Short-range Simulations Research Articles

Novel Solutions in Modeling of Anaerobic Digestion Process - Two-Phase AD Models Development and Comparison

Abstract This work focuses on Two Phase Anaerobic Digestion – a novel approach to split reactions’ chain in two separated tanks, each with specialized microbe community to achieve the best possible efficiency for each part of conversion. More specifically, the article tackle the topic of evaluation the possibility to adapt a mathematical model, previously dedicated for Autogenerative High Pressure Digestion (AHPD), to make use of it in simulation of Temperature Phased Anaerobic Digestion (TPAD) process. A comprehensive study of available solutions for biogas production simulation in conventional and TPAD configuration is additionally performed. Basing on its results, a reference model from literature, for comparison purpose is selected. Revisions and modifications, necessary to adjust previously developed model, to TPAD process, are described. Also, additional improvements like redesigned pH calculating algorithm is presented. Finally, the comparison between model predictions, a reference model and pilot-scale data is carried out. The results show that our current model needs further optimization, however even at this stage it provides acceptable results in short-range simulations (not longer than 42 days). Further works should focus on process stability improvement, especially in the thermophilic stage of biogas production. There's an opportunity for innovation as the research shows that requirement of accurate, large-scale optimized TPAD process model, is still not fulfilled.

Nuclear accident consequence assessment in Hong Kong using JRODOS

The JRODOS (Java-based Real-time Online DecisiOn Support) is a decision support system for off-site emergency management for releases of radioactive material into the environment. This paper documents the application of JRODOS by the Hong Kong Observatory in accident consequence assessment and emergency preparedness studies. For operational considerations, the most computational efficient dispersion model in JRODOS, ATSTEP, is adopted. Verification studies for JRODOS's ATSTEP model have been conducted. Comparison with tracer experiment results showed that under neutral atmospheric conditions and distances up to 50 km, the JRODOS simulation outputs were in general of the same order of magnitude with the tracer data. To further evaluate the capability of JRODOS in short-range simulation, a case study on the Fukushima nuclear power plant accident was also carried out. JRODOS was able to produce realistic simulation results which were comparable to the actual airborne monitoring data of the Cs-137 ground deposition from the Fukushima accident.Furthermore, the results of a comprehensive study to assess the potential consequences of accidents at a nearby nuclear power station are presented. Simulation using the French S3 source term for the Guangdong Nuclear Power Station at Daya Bay showed that the projected effective doses within Hong Kong remain far below the IAEA generic criteria of projected dose for urgent protective actions in sheltering/evacuation, while the projected equivalent dose in thyroid may meet the IAEA generic criteria for use of thyroid blocking agent at some areas in the northeastern part of Hong Kong, at distances of up to about 40 km from Daya Bay depending on the prevailing weather conditions in different seasons.

Avoiding numerical pitfalls in social force models

The social force model of Helbing and Molnár is one of the best known approaches to simulate pedestrian motion, a collective phenomenon with nonlinear dynamics. It is based on the idea that the Newtonian laws of motion mostly carry over to pedestrian motion so that human trajectories can be computed by solving a set of ordinary differential equations for velocity and acceleration. The beauty and simplicity of this ansatz are strong reasons for its wide spread. However, the numerical implementation is not without pitfalls. Oscillations, collisions, and instabilities occur even for very small step sizes. Classic solution ideas from molecular dynamics do not apply to the problem because the system is not Hamiltonian despite its source of inspiration. Looking at the model through the eyes of a mathematician, however, we realize that the right hand side of the differential equation is nondifferentiable and even discontinuous at critical locations. This produces undesirable behavior in the exact solution and, at best, severe loss of accuracy in efficient numerical schemes even in short range simulations. We suggest a very simple mollified version of the social force model that conserves the desired dynamic properties of the original many-body system but elegantly and cost efficiently resolves several of the issues concerning stability and numerical resolution.

