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Results for “Dimensionality 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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

AGC-2 Specimen Position Adjustment

This engineering calculations and analysis report (ECAR) calculates the Advanced Graphite Creep (AGC)-2 specimen position adjustments that result from (1) as-built dimension corrections of the Advanced Test Reactor (ATR) reactor, experiment, and specimens, (2) thermal growth of the capsule wall and graphite components, and (3) variable specimen elevations as the specimen stacks shrink due to radiation -induced shrinkage and axial load- induced creep. The specimen elevations listed in the experiment assembly documents do not accurately represent the actual specimen positions in relation to the reactor core mid-plane. Prior experiment insertion difficulties have shown that the distance from the reactor top head closure plate to the core is greater than the nominal elevations shown on the ATR facility drawings. Drawing 600001 lists the measured difference between the closure plate and the gearbox support beam as 0.313 in.ches greater than the facility drawings. Thus, the height of the specimens in relation to the core mid-plane is 0.313 in.ches higher than the nominal dimensions. AlsoIn addition, the overall dimension of the experiment is 0.084 in. shorter than the nominal drawing dimension on Drawing 601266 [1]. As a further adjustment, the average weld shrinkage of the three capsule welds is 0.035 each, which affects the as-built position of the lower isolation weld plate (i.e., Part #601266-9) onto which all graphite components are stacked. In addition to the as-built adjustments mentioned above, the specimens also are subject to a stack up error, which is due to their the thickness difference from nominal. This effect is most apparent in the center S-7 stack which that has a large number of specimens (i.e., 170 specimens). Per post-irradiation examination (PIE) measurements, the average S-7 specimen is about 0.0008 in. smaller than nominal. Even though this difference is small, when multiplied by the number specimens, the top specimen experiences an elevation reduction of 0.14 inches. Although dimensional changes were less pronounced in S-1 through S-6, specimen elevations were adjusted based on pre-irradiation length measurements.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Operator inference for non-intrusive model reduction of systems with non-polynomial nonlinear terms

Here in this work we present a non-intrusive model reduction method to learn low-dimensional models of dynamical systems with non-polynomial nonlinear terms that are spatially local and that are given in analytic form. In contrast to state-of-the-art model reduction methods that are intrusive and thus require full knowledge of the governing equations and the operators of a full model of the discretized dynamical system, the proposed approach requires only the non-polynomial terms in analytic form and learns the rest of the dynamics from snapshots computed with a potentially black-box full-model solver. The proposed method learns operators for the linear and polynomially nonlinear dynamics via a least-squares problem, where the given non-polynomial terms are incorporated on the right-hand side. The least-squares problem is linear and thus can be solved efficiently in practice. The proposed method is demonstrated on three problems governed by partial differential equations, namely the diffusion–reaction Chafee–Infante model, a tubular reactor model for reactive flows, and a batch-chromatography model that describes a chemical separation process. The numerical results provide evidence that the proposed approach learns reduced models that achieve comparable accuracy as models constructed with state-of-the-art intrusive model reduction methods that require full knowledge of the governing equations.

42 ENGINEERING↗
DOE OSTI2026

Computational discovery of a tetragonal MBene phase: diamond-shaped boron lattices, viable MAB precursors, and selective CO 2 reduction

MBenes are a class of two-dimensional transition-metal borides derived from layered MAB phases. Here, we use first-principles calculations to predict a new tetragonal MB (1 : 1) MBene phase featuring a diamond-shaped B–B lattice and systematically assess its phase stability, synthetic accessibility, and catalytic performance towards the CO 2 reduction reaction (CO 2 RR). Four tetragonal configurations are benchmarked against known hexagonal and orthorhombic phases, and a multi-tier screening identifies six robust members (CrB, FeB, MoB, WB, IrB, and PtB). Exploration of the I4/mmm MAB space reveals 11 viable precursors, including the experimentally synthesized Ir 2 ZnB 2 , which validates our screening approach. CO 2 RR free-energy diagrams show that CrB favors CH 3 OH while FeB, MoB, WB, IrB, and PtB preferentially yield HCOOH. The corresponding limiting potentials are competitive with, and in some cases superior to, those reported for state-of-the-art orthorhombic and hexagonal MBenes. These findings expand the MBene landscape by establishing a tetragonal phase with structural, synthetic, and catalytic promise.

