Integration of Simulation Tools for High Current Electron Sources
Explore the source record for details and available documents.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
SEARCH · Engineering Papers
Results for “Integrated Simulation”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Space-Charge Simulation of Integrable Rapid Cycling Synchrotron
Integrable optics is an innovation in particle accelerator design that enables strong nonlinear focusing without generating parametric resonances. We use the Synergia tracking code to investigate the application of integrable optics to high-intensity hadron rings. We consider an integrable rapid-cycling synchrotron (iRCS) designed to replace the Fermilab Booster. We find that incorporating integrability into the design suppresses the beam halo generated by a mismatched KV beam. Our iRCS design includes other features of modern ring design such as low momentum compaction factor and harmonically canceling sextupoles. Experimental tests of high-intensity beams in integrable lattices will take place over the next several years at the Fermilab Integrable Optics Test Accelerator (IOTA) and the University of Maryland Electron Ring (UMER).
43 PARTICLE ACCELERATORS↗
Simulations With the Marine Biogeochemistry Library (MARBL)
Abstract The Marine Biogeochemistry Library (MARBL) is a prognostic ocean biogeochemistry model that simulates marine ecosystem dynamics and the coupled cycles of carbon, nitrogen, phosphorus, iron, silicon, and oxygen. MARBL is a component of the Community Earth System Model (CESM); it supports flexible ecosystem configuration of multiple phytoplankton and zooplankton functional types; it is also portable, designed to interface with multiple ocean circulation models. Here, we present scientific documentation of MARBL, describe its configuration in CESM2 experiments included in the Coupled Model Intercomparison Project version 6 (CMIP6), and evaluate its performance against a number of observational data sets. The model simulates present‐day air‐sea CO 2 flux and many aspects of the carbon cycle in good agreement with observations. However, the simulated integrated uptake of anthropogenic CO 2 is weak, which we link to poor thermocline ventilation, a feature evident in simulated chlorofluorocarbon distributions. This also contributes to larger‐than‐observed oxygen minimum zones. Moreover, radiocarbon distributions show that the simulated circulation in the deep North Pacific is extremely sluggish, yielding extensive oxygen depletion and nutrient trapping at depth. Surface macronutrient biases are generally positive at low latitudes and negative at high latitudes. CESM2 simulates globally integrated net primary production (NPP) of 48 Pg C yr −1 and particulate export flux at 100 m of 7.1 Pg C yr −1 . The impacts of climate change include an increase in globally integrated NPP, but substantial declines in the North Atlantic. Particulate export is projected to decline globally, attributable to decreasing export efficiency associated with changes in phytoplankton community composition.
Meteorology & Atmospheric Sciences↗
Experiences Integrating Transmission and Distribution Simulations for DERs with the Integrated Grid Modeling System (IGMS)
This paper discusses the development of, approaches for, experiences with, and some results from a large-scale, high-performance-computer-based (HPC-based) co-simulation of electric power transmission and distribution systems using the Integrated Grid Modeling System (IGMS). IGMS was developed at the National Renewable Energy Laboratory (NREL) as a novel Independent System Operator (ISO)-to-appliance scale electric power system modeling platform that combines off-the-shelf tools to simultaneously model 100s to 1000s of distribution systems in co-simulation with detailed ISO markets, transmission power flows, and AGC-level reserve deployment. Lessons learned from the co-simulation architecture development are shared, along with a case study that explores the reactive power impacts of PV inverter voltage support on the bulk power system.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Multigrid methods with space–time concurrency
Here, we consider the comparison of multigrid methods for parabolic partial differential equations that allow space–time concurrency. With current trends in computer architectures leading towards systems with more, but not faster, processors, space–time concurrency is crucial for speeding up time-integration simulations. In contrast, traditional time-integration techniques impose serious limitations on parallel performance due to the sequential nature of the time-stepping approach, allowing spatial concurrency only. This paper considers the three basic options of multigrid algorithms on space–time grids that allow parallelism in space and time: coarsening in space and time, semicoarsening in the spatial dimensions, and semicoarsening in the temporal dimension. We develop parallel software and performance models to study the three methods at scales of up to 16K cores and introduce an extension of one of them for handling multistep time integration. We then discuss advantages and disadvantages of the different approaches and their benefit compared to traditional space-parallel algorithms with sequential time stepping on modern architectures.
