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Rainier Mesa/Shoshone Mountain Hydrostratigraphic Framework Model-Prime Model Model Development Process and Model Description, Revision 0
This document presents the results of revisions to the Rainier Mesa/Shoshone Mountain (RM/SM) hydrostratigraphic framework model (HFM) that was constructed for Corrective Action Unit (CAU) 99: Rainier Mesa/Shoshone Mountain. The revised HFM is described as the “RM/SM HFM-Prime” and serves as the base HFM. The modeling efforts and the revisions described in this report have been performed as part of the Underground Test Area (UGTA) Activity that was initiated by the U.S. Department of Energy (DOE), National Nuclear Security Administration Nevada Field Office (NNSA/NFO) to assess and evaluate the effects of the underground nuclear tests on groundwater at the Nevada National Security Site (NNSS) and vicinity. This document builds on the content of the internal Hydrostratigraphic Framework Model of the Rainier Mesa Area Phase I' - Base Model: Model Development Process & Data Package Description (N-I, 2014a) and specific document review comments from the UGTA RM/SM CAU Preemptive Review members (Zavarin, 2015). This document provides, where necessary, the justifications and rationale for interpretations and revisions to the hydrostratigraphic framework of the RM/SM CAU. The fundamental objective of the effort described in this report was to revise the RM/SM HFM, particularly in the areas and vicinity of the sub-CAU models, in order to incorporate some of the higher-resolution hydrogeologic interpretations used to construct several of the sub-CAU scale flow and transport models.
D-RM Builder: A software tool for generating fast and accurate nonlinear dynamic reduced models from high-fidelity models
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Kinetic Modeling of Secondary Organic Aerosol in a Weather-Chemistry Model: Parameterizations, Processes, and Predictions for GOAmazon
Secondary organic aerosol (SOA) forms and evolves in the atmosphere through many pathways and processes, over diverse spatial and time scales. Hence, there is a need to represent these widely-varying kinetic processes in large-scale atmospheric models to allow for accurate predictions of the abundance, properties, and impacts of SOA. In this work, we integrated a kinetic, process-level model (simpleSOM-MOSAIC) into a weather-chemistry model (WRF-Chem) to simulate the oxidation chemistry and microphysics of atmospheric SOA. simpleSOM-MOSAIC simulates multigenerational gas-phase chemistry, autoxidation reactions, heterogeneous oxidation, oligomerization, and phase-state-influenced gas/particle partitioning of SOA. As a case study, the integrated WRF-Chem-simpleSOM-MOSAIC (WC-SSM) model was used to simulate the photochemical evolution downwind of a large city (Manaus, Brazil) in the Amazon and, in turn, study the anthropogenic and biogenic interactions in an otherwise pristine environment. Consistent with previous work, we found that OA was enhanced by up to a factor of four in the urban plume due to elevated hydroxyl radical (OH) concentrations, relative to the background, and that this OA was dominated by SOA from biogenic precursors (80%). Further, in addition to accurately simulating the OA enhancement in the urban plume, the model reproduced the magnitude of the OA oxygen-to-carbon (O:C) ratio and broadly tracked the evolution of the aerosol size distribution. Our work highlights the importance of including an integrated, kinetic representation of SOA processes in an atmospheric model
WEC-SIM Phase 1 Validation Testing -- Numerical Modeling of Experiments: Preprint
The Wave Energy Converter Simulator (WEC-Sim) is an open-source code jointly developed by Sandia National Laboratories and the National Renewable Energy Laboratory. It is used to model wave energy converters subjected to operational and extreme waves. In order for the WEC-Sim code to be beneficial to the wave energy community, code verification and physical model validation is necessary. This paper describes numerical modeling of the wave tank testing for the 1:33-scale experimental testing of the floating oscillating surge wave energy converter. The comparison between WEC-Sim and the Phase 1 experimental data set serves as code validation. This paper is a follow-up to the WEC-Sim paper on experimental testing, and describes the WEC-Sim numerical simulations for the floating oscillating surge wave energy converter.
