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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 1,585 records · Page 88
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Ellipsometry characterization of polycrystalline ZnO layers with the modeling of carrier concentration gradient: Effects of grain boundary, humidity, and surface texture

Spectroscopic ellipsometry (SE) has been applied to study the effects of grain boundary, humidity, and surface texture on the carrier transport properties of Al-doped ZnO layers fabricated by dc and rf magnetron sputtering. In the SE analysis, the variation in the free carrier absorption toward the growth direction, induced by the ZnO grain growth on foreign substrates, has been modeled explicitly by adopting a multilayer model in which the optical carrier concentration (N{sub opt}) varies continuously with a constant optical mobility (μ{sub opt}). The effect of the grain boundary has been studied by comparing μ{sub opt} with Hall mobility (μ{sub Hall}). The change in μ{sub Hall}/μ{sub opt} indicates a sharp structural transition of the ZnO polycrystalline layer at a thickness of d ∼ 500 nm, which correlates very well with the structure confirmed by transmission electron microscopy. In particular, below the transition thickness, the formation of the high density grain boundary leads to the reduction in the μ{sub Hall}/μ{sub opt} ratio as well as N{sub opt}. As a result, we find that the thickness dependence of the carrier transport properties is almost completely governed by the grain boundary formation. On the other hand, when the ZnO layer is exposed to wet air at 85 °C, μ{sub Hall} reduces drastically with a minor variation of μ{sub opt} due to the enhanced grain boundary scattering. We have also characterized textured ZnO:Al layers prepared by HCl wet etching by SE. The analysis revealed that the near-surface carrier concentration increases slightly after the etching. We demonstrate that the SE technique can be applied to distinguish various rough textured structures (size ∼ 1 μm) of the ZnO layers prepared by the HCl etching.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

NeuDiff Agent: a governed AI workflow for single-crystal neutron crystallography

Large-scale facilities increasingly face analysis and reporting latency as a limiting step in scientific throughput, particularly for structural studies that require iterative reduction, integration, refinement and validation. To improve the time to result and analysis efficiency, NeuDiff Agent is introduced as a governed, tool-using AI workflow for TOPAZ at the Spallation Neutron Source. NeuDiff Agent takes instrument data through reduction, integration, refinement and validation to a validated crystal structure and a publication-ready CIF. NeuDiff Agent coordinates established crystallographic tools under explicit governance by restricting actions to allowlisted tools, enforcing fail-closed verification gates at key workflow boundaries, and capturing complete provenance for inspection, auditing and controlled replay. The present benchmark is limited to structural crystallography for periodic structures; magnetic structure analysis and incommensurate or superspace refinement are outside the scope of the current workflow. Performance is assessed using a fixed prompt protocol and repeated end-to-end runs with two large language model backends, with user and machine time partitioned and intervention burden and recovery behaviors quantified under gating. In a reference-case benchmark, NeuDiff Agent reduces wall time from 435 min (manual) to 86.5 ± 4.7 to 94.4 ± 3.5 min (4.6–5.0× faster) while producing a validated CIF with no checkCIF level A or B alerts. These results establish a practical route to deploy agentic AI in facility crystallography while preserving traceability and publication-facing validation requirements.

Xiao, Zhongcan [ORNL] (ORCID:0000000220761961)↗
NASA NTRS1983

Study of noise transmission through double wall aircraft windows

Analytical and experimental procedures were used to predict the noise transmitted through double wall windows into the cabin of a twin-engine G/A aircraft. The analytical model was applied to optimize cabin noise through parametric variation of the structural and acoustic parameters. The parametric study includes mass addition, increase in plexiglass thickness, decrease in window size, increase in window cavity depth, depressurization of the space between the two window plates, replacement of the air cavity with a transparent viscoelastic material, change in stiffness of the plexiglass material, and different absorptive materials for the interior walls of the cabin. It was found that increasing the exterior plexiglass thickness and/or decreasing the total window size could achieve the proper amount of noise reduction for this aircraft. The total added weight to the aircraft is then about 25 lbs.