97 MATHEMATICS AND COMPUTING↗
COSMIC MICROWAVE BACKGROUND INDUCED POLARIZATION FROM SINGLE SCATTERING BY CLUSTERS OF GALAXIES AND FILAMENTS
We present light-cone-integrated simulations of the cosmic microwave background (CMB) polarization signal induced by a single scattering in the direction of clusters of galaxies and filaments. We characterize the statistical properties of the induced polarization signals from the presence of the CMB quadrupole component (pqiCMB) and as the result of the transverse motion of ionized gas clouds with respect to the CMB rest frame (p{beta}{sup 2}{sub t}SZ). From adiabatic N-body/hydrodynamic simulations, we generated 28 random sky patches integrated along the light cone, each with about 0.86 deg{sup 2} and angular resolution of 6''. Our simulation method involves a box-stacking scheme that allows to reconstruct the CMB quadrupole component and the gas physical properties along the line of sight. We find that the linear polarization degree in the logarithmic scale of both effects follows approximately a Gaussian distribution and the mean total signal is about 10{sup -8} and 10{sup -10} for the pqiCMB and p{beta}{sup 2}{sub t}SZ effects, respectively. The polarization angle is consistent with a flat distribution in both cases. From the mean distributions of the polarization degree with redshift, the highest peak is found at z {approx_equal} 1 for the induced CMB quadrupole and at z {approx_equal} 0.5 for the kinematic component. Our results suggest that most of the contribution for the total polarization signal arises from z {approx}< 4 for the pqiCMB and z {approx}< 3 for p{beta}{sup 2}{sub t}SZ. The spectral dependency of both integrated signals is strong, increasing with the frequency, especially in the case of the p{beta}{sup 2}{sub t}SZ signal, which increases by a factor of 100 from 30 GHz to 675 GHz. The maxima values found at the highest frequency are about 3 {mu}K and 13 {mu}K for the pqiCMB and p{beta}{sup 2}{sub t}SZ, respectively. The angular power spectra of these effects peak at large multipoles l > 10{sup 4}, being of the order of 10{sup -5} {mu}K{sup 2} for pqiCMB polarization and 10{sup -7} {mu}K{sup 2} for the p{beta}{sup 2}{sub t}SZ effect. Therefore, these effects will not be a relevant source of contamination for measurements of the primary polarization modes, and at larger multipoles of roughly l > 40, 000, pqiCMB may be the dominant component over the primary and lensing signals.
79 ASTRONOMY AND ASTROPHYSICS↗
Combining On-Line Characterization Tools with Modern Software Environments for Optimal Operation of Polymerization Processes
Here, this paper discusses the initial steps towards the formulation and implementation of a generic and flexible model centric framework for integrated simulation, estimation, optimization and feedback control of polymerization processes. For the first time it combines the powerful capabilities of the automatic continuous on-line monitoring of polymerization system (ACOMP), with a modern simulation, estimation and optimization software environment towards an integrated scheme for the optimal operation of polymeric processes. An initial validation of the framework was performed for modelling and optimization using literature data, illustrating the flexibility of the method to apply under different systems and conditions. Subsequently, off-line capabilities of the system were fully tested experimentally for model validations, parameter estimation and process optimization using ACOMP data. Experimental results are provided for free radical solution polymerization of methyl methacrylate.
42 ENGINEERING↗
INtegrated Fluid & paRticles simulatioN cOde (INF&RNO) v1.0
INF&RNO (INtegrated Fluid & paRticle simulation cOde) is a 2D cylindrical (r-z) code to model the interaction of short laser pulse(s) with an underdense plasma. The code is based on an envelope model for the laser field, and the action of the laser on the plasma is modeled with the time-averaged ponderomotive force approximation. The code features an improved laser envelope solver which enables an accurate description of the laser pulse evolution deep into depletion even at a reasonably low resolution.
