Portrait of Kursat Kara

Kara Aerodynamics Research Laboratory

Kursat Kara, Ph.D.

  • Associate Professor, School of Mechanical and Aerospace Engineering
  • Associate Fellow, American Institute of Aeronautics and Astronautics (AIAA)
  • Oklahoma State University · Stillwater, Oklahoma

Our research integrates high-fidelity computational fluid dynamics (DNS and LES), physics-guided reduced-order modeling, and scientific machine learning to understand and predict complex unsteady flows. We develop computationally efficient predictive models that translate high-fidelity flow physics into real-time aerodynamic prediction, flow reconstruction, and decision support. Our work spans reentry-vehicle dynamic stability, ship airwakes, urban atmospheric turbulence, and hypersonic boundary-layer transition. These efforts aim to improve the prediction of vehicle stability and aerodynamic loads, anticipate turbulence-induced flight hazards, support autonomous flight planning and control, and enable more reliable prediction of hypersonic boundary-layer transition.

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At a glance

Kursat Kara directs the Kara Aerodynamics Research Laboratory (KARA Lab) at Oklahoma State University. Beyond the four areas above, the lab works on active flow control with sweeping jet actuators and on quantum computing for fluid dynamics. Its work has been funded by NASA, the U.S. Navy (NAWCAD), the FAA, NSF, and IBM Quantum.

He earned his Ph.D. at Old Dominion University, supported by NASA Langley, with a dissertation on hypersonic boundary-layer receptivity. He worked as a CFD engineer at New England Analytics on projects for Sikorsky Aircraft, was a postdoctoral scholar at Penn State on large-eddy simulation of hot supersonic jets, and was a founding faculty member of the first aerospace engineering program in the UAE at Khalifa University before joining OSU in 2019. He was elected an AIAA Associate Fellow in 2026. He serves on the editorial board of Scientific Reports and guest-edited its Quantum Computing collection (2024–2025).

Career path

  1. Ph.D., Aerospace Eng.Old Dominion University
  2. CFD/Aerospace EngineerNew England Analytics (Sikorsky)
  3. Postdoctoral ScholarPenn State
  4. Assistant ProfessorKhalifa University, UAE
  5. Assistant ProfessorOklahoma State University
  6. Associate ProfessorOklahoma State University

By the numbers

$3.5M+external research funding as PI or Co-PI
84peer-reviewed articles, including 42 journal papers
14journal papers in 2025–2026
29undergraduate researchers since 2019

Counts from the CV and Google Scholar, October 2026.

News

  • The AIAA Oklahoma Section won second place in the AIAA Section–Student Branch Partnership Award, with Kursat Kara as Technical Symposium advisor.
  • Kursat Kara was elected an AIAA Associate Fellow (Class of 2027).
  • Ashraf Kassem received the CEAT Dean's Overall Outstanding Graduate Student Award and the MAE Outstanding Graduate Student Award.
  • The U.S. Navy (NAWCAD) funded AIRWISE, our project on AI for real-time wake inference in ship environments.
  • NASA extended our Early Stage Innovations project on blunt-body dynamic stability through January 2027.
  • The lab published 14 journal papers, including six in Aerospace Science and Technology on entry-capsule dynamics.
  • The lab ran Qiskit Fall Fest 2025, three weekly workshops on qubits, superposition, and entanglement.
  • Kursat Kara received the CEAT Excellent Faculty Award. The lab also received $70,000 in IBM Quantum credits.

More updates on LinkedIn →

Research

Each project below pairs high-fidelity simulation with reduced-order or machine-learning models fast enough for design, uncertainty quantification, or real-time use.

Illustration: a blunt reentry capsule pitching about its center of gravity in an M∞ = 1.44 stream, with a detached bow shock and a turbulent wake; an inset plots the total angle of attack for two initial conditions: from a small initial angle of attack (2°) the oscillation keeps growing, and from a large one (20°) it decays to a limit cycle
NASA Early Stage Innovations · PI · 2023–2027

Dynamic stability of reentry capsules

Physics-Guided Multifidelity Learning for Characterization of Blunt-Body Dynamic Stability (80NSSC23K0231, with a paid extension)

Blunt reentry capsules can become dynamically unstable in pitch at supersonic and transonic speeds, where small-amplitude oscillations grow while large-amplitude oscillations decay to a limit cycle. The lab characterizes this behavior from free-flight CFD of the Genesis capsule, using multifidelity data fusion, Bayesian estimation of pitch-damping derivatives, neural-operator surrogates, dynamic mode decomposition, and equation discovery.

