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Results for “surface complexation modeling”

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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

FFT-based solver for higher-order and multi-phase-field fracture models applied to strongly anisotropic brittle materials

This paper presents the application of a fast Fourier transform (FFT) based method to solve two phase field models designed to simulate crack growth of strongly anisotropic materials in the brittle regime. By leveraging the ability of the FFT-based solver to generate solutions with higher-order and global continuities, we design two simple algorithms to capture the complex fracture patterns (e.g. sawtooth, and curved crack growth) common in materials with strongly anisotropic surface energy via the multi-phase-field and high-order phase-field frameworks. A staggered operator-split solver is used where both the balance of linear momentum and the phase field governing equations are formulated in the periodic domain. The unit phase field of the initial failure region is prescribed by the penalty method to alleviate the sharp material contrast between the initial failure region and the base material. The discrete frequency vectors are generalized to estimate the second and fourth order gradients such that the Gibbs effect near shape interfaces or jump conditions can be suppressed. Furthermore, a preconditioner is adopted to improve the convergence rate of the iterative linear solver. Three numerical experiments are used to systematically compare the performance of the FFT-based method in the multi-phase-field and high-order phase-field frameworks.

42 ENGINEERING↗
DOE OSTI2024

Efficient learning of accurate surrogates for simulations of complex systems

Machine learning methods are increasingly deployed to construct surrogate models for complex physical systems at a reduced computational cost. However, the predictive capability of these surrogates degrades in the presence of noisy, sparse or dynamic data. Here, we introduce an online learning method empowered by optimizer-driven sampling that has two advantages over current approaches: it ensures that all local extrema (including endpoints) of the model response surface are included in the training data, and it employs a continuous validation and update process in which surrogates undergo retraining when their performance falls below a validity threshold. We find, using benchmark functions, that optimizer-directed sampling generally outperforms traditional sampling methods in terms of accuracy around local extrema even when the scoring metric is biased towards assessing overall accuracy. Finally, the application to dense nuclear matter demonstrates that highly accurate surrogates for a nuclear equation-of-state model can be reliably autogenerated from expensive calculations using few model evaluations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

In situ ambient pressure XPS study of Pt/Cu(111) single-atom alloy in catalytically relevant reaction conditions

The presence of multiple reactant gases as well as reaction intermediates in a heterogeneous catalytic reaction results in a complex interaction between different components of the catalyst with each gas, which can alter the surface and chemical state of the catalyst differently than in the presence of an individual gas alone. In this study, we used in situ ambient pressure x-ray photoelectron spectroscopy to study the surface state of Pt/Cu(111) single-atom alloy model system in two catalytically relevant reaction conditions: CO 2 hydrogenation and CO oxidation. We found that the activation of CO 2 results in the formation of CO, which adsorbs on Pt sites at up to 400 K. In the presence of CO 2 and H 2 , Pt catalyzes the reverse water–gas shift reaction, which produces more CO and further stabilizes surface Pt atoms at 450 K. On the other hand, in CO oxidation condition, the presence of O 2 results in the formation of a thick Cu 2 O layer at higher temperatures, and Pt atoms are no longer detected in the surface and subsurface layers. When O 2 is introduced to the sample before CO, the formation of a complete Cu 2 O layer that covers all Pt atoms occurs immediately at room temperature. Finally, however, when CO is introduced at room temperature before O 2 , the presence of adsorbed CO on Pt sites stabilizes the surface Pt atoms and prevents the formation of a complete Cu 2 O layer, thus exposing the Pt atoms in 'holes' in the Cu 2 O layer.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Literature Review on the Theory and Measurement of Dry Sliding Friction of Metals

Understanding the friction behavior between two sliding bodies can inform the design of machines, processing of materials, and simulation of dynamic processes. Kinetic friction is a complex phenomenon that depends on a multitude of factors such as sliding velocity, normal force, contact area, surface roughness, material properties, lubrication conditions, and thermal effects. This literature review covers the major known effects of sliding velocity, normal load, and surface roughness on the measured kinetic friction coefficient in the context of microscopic friction phenomena. Classic macroscale friction models are reviewed to illustrate approaches for simulating friction behavior. Prominent experimental friction setups within the literature are discussed with respect to achievable velocity and pressure regimes. The background information gathered here will be used to inform experimental procedures and modeling strategies of the exploratory research (ER) project titled “Measurement of Dynamic Friction via Kolsky Bar” (20200418ER).

