• Original Article

    PREDICTION OF FUSELAGE FLOW SEPARATION AND DRAG CHARACTERISTICS OF A ROTORCRAFT USING THE LATTICE BOLTZMANN METHOD

    LBM 해석을 이용한 회전익기 동체의 박리 유동 및 항력 특성 분석

    D. Jeong, K.T. Park, C. Kim, S.H. Park, H. Jung, H. Lee

    정도윤, 박규태, 김찬영, 박수형, 정혜진, 이학진

    This study investigates the flow separation behavior on the upper and lower fuselage surfaces of a next-generation high-speed rotorcraft and its influence … + READ MORE
    This study investigates the flow separation behavior on the upper and lower fuselage surfaces of a next-generation high-speed rotorcraft and its influence on drag characteristics using lattice Boltzmann Method (LBM) simulations. The LBM results were validated against wind-tunnel experiments. The analysis revealed that adverse pressure gradients induced massive flow separation on both the upper engine-cowling and lower ramp regions. Early separation occurred on the upper surface, whereas delayed separation appeared on the lower surface. The separated flows from these regions interacted in the aft fuselage, forming large-scale wake structures that hindered pressure recovery and consequently increased pressure drag. As the angle of attack increased, the separation point on the upper surface moved forward, while that on the lower surface shifted rearward, exhibiting opposite trend. These results provide fundamental insights for designing drag-reduction devices and optimizing fuselage configurations in future rotorcraft development. - COLLAPSE
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    31 March 2026

  • Original Article

    DESIGN OF SUPERCRITICAL AIRFOIL FOR SHOCK WAVE REDUCTION USING XAI-BASED COUNTERFACTUAL EXPLANATIONS

    XAI 기반 반사실적 설명을 활용한 천음속 익형의 충격파 저감 설계

    S. Park, Y.E. Kang, Y. Hong, S. Lee

    박선영, 강유업, 홍윤표, 이상아

    Predicting and controlling shock waves in transonic airfoil design is critical for supercritical airfoils. While explainable artificial intelligence techniques interpret model predictions, … + READ MORE
    Predicting and controlling shock waves in transonic airfoil design is critical for supercritical airfoils. While explainable artificial intelligence techniques interpret model predictions, their use as active design tools remains limited. This study applies counterfactual explanations(CE) to generate actionable design alternatives for shock reduction. A multilayer perceptron surrogate model trained on computational fluid dynamics data with class shape transformation parametrization enables efficient geometry exploration. Four counterfactual explanation methods—gradient-based, random search, k-dimensional tree, and genetic algorithm—were compared to analyze design modification strategies. Results show that upper surface modifications in the leading edge and mid-chord regions are most critical for shock reduction, providing targeted design guidance. - COLLAPSE
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    31 March 2026

  • Original Article

    THE ADAPTATION OF IMPROVED NUMERICAL SCHEMES FOR PRESSURE BASED COMPRESSIBLE SOLVER BASED ON OPENFOAM

    OpenFOAM 기반의 압력 기반 압축성 유동 해석자를 위한 개선된 수치기법의 적용

    T.W. Kim

    김태우

    This study integrates density-based numerical schemes into a pressure-based solver to enhance compressible flow analysis. The AUSM+-up and HLLC schemes … + READ MORE
    This study integrates density-based numerical schemes into a pressure-based solver to enhance compressible flow analysis. The AUSM+-up and HLLC schemes were modified for the continuity equation flux and implemented within the OpenFOAM platform. The implementation was validated using 1D shock tube problems, confirming the successful integration of the schemes. Further simulations of 2D airfoils and 3D wings under transonic conditions demonstrate that the enhanced solver is highly effective and reliable. These results indicate that the adapting these schemes significantly enhances the performance of pressure-based solvers for complex compressible flows. - COLLAPSE
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    31 March 2026