Setup
Genesis sample return capsule; free-flight (free-to-oscillate) CFD at M∞ = 1.44, α0 = 2° and 20°; LES with Volcano ScaLES (in-kind licenses, Volcano Platforms).
Team
Ashraf Kassem and Shafi Al-Salman Romeo (Ph.D.); Kaden Thompson, Rachel Thibodeau, and Zachary Lohmar (undergraduate); alumnus Furkan Oz. Collaborator: Omer San.
Papers
Kassem et al., AST 2026, Tiwari et al., Sci. Rep. 2026, Romeo et al., AST 2025, Oz et al., JSR 2025
Illustration: the NATO Generic Destroyer, from its 1:50 baseline geometry, with wind over the deck shedding turbulent eddies from the superstructure and separating off the hangar into a recirculating airwake over the flight deck; mast anemometers feed the AIRWISE neural network, which outputs the inferred airwake for an approaching helicopter
U.S. Navy NAWCAD · PI · 2026–2028

Ship airwakes and real-time wake inference

Artificial Intelligence for Real-time Wake Inference in Ship Environments (AIRWISE), N00421-26-1-0004

Helicopter and drone operations at sea are limited by the turbulent airwake shed by a ship’s superstructure. AIRWISE runs GPU-accelerated LES of the NATO Generic Destroyer, validated against wind-tunnel measurements, and trains AI surrogates that infer the airwake in real time from sparse sensor data.

Setup
NATO Generic Destroyer; GPU-accelerated LES with Volcano ScaLES (in-kind licenses, Volcano Platforms), validated against 1:50-scale wind-over-deck velocities (NRC) and 1:200-scale surface pressure at several yaw angles.
Team
Mashrur Shejan (Ph.D.), Sabbir Hossain (M.Sc.), Gabe Kindt (undergraduate). Collaborator: Ryan Paul.
Status
Validation and airwake results presented at NAWCAD quarterly reviews, 2026.
Illustration: an atmospheric boundary-layer inflow, drawn as a mean power-law profile with an instantaneous fluctuating profile, enters a high-rise district; the towers shed turbulent eddies downstream, a vertical LES slice of streamwise velocity behind the towers shows their low-speed wakes, and an AAM VTOL aircraft flies above the district in this turbulent wind environment
FAA ASSURE · Co-PI · 2026–2028

Turbulence loading on AAM VTOL vehicles

A94: Effect of Turbulence on Aircraft Structural Loading of AAM VTOL Vehicles (25-C-UAS-OKSU-01)

Advanced air mobility (AAM) VTOL aircraft will take off and land among high-rise buildings, inside the most turbulent part of the urban atmospheric boundary layer. This ASSURE project creates a validated turbulent wind environment, an ABL inflow with building-induced wakes and gusts resolved by LES, for flight-dynamics simulation of AAM VTOL vehicles and the structural loads they experience.

Sketch of a downtown environment with high-rise buildings, turbulent wind swirling between them, and a small fixed-wing drone flying a path through target points
From the paper. A small UAV crossing a downtown wind field: it flies 12 paths, 30° apart in azimuth, through each of nine target points (green). Fig. 1, Krawczyk, Vuppala, Paul & Kara, Drones 2025, CC BY 3.0.
Setup
LES of the urban atmospheric boundary layer with Volcano ScaLES (in-kind licenses, Volcano Platforms).
Team
Mashrur Shejan (Ph.D.), Drew Moguin (undergraduate).
Related papers
Krawczyk et al., Aerospace 2024, Vuppala et al., Sci. Rep. 2024
Illustration: an LES domain over a city block with turbulent eddies shed behind each building, a reduced-order model panel, and a fixed-wing drone flying a wind-aware path
NSF National Robotics Initiative · PI · 2020–2025

Wind-aware navigation for drones in urban airspace

NRI: INT: Safe Wind-Aware Navigation for Collaborative Autonomous Aircraft in Low Altitude Airspace (1925147, with an REU supplement)

Small drones in cities fly through turbulent wind fields that mesoscale weather models do not resolve. The lab simulated the urban atmospheric boundary layer with LES, compressed it into POD–LSTM and convolutional-autoencoder reduced-order models, and coupled those winds to drone and AAM flight dynamics to support wind-aware navigation.