Comparision of Membrane Interactions of Acylated and Non-Acylated Lactoferricins by Solid-State NMR Spectroscopy and Molecular Dynamics Simulations

LfB6 (RRWQWR-NH2) is a tryptophan- and arginine-rich cationic antimicrobial peptide with broad spectrum activity derived from bovine lactoferrin. Membrane binding occurs via electrostatic interactions between arginines and negative charges on the bacterial cell membrane and intercalation of the tryptophans at the membrane interface. N-terminal acylation (CH3(CH2)4CO-RRWQWR-NH2; C6-LfB6) can enhance the antimicrobial activity (Greathouse et al. (2008) J. Pept. Sci. 14:1103). Solid-state 2H and 31P NMR spectroscopy combined with all-atom and coarse-grained molecular dynamics (CG-MD) simulations have confirmed subtle differences between 1 mol% LfB6 and C6-LfB in bilayers composed of 3:1 POPE:POPG (anionic, bacterial-like) and POPC (zwitterionic, mammalian-like). MD simulations reveal that the arginines of C6-LfB6 make first contact with POPE:POPG; whereas the C6 tails are first to contact POPC. LfB6 shows no sequence preference. Additionally, C6-LfB6 inserts more deeply than LfB6 into both membranes. Tryptophan emission fluorescence spectra suggest the tryptophans in LfB6 and C6-LfB6 are more water exposed in neutral compared to anionic membranes, while CG-MD simulations reveal that LfB6 comes off the POPC membrane, exposing the tryptophans to water. Acylation, therefore, increases the “stickiness” of the peptide for lipid bilayers. Although both peptides at 1 mol% show significant membrane effects during short range simulations, C6-LfB6 has less influence on lipid order. We now compare experimental and molecular dynamics results for LfB6 and C6- LfB6 at 4 mol%. Solid-state 2H NMR spectra indicate that C6-LfB6 has a greater effect on the lipid acyl chain order at 4 mol% compared to 1 mol%; whereas the effects of LfB6 are similar at both concentrations. Molecular dynamics simulations will be presented for comparison.

Time-domain simulation of long-range sound propagation in an atmosphere with temperature gradient

A numerical model for linearized Euler equations using finite difference in time-domain (FDTD) simulation is developed to simulate sound propagation with temperature gradient in the atmosphere. The speed of sound in the air varies with the temperature at different altitude above the ground due to the effect of temperature gradient. For sound propagation at long ranges, an algorithm of moving-frame method is implemented with parallel computation. The numerical results are compared with analytical solutions for sound propagation with downward and upward refraction caused by the speed of sound linearly increasing (downward refraction) or decreasing (upward refraction) with altitude. The 2D normal mode analytical solutions are used to compare the downward refraction results, and the residue series analytical solutions are used to compare the upward refraction results. The comparison show that the numerical simulation results agree very well with the analytical solutions for both downward refraction and upward refraction cases. Several examples of long- and short-range simulation results are then presented.

The impact of a thermodynamic sea-ice module in the COSMO numerical weather prediction model on simulations for the Laptev Sea, Siberian Arctic

Previous versions of the Consortium for Small-scale Modelling (COSMO) numerical weather prediction model have used a constant sea-ice surface temperature, but observations show a high degree of variability on sub-daily timescales. To account for this, we have implemented a thermodynamic sea-ice module in COSMO and performed simulations at a resolution of 15 km and 5 km for the Laptev Sea area in April 2008. Temporal and spatial variability of surface and 2-m air temperature are verified by four automatic weather stations deployed along the edge of the western New Siberian polynya during the Transdrift XIII-2 expedition and by surface temperature charts derived from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. A remarkable agreement between the new model results and these observations demonstrates that the implemented sea-ice module can be applied for short-range simulations. Prescribing the polynya areas daily, our COSMO simulations provide a high-resolution and high-quality atmospheric data set for the Laptev Sea for the period 14-30 April 2008. Based on this data set, we derive a mean total sea-ice production rate of 0.53 km 3 /day for all Laptev Sea polynyas under the assumption that the polynyas are ice-free and a rate of 0.30 km 3 /day if a 10-cm-thin ice layer is assumed. Our results indicate that ice production in Laptev Sea polynyas has been overestimated in previous studies. Keywords: Laptev Sea; polynya; mesoscale modelling (Published: 12 May 2011) Citation: Polar Research 2011, 30 , 6334, DOI: 10.3402/polar.v30i0.6334

