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Results for “structured model reduction”

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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Molecular characterization of AID-mediated reduction of hepatitis B virus transcripts

Hepatitis B virus (HBV) is the major cause of liver cirrhosis and hepatocellular carcinoma. After entering a hepatocyte, HBV forms a nuclear viral episome and produces pregenomic (pg) RNA with a stem–loop structure called an epsilon, which acts to signal encapsidation. We previously demonstrated that TGF-β upregulates activation-induced cytidine deaminase (AID) expression in hepatocytes, which in turn downregulates HBV transcripts by recruiting the RNA exosome complex. The molecular mechanism underlying AID-mediated HBV RNA reduction remains largely unclear. Here we used a pgRNA reporter system having a reporter gene within pgRNA to identify sis- and trans-acting elements in AID-mediated HBV RNA reduction. We found that the epsilon RNA and C-terminus of AID are required for AID-mediated HBV RNA reduction. Importantly, this reduction was reproduced in a hydrodynamic HBV transfection mouse model. The molecular mechanism of AID-mediated HBV RNA reduction is discussed. - Highlights: • AID-mediated HBV RNA reduction both in a cell line and in a HBV mouse model. • C-terminus of P protein/AID is essential for HBV RNA reduction by AID. • Epsilon RNA structure is required for AID-mediated HBV RNA reduction.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

Dual passivation of GaAs (110) surfaces using O{sub 2}/H{sub 2}O and trimethylaluminum

The nucleation and passivation of oxide deposition was studied on defect-free GaAs (110) surfaces to understand passivation of surfaces containing only III-V heterobonds. The passivation process on GaAs (110) was studied at the atomic level using scanning tunneling microscopy while the electronic structure was determined by scanning tunneling spectroscopy (STS). The bonding of the oxidant and reductant were modeled with density functional theory. To avoid Fermi level pinning during gate oxide atomic layer deposition, a dual passivation procedure was required using both a reductant, trimethylaluminum (TMA), and an oxidant, O{sub 2} or H{sub 2}O. Dosing GaAs (110) with TMA resulted in the formation of an ordered complete monolayer of dimethylaluminum which passivates the group V dangling bonds but also forms metal-metal bonds with conduction band edge states. These edge states were suppressed by dosing the surface with oxidants O{sub 2} or H{sub 2}O which selectively react with group III-aluminum bonds. The presence of an ordered Al monolayer with a high nucleation density was indirectly confirmed by XPS and STS.

36 MATERIALS SCIENCE↗
DOE OSTI2020

The Energy Modeling Forum (EMF)-30 Study on Short-Lived Climate Forcers: Introduction and Overview

The Energy Modeling Forum (EMF)-30 inter-comparison study on short-lived climate forcers (SLCFs) focuses on black carbon (BC) and methane, two of the most important warming SLCFs. The potential impact on climate change of methane and BC-focused emission reductions in the transport and residential sectors were quantified using nine integrated assessment models and a simple climate model. These SLCF reductions reduced global temperatures by 0.18–0.26 °C in 2050 across the models for central climate parameters, with methane being the dominant contributor. The incremental impact of SLCF-focused reductions is much smaller when combined with a broader climate policy. Individual modeling groups examined the health benefits of air pollutant reductions, climate policy co-benefits, and quantified market impacts from air pollutant damages. Model structure and set-up impact on results are discussed along with areas for future work.

Smith, Steven J.↗
DOE OSTI2021

Automated prediction of lattice parameters from X-ray powder diffraction patterns

A key step in the analysis of powder X-ray diffraction (PXRD) data is the accurate determination of unit-cell lattice parameters. This step often requires significant human intervention and is a bottleneck that hinders efforts towards automated analysis. This work develops a series of one-dimensional convolutional neural networks (1D-CNNs) trained to provide lattice parameter estimates for each crystal system. A mean absolute percentage error of approximately 10% is achieved for each crystal system, which corresponds to a 100- to 1000-fold reduction in lattice parameter search space volume. The models learn from nearly one million crystal structures contained within the Inorganic Crystal Structure Database and the Cambridge Structural Database and, due to the nature of these two complimentary databases, the models generalize well across chemistries. A key component of this work is a systematic analysis of the effect of different realistic experimental non-idealities on model performance. It is found that the addition of impurity phases, baseline noise and peak broadening present the greatest challenges to learning, while zero-offset error and random intensity modulations have little effect. However, appropriate data modification schemes can be used to bolster model performance and yield reasonable predictions, even for data which simulate realistic experimental non-idealities. In order to obtain accurate results, a new approach is introduced which uses the initial machine learning estimates with existing iterative whole-pattern refinement schemes to tackle automated unit-cell solution.