Large-eddy simulation of wind over a city block: translucent iso-volumes of velocity magnitude, 0 to 8 meters per second, wrap the buildings for a 135-degree inflow
From the paper. LES of the urban wind field: instantaneous iso-volumes of velocity magnitude for a 135° inflow. Fig. 9(d), Krawczyk, Vuppala, Paul & Kara, Drones 2025, CC BY 3.0.
Team
Alumnus Rohit Vuppala (Ph.D. 2024); undergraduates Tyler Landua, Zack Osborne, Gavin Sockey, Serge Amangoua, and Romain Bailey. Collaborators: He Bai, Jamey Jacob, Rushikesh Kamalapurkar, Samuel Vance, Ryan Paul.
Papers
Vuppala & Kara, AIP Adv. 2022, Vuppala et al., Sci. Rep. 2024, Krawczyk et al., Drones 9(5) 2025, Krawczyk et al., Drones 9(11) 2025
Illustration: a quantum computer with its qubit chip, a display of a quantum circuit and a 1D Burgers’ shock, and an arrow of increasing complexity toward a 3D aircraft model with tip vortices and a turbulent wake
IBM Quantum · PI · 2025

Quantum computing for fluid dynamics

Exploring Quantum Computing Paradigms for Fluid Dynamics ($70,000 in IBM Quantum credits)

Quantum algorithms may speed up the solution of the partial differential equations behind CFD. The lab developed a quantum PDE solver for the viscous Burgers’ equation and a Chebyshev-point formulation that improves its efficiency, and now builds hybrid classical–quantum lattice Boltzmann methods that target IBM quantum hardware.

Four snapshots of a Burgers’ equation shock: quantum (Q-CFD) and classical (C-CFD) solutions overlap
From the paper. Shock propagation in the viscous Burgers’ equation: quantum algorithm (Q-CFD) versus classical CFD (C-CFD). Oz et al., QIP 2022.
Team
Shafi Al-Salman Romeo (Ph.D.); alumnus Furkan Oz. Outreach: Qiskit Fall Fest.
Papers
Oz et al., Quantum Inf. Process. 2022, Oz, San & Kara, Sci. Rep. 2023
Illustration: two blunt cones in a Mach 6 free stream with acoustic disturbances. Without control, instability waves grow and the boundary layer transitions from laminar to turbulent flow, heating the aft surface. With a cooling strip and an acoustic metasurface, the waves are damped and the boundary layer stays laminar with low heat flux
NASA Oklahoma EPSCoR · PI · 2019, 2022–2023

Hypersonic boundary-layer transition

Direct Numerical Simulation of Broadband Acoustic Metasurfaces to Stabilize Acoustic Modes in Hypersonic Boundary Layer (Research Initiation Grant); Hypersonic Boundary-Layer Receptivity to Solid Particulates

Where the boundary layer turns turbulent sets the heating and drag on a hypersonic vehicle. DNS of Mach 6 flow over a blunt cone tracks how free-stream acoustic disturbances enter the boundary layer as first- and second-mode (Mack-mode) waves. Left alone, the waves grow, the flow transitions, and the surface heat flux rises; a localized cooling strip and an acoustic metasurface stabilize the waves and delay or eliminate transition, keeping the flow laminar.

Schematic of hypersonic receptivity: free-stream disturbance waves cross the bow shock, enter the boundary layer of a blunt body, and grow as instability waves over a cooling strip
From the paper. Receptivity of a hypersonic boundary layer over a blunt body with a local cooling strip. Oz et al., JSR 2023.
Team
Alumnus Furkan Oz (Ph.D. 2024); undergraduates Thomas Goebel, Jared Greif, Evan Evans, and Zackary Mitchell. Code on GitHub.
Papers
Oz et al., JSR 2023, Oz & Kara, Sci. Rep. 2024, Kara et al., AIAA J. 2011
Illustration: two wings beyond the stall angle of attack. The free stream approaches each leading edge. With actuators off, the flow separates from the leading edge into large eddies. With sweeping jet actuators on, fan-shaped jets keep the flow attached to the trailing edge. A fluidic oscillator shows the internal feedback channels, and an inset lift curve shows that the actuators delay stall to a higher angle of attack and raise the maximum lift coefficient
Khalifa University and KAIST · PI and Co-PI · 2016

