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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Computational study of the electrostatic potential and charges of multivalent ionic liquid molecules

The electrostatic interactions in ionic liquids (ILs) can be difficult to quantify, but this information is very valuable for improving intermolecular potentials for performing molecular-level simulations. Although polarizable forcefields can provide improved accuracy, the computational efficiency of fixed charge molecular models is often preferred for larger systems. Here, we propose a multi-scale screening approach for analyzing the charge scaling behavior and other electrostatic properties of ILs, which is more attainable than previous analysis methods that rely on the IL crystalline structures. Based on isolated molecules and small clusters, we study the electrostatic potential properties and charges of multivalent IL molecules using density-functional theory calculations and ab initio molecular dynamics simulations. The charges of the IL molecules are calculated and compared using several different charge estimation techniques. Although there are some differences among the estimated partial charges on the atoms, the total anion and cation charges are very similar. In conclusion, the charge scaling factors are substantial, and they are only slightly affected by the IL cluster size model.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

The behavior of methane–water mixtures under elevated pressures from simulations using many-body potentials

Non-polarizable empirical potentials have been proven to be incapable of capturing the mixing of methane–water mixtures at elevated pressures. Although density functional theory-based ab initio simulations may circumvent this discrepancy, they are limited in terms of the relevant time and length scales associated with mixing phenomena. Here, we show that the many-body MB-nrg potential, designed to reproduce methane–water interactions with coupled cluster accuracy, successfully captures this phenomenon up to 3 GPa and 500 K with varying methane concentrations. Two-phase simulations and long time scales that are required to fully capture the mixing, affordable due to the speed and accuracy of the MBX software, are assessed. Constructing the methane–water equation of state across the phase diagram shows that the stable mixtures are denser than the sum of their parts at a given pressure and temperature. As a result, we find that many-body polarization plays a central role, enhancing the induced dipole moments of methane by 0.20 D during mixing under pressure. Overall, the mixed system adopts a denser state, which involves a significant enthalpic driving force as elucidated by a systematic many-body energy decomposition analysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Microstructural pattern formation during liquid metal dealloying: Phase-field simulations and theoretical analyses

In recent years, liquid metal dealloying has emerged as a promising material processing method to generate micro- and nanoscale bicontinuous or porous structures. Most previous studies focused on the experimental characterization of the dealloying process and on the properties of the dealloyed materials, leaving the theoretical study incomplete to fully understand the fundamental mechanisms of the liquid metal dealloying process. In this paper, we use theoretical models and phase-field simulations to clarify the kinetics and pattern formation during liquid metal dealloying. Our investigation starts from a theoretical analysis of the 1D dissolution of a binary precursor alloy, which reveals that the 1D dissolution process involves two regimes. In the first regime, due to the low solubility of one of the elements in the melt, it accumulates at the solid-liquid interface, which reduces the dissolution kinetics. In the second regime, the interface kinetics reaches a stationary regime where both elements of the precursor alloy dissolve into the melt. Previous works revealed that in the early dealloying stage, the dealloying front is destabilized by an interfacial spinodal decomposition, which triggers the formation of interconnected ligaments. Here we extend this line of work by proposing a linear stability analysis able to predict the initial length scale of the ligaments formed in the initial stage of the dealloying. Combining this analysis with the 1D dissolution model proposed here enables us to better understand the initial conditions (composition of the precursor alloy and the melt) leading to a planar dissolution without interface destabilization. Finally, we report a strong influence of solid-state diffusion on dealloying. Although the solid-state diffusivity is four to five orders of magnitude smaller than in the liquid phase, it is found to affect both dissolution kinetics and ligament morphologies.