Vaicaitis, R.↗
NASA NTRS1993

Evaluation of passive and active vibration control mechanisms in a microgravity environment

The behavior of equipment and their light secondary attachments in large space structures under orbital excitation is studied. The equipment is modeled as a shear beam and its secondary attachment is treated as a single-degree-of-freedom lumped mass system. Peak responses of the equipment and its secondary system for a variety of vibration control mechanisms are evaluated. A novel active friction control mechanism, by varying the normal force, is suggested. The device uses a magnetic field control to minimize the stick condition, thereby reducing the overall structural response. The results show that the use of the passive vibration control devices could reduce the peak equipment responses to a certain extent. However, major reduction of vibration levels could be achieved only by the use of active devices. Using active control of the interface normal force, the peak responses of the equipment and its attachment are reduced by a factor of 10 over the fixed-base equipment response.

Ellison, J.↗
DOE OSTI2025

Heterointercalation in Chevrel-Phase Sulfides: A Model Periodic Solid for the Investigation of Chain Electron Transfer

Modulation of electron density localization on periodic crystal solids through electron transfer from interstitial cations can directly influence the bonding configurations of small-molecule intermediates at the catalyst binding site. This study presents the microwave-assisted solid-state synthesis of four heterointercalant Chevrel-phase (CP) sulfides with varying metal cation intercalants with compositional and electronic structure investigations of the electron density redistribution as a result of intercalation. The heterointercalant CP sulfides, with the general formula Cu x M y Mo 6 S 8 (where M = Cr, Mn, Fe, Ni; x, y = 1.5−2.5), are presented here for the probe reaction of electrochemical CO 2 reduction. A change in product selectivity is observed toward the production of methanol at low overpotentials of −0.5 V vs reversible hydrogen electrode (RHE), as a result of the intercalant combination present within the CP interstitial cavity. Structural confirmation of all materials was examined through Rietveld refinement of the powder X-ray diffraction (PXRD) data, high-resolution transmission electron microscopy (HR-TEM), and selected-area electron diffraction (SAED). Electron transfer from the intercalated metal cations to the Mo 6 S 8 cluster was investigated via X-ray photoelectron spectroscopy (XPS) of the intercalated metal cations and the chalcogenide cluster. Electron transfer was further confirmed through X-ray absorption analysis (XAS) of the K-edges of Mo and intercalants. Intermediate studies of electrochemical reduction of formaldehyde to methanol resulted in a faradaic efficiency of ∼78% methanol production on Cu x Ni y Mo 6 S 8 . The results presented herein identify distinct principles for materials design that can be utilized in other compositional spaces within the broad families of periodic crystal solids.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Active learning of ternary alloy structures and energies

Abstract Machine learning models with uncertainty quantification have recently emerged as attractive tools to accelerate the navigation of catalyst design spaces in a data-efficient manner. Here, we combine active learning with a dropout graph convolutional network (dGCN) as a surrogate model to explore the complex materials space of high-entropy alloys (HEAs). We train the dGCN on the formation energies of disordered binary alloy structures in the Pd-Pt-Sn ternary alloy system and improve predictions on ternary structures by performing reduced optimization of the formation free energy, the target property that determines HEA stability, over ensembles of ternary structures constructed based on two coordinate systems: (a) a physics-informed ternary composition space, and (b) data-driven coordinates discovered by the Diffusion Maps manifold learning scheme. Both reduced optimization techniques improve predictions of the formation free energy in the ternary alloy space with a significantly reduced number of DFT calculations compared to a high-fidelity model. The physics-based scheme converges to the target property in a manner akin to a depth-first strategy, whereas the data-driven scheme appears more akin to a breadth-first approach. Both sampling schemes, coupled with our acquisition function, successfully exploit a database of DFT-calculated binary alloy structures and energies, augmented with a relatively small number of ternary alloy calculations, to identify stable ternary HEA compositions and structures. This generalized framework can be extended to incorporate more complex bulk and surface structural motifs, and the results demonstrate that significant dimensionality reduction is possible in thermodynamic sampling problems when suitable active learning schemes are employed.

