• Atmosphere
• /
• v.29 no.5
• /
• pp.551-566
• /
• 2019

Quantitative understanding of a random error that is associated with Lagrangian particle dispersion modeling is a prerequisite for backward-in-time mode simulations. This study aims to quantify the random error of the WRF-FLEXPART model and suggest an optimum number of the Lagrangian particles for backward-in-time simulations over the Seoul metropolitan area. A series of backward-in-time simulations of the WRF-FLEXPART model has conducted at two receptor points by changing the number of Lagrangian particles and the relative error, as a quantitative indicator of random error, is analyzed to determine the optimum number of the release particles. The results show that in the Seoul metropolitan area a 1-day Lagrangian transport contributes 80~90% in residence time and ~100% in atmospheric enhancement of carbon monoxide. The relative errors in both the residence time and the atmospheric concentration enhancement are larger when the particles release in the daytime than in the nighttime, and in the inland area than in the coastal area. The sensitivity simulations reveal that the relative errors decrease with increasing the number of Lagrangian particles. The use of small number of Lagrangian particles caused significant random errors, which is attributed to the random number sampling process. For the particle number of 6000, the relative error in the atmospheric concentration enhancement is estimated as -6% ± 10% with reduction of computational time to 21% ± 7% on average. This study emphasizes the importance of quantitative analyses of the random errors in interpreting backward-in-time simulations of the WRF-FLEXPART model and in determining the number of Lagrangian particles as well.

• Proceedings of the KSME Conference
• /
• 2003.11a
• /
• pp.899-904
• /
• 2003

A new model for dense gas dispersion is formulated within the Lagrangian framework. In several accidental released situations, denser-than-air vapour clouds are formed which exhibit dispersion behavior markedly different from that observed for passive atmospheric pollutants. For relevant prediction of dense gas dispersion, the gravity and entrainment effects need to implemented. The model deals with negative buoyancy which is affected by gravity. Also, the model is subjected to entrainment. The mean downward motion of each particle was accounted for by considering the Langevin equation with buoyancy correction term.

• Journal of Korean Society for Atmospheric Environment
• /
• v.15 no.6
• /
• pp.757-765
• /
• 1999

Lagrangian particle dispersion model(LPDM) is an effective tool to calculate the dispersion from a point source since it dose not induce numerical diffusion errors in solving the pollutant dispersion equation. Fictitious particles are released to the atmosphere from the emission source and they are then transported by the mean velocity and diffused by the turbulent eddy motion in the LPDM. The concentration distribution from the dispersed particles in the calculation domain are finally estimated by applying a particle count method or a Gaussian kernel method. The two methods for calculating concentration profiles were compared each other and tested against the analytic solution and the tracer experiment to find the strength and weakness of each method and to choose computationally time saving method for the LPDM. The calculated concentrations from the particle count method was heavily dependent on the number of the particles released at the emission source. It requires lots fo particle emission to reach the converged concentration field. And resulting concentrations were also dependent on the size of numerical grid. The concentration field by the Gaussian kernel method, however, converged with a low particle emission rate at the source and was in good agreement with the analytic solution and the tracer experiment. The results showed that Gaussian kernel method was more effective method to calculate the concentrations in the LPDM.

• Journal of Korean Society for Atmospheric Environment
• /
• v.33 no.6
• /
• pp.583-592
• /
• 2017

A method is proposed for locating the heat sources from temperature observations, and its applicability is investigated for urban thermal environment simulations. A Lagrangian particle dispersion model, which is originally built for simulating the pollutants spread in the air, is exploited to identify the heat sources by transporting the Lagrangian heat particles backwards in time. The urban wind fields are estimated using a diagnostic meteorological model incorporating the morphological model for the urban canopy. The proposed method is tested for the horizontally homogeneous urban boundary layer problems. The effects of the turbulence levels and the computational time on the simulation are investigated.

• Journal of Korean Society of Coastal and Ocean Engineers
• /
• v.8 no.2
• /
• pp.185-192
• /
• 1996

In order to simulate the coastal dispersion effectively, hybrid concept of operator split Eulerian-lagrangian concentration model and random-walk particle tracking model are developed. Especially the random-walk model is adequate for region with steep slope of concentration. According to model tests, it agrees perfectly with analytical solution on around the source point for therefore. ▽C $\geq$ 0.005, meanwhile it shows poor results for ▽C$\leq$0.002. trial modeling for real situation therefore, random-walk model is applied for near field henceforth Eulerian-Lagrangian concentration model is adoped for whole domain so that overall performance and accuracy can be achieved by using developed hybrid model.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.28 no.1
• /
• pp.89-98
• /
• 2004

In this paper, we develope a dispersion model based on the Generalized Langevin Model. Thomson's well-mixed condition is the well known criterion to determine particle dispersion. But, it has 'non-uniqueness problem'. To resolve this, we adopt a turbulent model which is a new approach in this field of study. Our model was greatly simplified under the self-similarity condition, leaving model only two model constants $C_{0}$ and ${\gamma}$$_{5}$ that control the dispersion and spin which measures rotational property of the Lagrangian particle trajectory. We investigated the sign of spin as well as magnitude by using the Direct Numerical Simulation. Model calculations were performed on the neutrally stable boundary layer flow. We found that spin has weak effect on the particle dispersion but it shows the significant effect on the horizontal flux compared to the zero-spin model.

