat.” 

Changes in shape caused by the extreme force generated by the speed of hypersonic flight can knock these vehicles off their trajectory and make them harder to control. By better understanding where deformation occurs, researchers can wield superior control over the vehicles.

The researchers will use scientific machine learning methods to create computationally efficient models of the relationship between the deformations of the vehicles and the force applied during flight.

“Usually, it’s not possible to measure information like surface forces and torques while a vehicle is in flight,” said Jayant Sirohi, associate professor in the Department of Aerospace Engineering and Engineering Mechanics. “This information can be used to validate computer models and to help control the vehicle when it encounters uncertain conditions.”

The NASA university program encourages the participation of Historically Black Colleges and Universities, and the UT team is collaborating with Huston-Tillotson University, an Austin-based HBCU with 1,100 undergraduate students.

Huston-Tillotson is working to develop its own engineering program, creating a path to engineering for populations traditionally underrepresented in the industry. The university is made up of approximately 68% Black students and 30% Hispanic students. Today, it offers a pre-engineering program and a partnership with Prairie View A&M, where students go to complete engineering degrees.

Huston-Tillotson will offer a computational engineering course for each of the three years of the hypersonics project to get students ready to work as part of the project team. UT faculty will help with this course, and three to five students will be chosen each year to work on the NASA project.

The students will bring a unique viewpoint to the project based on their background, said Amanda Masino, director of Huston-Tillotson’s STEM Research Scholars program, which provides science, technology, engineering and math majors with research experience and mentoring.

“Assumptions about why work is being done, how data is collected and applied will always be biased by experience,” Masino said. “Having a diverse team with different perspectives ensures that bias in collecting and deploying data is minimized.”

Here’s a look at the roles of some of the other team members:

  • The University of Michigan is developing a sophisticated computational model that will be used to understand how the vehicle structures respond to force.
  • The University of Texas at San Antonio has a Mach 7 wind tunnel that the team will use to test its technology at high speeds.
  • Sandia National Laboratories will provide computer simulations of the flow around the hypersonic vehicles
  • Lockheed Martin Corp. will provide technical guidance on hypersonic flight systems

Testing the vehicles using the new sensing paradigm is still a couple of years down the road, researchers say. In the next few months, the main priorities will be designing and building computational and experimental models of hypersonic vehicles and developing the scientific machine learning technology that will help make the inferences about pressure applied to vehicles during flight.