Preliminary Study of California Wintertime Model Wet Bias

AbstractThe Weather Research and Forecasting (WRF) model version 3.0.1 is used in both short-range (days) and long-range (years) simulations to explore the California wintertime model wet bias. California is divided into four regions (the coast, central valley, mountains, and Southern California) for validation. Three sets of gridded surface observations are used to evaluate the impact of measurement uncertainty on the model wet bias. Short-range simulations are driven by the North American Regional Reanalysis (NARR) data and designed to test the sensitivity of model physics and grid resolution to the wet bias using eight winter storms chosen from four major types of large-scale conditions: the Pineapple Express, El Niño, La Niña, and synoptic cyclones. Control simulations are conducted with 12-km grid spacing (low resolution) but additional experiments are performed at 2-km (high) resolution to assess the robustness of microphysics and cumulus parameterizations to resolution changes. Additionally, long-range simulations driven by both NARR and general circulation model (GCM) data are performed at low resolution to gauge the impact of the GCM forcing on the model wet bias.These short- and long-range simulations show that low-resolution runs tend to underpredict precipitation in the coast region and overpredict it elsewhere in California. The sensitivity test of WRF physics in short-range simulations indicates that model precipitation depends most strongly on the microphysics scheme, though convective parameterization is also important, particularly near the coast. In contrast, high-resolution (2 km) simulation increases model precipitation in all regions. As a result, it improves the forecast bias in the coast region while it downgrades the model performance in the other regions. It is also found that the choice of validation dataset has a significant impact on the model wet bias of both short- and long-range simulations. However, this impact in long-range simulations appears to be a secondary contribution as compared to its counterpart from the GCM forcing.

Applications of the Adjoint Technique to Short-Range Ensemble Forecasting of Mesoscale Convective Systems

The feasibility of applying a mesoscale adjoint model to the creation of an ensemble for short-range simulations of mesoscale convective systems (MCSs) is explored. Because past studies show that forecasters routinely improve upon numerical guidance and can identify mesoscale-sized areas of forecast concern with a high level of skill, it is clear that forecasters have insights into the daily weather forecast problems that exceed what can be provided by a numerical weather prediction model. Using an adjoint model, one could develop a system in which a forecaster identifies the area of forecast concern and then designs a set of rapidly produced sensitivity experiments that evaluate the influence of key atmospheric parameters on the model forecast. The output from these sensitivity experiments is then used to create ensemble members for a short-range operational ensemble forecast, which is specifically designed to investigate the forecast concern of the day. This adjoint ensemble approach is tested for the 48-h period beginning 1200 UTC 27 May 1985, in which a long-lived MCS developed underneath a large-scale ridge. A mesoscale adjoint model is used to define the alterations to the model initial conditions necessary to evaluate the influences of key mesoscale structures, which the authors believe have a large influence on later convective development in the model. Results indicate that the adjoint technique is effective in creating the proper directional response in the model simulation. When compared to initial condition and model physics ensembles of this event, the adjoint ensemble produces more variance than the initial condition ensemble and almost as much variance as the model physics ensemble. However, the values of the equitable threat score and the ranked probability score are better for the adjoint ensemble between 6 and 24 h than for either of the other two ensembles. These results suggest that further exploration of ensembles that incorporate the experience and expertise possessed by forecasters is warranted.

Prediction of a Flash Flood in Complex Terrain. Part I: A Comparison of Rainfall Estimates from Radar, and Very Short Range Rainfall Simulations from a Dynamic Model and an Automated Algorithmic System

Operational prediction of flash floods caused by convective rainfall in mountainous areas requires accurate estimates or predictions of the rainfall distribution in space and time. The details of the spatial distribution are especially critical in complex terrain because the watersheds generally are small in size, and position errors in the placement of the rainfall can distribute the rain over the wrong watershed. In addition to the need for good rainfall estimates, accurate flood prediction requires a surface-hydrologic model that is capable of predicting stream or river discharge based on the rainfall-rate input data. In part 1 of this study, different techniques for the estimation and prediction of convective rainfall are applied to the Buffalo Creek, Colorado, flash flood of July 1996, during which over 75 mm of rain from a thunderstorm fell on the watershed in less than 1 h. The hydrologic impact of the rainfall was exacerbated by the fact that a considerable fraction of the watershed experienced a wildfire approximately two months prior to the rain event. Precipitation estimates from the National Weather Service Weather Surveillance Radar-1988 Doppler and the National Center for Atmospheric Research S-band, dual-polarization radar, collocated east of Denver, Colorado, were compared. Very short range simulations from a convection-resolving dynamic model that was initialized variationally using the radar reflectivity and Doppler winds were compared with simulations from an automated algorithmic forecast system that also employs the radar data. The radar estimates of rain rate and the two forecasting systems that employ the radar data have degraded accuracy by virtue of the fact that they are applied in complex terrain. Nevertheless, the dynamic model and automated algorithms both produce simulations that could be useful operationally for input to surface-hydrologic models employed for flood warning. Part 2 of this study, reported in a companion paper, describes experiments in which these radar-based precipitation estimates and dynamic model‐ and automated algorithm‐based precipitation simulations are used as input to a surface-hydrologic model for simulation of the stream discharge associated with the flood.