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At least 1,333 records · Page 74
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Weibull-Based Stochastic Simulation of Mud-Crack Damage Formation in an Environmental Barrier Coating

The integrated Finite Element Analysis Micromechanics Analysis Code/Ceramics Analysis and Reliability Evaluation of Structures (FEAMAC/CARES) program was used to simulate the formation of mudflat-cracks from thermomechanical loading on a multi-layered Environmental Barrier Coating (EBC) system deposited on a ceramic substrate. FEAMAC/CARES combines MAC/GMC (Micromechanics Analysis Code/Generalized Method of Cells) multiscale composite micromechanics code with CARES/Life probabilistic multiaxial failure criteria code and Abaqusfinite element analysis. In this work, step function elastic modulus reduction of randomly damaged finite elements was used to represent discrete cracking events. The use of many small-sized low-aspect-ratio finite elements enabled the depiction of crack boundaries and formation of mudflat patterned damage. Demonstrated examples include finite element models of button-sized disk-shaped 3-D specimen, and a 2-D model of through-the-thickness cross-section. All models were subjected to a progressive cool down from 1300 degrees Centigrade to room temperature. Mudflat crack damage in the coating system resulted from the buildup of residual tensile stresses between the individual material constituents from thermal expansion mismatch. A 2-parameter Weibull distribution characterized the coating layer stochastic strength response and the effect of the Weibull modulus on the formation of damage was studied here.

Residual Tensile Stress↗
NASA NTRS2022

Design of synthetic collagens that assemble into supramolecular banded fibers as a functional biomaterial testbed

Collagens are the most abundant proteins of the extracellular matrix, and the hierarchical folding and supramolecular assembly of collagens into banded fibers is essential for mediating cell-matrix interactions and tissue mechanics. Collagen extracted from animal tissues is a valuable commodity, but suffers from safety and purity issues, limiting its biomaterials applications. Synthetic collagen biomaterials could address these issues, but their construction requires molecular-level control of folding and supramolecular assembly into ordered banded fibers, comparable to those of natural collagens. Here, we show an innovative class of banded fiber-forming synthetic collagens that recapitulate the morphology and some biological properties of natural collagens. The synthetic collagens comprise a functional-driver module that is flanked by adhesive modules that effectively promote their supramolecular assembly. Multiscale simulations support a plausible molecular-level mechanism of supramolecular assembly, allowing precise design of banded fiber morphology. We also experimentally demonstrate that synthetic fibers stimulate osteoblast differentiation at levels comparable to natural collagen. This work thus deepens understanding of collagen biology and disease by providing a ready source of safe, functional biomaterials that bridge the current gap between the simplicity of peptide biophysical models and the complexity of in vivo animal systems.

Jinyuan Hu↗
DOE OSTI2023

Multiscale molecular simulations for the solvation of lignin in ionic liquids

Lignin, the second most abundant biopolymer found in nature, has emerged as a potential source of sustainable fuels, chemicals, and materials. Finding suitable solvents, as well as technologies for efficient and affordable lignin dissolution and depolymerization, are major obstacles in the conversion of lignin to value-added products. Certain ionic liquids (ILs) are capable of dissolving and depolymerizing lignin but designing and developing an effective IL for lignin dissolution remains quite challenging. To address this issue, the COnductor-like Screening MOdel for Real Solvents (COSMO-RS) model was used to screen 5670 ILs by computing logarithmic activity coefficients (ln(γ)) and excess enthalpies (H E ) of lignin, respectively. Based on the COSMO-RS computed thermodynamic properties (ln(γ) and H E ) of lignin, anions such as acetate, methyl carbonate, octanoate, glycinate, alaninate, and lysinate in combination with cations like tetraalkylammonium, tetraalkylphosphonium, and pyridinium are predicted to be suitable solvents for lignin dissolution. The dissolution properties such as interaction energy between anion and cation, viscosity, Hansen solubility parameters, dissociation constants, and Kamlet–Taft parameters of selected ILs were evaluated to assess their propensity for lignin dissolution. Furthermore, molecular dynamics (MD) simulations were performed to understand the structural and dynamic properties of tetrabutylammonium [TBA] + -based ILs and lignin mixtures and to shed light on the mechanisms involved in lignin dissolution. MD simulation results suggested [TBA] + -based ILs have the potential to dissolve lignin because of their higher contact probability and interaction energies with lignin when compared to cholinium lysinate.

