Using field inversion (FI) and symbolic regression (SR) to turn high-fidelity data into explicit, closed-form models: a mechanism-separated transition model for RANS (under review, AIAA Journal) and a vortex-aware reconstruction selector for shock-capturing schemes (under review, JCP).
An FP32 GPU finite-volume RANS/LES solver built around an exact discrete adjoint whose residual derivatives are generated by CuAD — a source-to-source automatic-differentiation compiler for CUDA. Drives gradient-based shape optimization, Cf/Cp inverse design, and Spalart–Allmaras field inversion (FIML) from Python, with the adjoint linear solve in double precision through PETSc. Validated on Schubauer–Klebanoff transitional flat plate, Mach-7 axisymmetric shock/boundary-layer interaction, and the ONERA M6 transonic wing.
An open-source, Tapenade-style source-to-source AD compiler for CUDA built on Clang LibTooling. It generates exact forward (tangent) and reverse (adjoint) GPU kernels from the original CUDA source, and powers the discrete adjoint of CuDAFLOW.
Solver-agnostic structured overset (chimera) mesh generation for rotors: wall-resolved (y+ ≈ 1) blade boundary-layer meshes from a plain-text planform table, full-rotor replication, and matched Cartesian background grids. Released on GitHub with a Zenodo DOI.
Comprehensive aerodynamic characterization of various eVTOL configurations and validation of the Lattice-Boltzmann Method (LBM) for near-ground operations.
Propeller–wing interaction modeling and wing design optimization within AMADEVS, a generalized aircraft modeling and multidisciplinary design environment developed at AVDL, SNU.
Developing a high-performance M&S environment and Coaxial rotor analysis module for advanced rotorcraft sizing and optimization.
Published in KSAS Fall Conference 2024, 2024
Integration of noise and signal propagation analysis for eVTOL operations.
Recommended citation: Taemin Jeong, Soonmyeong Kwon, Kwanghoon Yang, Seunghyun Joo, and Kwanjung Yee. (2024). “SNU-TIMPANI: Development of Integrated Noise and Electromagnetic Wave Analysis Tool for eVTOL.” KSAS Fall Conference 2024, pp. 320-321.
Published in KSAS Spring Conference 2025, 2025
Development of a generalized modeling framework for aircraft MDO.
Recommended citation: Jeonghwan Kim, Seongjoong Park, Seonghyun Jeong, Seunghyun Joo, Jaehyun Ryu, and Kwanjung Yee. (2025). “Generalized Modeling Architecture for Aerospace Multidisciplinary Analysis and Design.” KSAS Spring Conference 2025, pp. 593-594.
Published in KSCFE Spring Conference 2025, 2025
A study on the ground effect and downwash of eVTOL using LBM.
Recommended citation: Juneyoung Lim, Seunghyun Joo, and Kwanjung Yee. (2025). “Analysis of Downwash/Outwash Characteristics of eVTOL Configurations Using the Lattice Boltzmann Method.” KSCFE Spring Conference 2025.
Published in Asia Pacific International Symposium On Aerospace Technology (APISAT 2025), 2025
Analysis of aerodynamic characteristics of eVTOL configurations using the Lattice Boltzmann Method (LBM).
Recommended citation: Juneyoung Lim, Seunghyun Joo, and Kwanjung Yee. (2025). “Downwash/Outwash Analysis of eVTOL Configurations Using the Lattice-Boltzmann Method.” APISAT 2025.
Published in KSAS Fall Conference 2025, 2025
Development and Validation of a Low-Fidelity Analysis Tool for Conceptual Design Accounting for Anti-Icing Energy Requirement
Recommended citation: Heesang Shin, Younghyo Kim, Seunghyun Joo, Kwanjung Yee. (2026). “Development and Validation of a Low-Fidelity Analysis Tool for Conceptual Design Accounting for Anti-Icing Energy Requirement.” KSAS Fall Conference 2025, (TBD).
Published in AIAA SCITECH 2026 Forum, 2026
A generalized aircraft modeling and analysis environment with role-based components and a dual geometry–energy topology, demonstrated on Mars helicopter sizing, wing design under propeller–wing interaction, and UAM quadcopter handling qualities.
Recommended citation: Jeonghwan Kim, Seongjoong Park, Berenice Chatelain, Seonghyun Jeong, Jaehyun Ryu, Seunghyun Joo, and Kwanjung Yee. (2026). “AMADEVS: A Generalized Aircraft Modeling & Analysis Environment for Multidisciplinary Design.” AIAA SCITECH 2026 Forum, 1687.
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Published in KSAS Spring Conference 2026, 2026
Data-Driven Algebraic Turbulence Transition Modeling via Hierarchical Separation of Physical Transition Mechanisms
Recommended citation: Seunghyun Joo, Younghyo Kim, Kwanjung Yee. (2026). “Data-Driven Algebraic Turbulence Transition Modeling via Hierarchical Separation of Physical Transition Mechanisms.” KSAS Spring Conference 2026, (TBD).
Under Review, AIAA Journal, 2026
A closed-form, mechanism-separated laminar-to-turbulent transition model for the Spalart–Allmaras equation, built with field inversion and symbolic regression. Preprint: arXiv:2609.24162.
Recommended citation: Seunghyun Joo, Younghyo Kim, Kwanjung Yee. (2026). “Mechanism-Separated Closed-Form Transition Modeling via Field Inversion and Symbolic Regression.” Under review, AIAA Journal. arXiv:2609.24162.
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Under Review, Journal of Computational Physics (JCP), 2026
A vortex-aware (VA) reconstruction selector that reduces the numerical dissipation of under-resolved vortices in compressible flows while keeping the shock-capturing behavior of the baseline scheme.
Recommended citation: Seunghyun Joo, Yoonpyo Hong, Younghyo Kim, Kwanjung Yee. (2026). “Vortex-Aware Reconstruction Selection for Reducing Vortex Dissipation, Informed by Field Inversion and Symbolic Regression.” Under review, Journal of Computational Physics.
Work in Progress, 2026
The CFD residual and the periodic free-wake equilibrium residual are solved as one coupled system in both the primal and the adjoint, so the far wake is modeled cheaply but its design sensitivity is retained.
Recommended citation: Seunghyun Joo, Kwanjung Yee. “A Fully Coupled CFD–Free-Wake Discrete Adjoint for Rotor Design: Retaining Far-Wake Sensitivity.” Work in progress.
Preprint, arXiv (physics.flu-dyn), 2026
A source-to-source AD tool that writes tangent and adjoint CUDA kernels whose reverse sweep recomputes instead of storing, compared under one compiler with Clad and Enzyme and applied to a complete transonic Euler adjoint. Preprint: arXiv:2610.26694.
Recommended citation: Seunghyun Joo. (2026). “CuAD: Source-to-Source Automatic Differentiation of CUDA Kernels for Discrete Adjoints in Computational Fluid Dynamics.” arXiv preprint arXiv:2610.26694.
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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