36 MATERIALS SCIENCE↗
NASA NTRS2002

Modeling of the HiPco process for carbon nanotube production. I. Chemical kinetics

A chemical kinetic model is developed to help understand and optimize the production of single-walled carbon nanotubes via the high-pressure carbon monoxide (HiPco) process, which employs iron pentacarbonyl as the catalyst precursor and carbon monoxide as the carbon feedstock. The model separates the HiPco process into three steps, precursor decomposition, catalyst growth and evaporation, and carbon nanotube production resulting from the catalyst-enhanced disproportionation of carbon monoxide, known as the Boudouard reaction: 2 CO(g)-->C(s) + CO2(g). The resulting detailed model contains 971 species and 1948 chemical reactions. A second model with a reduced reaction set containing 14 species and 22 chemical reactions is developed on the basis of the detailed model and reproduces the chemistry of the major species. Results showing the parametric dependence of temperature, total pressure, and initial precursor partial pressures are presented, with comparison between the two models. The reduced model is more amenable to coupled reacting flow-field simulations, presented in the following article.

Evaluation Studies↗
NASA NTRS2011

Land Surface Verification Toolkit (LVT) - A Generalized Framework for Land Surface Model Evaluation

Model evaluation and verification are key in improving the usage and applicability of simulation models for real-world applications. In this article, the development and capabilities of a formal system for land surface model evaluation called the Land surface Verification Toolkit (LVT) is described. LVT is designed to provide an integrated environment for systematic land model evaluation and facilitates a range of verification approaches and analysis capabilities. LVT operates across multiple temporal and spatial scales and employs a large suite of in-situ, remotely sensed and other model and reanalysis datasets in their native formats. In addition to the traditional accuracy-based measures, LVT also includes uncertainty and ensemble diagnostics, information theory measures, spatial similarity metrics and scale decomposition techniques that provide novel ways for performing diagnostic model evaluations. Though LVT was originally designed to support the land surface modeling and data assimilation framework known as the Land Information System (LIS), it also supports hydrological data products from other, non-LIS environments. In addition, the analysis of diagnostics from various computational subsystems of LIS including data assimilation, optimization and uncertainty estimation are supported within LVT. Together, LIS and LVT provide a robust end-to-end environment for enabling the concepts of model data fusion for hydrological applications. The evolving capabilities of LVT framework are expected to facilitate rapid model evaluation efforts and aid the definition and refinement of formal evaluation procedures for the land surface modeling community.

Kumar, Sujay V.↗
DOE OSTI2025

Reduce Revenue Versus Increase Expenditure: Fires and Plant Invasion Drive Soil Carbon Loss With Different Mechanisms in a Mediterranean Shrubland

Fires and plant invasions threaten Mediterranean ecosystems substantially, particularly in the context of changing climate. Our study utilized a data-model integration approach to assess the response of soil organic carbon (SOC) to fires and plant invasion under three Shared Socio-Economic Pathway (SSP) scenarios (SSP1-26, SSP2-45, and SSP5-85). We parameterized the CLM-Microbe model and then investigated the individual and interactive impacts of fires and plant invasion on soil C by comparing factorial simulations of initialization (fire/no wildfire in 2021), fire module on/off, and with and without plant invasion during 2023–2100 in a Mediterranean ecosystem. The simulations indicated a marked C loss due to the 2021 wildfire, projected fires, and plant invasion across all future climate scenarios. Specifically, the 2021 wildfire, projected fires, and plant invasion reduced the SOC (0–30 cm) by 0.12, 0.26, and 0.15 kg C m −2 under SSP1-26, 0.12, 0.30, and 0.12 kg C m −2 under SSP2-45, and 0.12, 0.24, and 0.13 kg C m −2 under SSP5-85, respectively. However, fires and plant invasion decreased SOC through distinct mechanisms. The effects of the 2021 wildfire occurred due to its negative legacy on soil microbial community and, thus, litter accumulation, suppressing the formation of soil carbon via decomposition. Influences of projected fires happen via consuming fuel and suppressing carbon input to soils. In contrast, the impacts of plant invasions were due to enhanced microbial respiration, leading to C loss. In conclusion, these findings emphasize the need for tailored C sequestration strategies considering the disparate effects of fires and plant invasions in the Mediterranean climate.