Chemistry↗
DOE OSTI2020

Nonlinear Multiobjective MPC-Based Optimal Operation of a High Consistency Refining System in Papermaking

As one of the most important unit in the papermaking industry, the high consistency (HC) refining system is confronted with challenges such as improving pulp quality, energy saving, and emissions reduction in its operation processes. Here in this correspondence, an optimal operation of HC refining system is presented using nonlinear multiobjective model predictive control strategies that aim at set-point tracking objective of pulp quality, economic objective, and specific energy (SE) consumption objective, respectively. First, a set of input and output data at different times are employed to construct the subprocess model of the state process model for the HC refining system, and then the Wiener-type model can be obtained through combining the mechanism model of Canadian Standard Freeness and the state process model that determines their structures based on Akaike information criterion. Second, the multiobjective optimization strategy that optimizes both the set-point tracking objective of pulp quality and SE consumption is proposed simultaneously, which uses NSGA-II approach to obtain the Pareto optimal set. Furthermore, targeting at the set-point tracking objective of pulp quality, economic objective, and SE consumption objective, the sequential quadratic programming method is utilized to produce the optimal predictive controllers. In conclusion, the simulation results demonstrate that the proposed methods can make the HC refining system provide a better performance of set-point tracking of pulp quality when these predictive controllers are employed. In addition, while the optimal predictive controllers orienting with comprehensive economic objective and SE consumption objective, it has been shown that they have significantly reduced the energy consumption.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2006

Zero to Integration in Eight Months, the Dawn Ground Data System Engineering Challenge

The Dawn Project has presented the Ground Data System (GDS) with technical challenges driven by cost and schedule constraints commonly associated with National Aeronautics and Space Administration (NASA) Discovery Projects. The Dawn mission consists of a new and exciting Deep Space partnership among: the Jet Propulsion Laboratory (JPL), manages the project and is responsible for flight operation; Orbital Sciences Corporation (OSC), is the spacecraft builder and is responsible for flight system test and integration; and the University of California, at Los Angeles (UCLA), is responsible for science planning and operations. As a cost-capped mission, one of Dawn's implementation strategies is to leverage from both flight and ground heritage. OSC's ground data system is used for flight system test and integration as part of the flight heritage strategy. Mission operations, however, are to be conducted with JPL's ground system. The system engineering challenge of dealing with two heterogeneous ground systems emerged immediately. During the first technical interchange meeting between the JPL's GDS Team and OSC's Flight Software Team, August 2003, the need to integrate the ground system with the flight software was brought to the table. This need was driven by the project's commitment to enable instrument engineering model integration in a spacecraft simulator environment, for both demonstration and risk mitigation purposes, by April 2004. This paper will describe the system engineering approach that was undertaken by JPL's GDS Team in order to meet the technical challenge within a non-negotiable eight-month schedule. Key to the success was adherence to fundamental systems engineering practices: decomposition of the project request into manageable requirements; integration of multiple ground disciplines and experts into a focused team effort; definition of a structured yet flexible development process; definition of an in-process risk reduction plan; and aggregation of the intermediate products to an integrated final product. In addition, this paper will highlight the role of lessons learned from the integration experience. The lessons learned from an early GDS deployment have served as the foundation for the design and implementation of the Dawn Ground Data System.

Ground Data System (GDS)↗
DOE OSTI2017

Contextual Compression of Large-Scale Wind Turbine Array Simulations

Data sizes are becoming a critical issue particularly for HPC applications. We have developed a user-driven lossy wavelet-based storage model to facilitate the analysis and visualization of large-scale wind turbine array simulations. The model stores data as heterogeneous blocks of wavelet coefficients, providing high-fidelity access to user-defined data regions believed the most salient, while providing lower-fidelity access to less salient regions on a block-by-block basis. In practice, by retaining the wavelet coefficients as a function of feature saliency, we have seen data reductions in excess of 94 percent, while retaining lossless information in the turbine-wake regions most critical to analysis and providing enough (low-fidelity) contextual information in the upper atmosphere to track incoming coherent turbulent structures. Our contextual wavelet compression approach has allowed us to deliver interative visual analysis while providing the user control over where data loss, and thus reduction in accuracy, in the analysis occurs. We argue this reduced but contextualized representation is a valid approach and encourages contextual data management.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Evaluating Impacts of the Inflation Reduction Act and Bipartisan Infrastructure Law on the U.S. Power System