Benedetti, Carlo [Lawrence Berkeley National Labor↗
Integrated Modeling and Simulation Capability For Full Scale Multi-Physics Simulation and Visualization of Microreactor Concept
Microreactors are small mobile transportable nuclear reactors with thermal power less than 20 MWt that are designed to meet the needs of remote areas, military installations, emergency operations, and disaster relief zones. These reactor are concepts that are intended to be factory-manufacturable, easily transportable, and will allow for semi- or full-autonomous operation. To assist with development of microreactors, modeling and experimentation need to be performed with verification and validation to gain confidence in the models and tools. The objective of this study is apply advanced modeling and simulation capabilities being developed by the DOE Office of Nuclear Energy Nuclear Energy Advanced Modeling and Simulation (NEAMS) program to perform integrated modeling and simulation capability for multiphysics simulation of the microreactor concepts. This activity will provide an opportunity to exercise the codes to provide both gain experience and to provide feedback to the developers.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Accelerated path integral methods for atomistic simulations at ultra-low temperatures
Path integral methods provide a rigorous and systematically convergent framework to include the quantum mechanical nature of atomic nuclei in the evaluation of the equilibrium properties of molecules, liquids, or solids at finite temperature. Such nuclear quantum effects are often significant for light nuclei already at room temperature, but become crucial at cryogenic temperatures such as those provided by superfluid helium as a solvent. Unfortunately, the cost of converged path integral simulations increases significantly upon lowering the temperature so that the computational burden of simulating matter at the typical superfluid helium temperatures becomes prohibitive. Here we investigate how accelerated path integral techniques based on colored noise generalized Langevin equations, in particular the so-called path integral generalized Langevin equation thermostat (PIGLET) variant, perform in this extreme quantum regime using as an example the quasi-rigid methane molecule and its highly fluxional protonated cousin, CH{sub 5}{sup +}. We show that the PIGLET technique gives a speedup of two orders of magnitude in the evaluation of structural observables and quantum kinetic energy at ultralow temperatures. Moreover, we computed the spatial spread of the quantum nuclei in CH{sub 4} to illustrate the limits of using such colored noise thermostats close to the many body quantum ground state.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Path Integral Monte Carlo Simulations of Warm Dense Matter and Plasmas
New path integral Monte Carlo simulation (PIMC) techniques will be developed and applied to derive the equation of state (EOS) for the regime of warm dense matter and dense plasmas where existing first-principles methods cannot be applied. While standard density functional theory has been used to accurately predict the structure of many solids and liquids up to temperatures on the order of 10,000 K, this method is not applicable at much higher temperature where electronic excitations become important because the number of partially occupied electronic orbitals reaches intractably large numbers and, more importantly, the use of zero-temperature exchange-correlation functionals introduces an uncontrolled approximation. Here we focus on PIMC methods that become more and more efficient with increasing temperatures and still include all electronic correlation effects. In this approach, electronic excitations increase the efficiency rather than reduce it. While it has commonly been assumed such methods can only be applied to elements without core electrons like hydrogen and helium, we recently showed how to extend PIMC to heavier elements by performing the first PIMC simulations of carbon and water plasmas [Driver, Militzer, Phys. Rev. Lett. 108 (2012) 115502]. Here we propose to continue this important development to extend the reach of PIMC simulations to yet heavier elements and also lower temperatures. The goal is to provide a robust first-principles simulation method that can accurately and efficiently study materials with excited electrons at solid-state densities in order to access parts of the phase diagram such the regime of warm dense matter and plasmas where so far only more approximate, semi-analytical methods could be applied.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Using AI Simulations to Dynamically Model Multi-agent Multi-team Energy Systems
The complexity of energy systems is well known as they are complex and intricate systems. As a result, many extant studies have used many simplifications or generalizations that do not accurately reflect the nature of this complex system. In particular, most HVAC systems are modeled as a single unit, or several large units, rather than as a hierarchical composite (e.g., as a floor rather than as a collection of disparate rooms). The net result of this is that the simulations are too generic to perform meaningful analysis, machine learning, or integrated simulation. We propose using a multi-agent multi-team strategic simulations framework called SiMAMT to better define, model, simulate, and learn the HVAC environment. SiMAMT allows us to create distinct models for each type of room, hierarchically aggregate them into units (like floors, or sections), and then into larger sets (like buildings or a campus), and then perform a simulation that interacts with each sub-element individually, the teams of sub-elements collectively, and the entire set in aggregation. Further, and most importantly, we additionally model another ‘team’ within the simulation framework - the users of the systems. Again, each individual is modeled distinctly, aggregated into sub-sets, then collected into large sets. Each user, or agent, is performing on their own but with respect to the larger team goals. This provides a simulation that has a much higher model fidelity and more applicable results that match the real-world.