09 BIOMASS FUELS↗
DOE OSTI2022

Hierarchical deep reinforcement learning reveals a modular mechanism of cell movement

Time-lapse images of cells and tissues contain rich information about dynamic cell behaviours, which reflect the underlying processes of proliferation, differentiation and morphogenesis. However, we lack computational tools for effective inference. Here we exploit deep reinforcement learning (DRL) to infer cell–cell interactions and collective cell behaviours in tissue morphogenesis from three-dimensional (3D) time-lapse images. We use hierarchical DRL (HDRL), known for multiscale learning and data efficiency, to examine cell migrations based on images with a ubiquitous nuclear label and simple rules formulated from empirical statistics of the images. When applied to Caenorhabditis elegans embryogenesis, HDRL reveals a multiphase, modular organization of cell movement. Imaging with additional cellular markers confirms the modular organization as a novel migration mechanism, which we term sequential rosettes. Furthermore, HDRL forms a transferable model that successfully differentiates sequential rosettes-based migration from others. Our study demonstrates a powerful approach to infer the underlying biology from time-lapse imaging without prior knowledge.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Extreme shear-deformation-induced modification of defect structures and hierarchical microstructure in an Al–Si alloy

Abstract Extreme shear deformation is used for several material processing methods and is unavoidable in many engineering applications in which two surfaces are in relative motion against each other while in physical contact. The mechanistic understanding of the microstructural evolution of multi-phase metallic alloys under extreme shear deformation is still in its infancy. Here, we highlight the influence of shear deformation on the microstructural hierarchy and mechanical properties of a binary as-cast Al-4 at.% Si alloy. Shear-deformation-induced grain refinement, multiscale fragmentation of the eutectic Si-lamellae, and metastable solute saturated phases with distinctive defect structures led to a two-fold increase in the flow stresses determined by micropillar compression testing. These results highlight that shear deformation can achieve non-equilibrium microstructures with enhanced mechanical properties in Al–Si alloys. The experimental and computational insights obtained here are especially crucial for developing predictive models for microstructural evolution of metals under extreme shear deformation.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Structure of Offshore Low-Level Jet Turbulence and Implications to Mesoscale-to-Microscale Coupling

This paper explores realistic nonstationary atmospheric boundary layer (ABL) turbulence arising from nonstationarity at the mesoscale, particularly within offshore low-level jets with implications to offshore wind farms, using high-fidelity multiscale large-eddy simulations (LES). To this end, we analyzed the single-point turbulence statistical structure of a North-Atlantic offshore LLJ event simulated using high-resolution LES (AMR-Wind). The nonstationary LLJ is simulated using a mesoscale-to-microscale coupled (MMC) simulation procedure involving data assimilation of mesoscale velocity and temperature data from the Weather Research and Forecasting (WRF) model. Unlike the assimilation of mesoscale velocity data into the LES, the direct assimilation of temperature profiles had a strong impact on turbulence stratification, thereby causing erroneous predictions of turbulence both above and within the jet layer. Various approaches to mitigate this effect have resulted in multiple (four) variants of this MMC strategy. Outcomes from this work clearly show that the turbulence within the low-level jet is a strong function of the MMC approach as the turbulence structure within the low-level jet is dependent on the flux of residual turbulence from outside the jet, which in turn depends on the temperature forcing history. Additionally, the turbulence predicted by all these different methods (as well as the observation data) show similar deviations from equilibrium as evidenced by comparisons with idealized atmospheric turbulence structure obtained using the same numerical method. In general, we observe that the predicted LLJ turbulence tends to differ from canonical ABL turbulence with comparable shear. Particularly, the combination of shear and turbulence observed in such nonstationary low-level turbulence cannot be matched using equilibrium settings and therefore, represents a critical use-case for both testing and leveraging meso–micro coupling strategies.