Lu, Linguo [University of Puerto Rico, San Juan, P↗
DOE OSTI2015

Operational constraints and strategies for systems to effect the sustainable, solar-driven reduction of atmospheric CO 2

The operational constraints for a 6-electron/6-proton CO 2 reduction system that operates at the concentration of CO 2 in the current atmosphere (p CO 2 = 400 ppm) have been evaluated on a variety of scale lengths that span from laboratory scale to global scale. Due to the low concentration of CO 2 in the atmosphere, limitations due to mass transport of CO 2 from the tropopause have been evaluated through five different regions, each with different characteristic length scales: the troposphere; the atmospheric boundary layer (ABL); the canopy layer; a membrane layer; and an aqueous electrolyte layer. The resulting CO 2 conductances, and associated physical transport limitations, will set the ultimate limit on the efficiency and areal requirements of a sustainable solar-driven CO 2 reduction system regardless of the activity or selectivity of catalysts for reduction of CO 2 at the molecular level. At the electrolyte/electrode interface, the steady-state limiting current density and the concomitant voltage loss associated with the CO 2 concentration overpotential in a one-dimensional solar-driven CO 2 reduction cell have been assessed quantitatively using a mathematical model that accounts for diffusion, migration and convective transport, as well as for bulk electrochemical reactions in the electrolyte. At p CO 2 = 400 ppm, the low diffusion coefficient combined with the low solubility of CO 2 in aqueous solutions constrains the steady-state limiting current density to <0.1 mA cm -2 in a typical electrochemical cell with natural convection and employing electrolytes with a range of pH values. Hence, in such a system, the CO 2 capture area must be 100- to 1000-fold larger than the solar photon collection area to enable a >10% efficient solar-driven CO 2 reduction system (based on the solar collection area). This flux limitation is consistent with estimates of oceanic CO 2 uptake fluxes that have been developed in conjunction with carbon-cycle analyses for use in coupled atmosphere/ocean general circulation models. Two strategies to improve the feasibility of obtaining efficient and sustainable CO 2 transport to a cathode surface at p CO 2 = 400 ppm are described and modeled quantitatively. The first strategy employs yet unknown catalysts, analogous to carbonic anhydrases, that dramatically accelerate the chemically enhanced CO 2 transport in the aqueous electrolyte layer by enhancing the acid–base reactions in a bicarbonate buffer system. The rapid interconversion from bicarbonate to CO 2 in the presence of such catalysts near the cathode surface would in principle yield significant increases in the steady-state limiting current density and allow for >10% solar-fuel operation at the cell level. The second strategy employs a thin-layer cell architecture to improve the diffusive transport of CO 2 by use of an ultrathin polymeric membrane electrolyte. Rapid equilibration of CO 2 at the gas/electrolyte interface, and significantly enhanced diffusive fluxes of CO 2 in electrolytes, are required to increase the steady-state limiting current density of such a system. This latter approach however only is feasible for gaseous products, because liquid products would coat the electrode and therefore thicken the hydrodynamic boundary layer and accordingly reduce the diffusive CO 2 flux to the electrode surface. Regardless of whether the limitations due to mass transport to the electrode surface are overcome on the laboratory scale, at global scales the ultimate CO 2 flux limitations will be dictated by mass transport considerations related to transport of atmospheric CO 2 to the boundary plane of the solar-driven reactor system. The transport of CO 2 across the troposphere/ABL interface, the ABL/canopy layer interface, and the canopy layer/electrolyte interface have therefore been assessed in this work, to provide upper bounds on the ultimate limits for the solar-to-fuel (STF) conversion efficiency for systems that are intended to effect the reduction of atmospheric CO 2 in a sustainable fashion at global scale.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Transition metal-embedded two-dimensional C 3 N as a highly active electrocatalyst for oxygen evolution and reduction reactions

