Multiscale numerical weather prediction models transition from mesoscale, where turbulence is fully parameterized, to microscale, where the majority of highly energetic scales of turbulence are resolved. The turbulence gray-zone is situated between these two regimes and multiscale models must downscale through these resolutions. Here, we compare three multiscale simulations which vary by the parameterization used for turbulence and mixing within the gray-zone. The three parameterizations analyzed are the Mellor-Yamada Nakanishi and Niino (MYNN) Level 2.5 planetary boundary layer scheme, the TKE-1.5 large eddy simulation (LES) closure scheme, and a recently developed three-dimensional planetary boundary layer scheme based on the Mellor-Yamada model. The simulation domain includes complex (i.e., mountainous) terrain in Nevada that was instrumented with meteorological towers, profiling and scanning lidars, a tethered balloon, and a surface flux tower. Simulations are compared to each other and to observations, with assessment of model skill at predicting wind speed, wind direction and TKE, and qualitative evaluations of transport and dispersion of smoke from controlled releases. This analysis demonstrates that microscale predictions of transport and dispersion can be significantly influenced by the choice of turbulence and mixing parameterization in the terra incognita, particularly over regions of complex terrain and with strong local forcing. This influence may not be apparent in the analysis of model skill, and motivates future field campaigns involving controlled tracer releases and corresponding modeling studies of the turbulence gray-zone.

Current operational models for nuclear cloud rise over land were developed and validated using observations from shallow-buried or surface detonations, where lofted soil quickly mixed with fission products from the detonation. These models poorly predict fallout from elevated detonations near the fallout-free height of burst (FFHOB), where interactions with the ground are limited and the mixing of fission products and lofted soil is incomplete. Fallout-free is a misnomer at this HOB, as fallout was observed in these cases, but was below the levels of concern, especially off-grounds of the nuclear test site. To correctly characterize and model fallout from detonations near the FFHOB, models must be developed which can capture the stratified nature of the particle and activity-size distributions within the cloud. Previously, it was shown that the Weather Research and Forecasting (WRF) model can accurately simulate nuclear cloud rise for airbursts with little to no ground interactions (Arthur et al., 2021). That work is expanded here by (1) using a radiation-hydrodynamics code to improve the fireball initialization in WRF, (2) further developing an aerosol package from WRF-Chem to simulate lofted soil, and (3) combining the WRF cloud rise simulations with the operational models used at the National Atmospheric Release Advisory Center (NARAC) for fallout modeling. Using this combination of codes, the Upshot-Knothole Grable detonation, which was just below the FFHOB, is simulated from seconds after detonation through cloud rise and fallout, and results are compared to historical test data. The results show improved prediction of dose rate and highlight the need to correctly characterize the entrainment of material into the cloud and the subsequent mixing of fission products with entrained material.

Fast and accurate predictions of the flow and transport of materials in urban and complex terrain areas are challenging because of the heterogeneity of buildings and land features of different shapes and sizes connected by canyons and channels, which results in complex patterns of turbulence that can enhance material concentrations in certain regions. To address this challenge, we have developed an efficient three-dimensional computational fluid dynamics (CFD) code called Aeolus that is based on first principles for predicting transport and dispersion of materials in complex terrain and urban areas. The model can be run in a very efficient Reynolds average Navier–Stokes (RANS) mode or a detailed large eddy simulation (LES) mode. The RANS version of Aeolus was previously validated against field data for tracer gas and radiological dispersal releases. As a part of this work, we have validated the Aeolus model in LES mode against two different sets of data: (1) turbulence quantities measured in complex terrain at Askervein Hill; and (2) wind and tracer data from the Joint Urban 2003 field campaign for urban topography. As a third set-up, we have applied Aeolus to simulate cloud rise dynamics for buoyant plumes from high-temperature explosions. For all three cases, Aeolus LES predictions compare well to observations and other models. These results indicate that Aeolus LES can be used to accurately simulate turbulent flow and transport for a wide range of applications and scales.