42 ENGINEERING↗
NASA NTRS1982

Design considerations and experiences in the use of composite material for an aeroelastic research wing

Experiences in using composite skin material on an aeroelastic research wing used in flight flutter testing are described. Significant variations in skin shear modulus due to stress and temperature were encountered with the original fiberglass laminate skin designed to minimize wing torsional stiffness. These variations along with the sensitivity of wing torsional stiffness to the skin-to-frame attachment method complicated the structural model vibration mode predictions. A wing skin redesign with different fiber orientation and a reduction in the amount of skin-to-frame bonding resulted in more predictable modal characteristics without sacrificing design objectives. Design and modeling considerations for future applications are discussed.

Eckstrom, C. V.↗
NASA NTRS2009

Optmization and Fabrication Studies in the Development of Structurally Integrated Thermal Protection System Technology

NASA HYP M&S is pursuing the development of SITPS: 1) Working with HYP MDAO to formulate methodology to incorporate SITPS into hypersonic vehicle design trades. 2) SITPS-0 to SITPS-1 (FY10): a) Manufacturing development and weight reduction (5.8 to 3.1 lb(sub m)/sq ft); b) Structural testing to mature SITPS model. 3) SITPS-2 (FY11): a) Focus on panel closeout, panel-to-panel load transfer, and panel curvature. 4) Extend fabrication technology to include alternate cores and insulations (FY12).

Stephens, Craig A.↗
DOE OSTI2014

Warming shifts ‘worming’: effects of experimental warming on invasive earthworms in northern North America

Climate change causes species range shifts and potentially alters biological invasions. The invasion of European earthworm species across northern North America has severe impacts on native ecosystems. Given the long and cold winters in that region that to date supposedly have slowed earthworm invasion, future warming is hypothesized to accelerate earthworm invasions into yet non-invaded regions. Alternatively, warming-induced reductions in soil water content (SWC) can also decrease earthworm performance. We tested these hypotheses in a field warming experiment at two sites in Minnesota, USA by sampling earthworms in closed and open canopy in three temperature treatments in 2010 and 2012. Structural equation modeling revealed that detrimental warming effects on earthworm densities and biomass could indeed be partly explained by warming-induced reductions in SWC. The direction of warming effects depended on the current average SWC: warming had neutral to positive effects at high SWC, whereas the opposite was true at low SWC. Our results suggest that warming limits the invasion of earthworms in northern North America by causing less favorable soil abiotic conditions, unless warming is accompanied by increased and temporally even distributions of rainfall sufficient to offset greater water losses from higher evapotranspiration.

Science & Technology - Other Topics↗
DOE OSTI2018

Development and Verification of the Soil-Pile Interaction Extension for SubDyn

SubDyn is the substructure structural-dynamics module for the aero-hydro-servo-elastic tool FAST v8. SubDyn uses a finite-element model (FEM) to simulate complex multimember lattice structures connected to conventional turbines and towers, and it can make use of the Craig-Bampton model reduction. Here we describe the newly added capability to handle soil-pile stiffness and compare results for monopile and jacket-based offshore wind turbines as obtained with FAST v8, SACS, and EDP (the latter two are modeling software packages commonly used in the offshore oil and gas industry). The level of agreement in terms of modal properties and loads for the entire offshore wind turbine components is excellent, thus allowing SubDyn and FAST v8 to accurately simulate offshore wind turbines on fixed-bottom structures and accounting for the effect of soil dynamics, thus reducing risk to the project.