The Inflation Reduction Act of 2022 (IRA) and the Infrastructure Investment and Jobs Act of 2021, commonly referred to as the 'Bipartisan Infrastructure Law (BIL),' collectively represent the largest commitment of the U.S. Federal Government to invest in the modernization and decarbonization of the U.S. energy system. The Congressional Budget Office (CBO) estimates that total support for the broad range of climate and clean energy programs, tax credits, and other incentives authorized through the two laws will exceed $430 billion from 2022 through 2031 (CRS 2022; CBO 2021, 2022). While the climate and clean energy provisions are numerous and have the potential to impact all aspects of the U.S. energy system from fuel and electricity production to final consumption in industry, transportation, and buildings, the provisions relevant to the electricity sector - in particular the suite of tax credits for clean generation, storage, and carbon dioxide ( CO 2 ) capture and storage - are expected to be some of the most consequential in terms of emissions reduction and clean energy deployment (Larsen et al. 2022; Jenkins, Mayfield, et al. 2022; Mahajan et al. 2022; Zhao et al. 2022). In this report, we detail the methods and results of a study estimating the potential impacts of key provisions of IRA and BIL on the contiguous U.S. power sector from present day through 2030. The analysis employs an advanced power system planning model, the Regional Energy Deployment System (ReEDS), to evaluate how major provisions from both laws impact investment in and operation of utility-scale generation, storage, and transmission, and, in turn, how those changes impact power system costs, emissions, and climate and health damages. While not exhaustive in capturing every provision, the analysis estimates the possible scale of power-sector impacts that could result from the modeled provisions in IRA and BIL. The study is structured around two scenarios to evaluate the potential impacts of both laws on the power sector: 1) No New Policy: A counter-factual scenario that reflects all Federal and state policies enacted as of September 2022, with exception to IRA and BIL, and assumes load growth consistent with the Energy Information Administration's Annual Energy Outlook 2022 (AEO22) Reference case (EIA 2022a); 2) IRA-BIL: A scenario reflecting all Federal and state policies enacted as of September 2022, including key IRA and BIL provisions, most notably the investment and production tax credits for zero-carbon emitting electricity generation and storage (ITC and PTC), the tax credit for CO 2 capture and storage (45Q), and the tax credit for existing nuclear plants (described further in Section 2.3). To account for the impacts of IRA and BIL on electrification, assumes increased load growth consistent with a scaled version of the Medium Electrification scenario from the Electrification Futures Study (Mai et al. 2018). These scenarios are simulated across seven sets of assumptions with varying projected future electricity market conditions, including technology costs and performance, natural gas prices, and the degree of availability, feasibility, and cost of development of renewable resources, electricity transmission, and CO 2 pipeline, injection, and storage infrastructure. In addition, we simulate two sensitivities on the 'policy' treatment in which we vary key assumptions pertaining to the realized value of the clean electricity ITC and PTC: 1) the cost of monetization of tax credits, and 2) the level of bonus crediting realized by project developers. We demonstrate that IRA and BIL have the collective potential to drive substantial growth in clean electricity by 2030, while reducing costs for consumers, mitigating climate change, and decreasing the human health impacts of power sector emissions. However, we also demonstrate that if expected cost improvements of clean technologies are not realized and/or constraints on deployment driven by factors such as supply-chain challenges, regulatory hurdles, and the social acceptability of energy infrastructure development limit the rate of clean energy and associated infrastructure deployment (such as transmission), then the share of clean generation achieved and the associated emissions benefits realized may be substantively reduced.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

From electronic structure to model application of key reactions for gasoline/alcohol combustion: Hydrogen-atom abstractions by $CH_3\dot{O}$ radical

