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At least 1,693 records · Page 94
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Multi-Physics Surrogate Modeling with Dimensionality Reduction

This summary provides a new algorithm to construct surrogate models for multi-physics, nonlinear, high dimensional models which typically suffer from the curse of dimensionality. To deal with the high computational cost and difficult error estimation, we employ recent advances in reduced order modeling (ROM) techniques to do an error-preserving reduction for the number of influential model parameters, followed by a conventional surrogate construction process. To implement ROM for a multiphysics model, a range finding algorithm (RFA) is used to identify a linear transformation in which the number of influential parameters can be minimized and the errors resulting from the non-influential parameters can be upper-bounded. The proposed algorithm is applied to the MAMMOTH coupled code system developed at the Idaho National Laboratory. In particular, we investigate the reducibility of Rattlesnake and BISON codes and their multi-physics coupling. The most important result of this work is that the reduction obtained with coupled physics is more significant than that with individual physics models. This summary explores the application of ROM techniques on multi-physics coupling codes system. Concise methodology is provided and selected numerical results are provided to show that coupling system provides additional reduction at interfaces. Future work could be invested to determine the optimum surrogate model format. (authors)

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Challenges in Tracking Waste Reduction Performance Improvement in Manufacturing Plants

The recently released Circularity Gap Report 2023 by Circle Economy Foundation found that the circularity score for the global economy is declining. This indicates that material extraction from virgin sources is climbing over earlier years. As per data released by US Geological Survey, material use in the US economy has increased exponentially with the expanding US economy over last century. EPA tracked municipal solid waste from 1960 to 2018 and found that 50% of the waste is destined for landfills. EPA estimates that US industry is responsible for 2.7 billion ton of solid non-hazardous waste annually in our mostly linear economy model. The circular economy framework aims at decoupling economic value generation from virgin materials extraction from nature. The linear model of material extraction and disposal at end of life is highly unsustainable. Manufacturing companies have realized this and in their commitments to sustainability are adopting ambitious waste reduction targets. Through Better Plants program US Department of Energy has established Waste Reduction Network where it is offering technical assistance to partners to achieve these ambitious waste reduction goals. Basic requirements of establishing a target include identifying a baseline, quantifying waste performance, and measuring progress over time. One problem faced by industry is the unstandardized metrics to quantify waste performance that may not be well suited to demonstrate progress. In this paper, we research methods traditionally used to measure waste performance and highlight advantages, gaps and limitations of each method. Suitability of the methods applicable to different manufacturing circumstances are also examined. Finally, we present a case study of a large manufacturer that faced inconsistencies in their tracked measurement metric. A solution was proposed to alter the methodology to enable more accurate waste performance tracking against a baseline.

Chaudhari, Subodh↗
DOE OSTI2016

Facile Reductive Silylation of UO 2 2+ to Uranium(IV) Chloride

General reductive silylation of the UO 2 2+ cation occurs readily in a one-pot, two-step stoichiometric reaction at room temperature to form uranium(IV) siloxides. Addition of two equivalents of an alkylating reagent to UO 2 X 2 (L) 2 (X=Cl, Br, I, OTf; L=triphenylphosphine oxide, 2,2'-bipyridyl) followed by two equivalents of a silyl (pseudo)halide, R 3 Si-X (R=aryl, alkyl, H; X=Cl, Br, I, OTf, SPh), cleanly affords (R 3 SiO) 2 UX 2 (L) 2 in high yields. Support is included for the key step in the process, reduction of UVI to UV. This procedure is applicable to a wide range of commercially available uranyl salts, silyl halides, and alkylating reagents. Under this protocol, one equivalent of SiCl 4 or two equivalents of Me 2 SiCl 2 results in direct conversion of the uranyl to uranium(IV) tetrachloride. Full spectroscopic and structural characterization of the siloxide products is reported.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2022

Theoretical and Experimental Investigation of Functionalized Cyanopyridines Yield an Anolyte with an Extremely Low Reduction Potential for Nonaqueous Redox Flow Batteries