Flow-separation control with sweeping jet actuators

Sweeping Jet Actuator: An Innovative Approach for Separated Flow Control (Khalifa University Internal Research Fund); Design of Fluidic Oscillators for Active Flow Control (Khalifa University–KAIST Seed Project)

Sweeping jet actuators are fluidic oscillators with no moving parts: feedback channels make the exit jet sweep side to side. Without actuation, the flow over a wing separates; with the sweeping jets on, it stays attached even beyond the stall angle of attack. The lab studies how internal geometry and mass flow rate set the oscillation frequency and models the actuator with DMD-based and causal state-space reduced-order models.

Sweeping jet actuator geometry: inlet, mixing chamber, upper and lower feedback channels, and four exit-nozzle variants
From the paper. Sweeping jet actuator geometry and the exit-nozzle variants studied. Alam & Kara, Fluids 2022.
Team
Shafi Al-Salman Romeo (Ph.D.); alumni Bartosz Jurewicz Slupski (Ph.D.) and Mobashera Alam (M.Sc.); undergraduates Isaiah Richmond, Kaitlyn Matic, and Trevor Marshall.
Papers
Romeo & Kara, Mathematics 2026, Romeo et al., Aerospace 2026, Alam & Kara, Fluids 2022, Kara et al., AIAA J. 2018

Sponsors and funding

  • NASAEarly Stage Innovations; Oklahoma NASA EPSCoR
  • National Science FoundationNational Robotics Initiative, with REU; ACCESS computing
  • U.S. NavyNaval Air Warfare Center Aircraft Division (NAWCAD)
  • Federal Aviation AdministrationASSURE, the FAA Center of Excellence for UAS
  • IBM QuantumQuantum computing for fluid dynamics
  • AnsysCurriculum development
  • OCASTOklahoma Center for the Advancement of Science and Technology
  • Khalifa UniversityInternal research funds; joint seed grant with KAIST

Other support

  • PIClassical Aerodynamics Meet with Modern Engineering Simulation Tools. ANSYS curriculum development.2024–2025
  • Co-PINext Generation Smart Heatsinks. OCAST AR20-020.2021–2022

Computing and software support: allocations from NSF ACCESS (PHY260216, PHY260139, MTH220018), NASA High-End Computing, XSEDE, and IBM Quantum; in-kind Volcano ScaLES licenses from Volcano Platforms for GPU-native LES in the NASA ESI, NAWCAD AIRWISE, and FAA ASSURE A94 projects. Member institution, University Consortium for Applied Hypersonics (UCAH), since 2024.

Selected publications

84 peer-reviewed articles, including 42 journal papers and 42 conference papers (AIAA, AMS, and IEEE), plus 73 conference abstracts and posters. The full list is on Google Scholar and in the CV.