17 WIND ENERGY↗
DOE OSTI2022

Anomalies in the topology of the temperature fluctuations in the cosmic microwave background: An analysis of the NPIPE and FFP10 data releases

We present a topological analysis of the temperature fluctuation maps from the Planck 2020 Data Release 4 NPIPE dataset and the Planck 2018 Data Release 3 FFP10 dataset. We performed a multiscale analysis in terms of the homology characteristics of the maps, invoking relative homology to account for the analysis in the presence of masks. We performed our analysis for a range of smoothing scales spanning sub- and super-horizon scales corresponding to a full width at half maximum (FWHM) of 5',10',20',40',80',160',320', and 640', and employed simulations based on the standard model for comparison, which assumes the initial fluctuation field to be an isotropic and homogeneous Gaussian random field. Examining the behavior of topological components, represented by the 0D homology group, we find the observations to be approximately 2σ or less deviant from the simulations for all resolutions and scales for the NPIPE dataset. For the FFP10 dataset, we detect a 2.96σ deviation between the observations and simulations at N = 128, FWHM = 80'. For the topological loops, represented by the first homology group, the simulations and observations are consistent within 2σ for most resolutions and scales for both the datasets. However, for the NPIPE dataset, we observe a high deviation between the observation and simulations in the number of loops at FWHM = 320', but at a low dimensionless threshold ν = –2.5. Under a Gaussian assumption, this would amount to a deviation of ~4σ. However, the distribution in this bin is manifestly non-Gaussian and does not obey Poisson statistics either. In the absence of a true theoretical understanding, we simply note that the significance is higher than what may be resolved by 600 simulations, yielding an empirical p-value of at most 0.0016. Specifically in this case, our tests indicate that the numbers arise from a statistically stable regime, despite being based on small numbers. For the FFP10 dataset, the differences are not as strong as for the NPIPE dataset, indicating a 2.77σ deviation at this resolution and threshold. The Euler characteristic, which is the alternating sum of the ranks of relative homology groups, reflects the deviations in the components and loops. To assess the significance of combined levels for a given scale, we employed the empirical and theoretical versions of the χ 2 test as well as the nonparametric Tukey depth test. Although all statistics exhibit a stable distribution, we favor the empirical version of the χ 2 test in the final interpretation, as it indicates the most conservative differences. For the NPIPE dataset, we find that the components and loops differ at more than 95%, but agree within the 99% confidence level with respect to the base model at N = 32, FWHM = 320'. The Euler characteristic at this resolution displays a per mil deviation. In contrast, the FFP10 dataset shows that the observations are consistent with the base model within the 95% confidence level, at this and smaller scales. This is consistent with the observations of the Planck analysis pipeline via Minkowski functionals. For the largest smoothing scale, N = 16, FWHM = 640', both datasets exhibit an anomalous behavior of the loops, where FFP10 data exhibit a deviation that is larger by an order of magnitude than that of the NPIPE dataset. In contrast, the values for the topological components and the Euler characteristic agree between observations and model to within a confidence level of 99%. However, for the largest scales, the statistics are based on low numbers and may have to be regarded with caution. Even though both datasets exhibit mild to significant discrepancies, they also exhibit contrasting behaviors at various instances. Therefore, we do not find it feasible to convincingly accept or reject the null hypothesis. Disregarding the large-scale anomalies that persist at similar scales in WMAP and Planck, observations of the cosmic microwave background are largely consistent with the standard cosmological model within 2σ.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2009

Multiscale Computer Simulation of Tensile and Compressive Strain in Polymer- Coated Silica Aerogels

While the low thermal conductivities of silica aerogels have made them of interest to the aerospace community as lightweight thermal insulation, the application of conformal polymer coatings to these gels increases their strength significantly, making them potentially useful as structural materials as well. In this work we perform multiscale computer simulations to investigate the tensile and compressive strain behavior of silica and polymer-coated silica aerogels. Aerogels are made up of clusters of interconnected particles of amorphous silica of less than bulk density. We simulate gel nanostructure using a Diffusion Limited Cluster Aggregation (DLCA) procedure, which produces aggregates that exhibit fractal dimensions similar to those observed in real aerogels. We have previously found that model gels obtained via DLCA exhibited stress-strain curves characteristic of the experimentally observed brittle failure. However, the strain energetics near the expected point of failure were not consistent with such failure. This shortcoming may be due to the fact that the DLCA process produces model gels that are lacking in closed-loop substructures, compared with real gels. Our model gels therefore contain an excess of dangling strands, which tend to unravel under tensile strain, producing non-brittle failure. To address this problem, we have incorporated a modification to the DLCA algorithm that specifically produces closed loops in the model gels. We obtain the strain energetics of interparticle connections via atomistic molecular statics, and abstract the collective energy of the atomic bonds into a Morse potential scaled to describe gel particle interactions. Polymer coatings are similarly described. We apply repeated small uniaxial strains to DLCA clusters, and allow relaxation of the center eighty percent of the cluster between strains. The simulations produce energetics and stress-strain curves for looped and nonlooped clusters, for a variety of densities and interaction parameters.