microbe↗
DOE OSTI2022

Subgrid-scale horizontal and vertical variation of cloud water in stratocumulus clouds: a case study based on LES and comparisons with in situ observations

Abstract. Stratocumulus clouds in the marine boundary layer cover a large fraction of ocean surface and play an important role in the radiative energy balance of the Earth system. Simulating these clouds in Earth system models (ESMs) has proven to be extremely challenging, in part because cloud microphysical processes such as the autoconversion of cloud water into precipitation occur at scales much smaller than typical ESM grid sizes. An accurate autoconversion parameterization needs to account for not only the local microphysical process (e.g., the dependence on cloud water content qc and cloud droplet number concentration Nc) but also the subgrid-scale variability of the cloud properties that determine the process rate. Accounting for subgrid-scale variability is often achieved by the introduction of a so-called enhancement factor E. Previous studies of E for autoconversion have focused more on its dependence on cloud regime and ESM grid size, but they have largely overlooked the vertical dependence of E within the cloud. In this study, we use a large-eddy simulation (LES) model, initialized and constrained with in situ and surface-based measurements from a recent airborne field campaign, to characterize the vertical dependence of the horizontal variation of qc in stratocumulus clouds and the implications for E. Similar to our recent observational study (Zhang et al., 2021), we found that the inverse relative variance of qc, an index of horizontal homogeneity, generally increases from cloud base upward through the lower two-thirds of the cloud and then decreases in the uppermost one-third of the cloud. As a result, E decreases from cloud base upward and then increases towards the cloud top. We apply a decomposition analysis to the LES cloud water field to understand the relative roles of the mean and variances of qc in determining the vertical dependence of E. Our analysis reveals that the vertical dependence of the horizontal qc variability and enhancement factor E is a combined result of condensational growth throughout the lower portion of the cloud and entrainment mixing at cloud top. The findings of this study indicate that a vertically dependent E should be used in ESM autoconversion parameterizations.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2020

Thermal Decomposition of the Murchison CM2 Carbonaceous Chondrite: Implications of Space Weathering Processes for Sample Return Missions

Primitive carbonaceous asteroids are the target bodies for the JAXA Hayabusa2 mission to Ryugu and the NASA OSIRIS-REx mission to Bennu. Both asteroids share spectral characteristics of CI/CM type carbonaceous chondrites. Ryugu, in particular, appears to have undergone thermal processing that has modified its spectral properties. The nature and extent of space weathering processes on the surfaces of Bennu and Ryugu are under active investigation using remote sensing data from the missions [4] and through laboratory studies on analog materials. The analog studies are needed in order to understand the mineralogical and chemical changes that occur in space weathered samples that give rise to the observed optical effects measured by remote-sensing and to prepare for the analysis of returned samples. The space weathering effects of micrometeorite impact and solar wind irradiation on primitive carbonaceous chondrites have been simulated by analog studies on the Murchison CM2 chondrite. We performed a coordinated mineralogical, chemical and spectroscopic study to examine in detail the effects of thermal metamorphism on Murchison samples as an analog to processes that may have occurred on Ryugu. The bulk measurements including X-ray diffraction (XRD), Mössbauer spectroscopy, UV-VIS-NIR spectroscopy, thermogravimetric analysis, and evolved gas analysis are reported in a companion paper. Here we report on our preliminary nanoscale mineralogical and chemical analyses of pre- and post-heated Murchison samples using multiple electron beam techniques to understand how the mineralogical, chemical, and physical characteristics of carbonaceous chondrites change with increasing thermal effects.