Searching for highly efficient, stable and cost-effective catalysts for oxygen evolution reaction (OER) and oxygen reduction reaction (ORR) is vital to resolve energy security and environmental problems. Herein, by means of computational screening based on the density functional theory (DFT), we studied a wide range of transition metal (TM) atoms embedded into the double carbon vacancy of C 3 N monolayers (V CC ), denoted as TM-V CC (TM = Mn, Fe, Co, Ni, Cu, Ru, Rh, Pd, Ir and Pt), as efficient single-atom catalysts (SACs) for OER and ORR. The calculated results showed that all the considered TM-V CC composites exhibited a metallic feature that ensured efficient charge transfer during reactions. The interaction strength between intermediates and TM-V CC has direct correlation with the d-band center of TM, which can be tuned by changing the TM atoms with different numbers of d electrons. The best catalyst for OER was Rh-V CC with an overpotential (η OER ) of 0.35 V, followed by Co-V CC (0.43 V). For the ORR process, Rh-V CC exhibited the lowest ORR overpotential (η ORR ) of 0.27 V, followed by Co-V CC (0.42 V). The results suggested that the performances of the newly predicted Rh-V CC and Co-V CC SACs are comparable to those of the noble-metal benchmark catalysts for OER and ORR. Ab initio molecular dynamic simulations indicated that Rh-V CC and Co-V CC SACs could remain stable under 300 K and possessed high energy barriers to prevent the isolated Rh and Co atoms from clustering. Here our results highlight a new family of efficient and stable catalysts with a single atom anchored on carbon nitride-based materials, which provides a useful guideline for catalyst design and practical applications.

36 MATERIALS SCIENCE↗
DOE OSTI2012

(3 + 1)-dimensional cylindrical Korteweg-de Vries equation for nonextensive dust acoustic waves: Symbolic computation and exact solutions

By combining the effects of bounded cylindrical geometry, azimuthal and axial perturbations, the nonlinear dust acoustic waves (DAWs) in an unmagnetized plasma consisting of negatively charged dust grains, nonextensive ions, and nonextensive electrons are studied in this paper. Using the reductive perturbation method, a (3 + 1)-dimensional variable-coefficient cylindrical Korteweg-de Vries (KdV) equation describing the nonlinear propagation of DAWs is derived. Via the homogeneous balance principle, improved F-expansion technique and symbolic computation, the exact traveling and solitary wave solutions of the KdV equation are presented in terms of Jacobi elliptic functions. Moreover, the effects of the plasma parameters on the solitary wave structures are discussed in detail. The obtained results could help in providing a good fit between theoretical analysis and real applications in space physics and future laboratory plasma experiments where long-range interactions are present.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

On the reduction criterion for random quantum states

In this paper, we study the reduction criterion for detecting entanglement of large dimensional bipartite quantum systems. We first obtain an explicit formula for the moments of a random quantum state to which the reduction criterion has been applied. We show that the empirical eigenvalue distribution of this random matrix converges strongly to a limit that we compute, in three different asymptotic regimes. We then employ tools from free probability theory to study the asymptotic positivity of the reduction operators. Finally, we compare the reduction criterion with other entanglement criteria, via thresholds.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Higgs portals for thermal Dark Matter. EFT perspectives and the NMSSM

We analyze a low energy effective model of Dark Matter in which the thermal relic density is provided by a singlet Majorana fermion which interacts with the Higgs fields via higher dimensional operators. Direct detection signatures may be reduced if blind spot solutions exist, which naturally appear in models with extended Higgs sectors. Explicit mass terms for the Majorana fermion can be forbidden by a $Z_3$ symmetry, which in addition leads to a reduction of the number of higher dimensional operators. Moreover, a weak scale mass for the Majorana fermion is naturally obtained from the vacuum expectation value of a scalar singlet field. The proper relic density may be obtained by the $s$-channel interchange of Higgs and gauge bosons, with the longitudinal mode of the $Z$ boson (the neutral Goldstone mode) playing a relevant role in the annihilation process. This model shares many properties with the Next-to-Minimal Supersymmetric extension of the Standard Model (NMSSM) with light singlinos and heavy scalar and gauge superpartners. In order to test the validity of the low energy effective field theory, we compare its predictions with those of the ultraviolet complete NMSSM. Extending our framework to include $Z_3$ neutral Majorana fermions, analogous to the bino in the NMSSM, we find the appearance of a new bino-singlino well tempered Dark Matter region.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