  1. 2026
    Transition-aware learning for surrogate modeling of expensive engineering response surfacesS. A.-S. Romeo, K. KaraEngineering with Computers 42(6), 191
  2. 2026
    A knowledge-informed machine learning framework for multifidelity modelingS. A.-S. Romeo, K. Kara, O. SanMachine Learning for Computational Science and Engineering 2(1), 26doi:10.1007/s44379-026-00074-y
  3. 2026
    Causal state-space reduced-order modeling of sweeping jet actuators using internal mixing-chamber dynamicsS. A.-S. Romeo, K. KaraMathematics 14(10), 1694 · Feature paperdoi:10.3390/math14101694
  4. 2026
    Discovery of governing equations for reduced-order aerodynamic modes of atmospheric entry capsulesA. Kassem, S. A.-S. Romeo, B. Tiwari, L. Musharrat, O. San, K. KaraAerospace Science and Technology, 112454doi:10.1016/j.ast.2026.112454
  5. 2026
    Data-efficient neural operator surrogates for nonlinear pitch dynamics of blunt-body re-entry vehiclesB. Tiwari, A. Kassem, S. A.-S. Romeo, D. Ekelschot, C. Kazemba, K. Kara, O. SanAerospace Science and Technology, 112360doi:10.1016/j.ast.2026.112360
  6. 2026
    Emulator-accelerated Bayesian framework for nonlinear parameter estimation of entry capsule dynamic stabilityS. A.-S. Romeo, A. Kassem, B. Tiwari, L. Musharrat, O. San, K. KaraAerospace Science and Technology, 112342doi:10.1016/j.ast.2026.112342
  7. 2026
    Adaptive Bayesian learning for stability characterization of re-entry vehiclesB. Tiwari, L. Musharrat, S. A.-S. Romeo, A. Kassem, K. Kara, O. SanScientific Reports 16, 10267doi:10.1038/s41598-026-40068-7
  8. 2026
    Reduced-order modeling of sweeping jet actuators using eigenvalue-sorted dynamic mode decompositionS. A.-S. Romeo, M. Alam, K. KaraAerospace 13(2), 194doi:10.3390/aerospace13020194
  9. 2026
    Data-driven reduced-order modeling framework for blunt-body aerodynamic characterizationA. Kassem, S. A.-S. Romeo, B. Tiwari, O. San, K. KaraAerospace Science and Technology 171, 111615doi:10.1016/j.ast.2025.111615
  10. 2025
    Efficient coupling of urban wind fields and drone flight dynamics using convolutional autoencodersZ. Krawczyk, R. Paul, K. KaraDrones 9(11), 802doi:10.3390/drones9110802
  11. 2025
    Nonlinear parameter estimation for entry capsule dynamic stability analysis and uncertainty quantificationF. Oz, S. A.-S. Romeo, A. Kassem, D. Ekelschot, J. Schulz, C. Kazemba, O. San, K. KaraJournal of Spacecraft and Rocketsdoi:10.2514/1.A35998
  12. 2025
    Multifidelity data fusion for analyzing dynamic stability of atmospheric entry vehiclesS. A.-S. Romeo, A. Kassem, B. Tiwari, O. San, K. KaraAerospace Science and Technology 168, 111049doi:10.1016/j.ast.2025.111049
  13. 2025
    Bayesian framework for estimating dynamic stability derivatives in 6-DoF blunt-body entry vehiclesA. Kassem, S. A.-S. Romeo, B. Tiwari, O. San, K. KaraAerospace Science and Technology 167, 110646doi:10.1016/j.ast.2025.110646
  14. 2025
    Urban wind field effects on the flight dynamics of fixed-wing dronesZ. Krawczyk, R. K. S. S. Vuppala, R. Paul, K. KaraDrones 9(5), 362 · Feature paperdoi:10.3390/drones9050362
  15. 2024
    Evaluating reduced-order urban wind models for simulating flight dynamics of advanced aerial mobility aircraftZ. Krawczyk, R. K. S. S. Vuppala, R. Paul, K. KaraAerospace 11(10), 830doi:10.3390/aerospace11100830
  16. 2024
    Controlling hypersonic boundary layer transition with localized cooling and metasurface treatmentsF. Oz, K. KaraScientific Reports 14, 15928doi:10.1038/s41598-024-66867-4
  17. 2024
    An augmented physics informed neural network approach for blunt-body dynamicsS. A.-S. Romeo, F. Oz, A. Kassem, K. Kara, O. SanPhysics of Fluids 36(1), 017140doi:10.1063/5.0176702
  18. 2024
    Modeling advanced air mobility aircraft in data-driven reduced-order realistic urban windsR. K. S. S. Vuppala, Z. Krawczyk, R. Paul, K. KaraScientific Reports 14, 383doi:10.1038/s41598-023-50719-8
  19. 2023
    An efficient quantum partial differential equation solver with Chebyshev pointsF. Oz, O. San, K. KaraScientific Reports 13, 7767doi:10.1038/s41598-023-34966-3
  20. 2023
    Local wall cooling effects on hypersonic boundary-layer stabilityF. Oz, T. E. Goebel, J. S. Jewell, K. KaraJournal of Spacecraft and Rockets 60(2), 412–426doi:10.2514/1.A35404
  21. 2022
    A non-intrusive reduced order model using deep learning for realistic wind data generation for small unmanned aerial systems in urban spacesR. K. S. S. Vuppala, K. KaraAIP Advances 12(8) · codedoi:10.1063/5.0098835
  22. 2022
    Solving Burgers' equation with quantum computingF. Oz, R. K. S. S. Vuppala, K. Kara, F. GaitanQuantum Information Processing 21, 30doi:10.1007/s11128-021-03391-8
  23. 2022
    The influence of exit nozzle geometry on sweeping jet actuator performanceM. Alam, K. KaraFluids 7(2), 69doi:10.3390/fluids7020069
  24. 2018
    Flow-separation control using sweeping jet actuatorK. Kara, D. Kim, P. J. MorrisAIAA Journaldoi:10.2514/1.J056715
  25. 2011
    Effects of nose bluntness on hypersonic boundary-layer receptivity and stability over conesK. Kara, P. Balakumar, O. A. KandilAIAA Journaldoi:10.2514/1.J050032