17 WIND ENERGY↗
NASA NTRS2020

AERoBOND Project Summary

Under NASA’s Convergent Aeronautics Solutions (CAS) project, the Adhesive-Free Bonding of Complex Composites (AERoBOND) project investigated off-stoichiometric epoxy polymers for fast, reliable assembly of epoxy matrix composite structures. The project goal was to demonstrate feasibility of the AERoBOND joining method by demonstrating mechanical properties greater than 80% of conventional co-cured materials while reducing structure weight by 1%. The project consisted of three convergent research areas: material and process development, systems analysis, and material and process modeling. Material and process development was the largest component of AERoBOND with approximately 6 FTE and 1WYE of support to formulate and characterize new resins, prepare carbon fiber prepregs, fabricate laminates, measure mechanical properties, analyze failure results, and select material and process improvements. The systems analysis activity estimated the potential reduction in part count and aircraft weight by comparing models of composite wing boxes with no fasteners (co-cured structure), fasteners in major joints (co-cured stringers), and fasteners in all joints. The materials and process modeling activity included a molecular model of the AERoBOND materials system to predict mechanical properties of resins with offset stoichiometry and a process model to predict the effect of resin formulation and processing conditions on the extent of mixing and degree of cure in a finished joint. As the number of airline passenger trips doubles in the next 20 years (IATA/Tourism Economics Air Passenger Forecasts, April 2019), the increased demand for new commercial aircraft is now the single greatest technical challenge to the airframe manufacturing industry. To meet efficiency requirements, new aircraft must be fabricated primarily from high performance structural composites, but manufacturing processes are inherently slow with the largest bottleneck attributed to assembly and installation of fasteners (NASA/TM–2019-220428). Manufactures of commercial transport aircraft are compelled to install more than 100,000 redundant fasteners into bonded joints to prevent failures due to unpredictable weak bonds. In structural adhesive bonds, the interface between adherend and adhesive is nearly two-dimensional making it susceptible to minute quantities of contamination, which can cause weak bonds. Currently, bond strength assessment is only possible through destructive testing (i.e., breaking the joint). For these reasons, regulatory organizations such as the Federal Aviation Administration (FAA) often require redundant load paths in secondary-bonded, primary-structures to alleviate concerns with bond performance. The AERoBOND process enables reflow of matrix resin during assembly to eliminate the material discontinuity at the interface, thereby eliminating the dependence of mechanical performance on interfacial adhesion. The AERoBOND joint is equivalent to the interlaminar region obtained during a co-cure process, so joint performance depends on the cohesive properties of the matrix resin. Conventional co-cured structures, although too costly and complex for large-scale manufacturing, are trusted by manufacturers and regulators, and are certified for flight with few or no redundant fasteners.Systems analysis performed on a composite wing model at the scale of a single-aisle commercial transport aircraft indicated that >20,000 redundant fasteners per wing could be eliminated by implementing the AERoBOND joining method. A total weight reduction of 15% was predicted in a wing box by eliminating fasteners and thinning components that must no longer support localized fastener loads and accommodate fastener dimensions. Interlaminar shear fracture toughness measured by the end-notched flexure test was greater than 1 kJ/m2 (nearly 140% of the co-cured benchmark property), which is greatly in excess of the project goals for mechanical properties. Testing was planned to measure interlaminar tensile fracture toughness as well as interlaminar tensile and shear strengths using the same AERoBOND configuration, but was delayed due to closure of LaRC facilities during the COVID-19 pandemic. The AERoBOND process model is partially validated and available for experimental use. It allows the user to input AERoBOND process parameters such as material composition, laminate configuration, and cure cycle to predict the final cure state of the AERoBOND joint. A preliminary, multi-scale material model was developed to predict AERoBOND joint mechanical properties (stiffness and strength) based on the cure state of the joint provided by the process model. The timing for transition of this technology within NASA is excellent as NASA initiates new enduring projects to address composites manufacturing rate challenges. AERoBOND technology is well suited to AAVP/AATT objectives for rapid manufacturing of a composite wing. A minimal effort (1 FTE/$15k procurement/0 WYE) is proposed in FY21 to continue a minor mechanical testing effort and maintain a SAA with ASX composites to develop commercial quality prepreg material. An RFI with the composites industry is suggested to quantify the technology gap between the current TRL and the TRL needed for transition to industry. A moderate effort [3-4 FTE/$150k/1 WYE (~$115k)] is proposed in FY22 for the “high rate composites manufacturing” project currently in planning. The partnership with ASX Composites will be expanded to produce material for sub-element/element-scale “panel-off” activities. Industry partnerships with airframe manufacturers is an expected component to explore damage tolerance and environmental stability. Further development of multi-scale modeling tools (process model, meso-scale model, and molecular model) is planned to enhance and deliver tools for rapid manufacturing infusion.