Lee, S.↗
DOE OSTI2019

Projected Changes to Extreme Precipitation Along North American West Coast From the CESM Large Ensemble

Precipitation events along the North American (NA) west coast are strongly modulated by atmospheric rivers, yet the mechanisms of their influences on the probability distributions of precipitation events are not well studied. Simulations from the Community Earth System Model (CESM) large ensemble under global warming are investigated using a moisture budget conditioned onto precipitation events for the recurrence intervals ranging from 0.1 to 50 years. In the midlatitudes, the increases in precipitation intensity and accumulation for all events over the NA west coast are predominantly controlled by moisture increases. In contrast, changes in the subtropical precipitation distributions in southwestern NA are associated with moisture increases and duration decreases for all events, with additional dynamical amplification for the heaviest precipitation events. These interpretations from the conditional moisture budget are more consistent with future projection of atmospheric rivers than the conventional mean and transient decomposition of the moisture budget.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Dynamic Phasor Modeling of Various Multipulse Rectifiers and a VSI Fed by 18-Pulse Asymmetrical Autotransformer Rectifier Unit for Fast Transient Analysis

To study the fast modelling of dynamic characteristics caused by power electronic switching, the dynamic phasor (DP) theory based on the time-varying Fourier decomposition and frequency shifting is firstly applied to the modelling of various multipulse rectifiers. The DP model for symmetrical 12-pulse phase-shifting reactor rectifier unit (PSR-RU) is derived with model order reduction, relations between ac and dc terminals, and Taylor series expansion. The DP model of asymmetrical 18-pulse autotransformer rectifier unit (AT-RU) is proposed based on the switching functions expressed in the DP domain. Meanwhile, the DP model of voltage source inverter (VSI) fed by asymmetrical 18-pulse AT-RU is built with the harmonic state-space (HSS) equations. Under both balanced and unbalanced conditions, the good calculation accuracy and rapid simulation speed of the developed DP models are validated by the detailed time-domain (TD) simulation.

42 ENGINEERING↗
DOE OSTI2020

Shape-constrained Input Estimation for Efficient Multi-shaker Vibration Testing

Multi-shaker vibration testing is gaining interest from structural dynamics test engineers as it can provide a much more accurate match to complicated field vibration responses than traditional single-axis shaker tests. However, the force capabilities of the small modal shakers typically used in multi-shaker vibration tests has limited the achievable response levels. To date, most multi-shaker vibration tests have been performed using a variety of standard, commercially-available control systems. While these control systems are adequate for a wide range of multiple-input/multiple-output tests, their control algorithms have not been tailored for the specific problem of multi-shaker vibration tests: efficiently coordinating the various shakers to work together to achieve a desired response. Here, a new input estimation algorithm is developed and demonstrated using simulations and actual test data. This algorithm, dubbed shape-constrained input estimation, is shown to effectively coordinate multiple shakers using a set of constraint vectors based on the deflection shapes of the test structure. This is accomplished by using the singular vector shapes of the system frequency response matrix, which allows the constraint vectors to automatically change as a function of frequency. Simulation and test results indicate a significant reduction in the input forces required to achieve a desired response. Finally, the results indicate that shape-constrained input estimation is an effective method to achieve higher response levels from limited shaker forces which will enable higher level multi-shaker vibration tests to be performed.

42 ENGINEERING↗
DOE OSTI2021

A fast multi-fidelity method with uncertainty quantification for complex data correlations: Application to vortex-induced vibrations of marine risers

Here we develop a fast multi-fidelity modeling method for very complex correlations between high- and low-fidelity data by working in modal space to extract the proper correlation function. We apply this method to infer the amplitude of motion of a flexible marine riser in cross-flow, subject to vortex-induced vibrations (VIV). VIV are driven by an absolute instability in the flow, which imposes a frequency (Strouhal) law that requires a matching with the impedance of the structure; this matching is easily achieved because of the rapid parametric variation of the added mass force. As a result, the wavenumber of the riser spatial response is within narrow bands of uncertainty. Hence, an error in wavenumber prediction can cause significant phase-related errors in the shape of the amplitude of response along the riser, rendering correlation between low- and high-fidelity data very complex. Working in modal space as outlined herein, dense data from low-fidelity data, provided by the semi-empirical computer code VIVA, can correlate in modal space with few high-fidelity data, obtained from experiments or fully-resolved CFD simulations, to correct both phase and amplitude and provide predictions that agree very well overall with the correct shape of the amplitude response. We also quantify the uncertainty in the prediction using Bayesian modeling and exploit this uncertainty to formulate an active learning strategy for the best possible location of the sensors providing the high fidelity measurements.