Underlined authors are KARA Lab students.

Team

Ph.D., M.Sc., undergraduate, and high school researchers. Graduates now work at Oak Ridge National Laboratory, Fujitsu Research, SpaceX, and Boeing.

Graduate students

Ashraf Kassem
Ashraf KassemPh.D. CandidateEntry-capsule dynamics, equation discovery, reduced-order modeling. CEAT Dean's Outstanding Graduate Student (2026)ScholarGitHubLinkedIn
Shafi Al-Salman Romeo
Shafi Al-Salman RomeoPh.D. CandidateMultifidelity data fusion and uncertainty quantification for entry vehicles. MAE Outstanding Graduate Student (2025)ScholarGitHubLinkedIn
Mashrur Shejan
Mashrur ShejanPh.D. StudentLES of ship airwakes (NATO Generic Destroyer), urban turbulence and AAM VTOL vehiclesScholarLinkedIn
Sabbir Hossain
Sabbir HossainM.Sc. StudentAI for real-time wake inference in ship environments (AIRWISE)LinkedIn

Undergraduate researchers

Drew Moguin
Drew MoguinWake inference in urban environmentsLinkedIn
Gabe Kindt
Gabe KindtWake inference in ship environmentsLinkedIn
Kaden Thompson
Kaden ThompsonBlunt-body dynamic stabilityLinkedIn
Rachel Thibodeau
Rachel ThibodeauBlunt-body dynamic stabilityLinkedIn
Zachary Lohmar
Zachary LohmarBlunt-body dynamic stabilityLinkedIn

High school researchers

Aditya I.Quantum computing, fluid dynamics
Berrin A.Quantum computing, dynamical systems
Ceyda K.Flow separation control, graphic design
Otis H.Unmanned aerial systems, CFD

Alumni

Ph.D.

  • Furkan Oz2024 · R&D Associate, Oak Ridge National Laboratory
  • Rohit Vuppala2024 · Senior Researcher in Applied ML, Fujitsu Research
  • Bartosz Jurewicz Slupski2019, Khalifa University · Sr. R&D Programs & Engagement Manager, ASPIRE

M.Sc.

  • Mobashera Alam2022 · Flow Assurance Manager, JACAM Catalyst
  • Amani Bin Amro2019, Khalifa University · Principal Engineer, EDGE
  • Shamma Al Hemeiri2019, Khalifa University · Aerospace Engineer, Atlas Aerospace
  • Shaikha Al-Ali2019, Khalifa University · Systems Integration Engineer, UAE Ministry of Defence
  • Abdelnasir AlnaqbiKhalifa University

Undergraduate researchers

  • Peyton PiersonBuild Engineer, SpaceX (Starship)
  • Tyler LanduaBoeing
  • Thomas Goebel
  • Tuyen Nguyen
  • Joshua Exline
  • Yao Nayo (ROTC)
  • Zackary Mitchell
  • Brad Harris
  • Chris Dyke
  • Connor Kramer
  • Evan Evans
  • Gavin Smith
  • Gavin Sockey
  • Ilya Dzialendzik
  • Isaiah Richmond
  • Jared Greif
  • Kaitlyn Matic
  • Matthew Shell
  • Michael Cuthbertson
  • Romain Bailey (ROTC)
  • Serge Amangoua
  • Trevor Marshall
  • Tyler Cholerton
  • Zack Osborne

Teaching

Aerodynamics, computation, and data-driven methods. Recognized with the CEAT Excellent Faculty Award (2025), an ANSYS curriculum development grant (2024), and the President's Faculty Excellence Award for Outstanding Teaching at Khalifa University (2015).