Recent studies examine the potential for large urban fires ignited in a hypothetical nuclear exchange of one hundred 15 kt weapons between India and Pakistan to alter the climate (e.g., Mills et al., 2014, , and Reisner et al., 2018, ). In this study, the global climate forcing and response is predicted by combining two atmospheric models, which together span the micro-scale to global scale processes involved. Individual fire plumes are modeled using the Weather Research and Forecasting (WRF) model, and the climate response is predicted by injecting the WRF-simulated black carbon (BC) emissions into the Energy Exascale Earth System Model (E3SM) atmosphere model Version 1 (EAMv1). Consistent with previous studies, the radiative forcing depends on smoke quantity and injection height, examined here as functions of fuel loading and atmospheric conditions. If the fuel burned is 1 g cm(-2), BC is quickly removed from the troposphere, causing no global mean climate forcing. If the fuel burned is 16 g cm(-2) and 100 such fires occurred simultaneously with characteristics similar to historical large urban firestorms, BC reaches the stratosphere, reducing solar radiation and causing cooling at the Earth's surface. Uncertainties in smoke composition and aerosol representation cause large uncertainties in the magnitude of the radiative forcing and cooling. The approximately 4 yr duration of the radiative forcing is shorter than the 8 to 15 yr that has previously been simulated. Uncertainties point to the need for further development of potential nuclear exchange scenarios, quantification of fuel loading, and improved understanding of fire propagation and aerosol modeling.

A new 3D multiphase numerical capability is presented here for simulating multiphase flow regimes at all Mach numbers (M). The new method is a semi-implicit extension of the finite volume discrete equation method (DEM) of Chinnaya et al. (2004), which originally used explicit time-stepping. The capability is also developed to work with another extension of the DEM to moving grids for arbitrary Lagrangian-Eularian (ALE) methods detailed in Dunn (2011). Rather than solving all phase equations simultaneously, the DEM reduces the equations to a system of single-phase Riemann solves, where each phase has its own velocity and thermodynamic state. Exchanges between the phases are modeled through source terms accounting for the phase interactions. Since the original multiphase scheme uses an explicit time-advancement scheme, it has time step restrictions dictated by the speed of sound, which limits the model's ability to simulate weakly compressible flows which typically need to be integrated for longer time periods. Here, we extend the current multiphase formulation by implementing a pressure-correcting step to enable implicit calculations and remove acoustic time step limitations. The new semi-implicit algorithm allows use of relatively large time steps compared to an explicit method. Validation and benefits of the new implicit time-step method are illustrated using several examples including weakly compressible flows and strong shock waves. The scheme presented here is general and may be used for a variety of applications which require capabilities for handling multiphase flow at a wide range of Mach numbers. However, the goal of this research is to simulate all stages of high energy explosions, including the shock formation (high Mach numbers) and evolution of the buoyant cloud (low Mach numbers). Published by Elsevier Ltd.

Source-term estimation (STE) methods attempt to calculate the most-likely source characteristics of an atmospheric release given concentration observations. The quality of the STE depends partially on the time and space scales of the observations, sensor locations, and release parameters. We quantify the sensitivity of a probabilistic STE algorithm that was previously validated using observational data collected during a controlled tracer release experiment to the spatiotemporal resolution of observing networks. We have also made many improvements to the STE algorithm, which extends applicability to coarser-resolution observational datasets. Improvements include the use of a fully-connected deep neural network model emulator with dynamically optimized architecture and better goodness-of-fit (GoF) metrics to measure the discrepancy between model and observational data. The GoF metrics, which are more robust and resilient than previous metrics, are the Spearman rank-based correlation coefficient and a variation of the binary f 1 classification score adapted for regression problems. Using synthetically generated observations over East Asia, the skill of the improved algorithm is quantified over a broad range of sensor configurations and release scenarios. The evaluation is broken into three experiments. First, a validation study shows that the proposed GoF metrics perform more reliably than other possible candidates. Next, data-denial techniques are applied to a single release scenario over the Korean peninsula, where the skill of the inversion is shown to be highly sensitive to the number and location of deployed sensors but less sensitive to temporal resolution. Finally, the STE algorithm is tested for many release locations throughout the geographic model domain, where the STE algorithm performs well for all but a few cases. The results indicate that the STE algorithm provides informative source-parameter posterior probability distributions utilizing data collected by sparse sensor networks; however, the skill of the STE algorithm improves significantly for higher resolution networks.