42 ENGINEERING↗
DOE OSTI2023

Matrix Approach to Accelerate Spin-Up of CLM5

Numerical models have been developed to investigate and understand responses of biogeochemical cycle to global changes. Steady state, when a system is in dynamic equilibrium, is generally required to initialize these model simulations. However, the spin-up process that is used to achieve steady state pose a great burden to computational resources, limiting the efficiency of global modeling analysis on biogeochemical cycles. This study introduces a new Semi-Analytical Spin-Up (SASU) to tackle this grand challenge. We applied SASU to Community Land Model version 5 and examined its computational efficiency and accuracy. At the Brazil site, SASU is computationally 7 times more efficient than (or saved up to 86% computational cost in comparison with) the traditional native dynamics (ND) spin-up to reach the same steady state. Globally, SASU is computationally 8 times more efficient than the accelerated decomposition spin-up and 50 times more efficient than ND. In summary, SASU achieves the highest computational efficiency for spin-up on site and globally in comparison with other spin-up methods. It is generalizable to wide biogeochemical models and thus makes computationally costly studies (e.g., parameter perturbation ensemble analysis and data assimilation) possible for a better understanding of biogeochemical cycle under climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

VpROM: a novel variational autoencoder-boosted reduced order model for the treatment of parametric dependencies in nonlinear systems

Reduced Order Models (ROMs) are of considerable importance in many areas of engineering in which computational time presents difficulties. Established approaches employ projection-based reduction, such as Proper Orthogonal Decomposition. The limitation of the linear nature of such operators is typically tackled via a library of local reduction subspaces, which requires the assembly of numerous local ROMs to address parametric dependencies. Our work attempts to define a more generalisable mapping between parametric inputs and reduced bases for the purpose of generative modeling. We propose the use of Variational Autoencoders (VAEs) in place of the typically utilised clustering or interpolation operations, for inferring the fundamental vectors, termed as modes, which approximate the manifold of the model response for any and each parametric input state. The derived ROM still relies on projection bases, built on the basis of full-order model simulations, thus retaining the imprinted physical connotation. However, it additionally exploits a matrix of coefficients that relates each local sample response and dynamics to the global phenomena across the parametric input domain. The VAE scheme is utilised for approximating these coefficients for any input state. This coupling leads to a high-precision low-order representation, which is particularly suited for problems where model dependencies or excitation traits cause the dynamic behavior to span multiple response regimes. Moreover, the probabilistic treatment of the VAE representation allows for uncertainty quantification on the reduction bases, which may then be propagated to the ROM response. The performance of the proposed approach is validated on an open-source simulation benchmark featuring hysteresis and multi-parametric dependencies, and on a large-scale wind turbine tower characterised by nonlinear material behavior and model uncertainty.

Conditional VAEs↗
DOE OSTI2023

Signal Decomposition for Intrusion Detection in Reliability Assessment in Cyber Resilience (Summary Report)