Two-Dimensional N,S-Codoped Carbon/Co 9 S 8 Catalysts Derived from Co(OH) 2 Nanosheets for Oxygen Reduction Reaction

Investigation of highly active and cost-efficient electrocatalysts for oxygen reduction reaction is of great importance in a wide range of clean energy devices, including fuel cells and metal-air batteries. Herein, the simultaneous formation of Co9S8 and N,S-codoped carbon was achieved in a dual templates system. First, Co(OH)2 nanosheets and tetraethyl orthosilicate were utilized to direct the formation of two-dimensional carbon precursors, which were then dispersed into thiourea solution. After subsequent pyrolysis and templates removal, N/S-codoped porous carbon sheets confined Co9S8 catalysts (Co9S8/NSC) were obtained. Owing to the morphological and compositional advantages as well as the synergistic effects, the resultant Co9S8/NSC catalysts with modified doping level and pyrolysis degree exhibit superior ORR catalytic activity and long-term stability compared with the state-of-the-art Pt/C catalyst in alkaline media. Remarkably, the as-prepared carbon composites also reveal exceptional tolerance of methanol, indicating their potential applications in fuel cells.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Using a 3D Porous Flow-Through Electrode Geometry for High-Rate Electrochemical Reduction of CO 2 to CO in Ionic Liquid

Three-dimensional porous flow-through electrodes promise the realization of higher current densities in the electrochemical carbon dioxide reduction reaction (CO 2 RR) by overcoming mass-transport limitations associated with the diffusion of dissolved CO 2 in the bulk electrolyte. Using an ionic-liquid-based electrolyte, [EMIM]BF 4 , offers the additional benefit of higher CO 2 solubility compared to water-based electrolytes. Here, we quantitatively evaluated the effects of flow-driven reactant transport on electrode activity and selectivity for the electrochemical CO 2 RR in [EMIM]BF4. High-surface-area porous flow-through electrodes were fabricated by wet-chemical deposition of Ag nanoflowers on commercial macroporous Al foams. At the highest electrolyte flow-through rate of 100 mL min –1 , we observed a 70-fold increase in partial CO current density, and a 7-fold increase in the Faradaic efficiency toward CO, from 10% without flow to 75% with flow. At the same time, the selectivity changed from oxalate as the main product without flow to CO in the electrolyte flow-through configuration. These results demonstrate that the flow-through approach provides a promising path forward to control selectivity and to overcome mass-transport limitations of the electrochemical CO 2 RR.

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

An integrated manifold learning approach for high-dimensional data feature extractions and its applications to online process monitoring of additive manufacturing

As an effective dimension reduction and feature extraction technique, manifold learning has been successfully applied to high-dimensional data analysis. With the rapid development of sensor technology, a large amount of high-dimensional data such as image streams can be easily available. Thus, a promising application of manifold learning is in the field of sensor signal analysis, particular for the applications of online process monitoring and control using high-dimensional data. The objective of this study is to develop a manifold learning-based feature extraction method for process monitoring of Additive Manufacturing (AM) using online sensor data. Due to the non-parametric nature of most existing manifold learning methods, their performance in terms of computational efficiency, as well as noise resistance has yet to be improved. To address this issue, this study proposes an integrated manifold learning approach termed multi-kernel metric learning embedded isometric feature mapping (MKML-ISOMAP) for dimension reduction and feature extraction of online high-dimensional sensor data such as images. Based on the extracted features with the utilization of supervised classification and regression methods, an online process monitoring methodology for AM is implemented to identify the actual process quality status. Finally, in the numerical simulation and real-world case studies, the proposed method demonstrates excellent performance in both prediction accuracy and computational efficiency.