After performing a first multi-model exercise in 2015 a comprehensive and technically more demanding atmospheric transport modelling challenge was organized in 2016. Release data were provided by the Australian Nuclear Science and Technology Organization radiopharmaceutical facility in Sydney (Australia) for a one month period. Measured samples for the same time frame were gathered from six International Monitoring System stations in the Southern Hemisphere with distances to the source ranging between 680 (Melbourne) and about 17,000 km (Tristan da Cunha). Participants were prompted to work with unit emissions in pre-defined emission intervals (daily, half-daily, 3-hourly and hourly emission segment lengths) and in order to perform a blind test actual emission values were not provided to them. Despite the quite different settings of the two atmospheric transport modelling challenges there is common evidence that for long-range atmospheric transport using temporally highly resolved emissions and highly space-resolved meteorological input fields has no significant advantage compared to using lower resolved ones. As well an uncertainty of up to 20% in the daily stack emission data turns out to be acceptable for the purpose of a study like this. Model performance at individual stations is quite diverse depending largely on successfully capturing boundary layer processes. No single model meteorology combination performs best for all stations. Moreover, the stations statistics do not depend on the distance between the source and the individual stations. Finally, it became more evident how future exercises need to be designed. Set-up parameters like the meteorological driver or the output grid resolution should be pre-scribed in order to enhance diversity as well as comparability among model runs.

The maximum allowable level of drift in the linear attenuation coefficients (μ) for a Lawrence Livermore National Laboratory (LLNL) micro-computed tomography (MCT) system was determined to be 0.1%. After ~100 scans were acquired during the period of November 2014 to March 2015, the drift in μ for a set of six reference materials reached or exceeded 0.1%. Two strategies have been identified to account for or correct the drift. First, normalizing the 160 kV and 100 kV μ data by the μ of water at the corresponding energy, in contrast to conducting normalization at the 160 kV energy only, significantly compensates for measurement drift. Even after the modified normalization, μ of polytetrafluoroethylene (PTFE) increases linearly with scan number at an average rate of 0.00147% per scan. This is consistent with PTFE radiation damage documented in the literature. The second strategy suggested is the replacement of the PTFE reference with fluorinated ethylene propylene (FEP), which has the same effective atomic number (Ze) and electron density (ρe) as PTFE, but is 10 times more radiation resistant. This is important as effective atomic number and electron density are key parameters in analysis. The presence of a material with properties such as PTFE, when taken together with the remaining references, allows for a broad range of the (Ze, ρe) feature space to be used in analysis. While FEP is documented as 10 times more radiation resistant, testing will be necessary to assess how often, if necessary, FEP will need to be replaced. As radiation damage to references has been observed, it will be necessary to monitor all reference materials for radiation damage to ensure consistent x-ray characteristics of the references.

In the interest of promoting the international exchange of technical expertise, the US Department of Energy’s Office of Emergency Operations (NA-40) and the French Commissariat à l'Energie Atomique et aux énergies alternatives (CEA) requested that the National Atmospheric Release Advisory Center (NARAC) of Lawrence Livermore National Laboratory (LLNL) in Livermore, California host a joint table top exercise with experts in emergency management and atmospheric transport modeling. In this table top exercise, LLNL and CEA compared each other’s flow and dispersion models. The goal of the comparison is to facilitate the exchange of knowledge, capabilities, and practices, and to demonstrate the utility of modeling dispersal at different levels of computational fidelity. Two modeling approaches were examined, a regional scale modeling approach, appropriate for simple terrain and/or very large releases, and an urban scale modeling approach, appropriate for small releases in a city environment. This report is a summary of LLNL and CEA modeling efforts from this exercise. Two different types of LLNL and CEA models were employed in the analysis: urban-scale models (Aeolus CFD at LLNL/NARAC and Parallel- Micro-SWIFT-SPRAY, PMSS, at CEA) for analysis of a 5,000 Ci radiological release and Lagrangian Particle Dispersion Models (LODI at LLNL/NARAC and PSPRAY at CEA) for analysis of a much larger (500,000 Ci) regional radiological release. Two densely-populated urban locations were chosen: Chicago with its high-rise skyline and gridded street network and Paris with its more consistent, lower building height and complex unaligned street network. Each location was considered under early summer daytime and nighttime conditions. Different levels of fidelity were chosen for each scale: (1) lower fidelity mass-consistent diagnostic, intermediate fidelity Navier-Stokes RANS models, and higher fidelity Navier-Stokes LES for urban-scale analysis, and (2) lower-fidelity single-profile meteorology versus higher-fidelity three-dimensional gridded weather forecast for regional-scale analysis. Tradeoffs between computation time and the fidelity of the results are discussed for both scales. LES, for example, requires nearly 100 times more processor time than the mass-consistent diagnostic model or the RANS model, and seems better able to capture flow entrainment behind tall buildings. As anticipated, results obtained by LLNL and CEA at regional scale around Chicago and Paris look very similar in terms of both atmospheric dispersion of the radiological release and total effective dose. Both LLNL and CEA used the same meteorological data, Lagrangian particle dispersion models, and the same dose coefficients. LLNL and CEA urban-scale modeling results show consistent phenomenological behavior and predict similar impacted areas even though the detailed 3D flow patterns differ, particularly for the Chicago cases where differences in vertical entrainment behind tall buildings are particularly notable. Although RANS and LES (LLNL) models incorporate more detailed physics than do mass-consistent diagnostic flow models (CEA), it is not possible to reach definite conclusions about the prediction fidelity of the various models as experimental measurements were not available for comparison. Stronger conclusions about the relative performances of the models involved and evaluation of the tradeoffs involved in model simplification could be made with a systematic benchmarking of urban-scale modeling. This could be the purpose of a future US / French collaborative exercise.