Good, Brian↗
DOE OSTI2022

Analyzing the Multiscale Impacts of Implementing Energy-Efficient HVAC Improvements Through Energy Audits and Economic Input–Output Analysis

Abstract Heating, ventilation, and air-conditioning (HVAC) systems are usually an industry’s highest consumer of energy, most of which goes toward space cooling in buildings. Industrial energy-efficiency audits not only benefit manufacturers but also generate significant economic and environmental benefits to localities, states, and the nation. This article analyzes the micro- and macro scale impacts of implementing energy-efficient HVAC systems by integrating the industrial building energy data with the macroeconomic regional economic flow model. Micro-scale data include 10 years of historical energy, cost, and carbon dioxide savings achieved from energy-efficient HVAC implementation offered to manufacturers through industrial energy audits. The data were integrated into the macroeconomic modeling framework to illuminate the cascading regional economic impacts of implementing energy-efficient HVAC recommendations in manufacturing facilities. Results show that if recommendations had been implemented throughout all manufacturers in the region, $656 M energy costs would have been directly saved, 7.8 million metric tons of carbon dioxide emissions would have been avoided, and 4387 jobs could have been created, resulting in a total annual economic impact of $899 M stemming from direct, indirect, and induced impacts. The results offer insight into how industrial energy systems can be designed and provide models for how communities can accomplish a net-zero society.

Energy & Fuels↗
DOE OSTI2026

Development of Composite Photocatalyst Materials that are Highly Selective for Solar Hydrogen Production and their Evaluation in Z-Scheme Reactor Designs

The key technology gap preventing a vertically stacked dual-bed particle suspension reactor from achieving the DOE MYRD&D ultimate cost target for H 2 production remains the lack of materials in particle form factor that exhibit ≥10% solar-to-H 2 energy conversion (STH) efficiency as a suspension. Therefore, our project goals centered around strategies to increase the STH efficiency by enhancing photophysical properties of perovskite oxide particles including increased visible-light absorption, increased selectivity for electrocatalysis of the H 2 evolution reaction (HER) and the O 2 evolution reaction (OER) through development of ultrathin oxide coatings, correlating composition and structure to function, and improving understanding of multiscale transport and kinetic processes.

08 HYDROGEN↗
NASA NTRS2011

Monte Carlo Simulations of the Formation Flying Dynamics for the Magnetospheric Multiscale (MMS) Mission

The MMS mission is an ambitious space physics mission that will fly 4 spacecraft in a tetrahedron formation in a series of highly elliptical orbits in order to study magnetic reconnection in the Earth's magnetosphere. The mission design is comprised of a combination of deterministic orbit adjust and random maintenance maneuvers distributed over the 2.5 year mission life. Formal verification of the requirements is achieved by analysis through the use of the End-to-End (ETE) code, which is a modular simulation of the maneuver operations over the entire mission duration. Error models for navigation accuracy (knowledge) and maneuver execution (control) are incorporated to realistically simulate the possible maneuver scenarios that might be realized These error models, coupled with the complex formation flying physics, lead to non-trivial effects that must be taken into account by the ETE automation. Using the ETE code, the MMS Flight Dynamics team was able to demonstrate that the current mission design satisfies the mission requirements.