H-atom abstraction by methoxy radical ($CH_3\dot{O}$) plays an important role in capturing the kinetics of reactions between gasoline components and alcohols. This study focuses on determining the reaction rates and thermodynamic properties of methoxy radical reactions with five gasoline fuel components: n-heptane, iso-octane, 1-hexene, cyclopentane and toluene. Electronic structure calculations were per-formed for all the stationary points with M06-2X/6 -311 ++ g(d,p) method. G3 composite method with atomization method is used for determining Δ f H 0 of all the closed shell and radical species, using which the necessary thermodynamic data of all the species was determined. Coupled cluster theory QCISD(T)/cc-pVXZ (where X = D and T) and Møller-Plesset perturbation theory MP2/cc-pVXZ (where X = D, T and Q) were used to calculate single point energies. Subsequently, rate constants for all hydrogen atom ab-straction channels have been performed using conventional transition state theory with unsymmetric tunneling corrections. A systematic comparison of rates for abstraction from different sites within the same species and same site from different species is done in order to get insights into this reaction class. Here, the computed thermodynamic properties and rate constants were incorporated into a recent gasoline mechanism to investigate the impact of the calculations performed in this work. A shift in predicted NTC (negative temperature coefficient) behavior and significant reduction in model reactivity is observed upon incorporating the rates calculated herein.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems

In this work, we present Lift & Learn, a physics-informed method for learning low-dimensional models for large-scale dynamical systems. The method exploits knowledge of a system’s governing equations to identify a coordinate transformation in which the system dynamics have quadratic structure. This transformation is called a lifting map because it often adds auxiliary variables to the system state. The lifting map is applied to data obtained by evaluating a model for the original nonlinear system. This lifted data is projected onto its leading principal components, and low-dimensional linear and quadratic matrix operators are fit to the lifted reduced data using a least-squares operator inference procedure. Analysis of our method shows that the Lift & Learn models are able to capture the system physics in the lifted coordinates at least as accurately as traditional intrusive model reduction approaches. This preservation of system physics makes the Lift & Learn models robust to changes in inputs. Numerical experiments on the FitzHugh–Nagumo neuron activation model and the compressible Euler equations demonstrate the generalizability of our model.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Genetic algorithms for computational materials discovery accelerated by machine learning

Materials discovery is increasingly being impelled by machine learning methods that rely on pre-existing datasets. Where datasets are lacking, unbiased data generation can be achieved with genetic algorithms. Here a machine learning model is trained on-the-fly as a computationally inexpensive energy predictor before analyzing how to augment convergence in genetic algorithm-based approaches by using the model as a surrogate. This leads to a machine learning accelerated genetic algorithm combining robust qualities of the genetic algorithm with rapid machine learning. The approach is used to search for stable, compositionally variant, geometrically similar nanoparticle alloys to illustrate its capability for accelerated materials discovery, e.g., nanoalloy catalysts. The machine learning accelerated approach, in this case, yields a 50-fold reduction in the number of required energy calculations compared to a traditional “brute force” genetic algorithm. This makes searching through the space of all homotops and compositions of a binary alloy particle in a given structure feasible, using density functional theory calculations.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Experimental studies in magnetically induced transverse force-frequency effect in thin quartz microresonators

In this work, the transverse force-frequency sensitivity of magnetostrictive Metglas{sup ®} (Fe{sub 85}B{sub 5}Si{sub 10}) thin film coated AT-cut thickness shear mode quartz thin plate microresonator (500 μm × 500 μm × 19 μm) is experimentally measured and modeled in Lagrangian formulation by coupling magnetostrictive deformation equations with the basic plate equations from the theory of small deformation. The quartz plate resonator is fabricated by micromachining techniques and released into fixed-free structure using focused ion beam milling. Application of a magnetic field results in the out-of-plane bending of the structure due to elastic coupling between the magnetostrictive Metglas{sup ®} and quartz resonator layers. As a result of the transverse loading and out-of-plane bending, the admittance characteristics of the resonator shifts, and these shifts are recorded in real time utilizing a network analyzer. The sensitivity is experimentally measured to be 162.3 mdeg/Oe for phase, corresponding to a frequency sensitivity of Δf/H = 11 Hz/Oe. The equivalent force-frequency sensitivity can then be calculated as 2.36 μN/Hz using the developed model. The coupled domain analysis fits well with the experimental data. Further reduction of quartz thickness and optimization of the thickness ratio of the magnetostrictive to quartz layers offers the possibility of exploiting the stress sensitivity of plate microresonators as sensitive magnetic field sensors capable of low nanoTesla to picoTesla level magnetic flux densities.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Metal–ceramic composite structures for fabrication of high power density plasmonic devices