The complexity of assuring cyber resilience for physical process interactions in connected systems such as energy grids increases dramatically as the coupling between processes becomes more direct and responsive. An example of this growing complexity is provided by Integrated Energy Systems (IES), in which various processes such as nuclear heat generation and commodity production are being directly coupled for increased responsiveness to highly variable signals such as market pricing or electricity demand. As such, the potential attack surface of the coupled processes is larger than the two processes independently. Securing these complex systems requires two-fold monitoring: cybersecure monitoring for potential malicious incursion, and physics monitoring for system tampering. Physics monitoring includes analyzing the behavior of the signals within the system for anomalous behavior. This analysis has been shown to be insufficient if approached by only data-driven machine learning and artificial intelligence (MLAI) techniques or only low-level model comparison. Previous efforts at Purdue University suggested combining high-fidelity models with MLAI algorithms as a basis for a software tool for detecting anomalies in physical processes. This work built on that suggestion, developing an advanced library for signal decomposition and analysis using both MLAI and high-fidelity physics algorithms for greatly improved anomaly detection, especially false data injection. This software can be used as part of a secure imbedded intelligence (SEI) system designed under Consequence-driven Cyber-informed Engineering (CCE) for complex coupled systems. This library established a foundation for online and posteriori analysis of digital signals for the purpose of detecting potential malicious tampering in digital signals representing physical processes. Demonstrations carried out throughout the development highlight the effective use of characterization algorithms to detect signal perturbations, particularly triangle attack-style perturbations, in three wide-ranging applications: seismic monitoring, nuclear thermal hydraulics system simulation, and custom manufacturing.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1994

Turbulent transport in premixed flames

Simulations of planar, premixed turbulent flames with heat release were used to study turbulent transport. Reynolds stress and Reynolds flux budgets were obtained and used to guide the investigation of important physical effects. Essentially all pressure terms in the transport equations were found to be significant. In the Reynolds flux equations, these terms are the major source of counter-gradient transport. Viscous and molecular terms were also found to be significant, with both dilatational and solenoidal terms contributing to the Reynolds stress dissipation. The BML theory of premixed turbulent combustion was critically examined in detail. The BML bimodal pdf was found to agree well with the DNS data. All BML decompositions, through the third moments, show very good agreement with the DNS results. Several BML models for conditional terms were checked using the DNS data and were found to require more extensive development.

Rutland, C. J.↗
DOE OSTI2020

Molecular Origins of the Barriers to Proton Transport in Acidic Aqueous Solutions

The self-consistent iterative multistate empirical valence bond (SCI-MS-EVB) method is used to analyze the structure, thermodynamics, and dynamics of hydrochloric acid solutions. The reorientation timescales of irreversible proton transport are elucidated by simulating 0.43 M, 0.85 M, 1.68 M, and 3.26 M HCl solutions at 270 K, 285 K, 300 K, 315 K, and 330 K. The results indicate increased counter-ion pairing with increasing concentration, which manifests itself via a reduced hydronium oxygen–chloride (O*-Cl) structuring in the radial distribution functions. Increasing ionic concentration also reduces the diffusion of the hydrated excess protons, principally by reducing the contribution of the Grotthuss proton hopping (shuttling) mechanism to the overall diffusion process. In agreement with prior experimental findings, a decrease in the activation energy of reorientation timescales was also observed, which is explicitly explained using activated rate theory and an energy-entropy decomposition of the state-averaged radial distribution functions. Furthermore, these results provide atomistic verification of suggestions from recent two-dimensional infrared spectroscopy experiments that chloride anions (as opposed to hydrated excess protons) create entropic barriers to proton transport.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

CV4Quantum: Reducing the Sampling Overhead in Probabilistic Error Cancellation Using Control Variates

Quasiprobabilistic decompositions (QPDs) play a key role in maximizing the utility of near-term quantum hardware. For example, Probabilistic Error Cancellation (PEC) (an error mitigation technique) and circuit cutting (which enables large quantum computations to be performed on quantum hardware with a limited number of qubits) both involve QPDs. Computations based on QPDs typically incur large sampling overheads that grow exponentially with the number of error-terms mitigated or number of circuit-cuts employed, limiting their practical feasibility. In this work, we adapt the control variates variance reduction technique from the statistics literature in order to reduce the sampling overhead in QPD-based computations. We demonstrate our method using simulation experiments that mimic a realistic PEC scenario. In our experiments, we observed a more than 50% reduction in the number of samples needed to achieve a given precision, in more than 50% of the PEC-based estimations performed in the study when using our approach. We discuss how future research on constructing good control variates can lead to even stronger sampling overhead reduction.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