36 MATERIALS SCIENCE↗
DOE OSTI2021

Toward Hybrid Physics-Machine Learning to improve Land Surface Model predictions

A critical challenge for Land Surface Models (LSMs) is to simulate processes at the surface and the subsurface and their feedbacks to the atmosphere. Even using the same climate forcings, different LSMs predict different surface fluxes and soil moisture conditions due to differences in the formulations of individual processes, parameterizations, and representation of spatial heterogeneity. Ultimately, these differences contribute to the LSM prediction errors and uncertainty. This research seeks to address this challenge by coupling physics-based modeling with state-of-the-art machine learning (ML) techniques to describe complex physical and biogeochemical processes and narrow the gap between model predictions and observations.

58 GEOSCIENCES↗
DOE OSTI2022

Distributed Real-time Plume Monitoring for Deep Sea Mineral Extraction​

In the emerging industry of deep-sea mining for minerals and deposits (e.g. polymetallic nodules for nickel, cobalt, copper, and manganese), more data is required to understand the effects of sediment plume generation and predict the distribution of disturbed sediment. There are two main sources of plume generation, the first being at the active mining site where the “collector” directly removes the top layer of the sea floor. The other is the “midwater plume” consisting of unwanted sediment that was collected during extraction that is pumped back into the aphotic zone. The vast majority of plume generation is caused by the collector, causing detrimental and long-lasting impacts on seafloor ecosystems due to the lack of wave activity or strong currents at the sea floor. Therefore, it is crucial to invest in the infrastructure to support the study and constant monitoring over a large area of the sea floor where plume generation is present. Due to the limited number of usable channels and power requirements, current subsea wireless communications technologies are not well suited to instrumenting the large areas of the sea floor needed to monitor plume migration. The scope of this effort is to transition experimental demonstrations of high-bandwidth, full-duplex scalable underwater laser communications to the seafloor in an open ocean environment. Specifically tackling challenges associated with the dynamic nature of the subsea world, including but not limited to, deployment logistics, sustainability, and range. The goal is to enable the internet of underwater things for deep sea industries by broadening the capabilities of subsea communications. By using high-precision laser transmitters, many of the challenges current subsea optical systems face can be circumvented, such as power consumption, interference, and bandwidth limitations. This approach lends itself to wireless interlinking multi-node networks, in series or parallel, facilitating the implementation of a wide array of sensor types. This interlinking allows all the data gathered from the network to be processed through a single hardline uplink to the surface, lowering the complexity required for near real-time data processing. Additionally, the laser control systems produce metadata that can be used to help characterize the water column between the nodes. Combining data from various sensors such as turbidity, temperature, current velocity with metadata such as beam attenuation and deflection can produce a high-resolution model of sea floor conditions around an active mining zone. The resulting near real-time model can be used to optimize location and flow rate of the mining operation to minimize and quantify the environmental impact.

Mons, Ishan↗
DOE OSTI2024

Flashback characterization of additively manufactured swirl-stabilized fuel injector with varying surface roughness

This work investigates the effects of as-built surface roughness on the flashback propensity in additively manufactured (AM) swirl-stabilized lean premixed (LPM) fuel injectors. Adoption of AM for rapid prototyping and fabrication of complex fuel-flexible injector designs requires investigating surface roughness effects on the flow and flame stability characteristics of the combustor. Wall roughness increases the near-wall shear, which could alter the boundary layer structure and change the propensity for flame flashback. Accounting for the realistic as-built surface roughness is crucial in carrying out computational modeling and experimental analysis to establish a feedback loop for the precision designing of fuel-flexible injectors. The presented numerical analysis of as-built AM injector is an essential consideration to optimize injector design for aerodynamics and additive manufacturability.

Mohanty, Pratikshya↗
DOE OSTI2022

DECOVALEX-2019: An international collaboration for advancing the understanding and modeling of coupled thermo-hydro-mechanical-chemical (THMC) processes in geological systems

The DECOVALEX Project is an international research collaboration for advancing the understanding and modeling of coupled thermo-hydro-mechanical-chemical (THMC) processes in geological systems. DECOVALEX stands for “DEvelopment of COupled Models and VALidation against EXperiments”. The creation of this international initiative, now running for almost 30 years, was motivated by the recognition that prediction of these coupled effects is an essential part of the performance and safety assessment of geologic disposal systems for radioactive waste and spent nuclear fuel, and also for a range of other sub-surface engineering activities. DECOVALEX emphasizes joint analysis and comparative modeling of state-of-the-art field and laboratory experiments, across a range of host rock options and repository designs. Participating research teams are from radioactive waste management organizations, national research institutes, regulatory agencies, universities, and consulting groups, providing a wide range of perspectives and solutions to these complex problems. The most recent phase of the DECOVALEX Project, here referred to as DECOVALEX-2019, started in 2016 and ended in 2019. Modeling teams from 13 international partner organizations participated in the comparative evaluation of seven modeling tasks involving complex field and/or laboratory experiments. Furthermore, this Virtual Special Issue on DECOVALEX-2019 provides an in-depth overview of these collaborative research efforts and how these have advanced the state-of-the-art of understanding and modeling coupled THMC processes.