The purpose of this document is to familiarize the reader with the general configuration and operation of the natural gas pipelines in California and to discuss potential LLNL contributions that would support the Partnership for the 21st Century collaboration. First, pipeline infrastructure will be reviewed. Then, recent pipeline events will be examined. Selected current pipeline industry research will be summarized. Finally, industry acronyms are listed for reference.

Failure of a dam can have subtle beginnings. A small crack or dislocation at the interface of the concrete dam and the surrounding embankment soil initiated by, for example, a seismic or an explosive event can lead to a catastrophic failure of the dam. The dam may ‘self-rehabilitate’ if a properly designed granular filter is engineered around the embankment. Currently, the design criteria for such filters have only been based on experimental studies. We demonstrate the numerical prediction of filter effectiveness at the soil grain scale. This joint LLNL-ERDC basic research project, funded by the Department of Homeland Security’s Science and Technology Directorate (DHS S&T), consists of validating advanced high performance computer simulations of soil erosion and transport of grain- and dam-scale models to detailed centrifuge and soil erosion tests. Validated computer predictions highlight that a resilient filter is consistent with the current design specifications for dam filters. These predictive simulations, unlike the design specifications, can be used to assess filter success or failure under different soil or loading conditions and can lead to meaningful estimates of the timing and nature of full-scale dam failure.

An understanding of the inherent variability in micro-computed tomography (micro-CT) data is essential to tasks such as statistical process control and the validation of radiographic simulation tools. These data present unique challenges to variability analysis due to the relatively low resolution of radiographs, and also due to minor variations from run to run which can result in misalignment or magnification changes between repeated measurements of a sample. Such positioning changes artificially inflate the variability of the data in ways that mask true physical phenomena. We present a novel Bayesian nonparametric regression model that incorporates both additive and multiplicative measurement error in addition to heteroscedasticity to address this problem. We use this model to assess the effects of sample thickness and sample position on measurement variability for an aluminum specimen. Supplementary materials for this article are available online.

Numerical simulations using two hydrocodes were compared to near-field measurements of blast impulse associated with ideal and non-ideal explosives to gain insight into testing results and predict untested configurations. The recently developed kinetic plate test was designed to measure blast impulse in the near-field by firing spherical charges in close range from steel plates and probing plate acceleration using laser velocimetry. Plate velocities for ideal, non-ideal and aluminized explosives tests were modeled using a three dimensional hydrocode. The effects of inert additives in the explosive formulation were modeled using a 1-D hydrocode with multiphase flow capability using Lagrangian particles. The relative effect of particle impact on the plate compared to the blast wave impulse is determined and modeling is compared to free field pressure results.

Successful analysis of structural failures due to shock loading typically employs detailed three-dimensional codes that require excessive computational resources. Our interest is in developing more efficient approaches to evaluating the effects of damage on structures and personnel after a dynamic shock event. While several methods are currently available for a general analysis of structural failures under dynamic shock loads at a distance, we have developed a statistics based tool to predict the extent of damage for loads in close proximity to structures and quantify uncertainties. This tool is coupled with a fast-running, physics-based code that predicts the blast propagation in a tunnel of arbitrary complexity in order to form a single, user-friendly architecture to estimate the effects of blasts in tunnels. Use of such a fast-running tool facilitates scoping and planning of subsequent simulations using detailed, computationally expensive, models and provides a way to address parametric sensitivity.