36 MATERIALS SCIENCE↗
NASA NTRS1997

Controls/CFD Interdisciplinary Research Software Generates Low-Order Linear Models for Control Design From Steady-State CFD Results

The NASA Lewis Research Center is developing analytical methods and software tools to create a bridge between the controls and computational fluid dynamics (CFD) disciplines. Traditionally, control design engineers have used coarse nonlinear simulations to generate information for the design of new propulsion system controls. However, such traditional methods are not adequate for modeling the propulsion systems of complex, high-speed vehicles like the High Speed Civil Transport. To properly model the relevant flow physics of high-speed propulsion systems, one must use simulations based on CFD methods. Such CFD simulations have become useful tools for engineers that are designing propulsion system components. The analysis techniques and software being developed as part of this effort are an attempt to evolve CFD into a useful tool for control design as well. One major aspect of this research is the generation of linear models from steady-state CFD results. CFD simulations, often used during the design of high-speed inlets, yield high resolution operating point data. Under a NASA grant, the University of Akron has developed analytical techniques and software tools that use these data to generate linear models for control design. The resulting linear models have the same number of states as the original CFD simulation, so they are still very large and computationally cumbersome. Model reduction techniques have been successfully applied to reduce these large linear models by several orders of magnitude without significantly changing the dynamic response. The result is an accurate, easy to use, low-order linear model that takes less time to generate than those generated by traditional means. The development of methods for generating low-order linear models from steady-state CFD is most complete at the one-dimensional level, where software is available to generate models with different kinds of input and output variables. One-dimensional methods have been extended somewhat so that linear models can also be generated from two- and three-dimensional steady-state results. Standard techniques are adequate for reducing the order of one-dimensional CFD-based linear models. However, reduction of linear models based on two- and three-dimensional CFD results is complicated by very sparse, ill-conditioned matrices. Some novel approaches are being investigated to solve this problem.

Melcher, Kevin J.↗
DOE OSTI2023

Porosity evolution in proton irradiated microfine-grained POCO graphite

This work investigated the porosity evolution of POCO ZXF-5Q graphite that has been irradiated by 340 kW, 120 GeV protons inside NT02 target system in Fermilab's NuMI beamline. This POCO graphite has undergone direct bulk dimensional swelling at low dose irradiation and its local microstructural change is still not well-understood during this process. In this work, the (sub-) micrometre scale porosity from six locations across proton beam fluence and temperature gradients have been studied using focused ion beam-scanning electron microscopy (FIB-SEM) tomography. Here, a deep learning-based tomographic image segmentation technique has been established and implemented for porosity segmentation and quantification. It has been found that there is a decrease in the total volumetric percentage of the porosity at proton beam centre (~ 8 – 8.4 vol.%), by comparing to un-irradiated POCO (~ 12 – 13vol.%) and to beam 2σ and 5σ radii (~ 12vol.%). This decrease in porosity volume percentage was found to be caused by the reduction in pores with volumes > 0.1 μm 3 induced by material bulk dimensional swelling at proton beam centre area. The porosity reduction in relation to dimensional change and irradiation creep was discussed among with other contributing factors, and further investigations through well-controlled irradiation experiment are still needed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

Numerical Simulation and Model Analyses of In-situ Biogeochemical Remediation of Chromium at the LANL - 18306

