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Results for “simulation decomposition”

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.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Atomic resolution tracking of nerve-agent simulant decomposition and host metal–organic framework response in real space

Gas capture and sequestration are valuable properties of metal–organic frameworks (MOFs) driving tremendous interest in their use as filtration materials for chemical warfare agents. Recently, the Zr-based MOF UiO-67 was shown to effectively adsorb and decompose the nerve-agent simulant, dimethyl methylphosphonate (DMMP). Understanding mechanisms of MOF-agent interaction is challenging due to the need to distinguish between the roles of the MOF framework and its particular sites for the activation and sequestration process. Here, we demonstrate the quantitative tracking of both framework and binding component structures using in situ X-ray total scattering measurements of UiO-67 under DMMP exposure, pair distribution function analysis, and theoretical calculations. The sorption and desorption of DMMP within the pores, association with linker-deficient Zr6 cores, and decomposition to irreversibly bound methyl methylphosphonate were directly observed and analyzed with atomic resolution.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Fixed Depth Hamiltonian Simulation via Cartan Decomposition

Simulating quantum dynamics on classical computers is challenging for large systems due to the significant memory requirements. Simulation on quantum computers is a promising alternative, but fully optimizing quantum circuits to minimize limited quantum resources remains an open problem. In this study, we tackle this problem by presenting a constructive algorithm, based on Cartan decomposition of the Lie algebra generated by the Hamiltonian, which generates quantum circuits with time-independent depth. We highlight our algorithm for special classes of models, including Anderson localization in one-dimensional transverse field $\mathrm{XY}$ model, where $\mathscr{O}$(n 2 )-gate circuits naturally emerge. Compared to product formulas with significantly larger gate counts, our algorithm drastically improves simulation precision. In addition to providing exact circuits for a broad set of spin and fermionic models, our algorithm provides broad analytic and numerical insight into optimal Hamiltonian simulations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1991

Kinematic domain decomposition to simulate flows past moving objects

A new method is developed to solve the unsteady Navier-Stokes equations on a composite grid which consists of subdomain grids moving with respect to each other. These subdomains are structured grids with different topologies. However, their interfaces are unstructured in the sense that they may or may not arbitrarily overlap with each other. This method captures the boundary-motion-induced flow component. The method is demonstrated for a transonic flow past an airfoil which experiences a combined motion of pitching and plunging.

Baysal, Oktay↗
DOE OSTI2021

Drought legacies mediated by trait trade‐offs in soil microbiomes

Abstract Soil microbiomes play a key role in driving biogeochemical cycles of the Earth system. As drought frequency and intensity increase due to climate change, soil microbes and the processes they control will be impacted. Even after a drought ends, microbiomes and other systems take time to recover and may display a memory of previous climate conditions. Still, the mechanisms involved in these legacy effects remain unclear, making it difficult to predict climate and biogeochemical rates in the future. Here, we used a trait‐based microbiome model (DEMENTpy) to implement trade‐off‐mediated mechanisms that may lead to drought legacy effects on litter decomposition. Trade‐offs were assumed to follow the Y‐A‐S framework that defines three primary life‐history strategies of microorganisms: high growth Yield, resource Acquisition, and Stress tolerance. We represented cellular trade‐offs between osmolytes required for drought tolerance and investment in enzymes involved in litter decomposition. Simulations were run under varying levels of drought severity and dispersal. With high levels of dispersal, no legacy effects were predicted by DEMENTpy following drought. With limited dispersal, severe drought resulted in a persistent legacy of altered community‐level traits and reduced litter decomposition. Moderate drought resulted in a transient legacy that disappeared after two years, consistent with recent empirical observations in Southern California ecosystems. These results imply that greater movement along the trade‐off between enzyme investment and osmolyte production resulted in stronger legacy effects. More generally, factors that shift the position of a microbiome in YAS space may alter the legacy outcome following drought. Our trait‐based modeling study motivates additional empirical measurements to quantify YAS traits and trade‐offs that are needed to make accurate predictions of soil microbiome resilience and functioning. Also, our study illustrates an emerging approach for representing trait trade‐offs in microbiomes and vegetation that dictate ecosystem responses to drought and other environmental perturbations.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1990

Arcjet load characteristics

Experiments were conducted to define the interface characteristics and constraints of 1 kW class arcjets run on simulated decomposition products of hydrazine and power processors. The impacts of power supply output current ripple on arcjet performance were assessed by variation of the ripple frequency from 100 Hz to 100 kHz with 10 percent peak-to-peak ripple amplitude at 1.2 kW. Ripple had no significant effects on thrust, specific impulse or efficiency. The impact of output ripple on thruster lifetime was not assessed. The static and dynamic impedances of the arcjet were quantified with two thrusters of nearly identical configuration. Superposition of an AC component on the DC arc current was used to characterize the dynamic impedance as a function of flow rate and DC current level. A mathematical model was formulated from these data. Both the static and dynamic impedance magnitude were found to be dependent on mass flow rate. The amplitude of the AC component was found to have little effect on the dynamic impedance. Reducing the DC level from 10 to 8 amps led to a large change in the magnitude of the dynamic impedance with no observable phase change. The impedance data compared favorably between the two thrusters.