58 GEOSCIENCES↗
DOE OSTI2020

New H2O–H2O collisional rate coefficients for cometary applications

ABSTRACT We re-introduce a semiclassical methodology based on theories developed for the determination of broadening coefficients. We show that this simple and extremely fast methodology provides results that are in good agreement with results obtained using the more sophisticate MQCT approach. This semiclassical methodology could be an alternative approach which allows to provide large sets of collisional data for very complex molecular systems. It saves time both on the determination of potential energy surfaces and on the collisional dynamical calculations. In addition, this paper provides more complete sets of rotational de-excitation cross-sections and rate coefficients of H2O perturbed by a thermal average of water molecules. Those data can be used in the radiative transfer modelling of cometary atmospheres.

Boursier, C.↗
DOE OSTI2022

Geometric prior of multi-resolution yielding manifolds and the local closest point projection for nearly non-smooth plasticity

Elastoplasticity models often introduce a scalar-valued yield function to implicitly represent the boundary between elastic and plastic material states. Here this paper introduces a new alternative where the yield envelope is represented by a manifold of which the topology and the geometry are learned from a set of data points in a parametric space (e.g. principal stress space, π-plane). Here, deep geometric learning enables us to reconstruct a highly complex yield envelope by breaking it down into multiple coordinate charts. The global atlas that consists of these coordinate charts in return allows us to represent the yield surface via multiple overlapping patches, each with a specific local parametrization. This setup provides several advantages over the classical implicit function representation approach. For instance, the availability of coordinate charts enables us to introduce an alternative stress integration algorithm where the trial stress may project directly on a local patch and hence circumvent the issues related to non-smoothness and the lack of convexity of yield surfaces. Meanwhile, the local parametric approach also enables us to predict hardening/softening locally in the parametric space, even without complete knowledge of the yield surface. Comparisons between the classical yield function approach on the non-smooth plasticity and anisotropic cam-clay plasticity model are provided to demonstrate the capacity of the models for highly precise yield surface and the feasibility of the implementation of the learned model in the local stress integration algorithm.

42 ENGINEERING↗
DOE OSTI2020

Topological analysis of X-ray CT data for the recognition and trending of subtle changes in microstructure under material aging

X-ray computed tomography (CT) is an established non-destructive tool for 3D imaging of multiphasic composites. Numerous applications of X-ray CT in medical diagnosis and materials characterization have been reported, many involving field-specific innovations in the imaging technology itself. Yet, quantitative summarization to link image features to properties of interest has been rare. Here we address this issue by employing state-of-the-art technics in scalar field topology for the summarization of X-ray CT images of an example biphasic system. By varying processing-parameters we create different microstructures, evolve them through accelerated thermal aging, CT-image them pre- and post-aged, and demonstrate the ability of our image summarization method to systematically track process- and age-related changes, which can often be very subtle. A novel aspect of the algorithm involves recognition over multiple resolution levels, which provides deeper insight into the pattern relationship between grain-like features and their neighbors. The method is general, adaptable to diverse image reconstruction methods and materials systems, and particularly useful in applications where practical constraints on the sample-size limits the reliable use of more complex models, e.g., convolutional neural networks.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Automated descriptor selection, volcano curve generation, and active site determination using the DescMAP software

The material space for catalyst discovery is expansive. Volcano curves are traditionally employed to provide physical insights into optimal catalyst characteristics for new material selection. Their generation lies on a single descriptor picked using expert knowledge. Here we present DescMAP, a Python-based software, to automate the selection of descriptors, the generation of volcano maps, and the identification of active sites for structure-sensitive reactions. Here, we consider traditional energy-based and geometric descriptors for structure-sensitive reactions. DescMAP is integrated with the Virtual Kinetic Laboratory (VLab) to provide multiple functionalities. It inputs spreadsheets or template files for flexibility and outputs interactive graphs for post-processing. We demonstrate its features using the non-oxidative dehydrogenation of ethane to ethylene over (111) closed-packed surfaces and the methane total oxidation over various Pt facets. It can be easily applied to other complex chemistries and achieves quick screening of potential catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Importance of van der Waals interactions for ab initio studies of topological insulators

