arXiv subjects

Sangjoon Lee

Publications and source records attributed to Sangjoon Lee.

9 recordsLinked to original sources

arXiv2026

Bayesian optimization and topographic exploration of drag-reducing dimples for aerodynamic surfaces

Dimples offer a promising route to reducing drag on aerodynamic surfaces. However, whether such shallow concavities yield a net benefit depends sensitively on their topography, which demands systematic mapping and exploration of their comprehensive design space. In this study, dimple design is examined as a mixed-variable optimization over four design variables: dimple type, depth, in-plane scale, and streamwise stretch. The design space is explored using MixMOBO, a Bayesian optimizer, coupled with immersed-boundary large eddy simulations of channel flows at a constant flow rate corresponding to a flat channel at a friction Reynolds number of 180. The optimal solution, a relatively deep, fully packed, streamwise-elongated diamond dimple, attains a 13.2% drag reduction, notably above previously reported values. A Gaussian process metamodel sensitivity analysis identifies dimple topology as the dominant factor, with coverage and elongation acting mainly through interactions, and depth itself carrying no universal sign. A near-wall flow analysis links the leading designs to fully attached, groove-like flow, whereas poorer designs tend to produce local flow separation that incurs adverse form drag. From these findings, key design insights for drag-reducing dimples are provided.

physics.flu-dyn↗

arXiv2026

Lattice dynamics and the spectroscopic signatures of H-bond disorder in $\delta$-AlOOH

Raman and infrared anomalies associated with H-bond symmetrization in $\delta$-AlOOH, including mode softening and linewidth broadening at 5-10 GPa, occur at significantly lower pressures than predicted by static harmonic theory. To resolve this discrepancy, we combine harmonic phonon calculations with strongly constrained and appropriately normed (SCAN)-based deep-potential molecular dynamics and phonon quasiparticle analysis at 300 K. This framework extracts temperature- and pressure-dependent frequencies and lifetimes from long-time trajectories, capturing the branch reorganization and rapid linewidth growth characteristic of the disordering regime. Incorporating quasiparticle renormalization and directional longitudinal-optical-transverse-optical (LO-TO) splitting further yields near-quantitative agreement with the ambient-pressure OH-stretching Raman multiplet. These results identify finite-temperature dynamical effects and the progressive loss of spectral coherence as the origin of the spectroscopic signatures of H-bond symmetrization.

cond-mat.mtrl-sci↗

arXiv2025

Airfoil optimization using Design-by-Morphing with minimized design-space dimensionality

Effective airfoil geometry optimization requires exploring a diverse range of designs using as few design variables as possible. This study introduces AirDbM, a Design-by-Morphing (DbM) approach specialized for airfoil optimization that systematically reduces design-space dimensionality. AirDbM selects an optimal set of 12 baseline airfoils from the UIUC airfoil database, which contains over 1,600 shapes, by sequentially adding the baseline that most increases the design capacity. With these baselines, AirDbM reconstructs 99 % of the database with a mean absolute error below 0.005, which matches the performance of a previous DbM approach that used more baselines. In multi-objective aerodynamic optimization, AirDbM demonstrates rapid convergence and achieves a Pareto front with a greater hypervolume than that of the previous larger-baseline study, where new Pareto-optimal solutions are discovered with enhanced lift-to-drag ratios at moderate stall tolerances. Furthermore, AirDbM demonstrates outstanding adaptability for reinforcement learning (RL) agents in generating airfoil geometry when compared to conventional airfoil parameterization methods, implying the broader potential of DbM in machine learning-driven design.

cs.LG↗

arXiv2024

Transient growth of a wake vortex and its initiation via inertial particles

The transient dynamics of a wake vortex, modelled as a strong swirling $q$-vortex, are investigated with a focus on optimal transient growth driven by continuous eigenmodes associated with continuous spectra. The pivotal contribution of viscous critical-layer eigenmodes (Lee & Marcus, J. Fluid Mech., vol. 967) amongst the entire eigenmode families to optimal perturbations is numerically confirmed, utilising a spectral collocation method for a radially unbounded domain that ensures correct analyticity and far-field behaviour. The consistency of the numerical method across different sensitivity tests supports the reliability of the results and provides flexibility for tuning. Both axisymmetric and helical perturbations with axial wavenumbers of order unity or less are examined through linearised theory and non-linear simulations, yielding results that align with existing literature on energy growth curves and optimal perturbation structures. The initiation process of transient growth is also explored, highlighting its practical relevance. Inspired by ice crystals in contrails, the backward influence of inertial particles on the vortex flow, particularly through particle drag, is emphasised. In the pursuit of optimal transient growth, particles are initially distributed at the periphery of the vortex core to disturb the flow. Two-way coupled vortex-particle simulations reveal clear evidence of optimal transient growth during ongoing vortex-particle interactions, reinforcing the robustness and significance of transient growth in the original non-linear vortex system over finite time periods.

physics.flu-dyn↗

arXiv2024

Perturbation analysis of triadic resonance in columnar vortices: selection rules and the roles of external forcing and critical layers