Hamley, John A.↗
NASA NTRS1990

Arcjet load characteristics

Experiments were conducted to define the interface characteristics and constraints of 1 kW class arcjets run on simulated decomposition products of hydrazine and power processors. The impacts of power supply output current ripple on arcjet performance were assessed by variation of the ripple frequency from 100 Hz to 100 kHz with 10 percent peak-to-peak ripple amplitude at 1.2 kW. Ripple had no significant effects on thrust, specific impulse or efficiency. The impact of output ripple on thruster lifetime was not assessed. The static and dynamic impedances of the arcjet were quantitied with two thrusters of nearly identical configuration. Superposition of an AC component on the DC arc current was used to characterize the dynamic impedance as a function of flow rate and DC current level. A mathematical model was formulated from these data. Both the static and dynamic impedance magnitude were found to be dependent on mass flow rate. The amplitude of the AC component was found to have little effect on the dynamic impedance. Reducing the DC level from 10 to 8 amps led to a large change in the magnitude of the dynamic impedance with no observable phase change. The impedance data compared favorably between the two thrusters.

Hamley, John A.↗
NASA NTRS1990

Concurrent Algorithm For Particle-In-Cell Simulations

Separate decompositions used for particle-motion and field calculations. General Concurrent Particle-in-Cell (GCPIC) algorithm used to implement motions of individual plasma particles (ions and electrons) under influence of particle-in-cell (PIC) computer codes on concurrent processors. Simulates motions of individual plasma particles under influence of electromagnetic fields generated by particles themselves. Performed to study variety of nonlinear problems in plasma physics, including magnetic and inertial fusion, plasmas in outer space, propagation of electron and ion beams, free-electron lasers, and particle accelerators.

Liewer, Paulett C.↗
NASA NTRS1990

Adaptive domain decomposition for Monte Carlo simulations on parallel processors

A method is described for performing direct simulation Monte Carlo (DSMC) calculations on parallel processors using adaptive domain decomposition to distribute the computational work load. The method has been implemented on a commercially available hypercube and benchmark results are presented which show the performance of the method relative to current supercomputers. The problems studied were simulations of equilibrium conditions in a closed, stationary box, a two-dimensional vortex flow, and the hypersonic, rarefield flow in a two-dimensional channel. For these problems, the parallel DSMC method ran 5 to 13 times faster than on a single processor of a Cray-2. The adaptive decomposition method worked well in uniformly distributing the computational work over an arbitrary number of processors and reduced the average computational time by over a factor of two in certain cases.

Wilmoth, Richard G.↗
NASA NTRS1991

Adaptive domain decomposition for Monte Carlo simulations on parallel processors

A method is described for performing direct simulation Monte Carlo (DSMC) calculations on parallel processors using adaptive domain decomposition to distribute the computational work load. The method has been implemented on a commercially available hypercube and benchmark results are presented which show the performance of the method relative to current supercomputers. The problems studied were simulations of equilibrium conditions in a closed, stationary box, a two-dimensional vortex flow, and the hypersonic, rarefied flow in a two-dimensional channel. For these problems, the parallel DSMC method ran 5 to 13 times faster than on a single processor of a Cray-2. The adaptive decomposition method worked well in uniformly distributing the computational work over an arbitrary number of processors and reduced the average computational time by over a factor of two in certain cases.

Wilmoth, Richard G.↗
DOE OSTI2023

Extreme-scale stochastic optimization and simulation via learning-enhanced decomposition and parallelization (Final Technical Report)

Stochastic optimization and simulation models ubiquitously arise in designing and operating complex service/engineering systems. They can be extreme in scale due to high-dimensional data and decisions, and can also involve decisions made sequentially in response to newly revealed data, both causing significant computational challenge. The objective of this research is to explore a unified framework that integrates machine learning with discrete optimization and risk-averse modeling, to improve the efficiency of decomposition paradigms for stochastic optimization and simulations at extreme scale. The models we consider represent a broad class of complex decision-making problems, where 0-1 or continuous decisions are made before and/or after knowing multiple sources of uncertainties that could be correlated. We will employ machine learning methods to dynamically decide and prioritize computational procedures, including cut generation, branching, and bounding of the optimal objective. Furthermore, the research will shed new lights on the traditional decomposition algorithms for extreme-scale computing. Deliverables of the research include new modeling and computational methods for advancing the state-of-the-art research in optimization and simulation, bringing many relevant risk-averse, data-driven optimization problems in practice within the range of tractability. Examples include distributed computing server scheduling and sensor deployment for monitoring critical infrastructures. Success in this effort will enable progress in solving multiple extreme-scale problems in the complex system design and operations arising from DoE missions in energy, environment, and national security.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Communication—First-Principles Simulations of LiPF 6 Decomposition in Ethylene Carbonate-Based Electrolytes

We revisit a theoretical result by Okamoto (2013 Journal of The Electrochemical Society , 160 , A404) who calculated the energy barrier for the decomposition of lithium hexafluorophosphate (LiPF 6 ) into LiF + PF 5 when solvated in Ethylene carbonate (EC)-based electrolyte. Using different numerical techniques to discretize the Density Functional Theory (DFT) equations, and different continuum solvation models with the same dielectric constant, our results largely confirm the original calculation. However, simulations with a higher dielectric permittivity value, closer to that of EC, show a lower energy barrier. More importantly, First-Principles simulations with an explicit solvent show a substantially lower energy barrier.