We investigate the lattice and electronic structures of the bulk and surface of the prototypical layered topological insulators Bi 2 Se 3 and Bi 2 Te 3 using ab initio density functional methods, and systematically compare the results of different methods of including van der Waals (vdW) interactions. We show that the methods utilizing semi-empirical energy corrections yield accurate descriptions of these materials, with the most precise results obtained by properly accounting for the long-range tail of the vdW interactions. The bulk lattice constants, distances between quintuple layers and the Dirac velocity of the topological surface states (TSS) are all in excellent agreement with experiment. In Bi 2 Te 3 , hexagonal warping of the energy dispersion leads to complex spin textures of the TSS at moderate energies, while in Bi 2 Se 3 these states remain almost perfectly helical away from the Dirac point, showing appreciable signs of hexagonal warping at much higher energies, above the minimum of the bulk conduction band. Furthermore, our results establish a framework for unified and systematic self-consistent first principles calculations of topological insulators in bulk, slab and interface geometries, and provides the necessary first step toward ab initio modeling of topological heterostructures.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Multimodal imaging of oil-in-water bilgewater emulsion and biofilms

Bilgewater emulsions, consisting of oil and surfactant mixtures in water, affect many aquatic species. Thus development of methods and techniques to treat or mitigate the formation and undesired consequences of shipboard emulsions are urgently needed. This work presents new results of the evolution of the bilgewaer chemical and physical properties using in situ imaging. A Navy O/W emulsion consisting of three oils and a detergent mixture was used as the synthetic bilgewater model [1, 2]. Synthetic oil-in-water (O/W) emulsion droplets were seled in a vacuum compatible microfluidic sample holder and imaged using a suite of tools including scanning electron microscopy (SEM), time-of-flight secondary ion mass spectrometry (ToF-SIMS), confocal laser scanning microscopy, and optical microscopy, highlighting the advantage of multiscale analysis and its applications in waste reduction and treatment in the environment. The transferrable and vacuum compatible microfluidic interface, System for Analysis at the Liquid Vacuum Interface (SALVI), was used in this study. SALVI enabled surface analysis of liquids and liquid-solid interactions using ToF-SIMS and SEM [3, 4]. Its detection window is an aperture of 2 ?m in diameter open to vacuum, permitting direct detection of the liquid surface. Liquid is withheld by surface tension within the aperture. The interface is composed of a silicon nitride membrane and polydimethylsiloxane microchannel [3, 4]. A variety of samples including complex liquid mixtures, ionic liquids, single mammalian cells, live biofilms, and solid-electrolyte interface have been analyzed using in situ imaging [5, 6]. The feasibility of using SALVI for in situ SEM imaging of nanoparticles and colloids in liquid was recently demonstrated [7]. This paper shows new findings of multimodal imaging and analysis of synthesized emulsion particles in water. Figure 1 depicts a schematic of multimodal in situ imaging of bilgewater emulsion using SEM and ToF-SIMS. Furthermore, we study the interactions of microbes and bilgewater emulsion. Microbes play an important role in the ocean. We hypothesize that microbes release organics that can act as surfactants that affect bilgewater formation or weakening. We present the first systematic study of emulsions and biofilms and investigate the effects of biofilms on bilgewater emulsions. Pseudomonas was selected as a model strain. Biofilms were cultured in a microchannel to allow culture [8]. Once a thick layer of biofilms was formed, the medium solution was changed to a mixture consisting of 50 % bilgewater emulsion. Dispersed biofilms were collected at 24 hrs. and 48 hrs. after emulsions were introduced into the channel. Bilgewater emulsions, biofilms, and mixtures of bilgewater emulsions and biofilms were analyzed using multiple in situ and ex situ techniques as described earlier. Our findings indicate that biofilms change the chemical makeup of the emulsion surface compositions and emulsion droplet size distribution, confirming the hypothesis that extracellular polymeric substance (EPS) related components released from biofilms can function as surfactants and change the oil-in-water interfaces.

bilgewater, multimodal imaging, biofilms↗
DOE OSTI2026

Elucidating the Structural and Electronic Effects of Ni and Mn Cationic Incorporation on CoOOH for Efficient Benzyl Alcohol Electrooxidation