The remarkable robustness of columnar vortices suggests the existence of fundamental constraints that prevent spontaneous disintegration. In this work, we investigate the weakly nonlinear stability of such flows, demonstrating that the triadic resonance of wave modes is governed by a set of hydrodynamic ``selection rules''. By employing a multi-scale perturbation analysis, we prove that resonant interactions between smooth neutral modes, specifically regular Kelvin waves and discrete critical layer modes with passive singularities, are strictly conservative and confined to the Manley--Rowe relations. Using wave pseudoenergy within a large-$k$ WKBJ framework, we show that these rules topologically prohibit intrinsic instability, analogous to the forbidden transitions of quantum mechanics. Consequently, the breakdown of a columnar vortex requires a specific symmetry-breaking mechanism to overcome this barrier. We identify two distinct pathways: (1) \textit{Parametric instability}, a limiting case where one mode is maintained externally. By generalising beyond the specific spatial and temporal assumptions of classical studies (e.g., elliptical instability) and leveraging a tuning method based on non-degenerate perturbation theory, our framework admits arbitrary driving frequencies and identifies new instability configurations involving discrete critical layer modes. (2) \textit{Active critical layers}, where an embedded wave-mean resonance enables the direct, non-conservative extraction of mean-flow energy. These findings provide theoretical guidance for flow control, suggesting that aircraft wake vortex mitigation requires either tuned external forcing or the excitation of critical layers (e.g., via thermal stratification) to trigger the forbidden transitions.

physics.flu-dyn↗

arXiv2023

A Data-Driven Digital Twin Network Architecture in the Industrial Internet of Things (IIoT) Applications

A new network named the "Digital Twin Network" (DTN) uses the "Digital Twin" (DT) technology to produce virtual twins of real things. The network load and size continue to grow as a result of the development of 5G, the Internet of Things, and cloud computing technology as well as the advent of new network services. As a result, network operation and maintenance are becoming more difficult. A digital twin connects the real and digital worlds, exchanging data in both directions and revealing information about the progression of a network process. The framework of the Industrial Internet of Things, data processing, and digital twin network is taken into consideration in this article as a key aspect. This paper proposed a data-driven digital twin network architecture, that comprises the physical network layer (PNL), the digital twin layer(DTL), the application layer (AL), and what those layers encompass and beyond. Also, we presented DTN data types and protocols to be used for data integration.

cs.NI↗

arXiv2023

Residual Physics Learning and System Identification for Sim-to-real Transfer of Policies on Buoyancy Assisted Legged Robots

The light and soft characteristics of Buoyancy Assisted Lightweight Legged Unit (BALLU) robots have a great potential to provide intrinsically safe interactions in environments involving humans, unlike many heavy and rigid robots. However, their unique and sensitive dynamics impose challenges to obtaining robust control policies in the real world. In this work, we demonstrate robust sim-to-real transfer of control policies on the BALLU robots via system identification and our novel residual physics learning method, Environment Mimic (EnvMimic). First, we model the nonlinear dynamics of the actuators by collecting hardware data and optimizing the simulation parameters. Rather than relying on standard supervised learning formulations, we utilize deep reinforcement learning to train an external force policy to match real-world trajectories, which enables us to model residual physics with greater fidelity. We analyze the improved simulation fidelity by comparing the simulation trajectories against the real-world ones. We finally demonstrate that the improved simulator allows us to learn better walking and turning policies that can be successfully deployed on the hardware of BALLU.

cs.RO↗

arXiv2022

Linear stability analysis of wake vortices by a spectral method using mapped Legendre functions

A spectral method using associated Legendre functions with algebraic mapping is developed for a linear stability analysis of wake vortices. These functions serve as Galerkin basis functions, capturing correct analyticity and boundary conditions for vortices in an unbounded domain. The incompressible Euler or Navier-Stokes equations linearised on a swirling flow are transformed into a standard matrix eigenvalue problem of toroidal and poloidal streamfunctions, solving perturbation velocity eigenmodes with their complex growth rate as eigenvalues. This reduces the problem size for computation and distributes collocation points adjustably clustered around the vortex core. Based on this method, strong swirling $q$-vortices with linear perturbation wavenumbers of order unity are examined. Without viscosity, neutrally stable eigenmodes associated with the continuous eigenvalue spectrum having critical-layer singularities are successfully resolved. The inviscid critical-layer eigenmodes numerically tend to appear in pairs, implying their singular degeneracy. With viscosity, the spectra pertaining to physical regularisation of critical layers stretch out toward an area, referring to potential eigenmodes with wavepackets found by Mao & Sherwin (2011). However, the potential eigenmodes exhibit no spatial similarity to the inviscid critical-layer eigenmodes, doubting that they truly represent the viscous remnants of the inviscid critical-layer eigenmodes. Instead, two distinct continuous curves in the numerical spectra are identified for the first time, named the viscous critical-layer spectrum, where the similarity is noticeable. Moreover, the viscous critical-layer eigenmodes are resolved in conformity with the $Re^{-1/3}$ scaling law. The onset of the two curves is believed to be caused by viscosity breaking the singular degeneracy.

physics.flu-dyn↗

arXiv2022

Airfoil Optimization using Design-by-Morphing

We present Design-by-Morphing (DbM), a novel design methodology applicable to creating a search space for topology optimization of 2D airfoils. Most design techniques impose geometric constraints and sometimes designers' bias on the design space itself, thus restricting the novelty of the designs created, and only allowing for small local changes. We show that DbM methodology does not impose any such restrictions on the design space and allows for extrapolation from the search space, thus granting truly radical and large search space with a few design parameters. In comparison to other shape design methodologies, we apply DbM to create a search space for 2D airfoils. We optimize this airfoil shape design space for maximizing the lift-over-drag ratio, $CLD_{max}$, and stall angle tolerance, $\Delta \alpha$. Using a bi-objective genetic algorithm to optimize the DbM space, it is found that we create a Pareto-front of radical airfoils exhibiting remarkable properties for both objectives.

math.GT↗