Frank Louis Palmieri↗
DOE OSTI2025

Many-body expansion based machine learning models for octahedral transition metal complexes

Abstract Graph-based machine learning (ML) models for material properties show great potential to accelerate virtual high-throughput screening of large chemical spaces. However, in their simplest forms, graph-based models do not include any 3D information and are unable to distinguish stereoisomers such as those arising from different orderings of ligands around a metal center in coordination complexes. In this work we present a modification to revised autocorrelation descriptors, a molecular graph featurization method, for predicting spin state dependent properties of octahedral transition metal complexes (TMCs). Inspired by analytical semi-empirical models for TMCs, the new modeling strategy is based on the many-body expansion (MBE) and allows one to tune the captured stereoisomer information by changing the truncation order of the MBE. We present the necessary modifications to include this approach in two commonly used ML methods, kernel ridge regression and feed-forward neural networks. On a test set composed of all possible isomers of binary TMCs, the best MBE models achieve mean absolute errors (MAEs) of 2.75 kcal mol −1 on spin-splitting energies and 0.26 eV on frontier orbital energy gaps, a 30%–40% reduction in error compared to models based on our previous approach. We also observe improved generalization to previously unseen ligands where the best-performing models exhibit MAEs of 4.00 kcal mol −1 (i.e. a 0.73 kcal mol −1 reduction) on the spin-splitting energies and 0.53 eV (i.e. a 0.10 eV reduction) on the frontier orbital energy gaps. Because the new approach incorporates insights from electronic structure theory, such as ligand additivity relationships, these models exhibit systematic generalization from homoleptic to heteroleptic complexes, allowing for efficient screening of TMC search spaces.

Meyer, Ralf (ORCID:0000000322360261)↗
DOE OSTI2015

The effect of large-scale model time step and multiscale coupling frequency on cloud climatology, vertical structure, and rainfall extremes in a superparameterized GCM

The effect of global climate model (GCM) time step—which also controls how frequently global and embedded cloud resolving scales are coupled—is examined in the Superparameterized Community Atmosphere Model ver 3.0. Systematic bias reductions of time-mean shortwave cloud forcing (~10 W/m 2 ) and longwave cloud forcing (~5 W/m 2 ) occur as scale coupling frequency increases, but with systematically increasing rainfall variance and extremes throughout the tropics. An overarching change in the vertical structure of deep tropical convection, favoring more bottom-heavy deep convection as a global model time step is reduced may help orchestrate these responses. The weak temperature gradient approximation is more faithfully satisfied when a high scale coupling frequency (a short global model time step) is used. These findings are distinct from the global model time step sensitivities of conventionally parameterized GCMs and have implications for understanding emergent behaviors of multiscale deep convective organization in superparameterized GCMs. Lastly, the results may also be useful for helping to tune them.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Process-Structure Linkages Using a Data Science Approach: Application to Simulated Additive Manufacturing Data

A novel data science workflow is developed and demonstrated to extract process-structure linkages (i.e., reduced-order model) for microstructure evolution problems when the final microstructure depends on (simulation or experimental) processing parameters. Our workflow consists of four main steps: data pre-processing, microstructure quantification, dimensionality reduction, and extraction/validation of process-structure linkages. These methods that can be employed within each step vary based on the type and amount of available data. In this paper, this data-driven workflow is applied to a set of synthetic additive manufacturing microstructures obtained using the Potts-kinetic Monte Carlo (kMC) approach. Additive manufacturing techniques inherently produce complex microstructures that can vary significantly with processing conditions. Using the developed workflow, a low-dimensional data-driven model was established to correlate process parameters with the predicted final microstructure. In addition, the modular workflows developed and presented in this work facilitate easy dissemination and curation by the broader community.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Machine learning coupled multi-scale modeling for redox flow batteries

The reaction distribution in macro or device-scale has been studied for redox flow batteries. The reaction distribution on electrode pore-scale structure however is not well understood, lacking especially on how the reaction distribution on the pore-scale may impact the overall performance of a flow battery. This study introduces for the first time a framework of a multi-scale model that provides understanding of the relationship between the pore-scale electrode structure reaction and the device-scale electrochemical reaction uniformity within the flow battery. A reduced order model is constructed based on 128 pore-scale simulations, which provide a quantitative relationship between the battery operation conditions (inlet velocity, current density, inlet concentration) and the surface reaction uniformity for the pore-scale sample. The multi-scale framework upscales this pore-scale surface reaction uniformity to device-scale combined uniformity. Based on the multi-scale model, a time-varying optimization of the inlet velocity is established, leading to significant reduction on pump power consumption with targeted surface reaction uniformity. The multi-scale model establishes the critical link between the micro-structure of a flow battery component and its performance at the macro-scale, therefore providing rationale for further operational or material optimization.

flow batteries, machine learning, multi-scale mode↗
DOE OSTI2018

On the Nanoscale Structure of K x Fe 2-y Ch 2 (Ch = S, Se): A Neutron Pair Distribution Function View