FVCOM-plume – A Three-dimensional Lagrangian Outfall Plume Dilution and Transport Model for Dynamic Tidal Environments: Model Development
Although numerous steady-state effluent plume dilution models are in use, their application in tidal environments remains a challenge. Three-dimensional dynamic circulation models are also inadequate, often due to the lack of required resolution and simplifying assumptions. To overcome these limitations, we present FVCOM-plume—an outfall plume dilution and transport model that operates within the Finite Volume Coastal Ocean Model (FVCOM) framework. It provides simultaneous inclusion of near-field dilution and far-field plume transport processes. The near-field is based on UM3 model using Lagrangian Control Volume approach to compute buoyant plume trajectory and dilution from multiport diffusers. The far-field uses neutrally buoyant particles with point masses and the random walk method to solve unsteady advection-diffusion processes. A density kernel approach is used to compute concentrations at point locations and analyze far-field plume characteristics. The results demonstrate the ability of FVCOM-plume to simultaneously capture near-field and far-field effluent plume dynamics in tidal environments.
A framework for modeling and optimizing dynamic systems under uncertainty
Algebraic modeling languages (AMLs) have drastically simplified the implementation of algebraic optimization problems. However, there are still many classes of optimization problems that are not easily represented in most AMLs. These classes of problems are typically reformulated before implementation, which requires significant effort and time from the modeler and obscures the original problem structure or context. In this work we demonstrate how the Pyomo AML can be used to represent complex optimization problems using high-level modeling constructs. We focus on the operation of dynamic systems under uncertainty and demonstrate the combination of Pyomo extensions for dynamic optimization and stochastic programming. We use a dynamic semibatch reactor model and a large-scale bubbling fluidized bed adsorber model as test cases.
Lithium-ion battery cell-level control using constrained model predictive control and equivalent circuit models
Not Available
Modeling turbulence–chemistry interaction in lean premixed hydrogen flames with a strained flamelet model
Premixed flames in the thin and broken reaction zones regime exhibit strong non-linear turbulence–chemistry interactions and are hence challenging to model. In the present study, a direct numerical simulation (DNS) database for a hydrogen–air flame is used to understand the effect of turbulence–chemistry interactions on the flame speed. To this end, statistics of the local flame front displacement speed are investigated and the roles of flame stretch due to curvature and strain are analyzed. The local flame speed is found to be on average around 30% lower than the laminar burning velocity, which is shown to be a consequence of strain. Strain effects are then modeled by a recently proposed strained flamelet model, which is validated a priori against the DNS in terms of the flame speed and the reaction source term. The strained flamelet model leads to a moderate improvement of the flame speed prediction and is able to reproduce the decrease of the local flame speed. Moreover, it significantly improves the source term closure, which is demonstrated on the basis of the optimal estimator approach and, overall, describes the investigated DNS flame with very good accuracy.
Value of model enhancements: quantifying the benefit of improved transmission planning models
Not provided.
Exploring magnetized liner inertial fusion with a semi-analytic model
In this paper, we explore magnetized liner inertial fusion (MagLIF) [S. A. Slutz et al., Phys. Plasmas 17, 056303 (2010)] using a semi-analytic model [R. D. McBride and S. A. Slutz, Phys. Plasmas 22, 052708 (2015)]. Specifically, we present simulation results from this model that: (a) illustrate the parameter space, energetics, and overall system efficiencies of MagLIF; (b) demonstrate the dependence of radiative loss rates on the radial fraction of the fuel that is preheated; (c) explore some of the recent experimental results of the MagLIF program at Sandia National Laboratories [M. R. Gomez et al., Phys. Rev. Lett. 113, 155003 (2014)]; (d) highlight the experimental challenges presently facing the MagLIF program; and (e) demonstrate how increases to the preheat energy, fuel density, axial magnetic field, and drive current could affect future MagLIF performance.
LOW MACH NUMBER MODELING OF CONVECTION IN HELIUM SHELLS ON SUB-CHANDRASEKHAR WHITE DWARFS. II. BULK PROPERTIES OF SIMPLE MODELS
The dynamics of helium shell convection driven by nuclear burning establish the conditions for runaway in the sub-Chandrasekhar-mass, double-detonation model for SNe Ia, as well as for a variety of other explosive phenomena. We explore these convection dynamics for a range of white dwarf core and helium shell masses in three dimensions using the low Mach number hydrodynamics code MAESTRO. We present calculations of the bulk properties of this evolution, including time-series evolution of global diagnostics, lateral averages of the 3D state, and the global 3D state. We find a variety of outcomes, including quasi-equilibrium, localized runaway, and convective runaway. Our results suggest that the double-detonation progenitor model is promising and that 3D dynamic convection plays a key role.