Franklin, D. Michael↗
Estimating the Likelihood of GHG Concentration Scenarios From Probabilistic Integrated Assessment Model Simulations
The climate scenarios that form the basis for current climate risk assessments have no assigned probabilities, and this impedes the analysis of future climate risks. This paper proposes an approach to estimate the probability of carbon dioxide (CO 2 ) concentration scenarios used in key climate change modeling experiments. It computes the CO 2 emissions compatible with the concentrations prescribed by Coupled Model Intercomparison Project Phase 5 (CMIP5) and CMIP6 experiments. The distribution of these compatible cumulative emissions is interpreted as the likelihood of future emissions given a concentration pathway. Using Bayesian analysis, the probability of each pathway can be estimated from a probabilistic sample of future emissions. The approach is demonstrated with five probabilistic CO 2 emission simulation ensembles from four Integrated Assessment Models (IAM), leading to independent estimates of the likelihood of the CO 2 concentration of Representative Concentration Pathways (RCP) and Shared Socioeconomic Pathways (SSP). Results suggest that SSP5-8.5 is unlikely for the second half of the 21st century, but offer no clear consensus on which of the remaining scenarios is most likely. Estimates of likelihoods of CO 2 concentrations associated with RCP and SSP scenarios are affected by sampling errors, differences in emission sources simulated by the IAMs, and a lack of a common experimental framework for IAM simulations. These shortcomings, along with a small IAM ensemble size, limit the applicability of the results presented here. Novel joint IAM and the Earth System Model experiments are needed to deliver actionable probabilistic climate risk assessments.
54 ENVIRONMENTAL SCIENCES↗
Solving iTOUGH2 simulation and optimization problems using the PEST protocol
The PEST protocol has been implemented into the iTOUGH2 code, allowing the user to link any simulation program (with ASCII-based inputs and outputs) to iTOUGH2's sensitivity analysis, inverse modeling, and uncertainty quantification capabilities. These application models can be pre- or post-processors of the TOUGH2 non-isothermal multiphase flow and transport simulator, or programs that are unrelated to the TOUGH suite of codes. PEST-style template and instruction files are used, respectively, to pass input parameters updated by the iTOUGH2 optimization routines to the model, and to retrieve the model-calculated values that correspond to observable variables. We summarize the iTOUGH2 capabilities and demonstrate the flexibility added by the PEST protocol for the solution of a variety of simulation-optimization problems. In particular, the combination of loosely coupled and tightly integrated simulation and optimization routines provides both the flexibility and control needed to solve challenging inversion problems for the analysis of multiphase subsurface flow and transport systems.
54 ENVIRONMENTAL SCIENCES↗
Informing nuclear physics via machine learning methods with differential and integral experiments
Information from differential nuclear-physics experiments and theory is often too uncertain to accurately define nuclear-physics observables such as cross sections or energy spectra. Integral experimental data, representing the applications of these observables, are often more precise but depend simultaneously on too many of them to unambiguously identify issues in the observable with human expert analysis alone. Here, we explore how we can leverage physics knowledge gained from differential experimental data, nuclear theory, integral experiments, and neutron-transport calculations to better understand nuclear-physics observables in the context of the application area represented by integral experiments. We support this task with machine-learning methods to discern trends in a large amount of convoluted data. Differential and integral information was used in an analysis augmented by the random forest and the Shapley additive explanations metric. We chose as an application area one that is represented by criticality measurements and pulsed-sphere neutron-leakage spectra. We show one representative example ( 241 Pu fission observables) where the combination of differential and integral information allowed to resolve issues in data representing these observables. As a starting point, the machine learning (ML) algorithms highlighted several observables as leading potentially to bias in simulating integral experiments. Differential information, paired with sensitivity to integral quantities, allowed us then to pinpoint one specific observable ( 241 Pu fission cross section) as the main driver of bias. The comparison to integral experiments, on the other hand, allowed us to indicate a likely reliable experiment among several discrepant ones for this observables. In other cases (e.g., 239 Pu observables), we were not able to resolve the confounding introduced by integral experiments but instead highlighted the need for targeted new experiments and theory developments to better constrain the nuclear-physics space for the application area represented by integral experiments. We were able to combine information from differential experimental data, nuclear-physics theory, integral experiments, and neutron-transport simulations of the latter experiments with the help of the random forest algorithm and expert judgment. This combination of knowledge allows to improve our description of nuclear-physics observables as applied to a particular application area.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