• Proceedings of the Korea Air Pollution Research Association Conference
• /
• 2000.04a
• /
• pp.167-168
• /
• 2000

복잡한 구조를 갖고 시간에 따라서 변하는 바람장내에서 공장굴뚝과 같은 점오염원에서 배출되는 오염물질의 확산을 계산하기 위해서 라그란지안 입자확산모텔(Lagrangian Particle Dispersion Model, LPDM)을 사용하는 것이 최근의 연구 동향이다. 구윤서(1999a, 1999b)는 중립 및 안정한 대기조건에서 바람장 계산시 비평형 2.5 난류모델을 이용한 LPDM을 개발하여 복잡한 대기흐름내 확산현상을 보다 정확히 모사할 수 있는 LPDM을 제시하였다. (중략)

• Journal of Korean Society of Coastal and Ocean Engineers
• /
• v.10 no.2
• /
• pp.73-82
• /
• 1998

In order to simulate surface discharged heat dispersion in costal area, a 2-dimensional Eulerian-Lagrangian model for far field and semi-active particle tracking random walk model in near field has been combined. The mass of discharged heat water in near field has treated as particles with buoyancy and this is eventually converted to horizontal additive dispersion in random walk equations. This model is applied to both a simplified coastal geometry and a real site. In simple application it can simulate plume-like characteristics around discharging point than a near field-model, CORMIX/3. Actual application in the Chonsu Bay shows farther spreading of heat water in near field comparing the observed data, and this shows that the developed model might be applied with satisfaction.

• Journal of Korean Society for Atmospheric Environment
• /
• v.19 no.3
• /
• pp.249-261
• /
• 2003

A three-dimensional mesoscale atmospheric dispersion modeling system consisting of the Lagrangian particle dispersion model (LPDM) and the meteorological mesoscale model (MM5) was employed to simulate the transport and dispersion of non-reactive pollutant during the nuclear spill event occurred from Sep. 31 to Oct. 3, 1999 in Tokaimura city, Japan. For the comparative analysis of numerical experiment, two more sets of foreign mesoscale modeling system; NCEP (National Centers for Environmental Prediction) and DWD (Deutscher Wetter Dienst) were also applied to address the applicability of air pollution dispersion predictions. We noticed that the simulated results of horizontal wind direction and wind velocity from three meteorological modeling showed remarkably different spatial variations, mainly due to the different horizontal resolutions. How-ever, the dispersion process by LPDM was well characterized by meteorological wind fields, and the time-dependent dilution factors ($\chi$/Q) were found to be qualitatively simulated in accordance with each mesocale meteorogical wind field, suggesting that LPDM has the potential for the use of the real time control at optimization of the urban air pollution provided detailed meteorological wind fields. This paper mainly pertains to the mesoscale modeling approaches, but the results imply that the resolution of meteorological model and the implementation of the relevant scale of air quality model lead to better prediction capabilities in local or urban scale air pollution modeling.

• Nuclear Engineering and Technology
• /
• v.55 no.9
• /
• pp.3367-3382
• /
• 2023

Smoothed Particle Hydrodynamics (SPH) is a Lagrangian computational fluid dynamics method that has been widely used in the analysis of physical phenomena characterized by large deformation or multi-phase flow analysis, including free surface. Despite the recent implementation of eddy-viscosity models in SPH methodology, sophisticated turbulent analysis using Lagrangian methodology has been limited due to the lack of computational performance and numerical consistency. In this study, we implement the standard and dynamic Smagorinsky model and dynamic Vreman model as sub-particle scale models based on a weakly compressible SPH solver. The large eddy simulation method is numerically identical to the spatial discretization method of smoothed particle dynamics, enabling the intuitive implementation of the turbulence model. Furthermore, there is no additional filtering process required for physical variables since the sub-grid scale filtering is inherently processed in the kernel interpolation. We simulate lid-driven flow under transition and turbulent conditions as a benchmark. The simulation results show that the dynamic Vreman model produces consistent results with experimental and numerical research regarding Reynolds averaged physical quantities and flow structure. Spectral analysis also confirms that it is possible to analyze turbulent eddies with a smaller length scale using the dynamic Vreman model with the same particle size.