Abstract Cyanopyridines and cyanophenylpyridines were investigated as anolytes for nonaqueous redox flow batteries (RFBs). The three isomers of cyanopyridine are reduced at potentials of −2.2 V or lower vs. ferrocene +/0 (Fc +/0 ), but the 3‐CNPy⋅ − radical anion forms a sigma‐dimer that is re‐oxidized at E ≈−1.1 V, which would lead to poor voltaic efficiency in a RFB. Bulk electrochemical charge‐discharge cycling of the cyanopyridines in acetonitrile and 0.50 M [NBu 4 ][PF 6 ] shows that 2‐CNPy and 4‐CNPy lose capacity quickly under these conditions, due to irreversible chemical reaction/decomposition of the radical anions. Density‐functional theory (DFT) calculations indicated that adding a phenyl group to the cyanopyridines would, for some isomers, limit dimerization and improve the stability of the radical anions, while shifting their E 1/2 only about +0.10 V relative to the parent cyanopyridines. Among the cyanophenylpyridines, 3‐CN‐6‐PhPy and 3‐CN‐4‐PhPy are the most promising as anolytes. They exhibit reversible reductions at E 1/2 =−2.19 and −2.22 V vs. ferrocene +/0 , respectively, and retain about half of their capacity after 30 bulk charge‐discharge cycles. An improved version of 3‐CN‐6‐PhPy with three methyl groups (3‐cyano‐4‐methyl‐6‐(3,5‐dimethylphenyl)pyridine) has an extremely low reduction potential of −2.50 V vs. Fc +/0 (the lowest reported for a nonaqueous RFB anolyte) and loses only 0.21 % of capacity per cycle during charge‐discharge cycling in acetonitrile.

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

Binary Atomically Dispersed Metal-Site Catalysts with Core–Shell Nanostructures for O 2 and CO 2 Reduction Reactions

Engineering atomically dispersed metal site catalysts with controlled local coordination environments and 3D nanostructures effectively improves the catalytic performance for the oxygen reduction reaction (ORR) and the carbon dioxide reduction reaction (CO 2 RR), which are critical for clean energy conversion and chemical production. Herein, an innovative approach for preparing core-shell nanostructured catalysts with different single-metal sites in the core and the shell, respectively, is developed. In particular, as the shell precursors, covalent organic polymers with a thin layered structure that is polymerized in situ and coated on a metal-doped ZIF-derived carbon core are used, followed by a controlled thermal activation. The selective combination and construction of different metal sites increase active site density in the surface layers, promote structural robustness, facilitate mass/charge transfer, and yield a possible synergy of active sites in the core and the shell. The p-FeNC(shell)@CoNC(core), consisting of a polymerized FeTPPCl-derived carbon layer (p-FeNC) on a Co-doped ZIF-derived carbon (CoNC), exhibits remarkable ORR activity and stability in acidic media along with encouraging durability in H 2 –air fuel cells. Likewise, a p-FeNC(shell)@NiNC(core) catalyst demonstrates outstanding CO 2 RR activity and stability. Hence, integrating two appropriate single-metal sites in core and shell precursors, respectively, can modulate morphological and catalytic properties for a possible synergy toward different electrocatalysis processes.

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

Accelerating uncertainty quantification in incremental dynamic analysis using dimension reduction-based surrogate modeling

We propose a surrogate modeling framework based on dimension reduction to facilitate the quantification of seismic risk of structural systems in performance-based earthquake engineering. The framework adopts incremental dynamic analysis (IDA) for addressing hazard variability, and promotes significant computational efficiency improvement for propagating epistemic uncertainties associated with the structural models. It utilizes both linear and nonlinear dimension reduction approaches, equipped with inverse mappings, to learn a functional between the input parameter space (e.g., the epistemic uncertainties of the structure) to the high-dimensional output space created through the IDA implementation across different ground motions and seismic intensity levels. Polynomial chaos expansion is adopted as the surrogate model to learn this functional in the reduced space. A nine-story steel moment-resisting frame with uncertain structural properties is used as a testbed. Furthermore, we select the seismic fragility curves as a measure of the structure’s seismic performance, since it provides an estimate of the probability of entering specified damage states for given levels of ground shaking.

42 ENGINEERING↗
DOE OSTI2016

Data-driven operator inference for nonintrusive projection-based model reduction

We presenta a nonintrusive projection-based model reduction method for full models based on time-dependent partial differential equations. Projection-based model reduction constructs the operators of a reduced model by projecting the equations of the full model onto a reduced space. Traditionally, this projection is intrusive, which means that the full-model operators are required either explicitly in an assembled form or implicitly through a routine that returns the action of the operators on a given vector; however, in many situations the full model is given as a black box that computes trajectories of the full-model states and outputs for given initial conditions and inputs, but does not provide the full-model operators. Our nonintrusive operator inference approach infers approximations of the reduced operators from the initial conditions, inputs, trajectories of the states, and outputs of the full model, without requiring the full-model operators. Our operator inference is applicable to full models that are linear in the state or have a low-order polynomial nonlinear term. The inferred operators are the solution of a least-squares problem and converge, with sufficient state trajectory data, in the Frobenius norm to the reduced operators that would be obtained via an intrusive projection of the full-model operators. Our numerical results reflect operator inference on a linear climate model and on a tubular reactor model with a polynomial nonlinear term of third order.