Comparative exploration of the nanometer-scale atomic structure of K x Fe 2-y Ch 2 (Ch = S, Se) was performed using neutron total scattering-based atomic pair distribution function (PDF) analysis of 5 K powder diffraction data in relation to physical properties. Whereas K x Fe 2-y Se 2 is a superconductor with a transition temperature of about 32 K, the isostructural sulphide analogue is not, which instead displays a spin glass semiconducting behavior at low temperatures. The PDF analysis explores phase separated and disordered structural models as candidate descriptors of the low temperature data. For both materials, the nanoscale structure is well described by the iron (Fe)-vacancy-disordered K 2 Fe 5-y Ch 5 (I4/m) model containing excess Fe. An equally good description of the data is achieved by using a phase separated model comprised of I4/m vacancy-ordered and I4/mmm components. The I4/mmm component appears as a minority phase in the structure of both K x Fe 2-y Se 2 and K x Fe 2-y S 2 , and with similar contribution, implying that the phase ratio is not a decisive factor influencing the lack of superconductivity in the latter. Comparison of structural parameters of the Fe-vacancy-disordered model indicates that the replacement of selenium (Se) by sulphur (S) results in an appreciable reduction in the Fe-Ch interatomic distances and anion heights, while simultaneously increasing the irregularity of FeCh 4 tetrahedra, suggesting the more significant influence of these factors. Finally, structural features are also compared to the non-intercalated FeSe and FeS parent phases, providing further information for the discussion about the influence of the lattice degrees of freedom on the observed properties in layered iron chalcogenides.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

The Climate Response to Emissions Reductions Due to COVID-19: Initial Results From CovidMIP

Many nations responded to the COVID-19 pandemic by restricting travel and other activities during 2020, resulting in temporarily reduced emissions of CO 2 , other greenhouse gases and ozone and aerosol precursors. We perform a coordinated Intercomparison, CovidMIP, of Earth System model simulations to assess the impact on climate of these emissions reductions. Eleven models performed multiple initial-condition ensembles to produce over 280 simulations spanning both initial condition and model structural uncertainty. We find model consensus on reduced aerosol amounts (particularly over East Asia) and associated increases in surface shortwave radiation levels. However, any impact on near-surface temperature or rainfall during 2020-2024 is extremely small and is not detectable in this initial analysis. Regional analyses on a finer scale, and closer attention to extremes (especially linked to changes in atmospheric composition and air quality) are required to test the impact of COVID-19-related emission reductions on near-term climate.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2021

The Climate Response to Emissions Reductions due to COVID-19: Initial Results from CovidMIP

Many nations responded to the COVID-19 pandemic by restricting travel and other activities during 2020, resulting in temporarily reduced emissions of CO2, other greenhouse gases and ozone and aerosol precursors. We present the initial results from a coordinated Intercomparison, CovidMIP, of Earth system model simulations which assess the impact on climate of these emissions reductions. Twelve models performed multiple initial-condition ensembles to produce over 300 simulations spanning both initial condition and model structural uncertainty. We find model consensus on reduced aerosol amounts (particularly over southern and eastern Asia) and associated increases in surface shortwave radiation levels. However, any impact on near-surface temperature or rainfall during 2020-2024 is extremely small and is not detectable in this initial analysis. Regional analyses on a finer scale, and closer attention to extremes (especially linked to changes in atmospheric composition and air quality) are required to test the impact of COVID- 19-related emission reductions on near-term climate.

Chris D. Jones↗
DOE OSTI2023

Triarylmethyl cation redox mediators enhance Li–O 2 battery discharge capacities

A major impediment to Li–O 2 battery commercialization is the low discharge capacities resulting from electronically insulating Li 2 O 2 film growth on carbon electrodes. Redox mediation offers an effective strategy to drive oxygen chemistry into solution, avoiding surface-mediated Li 2 O 2 film growth and extending discharge lifetimes. As such, the exploration of diverse redox mediator classes can aid the development of molecular design criteria. Here, in this work, we report a class of triarylmethyl cations that are effective at enhancing discharge capacities up to 35-fold. Surprisingly, we observe that redox mediators with more positive reduction potentials lead to larger discharge capacities because of their improved ability to suppress the surface-mediated reduction pathway. This result provides important structure–property relationships for future improvements in redox-mediated O 2 /Li 2 O 2 discharge capacities. Furthermore, we applied a chronopotentiometry model to investigate the zones of redox mediator standard reduction potentials and the concentrations needed to achieve efficient redox mediation at a given current density. We expect this analysis to guide future redox mediator exploration.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