Schiff, Conrad↗
DOE OSTI2023

Coupled Monte Carlo Transport and Conjugate Heat Transfer for Wire-Wrapped Bundles Within the MOOSE Framework

Cardinal is an open-source application that couples OpenMC Monte Carlo transport and NekRS computational fluid dynamics (CFD) to the Multiphysics Object-Oriented Simulation Environment (MOOSE), closing neutronics and thermal-fluid gaps in conducting high-resolution multiscale and multiphysics analyses of nuclear systems. We first provide a brief introduction to Cardinal's software design, data mapping, and coupling strategy to highlight our approach to overcoming common challenges in high-fidelity multiphysics simulations. Here we then present two Cardinal simulations for hexagonal pin bundles. The first is a validation of Cardinal's conjugate heat transfer coupling of NekRS's Reynolds-Averaged Navier Stokes model with MOOSE's heat conduction physics for a bare seven-pin Freon-12 bundle flow experiment. Predictions for pin surface temperatures under three different heating modes agree reasonably well with experimental data and similar CFD modeling from the literature. The second simulation is a multiphysics coupling of OpenMC, NekRS, and BISON for a reduced-scale, seven-pin wire-wrapped version of an Advanced Burner Reactor bundle. Wire wraps are approximated using a momentum source model, and coupled predictions are provided for velocity, temperature, and power distribution.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Closing the Gap Between Modeling and Experiments in the Self-assembly of Biomolecules at Interfaces and in Solution

Molecular self-assembly is a powerful tool in materials design, wherein non-covalent interactions like electrostatic, hydrophobic, hydrogen bonding, and van der Waals can be exploited to produce supramolecular nanostructures that are functional and highly tunable. Biomolecules are attractive building blocks, as they are biocompatible, biodegradable and adopt a wide array of higher order structures. Moreover, naturally occurring protein systems display a manifold of structures and interactions that can be replicated in synthetic biomolecules. In this perspective, we highlight advances in multiscale simulation techniques across broad spatiotemporal scales that can aid in characterizing self-assembly of hybrid and hierarchical bionanomaterial systems, with an emphasis on physics-based simulation approaches currently employed to study biomolecules at mineral interfaces. The power of these approaches is highlighted across a few recent areas where molecular simulations have advanced our understanding of self-assembly spanning peptides to protein self-assembly. Looking forward, we discuss how in the near future emerging methods in statistical and machine learning will advance this research field in all areas from expanding the capabilities of physics-based simulation methods to enabling new analyses of high throughput experiments. These advances will pave the way for understanding the molecular recognition patterns in systems that are dictated by self-assembly - biomineralizing peptides, hierarchical peptoids, and large protein assemblies, and will aid in the development of a new synthesis science for achieving precise molecular control in materials design

Sampath, Janani↗
NASA NTRS2010

Multiscale Simulations of Magnetic Island Coalescence

We describe a new interactive parallel Adaptive Mesh Refinement (AMR) framework written in the Python programming language. This new framework, PyAMR, hides the details of parallel AMR data structures and algorithms (e.g., domain decomposition, grid partition, and inter-process communication), allowing the user to focus on the development of algorithms for advancing the solution of a systems of partial differential equations on a single uniform mesh. We demonstrate the use of PyAMR by simulating the pairwise coalescence of magnetic islands using the resistive Hall MHD equations. Techniques for coupling different physics models on different levels of the AMR grid hierarchy are discussed.

Dorelli, John C.↗
DOE OSTI2026

Hydration-Controlled Proton Transport in Respiratory Complex I

Proton pumping by respiratory complex I is one essential element for generating the proton motive force that drives ATP synthesis in mitochondria. Although it is understood that electrons from NADH reduce ubiquinone at the peripheral arm and that four protons are transferred in the membrane domain, the mechanism by which this redox reaction initiates proton translocation remains unclear. A lateral pathway linking the quinone binding site to the membrane domain via ND1, ND3, and ND4L subunits has been proposed as a possible initial path of an excess proton. However, experimental structures indicate that the hydration connectivity between D66 ND3 and E34 ND4L is comparatively weaker than in neighboring segments, suggesting a potential regulatory point for proton transfer. Using multiscale reactive molecular dynamics (MS-RMD) and a water wire connectivity metric, we directly simulate proton transport through this region as coupled to the hydration by water molecules. Our results reveal that proton transfer is thermodynamically feasible when transient hydration aligns with the presence of an excess proton, revealing the strong coupling between hydration and proton transfer (PT) in this region of Complex I. These findings support a model where proton injection enhances local hydration, dynamically opening the pathway for proton transfer and regulating the onset of proton pumping in Complex I.