42 ENGINEERING↗
DOE OSTI2016

Scalable posterior approximations for large-scale Bayesian inverse problems via likelihood-informed parameter and state reduction

Two prime bottlenecks to the solution of large-scale Bayesian inverse problems are the scaling of posterior sampling algorithms to high-dimensional parameter spaces and the computational cost of forward model evaluations. However, incomplete or noisy data, the state variation and parameter dependence of the forward model, and correlations in the prior collectively provide useful structure that can be exploited for dimension reduction in this setting—both in the parameter space of the inverse problem and in the state space of the forward model. To this end, we show how to jointly construct low-dimensional subspaces of the parameter space and the state space in order to accelerate the Bayesian solution of the inverse problem. As a byproduct of state dimension reduction, we also show how to identify low-dimensional subspaces of the data in problems with high-dimensional observations. These subspaces enable approximation of the posterior as a product of two factors: (i) a projection of the posterior onto a low-dimensional parameter subspace, wherein the original likelihood is replaced by an approximation involving a reduced model; and (ii) the marginal prior distribution on the high-dimensional complement of the parameter subspace. We introduce and compare several strategies for constructing these subspaces using only a limited number of forward and adjoint model simulations. The resulting posterior approximations can rapidly be characterized using standard sampling techniques, e.g., Markov chain Monte Carlo. Two numerical examples demonstrate the accuracy and efficiency of our approach: inversion of an integral equation in atmospheric remote sensing, where the data dimension is very high; and the inference of a heterogeneous transmissivity field in a groundwater system, which involves a partial differential equation forward model with high dimensional state and parameters.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Single-atom catalysts with anionic metal centers: promising electrocatalysts for the oxygen reduction reaction and beyond

Ongoing efforts to develop single-atom catalysts (SACs) for the oxygen reduction reaction (ORR) typically focus on SACs with cationic metal centers, while SACs with anionic metal centers (anionic SACs) have been generally neglected. However, anionic SACs may offer excellent active sites for ORR, since anionic metal centers could facilitate the activation of O2 by back donating electrons to the antibonding orbitals of O2. In this work, we propose a simple guideline for designing anionic SACs: the metal centers should have larger electronegativity than the surrounding atoms in the substrate on which the metal atoms are supported. By means of density functional theory (DFT) simulations, we identified 13 anionic metal centers (Co, Ni, Cu, Ru, Rh, Pd, Ag, Re, Os, Ir, Pt, Au, and Hg) dispersed on pristine or defective antimonene substrates as new anionic SACs, among which anionic Au and Co metal centers exhibit limiting potentials comparable to, or even better than, conventional Pt-based catalysts towards ORR. We also found that anionic Os and Re metal centers on the defective antimonene can electrochemically catalyze the nitrogen reduction reaction (NRR) with a limiting potential close to that of stepped Ru(0001). Overall, our work shows promise towards the rational design of anionic SACs and their utility for applications as electrocatalysts for ORR and other important electrochemical reactions.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Electrocatalytic CO 2 Reduction to Fuels: Progress and Opportunities

The electrochemical reduction of CO 2 continues to be an appealing option for storing renewable energy in a chemical form. In this review, we assess progress in designing catalysts that convert CO 2 to high energy density products. We explain how reaction data can be reported to reflect the intrinsic properties of the catalyst. This analysis shows that limited advances have been made in improving the performance of Cu. We suggest that opportunities remain using bimetallic catalysts that are resistant to dealloying. While aqueous systems are instrumental to developing our understanding of this chemistry, gas-fed systems that operate at high current densities must be developed. Although obstacles remain for practical application of CO 2 reduction, advances in fundamental understanding made over the years give reason for optimism.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Investigating the Role of Copper Oxide in Electrochemical CO 2 Reduction in Real Time

Copper oxides have been of considerable interest as electrocatalysts for CO 2 reduction (CO2R) in aqueous electrolytes. Yet, their role as an active catalyst in reducing the required overpotential and improving the selectivity of reaction compared with that of polycrystalline copper remains controversial. In this work, we present the use of selected-ion flow tube mass spectrometry, in concert with chronopotentiometry, in situ Raman spectroscopy, and computational modeling, to investigate CO2R on C u 2O nanoneedles, Cu 2 O nanocrystals, and Cu 2 O nanoparticles. We show experimentally that the selective formation of gaseous C2 products (i.e., ethylene) in CO 2 R is preceded by the reduction of the copper oxide (Cu 2 OR) surface to metallic copper. On the basis of density functional theory modeling, CO2R products are not formed as long as Cu 2 O is present at the surface because Cu 2 OR is kinetically and energetically more favorable than CO2R.