25 ENERGY STORAGE↗
DOE OSTI2026

Efficient simulation of optical spectra via machine learning and physical decomposition of environmental effects

Simulations of optical spectra can provide key insights to aid experimental interpretation of electronic excitation phenomena. For chromophores in the condensed phase, these spectra, which incorporate the coupling between electronic excitation and molecular and solvent nuclear motions, can be simulated using excitation energies obtained from molecular dynamics simulations of the chromophore and solvent. Here, we present a hybrid scheme that exploits machine learning and physically informed spectral densities to show that as few as 25 ground and excited state energetic gradient calculations can be used to construct models that accurately predict environment-influenced vibronic coupling in optical spectra. We demonstrate our approach for the green fluorescent protein chromophore in water and the cresyl violet chromophore in methanol. We show that our hybrid approach, employing a machine learning model for the high-frequency spectral density and an ab initio parameterized Debye spectral density for the low-frequency, results in a systematic improvement of the optical absorption lineshape, leading to a simple machine learning scheme that can be used for the simulation of spectral densities and optical spectra.

Snider, Andrew [Univ. of California, Merced, CA (U↗
DOE OSTI2022

Climate-Driven Legacies in Simulated Microbial Communities Alter Litter Decomposition Rates

The mechanisms underlying diversity-functioning relationships have been a consistent area of inquiry in biogeochemistry since the 1950s. Though these mechanisms remain unresolved in soil microbiomes, many approaches at varying scales have pointed to the same notion—composition matters. Confronting the methodological challenge arising from the complexity of microbiomes, this study used the model DEMENTpy, a trait-based modeling framework, to explore trait-based drivers of microbiome-dependent litter decomposition. We parameterized DEMENTpy for five sites along a climate gradient in Southern California, United States, and conducted reciprocal transplant simulations analogous to a prior empirical study. The simulations demonstrated climate-dependent legacy effects of microbial communities on plant litter decomposition across the gradient. This result is consistent with the previous empirical study across the same gradient. An analysis of community-level traits further suggests that a 3-way tradeoff among resource acquisition, stress tolerance, and yield strategies influences community assembly. Simulated litter decomposition was predictable with two community traits (indicative of two of the three strategies) plus local environment, regardless of the system state (transient vs. equilibrium). Although more empirical confirmation is still needed, community traits plus local environmental factors (e.g., environment and litter chemistry) may robustly predict litter decomposition across spatial-temporal scales. In conclusion, this study offers a potential trait-based explanation for climate-dependent community effects on litter decomposition with implications for improved understanding of whole-ecosystem functioning across scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A quasi-static particle-in-cell algorithm based on an azimuthal Fourier decomposition for highly efficient simulations of plasma-based acceleration: QPAD

The three-dimensional (3D) quasi-static particle-in-cell (PIC) algorithm is a very efficient method for modeling short-pulse laser or relativistic charged particle beam–plasma interactions. In this algorithm, the plasma response, i.e., plasma wave wake, to a non-evolving laser or particle beam is calculated using a set of Maxwell’s equations based on the quasi-static approximate equations that exclude radiation. The plasma fields are then used to advance the laser or beam forward using a large time step. The algorithm is many orders of magnitude faster than a 3D fully explicit relativistic electromagnetic PIC algorithm. It has been shown to be capable to accurately model the evolution of lasers and particle beams in a variety of scenarios. Additionally, at the same time, an algorithm in which the fields, currents and Maxwell equations are decomposed into azimuthal harmonics has been shown to reduce the algorithmic complexity of a 3D explicit PIC algorithm to that of a 2D algorithm when the expansion is truncated while maintaining accuracy for problems with near azimuthal symmetry. This hybrid algorithm uses a PIC description in r–z and a gridless description in . We describe a novel method that combines the quasi-static and hybrid PIC methods. This algorithm expands the fields, charge and current density into azimuthal harmonics. A set of the quasi-static field equations is derived for each harmonic. The complex amplitudes of the fields are then solved using the finite difference method. The beam and plasma particles are advanced in Cartesian coordinates using the total fields. Details on how this algorithm was implemented using a similar workflow to an existing quasi-static code, QuickPIC, are presented. The new code is called QPAD for QuickPIC with Azimuthal Decomposition. Benchmarks and comparisons between a fully 3D explicit PIC code (OSIRIS), a full 3D quasi-static code (QuickPIC), and the new quasi-static PIC code with azimuthal decomposition (QPAD) are also presented.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