Chemical structure↗
DOE OSTI2023

Energy transfer and scale dynamics in 2D and 3D laser-driven jets

We demonstrate a methodology for diagnosing the multiscale dynamics and energy transfer in complex HED flows with realistic driving and boundary conditions. The approach separates incompressible, compressible, and baropycnal contributions to energy scale-transfer and quantifies the direction of these transfers in (generalized) wavenumber space. We use this to compare the kinetic energy (KE) transfer across scales in simulations of 2D axisymmetric vs fully 3D laser-driven plasma jets. Using the FLASH code, we model a turbulent jet ablated from an aluminum cone target in the configuration outlined. We show that, in addition to its well known bias for underestimating hydrodynamic instability growth, 2D modeling suffers from significant spurious energization of the bulk flow by a turbulent upscale cascade. In 2D, this arises as vorticity and strain from instabilities near the jet's leading edge transfer KE upscale, sustaining a coherent circulation that helps propel the axisymmetric jet farther (⁠ ≈25% by 3.5 ns) and helps keep it collimated. In 3D, the coherent circulation and upscale KE transfer are absent. Here, the methodology presented here may also help with inter-model comparison and validation, including future modeling efforts to alleviate some of the 2D hydrodynamic artifacts highlighted in this study.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Large-eddy simulation of an atmospheric bore and associated gravity wave effects on wind farm performance in the southern Great Plains

Gravity waves are a common occurrence in the atmosphere, with a variety of generation mechanisms. Their impact on wind farms has only recently gained attention, with most studies focused on wind farm-induced gravity waves. In this study, the interaction between a wind farm and gravity waves generated by an atmospheric bore event is assessed using multiscale large-eddy simulations. The atmospheric bore is created by a thunderstorm downdraft from a nocturnal mesoscale convective system (MCS). The associated gravity waves impact the wind resource and power production at a nearby wind farm during the American Wake Experiment (AWAKEN) in the US southern Great Plains. A two-domain nested setup (Δx=300 and 20 m) is used in the Weather Research and Forecasting (WRF) model, forced with data from the High-Resolution Rapid Refresh model, to capture both the formation of the bore and its interaction with individual wind turbines. The MCS is resolved on the large outer domain, where the structure of the bore and the associated gravity waves are found to be especially sensitive to parameterized microphysics processes. On the finer inner domain, gravity wave interactions with individual wind turbines are resolved; wake dynamics are captured using a generalized actuator disk parameterization in WRF. The gravity waves are found to have a strong effect on the atmosphere above the wind farm; however, the effect of the waves is more nuanced closer to the surface where there is additional turbulence, both ambient and wake-generated. Notably, the gravity waves modulate the mesoscale environment by weakening and dissipating the preexisting low-level jet, which reduces hub-height wind speed and hence the simulated power output, which is confirmed by the observed supervisory control and data acquisition (SCADA) power data. Additionally, the gravity waves induce local wind direction variations correlated with fluctuations in pressure, which lead to fluctuations in the simulated power output as various turbines within the farm are subjected to waking from nearby turbines.

17 WIND ENERGY↗
NASA NTRSDate not supplied

Multiscale Simulation of Deployable Composite Structures

In this paper, a multiscale simulation method for analyzing deployable composite structures is presented. Effective shell properties of the composites are obtained based on Mechanics of Structure Genome (MSG) homogenization, and then implemented into a user-subroutine UGENS for structural simulation with shell elements in Abaqus. The column bending test(CBT) of a flat thin flexure and lenticular composite boom in a simplified deployer structure are studied for demonstration. A viscoelastic material model with direct integration is adopted in this paper. The CBT simulation shows good agreement with experiments during relaxation, while errors are observed when comparing residual deformation. It is shown that this CBT model can be calibrated to CBT test results. For the lenticular boom analysis, the complete process of flattening, coiling, stowage, deployment and recovery is simulated with the viscoelastic shell model.

Finite element analysis↗