Environmental management problems associated with contamination of groundwater resources with heavy metals (such as chromium) typically require complex biogeochemical model simulations and analysis of in situ remediation. Frequently, the in situ remediation is the only viable treatment option. In these cases, a three-dimensional reactive transport model of relevant biogeochemical processes is invaluable. Recently, we have developed a novel unique biogeochemical simulator called CHROTRAN (http://chrotran.lanl.gov; Hansen et al. 2017, Vesselinov et al. 2018a) capable to represent the full three-dimensional dynamics of processes associated with in situ biogeochemical remediation of heavy metals in heterogeneous aquifers. CHROTRAN accounts for spatial and temporal transients of (1) the heavy metal to be remediated, (2) introduced amendments (e.g., through well injection), and (3) biomass growth and decay. The introduced amendments can include (1) an electron donor (e.g., molasses), (2) a nontoxic-conservative bio-inhibitor (e.g., ethanol), and (3) a biocide (e.g., dithionite). In addition, direct abiotic reduction by donor-metal interaction as well as donor-driven biomass growth and bio-reduction are modeled in CHROTRAN. Other critical processes, such as donor sorption, bio-fouling and biomass death are also modeled. CHROTRAN can simulate the groundwater flow dynamics introduced due a series of injection and extraction wells and account for reduction of aquifer permeability near the wells due bio clogging. CHROTRAN is based on the existing PFLOTRAN (http://pflotran.org; Lichtner et al, 2017a) code framework, taking advantage of the reaction interface allowing for implementation of complex model features and leverage other facets of PFLOTRAN, such as multi-component geochemistry and high-performance computing. Here, we present numerical simulations and model analyses related to the Los Alamos National Laboratory (LANL) chromium site. The analyses are performed by coupling of CHROTRAN with MADS (Model Analysis and Decision Support; http://mads.lanl.gov; Vesselinov et al., 2018b). We present analyses related to uncertainty quantification of model predictions, sensitivity of model parameters and remedial decision support. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

Influence of Contrast Agent Dilution on Ballon Deflation Time and Visibility During Tracheal Balloon Dilation: A 3D Printed Phantom Study

PurposeTo determine the effect of contrast medium dilution during tracheal balloon dilation on balloon deflation time and visibility using a 3-dimensional (3D) printed airway phantom.Materials and MethodsA comparison study to investigate balloon deflation times and image quality was performed using two contrast agents with different viscosities, i.e., iohexol and ioxithalamate, and six contrast dilutions with a 3D printed airway phantom.ResultsCompared to 1:0 concentration, 3:1, 2:1, 1:1, 1:2, and 1:3, contrast/saline ratios resulted in a 46% (56.2 s), 59.8% (73.1 s), 74.9% (91.6 s), 81.7% (99.8 s), and 83.5% (102 s) reduction for iohexol, respectively, and a 51.8% (54.7 s), 63.8% (67.6 s), 74.7% (79.2 s), 80.5% (85.3 s), and 82.4% (87.4 s) reduction for ioxithalamate, respectively, in the mean balloon deflation time, although at the expense of decreased balloon opacity (3.5, 6.9, 11.1, 12.4, and 13.9%, for iohexol, respectively, and 3.2, 6, 9.6, 10.8, and 12.4%, for ioxithalamate, respectively).ConclusionsUse of a lower viscosity contrast agent and higher contrast dilution is considered to be able to reduce balloon deflation times and then simultaneously decrease visualization of balloons. The rapid balloon deflation time is likely to improve the safe performance of interventional procedures.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

A deep learning enabler for nonintrusive reduced order modeling of fluid flows

Here in this paper, we introduce a modular deep neural network (DNN) framework for data-driven reduced order modeling of dynamical systems relevant to fluid flows. We propose various DNN architectures which numerically predict evolution of dynamical systems by learning from either using discrete state or slope information of the system. Our approach has been demonstrated using both residual formula and backward difference scheme formulas. However, it can be easily generalized into many different numerical schemes as well. We give a demonstration of our framework for three examples: (i) Kraichnan-Orszag system, an illustrative coupled nonlinear ordinary differential equation, (ii) Lorenz system exhibiting chaotic behavior, and (iii) a nonintrusive model order reduction framework for the two-dimensional Boussinesq equations with a differentially heated cavity flow setup at various Rayleigh numbers. Using only snapshots of state variables at discrete time instances, our data-driven approach can be considered truly nonintrusive since any prior information about the underlying governing equations is not required for generating the reduced order model. Our a posteriori analysis shows that the proposed data-driven approach is remarkably accurate and can be used as a robust predictive tool for nonintrusive model order reduction of complex fluid flows.

42 ENGINEERING↗