MAE 3293 · Undergraduate · Every fall since 2019

Fundamentals of Aerodynamics

The junior aerodynamics course, taught to 90–160 students a year. Calculators: compressible aerodynamics.

MAE 5943 · Graduate · 2020, 2023

Unsteady Aerodynamics and Aeroacoustics

Governing equations for unsteady flows; linear unsteady aerodynamics of isolated and cascaded lifting surfaces; acoustics in moving media and three-dimensional ducts; sound generation by airfoils, cascades, rotor–stator interaction, propellers, and jets.

MAE 5010 · Graduate · 2022

Hydrodynamic Stability and Transition to Turbulence

Linear stability, receptivity, and transition.

MAE 5010 · Graduate · 2021

Quantum Computational Fluid Dynamics

Quantum algorithms for PDEs and their application to fluid flows.

Khalifa University · 2010–2018

Aerospace curriculum

Low- and high-speed aerodynamics, CFD, boundary-layer analysis, numerical methods, aircraft performance, and senior design.

Outreach, honors, and service

The lab runs quantum computing events with IBM Quantum and hosts high school researchers.

Qiskit Fall Fest 2025 banner, KARA Lab, Oklahoma State University
Oct – Nov 2025

Qiskit Fall Fest 2025

Three weekly workshops on qubits, superposition, and entanglement, taught by Kursat Kara with Ashraf Kassem and Shafi Al-Salman Romeo.

Qiskit Fall Fest 2024 banner, KARA Lab, Oklahoma State University, November 9, 2024
Nov 9, 2024

Qiskit Fall Fest at OSU

Kursat Kara chaired the organizing committee and gave the main talk, "Quantum Computing for Fluid Dynamics."

Honors and awards

  • Associate Fellow, American Institute of Aeronautics and Astronautics (Class of 2027)
  • AIAA Section–Student Branch Partnership Award, second place (Oklahoma Section; Technical Symposium advisor)
  • Excellent Faculty Award, College of Engineering, Architecture and Technology, Oklahoma State University
  • Principal investigator, NASA Early Stage Innovations award
  • President's Faculty Excellence Award for Outstanding Teaching, Khalifa University

Professional service

  • MAE representative, CEAT Academic Council
  • Editorial Board, Scientific Reports; Guest Editor, Quantum Computing collection (2024–2025)
  • NSF ACCESS Allocation Review Committee; NSF NAIRR Pilot Review Committee (2024–); Executive Committee, NERSC Users Group (NUGEX)
  • Review panels for NSF Fluids ERI, NASA TechLeap, NASA EPSCoR R3, NASA Next Gen STEM, DOE ASCR, and the Naval Research Laboratory
  • Technical Chair, AIAA Applied Aerodynamics Conference at AVIATION 2016 and SciTech 2018; Technical Co-Chair at AVIATION 2014 and 2020; Co-Chair, 40th AIAA/ASME Symposium (2021)
  • Member, AIAA Applied Aerodynamics Technical Committee
  • Member, American Physical Society

Lab life

Defenses, conferences, and internships.

Rohit Vuppala presenting at the U.S. National Congress on Computational Mechanics
Rohit Vuppala at USNCCM
Kursat Kara and Furkan Oz in doctoral regalia
Furkan Oz's graduation
Kursat Kara and Furkan Oz after the Ph.D. defense
Furkan Oz's Ph.D. defense
Kursat Kara and Rohit Vuppala outside the engineering building
Rohit Vuppala's Ph.D. defense
Ashraf Kassem and Shafi Romeo at an AIAA conference
Ashraf and Shafi at AIAA
Lab members at the NASA Research Park sign at Ames
Internships at NASA Ames
Lab members with posters at the MAE graduate research symposium
MAE Research Symposium, 2022
Kursat Kara with students beside a research poster
Poster session, 2022
Conference attendees under an SR-71 at the APS DFD 2019 reception
APS DFD, 2019

Contact

Email
kursat.kara@okstate.edu
Phone
(405) 744-5900
Mail
School of Mechanical and Aerospace Engineering
300 Engineering South
Oklahoma State University
Stillwater, OK 74078

Prospective students

We look for students who enjoy both the physics and the code: fluid mechanics, numerical methods, HPC, and machine learning or quantum computing. Email a CV, transcripts, and a short note on which project interests you.

OSU undergraduates are welcome to ask about research positions at any time.