A novel but relatively easy-to-implement mitigation concept to enforce standoff distance and reduce shock loading on a vertical, partially-submerged structure is evaluated experimentally using scaled aquarium experiments and numerically using a high-fidelity finite element code. Scaled, water-tamped explosive experiments were performed using aquariums of different sizes. The effectiveness of different mitigation configurations, including air-filled media and an air gap, is assessed relative to an unmitigated detonation using the same charge weight and standoff distance. Experiments using an air-filled media mitigation concept effectively dampen the explosive response of an aluminum plate and reduce the final displacement at plate center by approximately half. Experiments using an air-gap resulted in a focused water slug hitting the plate, an effect we hypothesize to be due to water encasement of the charge. Finite element simulations used for the initial experimental design compare very well to experiments both spatially and temporally for the unmitigated case and for the air-filled media mitigation; simulations accounting for water encasement bound air gap experiments. Details of numerical and experimental approach are provided as well as a discussion of results.

Hybrid system modeling is an emerging field in flow and transport in porous fractured media. Known the effective behavior of an engineered system, such as geothermal system, at different scale is essential to successfully design and predict complex operational conditions. A major issue to overcome when characterizing a deep fractured reservoir is that of data limitation due to accessibility and affordability. Moreover, the ability to map discontinuities in the rock with available geological and geophysical tools tends to decrease particularly as the scale of the discontinuity goes down. Geological characterization data include, but are not limited to, measurements of fracture density, orientation, extent, and aperture. All of which are taken at the field scale through a very sparse limited number of deep boreholes. These types of data are often reduced to probability distribution functions for predictive modeling and simulation in a stochastic discrete framework. Stochastic discrete fracture network (SDFN) models enable, through Monte Carlo simulations, the probabilistic assessment of flow and transport phenomena that are not adequately captured using continuum models. Despite the fundamental uncertainties inherited within the probabilistic reduction of the sparse data collected, very little work has been conducted on quantifying uncertainty on the reduced probabilistic distribution functions. Using nested Monte Carlo simulations, we investigated the impact of parameter uncertainties of the discrete fracture network on the flow, heat and mass transport using physical characteristics such as the hydraulic conductivity tensor, production temperatures and peak arrival time. INTRODUCTION & PROBLEM STATEMENT The logical steps that must be taken to complete an EGS reservoir project are (Figure 1): (1) finding and characterizing a site by drilling and logging exploratory wells; (2) creating the reservoir by drilling an injection well, stimulating the fractures and drilling extraction well(s); and (3) operating the reservoir by completing and verifying the circulation loop and installing operating equipment (DOE, 2008). Despite that geothermal energy is a mature geosciences energy technology, the success of old and new approaches remain limited by the sparse knowledge gained from the geological site characterization and the fundamental uncertainties inherited within the probabilistic reduction of the sparse data collected and their adequacy for the mechanical and chemical stimulation of geothermal systems. Figure 1: Logical steps to complete an EGS reservoir

offshore and in complex terrain. The modeling capability would include uncertainty quantification. Finally, the application of the modeling tool and existing global climate change predictions would enable the delineation of the likely effects of climate change on wind resources. Siemens was to provide high time resolution hub-height wind speed and other meteorological data streams, including temperature profiles from wind farms, for LLNL to incorporate into the modeling system, to validate and tune this forecasting model for their locations of interest. These data streams would also be used for longer-term studies of correlations of wind resources to climate oscillations to indicate how long-term climate change trends may affect the available wind resource. Siemens would also provide information and observations of turbine wakes for incorporation into the modeling tool. By implementing state-of-the-art turbulence parameterizations into a simulation model and/or ensembles of simulation models, and by integrating real-time hub height wind speed and other meteorological datastreams from wind farms into that model or ensemble of models, LLNL would develop a forecasting tool that could be implemented by Siemens as an add-on to existing wind farm control and monitoring software to provide owners with useful resource forecasting. The desired outcome was that the accuracy level of the output would be sufficient to substantiate power output commitments. The final deliverable for this work would consist of a document outlining the algorithms and software tools that could be integrated into Siemens Wind Park Supervisor.