Transition-metal oxyhydroxides such as CoOOH are promising low-cost electrocatalysts for the selective electrooxidation of organic molecules, yet the influence of ubiquitous transition-metal impurities on their performance and durability remains poorly understood. Here, we experimentally probed the individual and synergistic electrochemical and structural effects of Ni and Mn incorporations into model CoOOH electrocatalysts toward an efficient benzyl alcohol oxidation reaction (BAOR). Comprehensive electrochemical, microscopic, and spectroscopic analyses reveal that Ni incorporation enhances charge-transfer kinetics and overall activity through the formation of catalytically active Ni 3+ sites, whereas Mn exhibited a more complex but interesting role. At the early stages of operation, Mn 4+ acts as a stabilizing surface layer that mitigates catalyst degradation but partially blocks Co sites before they undergo gradual leaching. The concurrent incorporation of both Ni and Mn yields a trimetallic 2NMC@NF electrocatalyst that integrates the activity benefits of Ni with the stability conferred by Mn, achieving 92.9% benzyl alcohol conversion and 91.4% Faradaic efficiency after 24 h at 1.5 V vs RHE. These findings elucidate how trace Ni and Mn impurities, often introduced from electrolytes or external sources, can modulate the lattice and electronic structure of CoOOH, offering a design strategy for enhancing both activity and long-term stability in electrocatalytic organic oxidation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Exploring Causal Physical Mechanisms via Non-Gaussian Linear Models and Deep Kernel Learning: Applications for Ferroelectric Domain Structures

Rapid emergence of multimodal imaging in scanning probe, electron, and optical microscopies has brought forth the challenge of understanding the information contained in these complex data sets, targeting the intrinsic correlations between different channels, and further exploring the underpinning causal physical mechanisms. Here, we develop such an analysis framework for Piezoresponse Force Microscopy. We argue that under certain conditions, we can bootstrap experimental observations with the prior knowledge of materials structure to get information on certain nonobserved properties, and demonstrate linear causal analysis for PFM observables. We further demonstrate that the strength of individual causal links between complex descriptors can be ascertained using the deep kernel learning (DKL) model. In this DKL analysis, we use the prior information on domain structure within the image to predict the physical properties. This analysis demonstrates the correlative relationships between morphology, piezoresponse, elastic property, etc., at nanoscale. The prediction of morphology and other physical parameters illustrates a mutual interaction between surface condition and physical properties in ferroelectric materials. Overall, this analysis is universal and can be extended to explore the correlative relationships of other multichannel data sets, and allow for high-fidelity reconstruction of underpinning functionalities and physical mechanisms.

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

Benchmarking and Exploring Parameter Space of the 2-Phase Bubble Tracking Model for Liquid Mercury Target Simulation

High intensity proton pulses strike the Spallation Neutron Source (SNS)’s mercury target to provide bright neutron beams. These strikes deposit extensive energy into the mercury and its steel vessel. Prediction of the resultant loading on the target is difficult when helium gas is intentionally injected into the mercury to reduce the loading and to mitigate the pitting damage on the vessel. A 2-phase material model that incorporates the Rayleigh-Plesset (R-P) model is expected to address this complex multi-physics dynamics problem by including the bubble dynamics in the liquid mercury. We present a study comparing the measured target strains in the SNS target station with the simulation results of the solid mechanics simulation framework. We investigate a wide range of various physical model parameters, including the number of bubble families, bubble size distribution, viscosity, surface tension, etc. to understand their impact on simulation accuracy. Our initial findings reveal that using 8-10 bubble families in the model renders a simulation strain envelope that covers the experimental ones. Further optimization studies are planned to predict the strain response more accurately.

Lin, Lianshan↗
DOE OSTI2024

Modeling Surface-Enhanced Raman Scattering of Au-Pyrazine and Au-Pyrazine-Au Nanorod Dimer Systems with the TD-DFTB Method

This study investigates Raman scattering from a pyrazine molecule adsorbed on gold nanorod surfaces using the time-dependent density functional tight-binding (TD-DFTB) method. We analyze surface enhanced Raman scattering (SERS) of the molecule for two configurations: one where the molecule is adsorbed on a single gold nanorod forming Au-pyrazine complex, and another where it forms a nano-junction Au-pyrazine-Au dimer system. These two configurations offer distinct chemical environments for the molecule and different local field enhancements, with the dimer systems generating enhanced hotspot regions at the junctions. We present results for nanojunction, Au-pyrazine-Au dimer structures, for variable nanogap sizes that lead to shift in the plasmon energy. The study identifies contributions to the SERS enhancements from chemical, electromagnetic, and resonance charge transfer mechanisms. Our results show that the chemical mechanism provides enhancement factors in the range of 10 2 –10 3 , while the electromagnetic enhancements are on the order of 10 3 –10 5 for monomer systems and 10 6 –10 8 for dimer structures. We present results for a nanorod with a length of 5.3 nm and a width of 0.52 nm, consisting of 121 gold atoms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