Accuracy Of Transport Research Articles

A conservative Eulerian-Lagrangian decomposition principle for the solution of multi-scale flow problems at high Schmidt or Prandtl numbers

The simulation of turbulent flow that involves scalar transport at high Schmidt or Prandtl numbers is a major challenge. Enhanced Schmidt and Prandtl numbers demand an excessive increase in numerical resolution. Otherwise, the accuracy of transport would suffer substantially through unresolved information and numerical diffusion. With the aim of providing an efficient alternative for such applications, this paper presents a simulation method that is based on a novel Eulerian-Lagrangian decomposition principle (ELD) of the transported quantity. Low-pass filtering of the initial scalar quantity field separates it into a smooth low-frequency component and a fine-structured high-frequency component. The low-frequency component is represented and transported according to the Eulerian description by applying the Finite Volume Method (FVM) with a numerical resolution according to the Kolmogorov scale. The high-frequency component is translated into the Lagrangian description by the formation of particles, which are transported in parallel. By exchanging information between the two components, a re-initialisation mechanism ensures that the frequency-based decomposition is maintained throughout the simulation. Such ELD approach combines the efficiency of the FVM with the numerical stability of Lagrangian particles. As a result of the frequency-separation, the latter are by principle limited to zones of small scales and thus effectively complement the FVM. Furthermore, the particle information allows details of the scalar quantity field to be reconstructed that extend into the sub-grid level. By using a mixing layer setup, the proposed method is tested for a range of Schmidt numbers, and the numerical costs are considered and discussed.

A comparison of interface tracking methods in a low-Mach formulation

The objective of the present work is to compare the main Eulerian sharp-interface tracking methods using up-to-date numerical strategies from the literature. The study is focused on the specific case of low-Mach number formulation for the phase dynamics, and on a finite-volume cartesian-grid discretization. Volume of fluid and Level Set methods both rely on the resolution of an Eulerian transport equation that describes the interface and an additional step to prevent huge deviation from the interface representation due to numerical diffusion and dispersion in the transport step. In the VOF framework, this results in the transport of the volume fraction with a piecewise linear construction of the interface (PLIC). In the LS framework, this takes the form of a distance function transport which can be either a signed distance (here called Standard Level Set (SLS)) or an hyperbolic tangent (called the Conservative Level Set (CLS)) followed by a reinitialization step which ensures that the transported variable remains a signed distance or an hyperbolic tangent respectively. For each method, the numerical scheme used for advection and additional step are selected because of their proven accuracy and effectiveness in the literature for our specific framework. Our comparison is based on 2D and 3D canonical cases of the literature (Zalesak’s Disk rotation, vortex-in-a-box, sphere deformation). Our attention is drawn on a detailed analysis of mass conservation and transport accuracy, with the use of shared metrics for all methods.

Improved Accuracy in the 2-D/1-D Method with Anisotropic Transverse Leakage and Cross-Section Homogenization

The Two-Dimensional (2-D)/One-Dimensional (1-D) method allows pin-resolved computational transport solutions for large, full-core light water reactor simulations at relatively low computational cost compared to a true three-dimensional (3-D) transport method. The 2-D/1-D method constructs an approximation to the 3-D transport equation with (1) a 2-D transport equation in the radial variables and , discretized on a fine radial spatial grid, and (2) a 1-D transport (or approximate PN) equation in the axial variable , discretized on a radially coarse spatial grid. The 2-D and 1-D equations are coupled through transverse leakage (TL) terms. In this paper, a new 2-D/1-D P3 method with anisotropic transverse leakages and anisotropic homogenized 1-D cross sections (XSs) is proposed to improve the accuracy of conventional 2-D/1-D with pin homogenization. It is shown that only the polar component of the anisotropic homogenized XS has a significant effect on the solution; the azimuthal component is negligible. However, the polar and azimuthal components of the leakage terms are both important. The new method is implemented in the 2-D/1-D code Michigan PArallel Characteristics Transport (MPACT). The method in this paper is shown to achieve nearly 3-D transport accuracy with sufficient refinement in space and angle. The improvement of this new method compared to the previous 2-D/1-D method in MPACT is most notable in problems with strong axial leakage and sharp axial discontinuities, such as control rod tips or part-length rods. The method is computationally more expensive than the existing 2-D/1-D method with isotropic TL and XSs, but this additional cost may be justified when the axial flux shape does not vary smoothly due to axial heterogeneity and needs to be resolved well.

Toward reduced transport errors in a high resolution urban CO2 inversion system

We present a high-resolution atmospheric inversion system combining a Lagrangian Particle Dispersion Model (LPDM) and the Weather Research and Forecasting model (WRF), and test the impact of assimilating meteorological observation on transport accuracy. A Four Dimensional Data Assimilation (FDDA) technique continuously assimilates meteorological observations from various observing systems into the transport modeling system, and is coupled to the high resolution CO2 emission product Hestia to simulate the atmospheric mole fractions of CO2. For the Indianapolis Flux Experiment (INFLUX) project, we evaluated the impact of assimilating different meteorological observation systems on the linearized adjoint solutions and the CO2 inverse fluxes estimated using observed CO2 mole fractions from 11 out of 12 communications towers over Indianapolis for the Sep.-Nov. 2013 period. While assimilating WMO surface measurements improved the simulated wind speed and direction, their impact on the planetary boundary layer (PBL) was limited. Simulated PBL wind statistics improved significantly when assimilating upper-air observations from the commercial airline program Aircraft Communications Addressing and Reporting System (ACARS) and continuous ground-based Doppler lidar wind observations. Wind direction mean absolute error (MAE) decreased from 26 to 14 degrees and the wind speed MAE decreased from 2.0 to 1.2 m s–1, while the bias remains small in all configurations (< 6 degrees and 0.2 m s–1). Wind speed MAE and ME are larger in daytime than in nighttime. PBL depth MAE is reduced by ~10%, with little bias reduction. The inverse results indicate that the spatial distribution of CO2 inverse fluxes were affected by the model performance while the overall flux estimates changed little across WRF simulations when aggregated over the entire domain. Our results show that PBL wind observations are a potent tool for increasing the precision of urban meteorological reanalyses, but that the impact on inverse flux estimates is dependent on the specific urban environment.

Regression Analysis for Transport Trip Generation Evaluation

The paper focuses on transportation trip generation models based on mixed-use and transport infrastructure near the site. Transport trip generation models are considered with an aim to improve the accuracy of transport generated trips. Information systems are reviewed, and “smart growth” criteria that could affect the accuracy of trip generation models are also identified. Experimental results of transport generated trips based on linear regression equations and “smart growth” tools are demonstrated.