  • Original Article

    EFFECTS OF TURBULENCE MODELS ON PREDICTION ACCURACY OF THERMAL NOx EMISSIONS IN A MICROMIX COMBUSTOR

    마이크로믹스 수소 연소기에서 난류모델이 열적 NOx 발생량 예측 정확도에 미치는 영향

    S. Tamang, H. Park

    따망사전, 박희성

    In this study, we investigated the effects of turbulence model selection and equivalence ratio on the internal flow field, flame temperature distribution, … + READ MORE
    In this study, we investigated the effects of turbulence model selection and equivalence ratio on the internal flow field, flame temperature distribution, and thermal NOx emissions in a single-nozzle micromix combustor. The combustor was designed to accelerate the oxidizer flow through an air-guide passage, while hydrogen was injected perpendicularly through a dedicated fuel nozzle. This configuration promotes microscale fuel-air mixing within the shear-layer region generated by the inner and outer vortex pair. To achieve these objectives, three-dimensional RANS simulations were performed using the commercial numerical code ANSYS Fluent 25R2. Combustion was modeled using a finite-rate/eddy-dissipation approach, and thermal NOx was quantified at the outlet and normalized to 15% O2 (dry). The results demonstrate that the choice of turbulence model strongly affects the oxidizer flow field, leading to either strengthened or weakened vortex structures and a corresponding change in the high-temperature region along the shear layer. The realizable k-ε model predicts the strongest recirculation and the highest local velocities. Thermal NOx increases with equivalence ratio and reaches a maximum value at ER = 0.47. Among the tested models, the realizable k-ε model shows the highest agreement with the experimental data. Consequently, turbulence model selection significantly affects the predicted temperature field and NOx trend and should therefore be treated as a key factor in micromix combustor design assessments. - COLLAPSE
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    31 March 2026

  • Original Article

    AN EFFICIENT WALL-DISTANCE COMPUTATION IN RANS FLOW SIMULATION USING KD-TREE

    KD-트리를 활용한 RANS 유동 해석에서의 효율적인 벽면 거리 계산 기법

    C.W. Jeong, H. Lee, J.S. Park

    정채원, 이하은, 박진석

    In this study, a KD-tree-based wall-distance computation method is proposed to improve computational efficiency in compressible turbulent flow simulations on large unstructured … + READ MORE
    In this study, a KD-tree-based wall-distance computation method is proposed to improve computational efficiency in compressible turbulent flow simulations on large unstructured grids. In Reynolds-averaged Navier-Stokes (RANS) turbulence modeling, accurate evaluation of the wall distance is essential for predicting near-wall turbulent behavior, while conventional Brute-force approaches suffer from rapidly increasing computational cost as the grid size grows. To improve computational efficiency, a two-step KD-tree search strategy is introduced to significantly reduce the number of wall elements involved in the distance calculation: candidate wall elements are first identified through a KD-tree-based proximity search, and the exact wall distance is then computed only for the selected candidates using a point-to-triangle geometric formulation. The proposed method is validated using the HB-2 configuration, the ONERA M6 wing, and the NASA CRM aircraft, demonstrating a substantial reduction in computational time compared to the Brute-force approach while maintaining reliable aerodynamic prediction accuracy. - COLLAPSE
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    31 March 2026

  • Original Article

    COMPARISON OF AI TURBULENCE PREDICTION PERFORMANCE ACROSS TIME SCALE: A COMPARATIVE STUDY OF U-NET AND DIFFUSION MODEL

    시간 간격에 따른 인공지능 난류 예측 성능 비교: U-Net과 Diffusion Model의 비교 연구

    J. Kang, M. Oh, J. Jeon, S. Lee

    강지원, 오민혁, 전준구, 이상승

    This study investigates the impact of time increments on the autoregressive prediction of two-dimensional turbulence slices extracted from three-dimensional direct numerical simulations, … + READ MORE
    This study investigates the impact of time increments on the autoregressive prediction of two-dimensional turbulence slices extracted from three-dimensional direct numerical simulations, comparing a U-Net with a diffusion-based generative model. Experiments demonstrate that the diffusion model significantly outperforms the deterministic approach, reducing the rollout-averaged mean squared error by 28.3% and relative energy error by 61.9%. Notably, the generative model effectively mitigates the smoothing of fine-scale structures and error accumulation observed in deterministic methods, preserving physical statistics such as energy spectra even at large time steps. Although the diffusion-based approach incurs higher computational costs, these findings highlight its superior capability in maintaining physical consistency for long-term turbulence prediction under partial observation conditions. - COLLAPSE
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    31 March 2026

  • Original Article

    EFFECTS OF GUIDE VANE INSTALLATION ON FLOW UNIFORMITY INSIDE DUCT SYSTEM

    가이드 베인의 설치가 덕트 내부의 유동 균일성에 미치는 영향

    J. Ko, J.H. Kim, H. Lim, C. Kang

    고정훈, 김준희, 임호, 강창우

    A design of the guide vane installed inside a duct system is presented to emit air uniformly to the outlet. The numerical … + READ MORE
    A design of the guide vane installed inside a duct system is presented to emit air uniformly to the outlet. The numerical simulation is performed to analyze the flow field. The effect of guide vanes on the flow uniformity is investigated. The design parameters such as location and number of guide vane are varied to examine their impact on standard deviation of flow velocity at the measurement location. When the 10 short straight guide vanes are installed, the standard deviation of the flow velocity is reduced by approximately 42% compared to the basic duct design. - COLLAPSE
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    31 March 2026
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