Can reanalysis products outperform mesoscale numerical weather prediction models in modeling the wind resource in simple terrain?
Mesoscale numerical weather prediction (NWP) models are generally considered more accurate than reanalysis products in characterizing the wind resource at heights of interest for wind energy, given their finer spatial resolution and more comprehensive physics. However, advancements in the latest ERA-5 reanalysis product motivate an assessment on whether ERA-5 can model wind speeds as well as a state-of-the-art NWP model – the Weather Research and Forecasting (WRF) Model. We consider this research question for both simple terrain and offshore applications. Specifically, we compare wind profiles from ERA-5 and the preliminary WRF runs of the Wind Integration National Dataset (WIND) Toolkit Long-term Ensemble Dataset (WTK-LED) to those observed by lidars at a site in Oklahoma, United States, and in a United States Atlantic offshore wind energy area. We find that ERA-5 shows a significant negative bias (~-1ms-1) at both locations, with a larger bias at the land-based site. WTK-LED-predicted wind speed profiles show a limited negative bias (~-0.5ms-1) offshore and a slight positive bias (~+0.5ms-1) at the land-based site. On the other hand, we find that ERA-5 outperforms WTK-LED in terms of the centered root-mean-square error (cRMSE) and correlation coefficient, for both the land-based and offshore cases, in all atmospheric stability conditions. We find that WTK-LED's higher cRMSE is caused by its tendency to overpredict the amplitude of the wind speed diurnal cycle. At the land-based site, this is partially caused by wind plant wake effects not being accurately captured by WTK-LED.
Lithium-ion battery physics and statistics-based state of health model
A pseudo-2d model using COMSOL Multiphysics® software is developed to simulate performance and performance degradation of Li-ion batteries consisting of layered and olivine cathodes with graphite anode when subjected to peak shaving grid service. Multiple degradation pathways are considered, including solid electrolyte interphase (SEI) formation and breakdown at the anode, cathode dissolution and its synergistic effect on SEI formation at the anode. The model is validated by simulating commercial cylindrical cell performance. A global model is developed to simulate performance across all chemistries, along with individual chemistry models using global model parameters as initial values. There is good agreement between these models for various optimization parameters such as SEI equilibrium potential, cathode dissolution exchange current density, solvent diffusivity in the SEI and SEI ionic conductivity. To circumvent time constraints related to the COMSOL model, a 0d global model is developed which fits data well and provides more clarity on differences in cathode dissolution exchange current density. Again, good agreement for various optimization parameters is obtained among the COMSOL global & individual chemistry models and the 0-d model. The lessons learned from the physics-based model is used to develop a top down statistics-based model using current, voltage and anode volumetric change per mole lithium intercalated, along with their interactions as degradation predictors. This model predicts out of sample degradation for multiple grid services and electric vehicle drive cycle with high accuracy and provides the pathway to develop an efficient battery management system combining machine learning and findings from physics-based computationally intensive algorithms.
Mesoscale to microscale wind farm flow modeling and evaluation
The increasing size of wind turbines, with rotors already spanning more than 150 m diameter and hub heights above 100 m, requires proper modeling of the atmospheric boundary layer (ABL) from the surface to the free atmosphere. Furthermore, large wind farm arrays create their own boundary layer structure with unique physics. This poses significant challenges to traditional wind engineering models that rely on surface-layer theories and engineering wind farm models to simulate the flow in and around wind farms. However, adopting an ABL approach offers the opportunity to better integrate wind farm design tools and meteorological models. The challenge is how to build the bridge between atmospheric and wind engineering model communities and how to establish a comprehensive evaluation process that identifies relevant physical phenomena for wind energy applications with modeling and experimental requirements. A framework for model verification, validation, and uncertainty quantification is established to guide this process by a systematic evaluation of the modeling system at increasing levels of complexity. In terms of atmospheric physics, ‘building the bridge’ means developing models for the so-called ‘terra incognita,’ a term used to designate the turbulent scales that transition from mesoscale to microscale. This range of scales within atmospheric research deals with the transition from parameterized to resolved turbulence and the improvement of surface boundary-layer parameterizations. The coupling of meteorological and wind engineering flow models and the definition of a formal model evaluation methodology, is a strong area of research for the next generation of wind conditions assessment and wind farm and wind turbine design tools. Some fundamental challenges are identified in order to direct future research in this area.