The recent decade brought many advances to plasmonics, but high power density plasmonic antennas designed to behave as heaters or operate in high temperature environments are still facing material stability challenges preventing their ultimate use. Gold has been the optimal choice among plasmonic materials but experiences morphology changes at temperature that result in device efficiency reduction and failure. Bulk titanium nitride has been explored as a solution but has deal-breaking tradeoffs in device quality factor. In this paper, we explore via proof-of-concept the use of a metal–ceramic composite structure to determine whether a bulk Au nanorod can provide strong plasmonic resonances while coated with an ultrathin conformal layer of titanium nitride or silica to provide morphological stability and sufficient plasmonic activity without excessive resonance quality degradation. We show SEM-level morphological stability for temperatures up to 500 °C with coatings below 4 nm. Computer modeling suggests the ultrathin titanium nitride has detrimental effects on the strong plasmonic resonances of a Au nanorod. Here, we then looked into other possible coatings for solutions to stabilize high power density plasmonic antennas including plasmonic oxides, metal adhesion layers, and silica, the latter appearing to be the best option while lowering the overall peak electric field intensity, the silica increases the electric field intensity at its boundary.

36 MATERIALS SCIENCE↗
NASA NTRS1977

Analytical and numerical investigation of structural response of compliant wall materials

Surface motion of compliant walls in drag reduction experiments was analyzed. The spectrum of surface motion indicates that membranes over deep cavities respond at low frequencies and large wavelengths. The membrane over a deep cavity is therefore found not to yield the desired reponse predicted by the postulated mechanism. The membrane over a thin air gap is found to act as a wavelength chopper, and analysis of the nonlinear response of the compliant surface indicates its possible suitability for compliant wall experiments. Periodic structures are found to lock in the desired wavelengths of motion. Laminated structures are found to be very ineffective as compliant models, except when there is no bonding between the membrane and the backing. Computer programs developed for these analyses are documented.

Goglia, G. L.↗
NASA NTRS1988

Effects of state recovery on creep buckling under variable loading

Structural alloys embody internal mechanisms that allow recovery of state with varying stress and elevated temperature, i.e., they can return to a softer state following periods of hardening. Such material behavior is known to strongly influence structural response under some important thermomechanical loadings, for example, that involving thermal ratchetting. The influence of dynamic and thermal recovery on the creep buckling of a column under variable loading is investigated. The column is taken as the idealized (Shanley) sandwich column. The constitutive model, unlike the commonly employed Norton creep model, incorporates a representation of both dynamic and thermal (state) recovery. The material parameters of the constitutive model are chosen to characterize Narloy Z, a representative copper alloy used in thrust nozzle liners of reusable rocket engines. Variable loading histories include rapid cyclic unloading/reloading sequences and intermittent reductions of load for extended periods of time; these are superimposed on a constant load. The calculated results show that state recovery significantly affects creep buckling under variable loading.

Robinson, D. N.↗
NASA NTRS1989

Precision pointing compensation for DSN antennas with optical distance measuring sensors

The pointing control loops of Deep Space Network (DSN) antennas do not account for unmodeled deflections of the primary and secondary reflectors. As a result, structural distortions due to unpredictable environmental loads can result in uncompensated boresight shifts which degrade pointing accuracy. The design proposed here can provide real-time bias commands to the pointing control system to compensate for environmental effects on pointing performance. The bias commands can be computed in real time from optically measured deflections at a number of points on the primary and secondary reflectors. Computer simulations with a reduced-order finite-element model of a DSN antenna validate the concept and lead to a proposed design by which a ten-to-one reduction in pointing uncertainty can be achieved under nominal uncertainty conditions.

Scheid, R. E.↗