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

Quantitative Proteomics and Quantitative PCR as Predictors of cis -1,2-Dichlorethene and Vinyl Chloride Reductive Dechlorination Rates in Bioaugmented Aquifer Microcosms

Quantitative measurement of process-specific biomarker genes of Dehalococcoides mccartyi (Dhc) supports monitoring at chlorinated ethene contaminated sites. In this study, we varied Dhc cell abundances from ~103 to 108 cells/mL in aquifer microcosms and correlated the corresponding reductive dehalogenase (RDase) gene and RDase protein abundances with measured reductive dechlorination (RD) rates of cis-1,2-dichloroethene (cDCE) and vinyl chloride (VC). An additional set of microcosms tested the RD rate-predictive power of the regression analyses. These efforts revealed (1) that targeted proteomics quantifies Dhc biomarker proteins (e.g., TceA and VcrA, OmeA) over a relevant range of Dhc cell densities, and (2) that protein and gene abundances can predict RD rates. Protein detection limits translated to a rate coefficient of 10 –4 day –1 (0.04 year –1 ) for both k cDCE and k vc , which is within the range observed at sites undergoing monitored natural attenuation (MNA) (i.e., without the implementation of enhanced bioremediation treatment). Rates predicted using a combination of quantitative biomarker gene and protein measurements generally resulted in the best match with experimentally determined rate constants. These new findings provide evidence that quantitative biomarker measurements may be useful predictors of in situ RD rates, which would constitute a major advance for the cost-effective management of contaminated sites.

42 ENGINEERING↗
DOE OSTI2024

1 H -1,2,3-Triazol-5-ylidenes as Catalytic Organic Single-Electron Reductants

Most of the known single-electron reductants are either metal based reagents, used in stoichiometric amount, or a combination of an organic species and a photocatalyst. Here we report that 1H-1,2,3-triazol-5-ylidenes act not only as stoichiometric one-electron donors, but also as catalytic organic reducing agents, without the need of a photocatalyst. As a proof of concept, we studied the reduction of quinones, which are well-known electron conveyors involved in various biological and industrial processes. Furthermore, this work also provides experimental evidence for the formation of a bis(triazolium)carbonate adduct, which acts as the resting state of the catalytic cycle and as the carbene reservoir.

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

S-OPT: A Points Selection Algorithm for Hyper-Reduction in Reduced Order Models

While projection-based reduced order models can reduce the dimension of full order solutions, the resulting reduced models may still contain terms that scale with the full order dimension. Hyper-reduction techniques are sampling-based methods that further reduce this computational complexity by approximating such terms with a much smaller dimension. The goal of this work is to introduce the points selection algorithm developed by Shin and Xiu as a hyper-reduction method. The selection algorithm was originally proposed as a stochastic collocation method for uncertainty quantification. Since the algorithm aims at maximizing a quantity $\mathcal{S}$ that measures both the column orthogonality and the determinant, we refer to the algorithm as S-OPT. Numerical examples are provided to demonstrate the performance of S-OPT and to compare its performance with a gappy proper orthogonal decomposition (POD) algorithm. Here, we found that using the S-OPT algorithm is shown to predict the full order solutions with higher accuracy than gappy POD especially when the number of sampling points is small, although we note that S-OPT shows slow asymptotic convergence with respect to the number of samples for some applications, e.g., Lagrangian hydrodynamics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Transportation Energy Futures Series: Effects of Travel Reduction and Efficient Driving on Transportation: Energy Use and Greenhouse Gas Emissions

Since the 1970s, numerous transportation strategies have been formulated to change the behavior of drivers or travelers by reducing trips, shifting travel to more efficient modes, or improving the efficiency of existing modes. This report summarizes findings documented in existing literature to identify strategies with the greatest potential impact. The estimated effects of implementing the most significant and aggressive individual driver behavior modification strategies range from less than 1% to a few percent reduction in transportation energy use and GHG emissions. Combined strategies result in reductions of 7% to 15% by 2030. Pricing, ridesharing, eco-driving, and speed limit reduction/enforcement strategies are widely judged to have the greatest estimated potential effect, but lack the widespread public acceptance needed to accomplish maximum results. This is one of a series of reports produced as a result of the Transportation Energy Futures (TEF) project, a Department of Energy-sponsored multi-agency project initiated to pinpoint underexplored strategies for abating GHGs and reducing petroleum dependence related to transportation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2013

Transportation Energy Futures Series: Effects of Travel Reduction and Efficient Driving on Transportation: Energy Use and Greenhouse Gas Emissions

Since the 1970s, numerous transportation strategies have been formulated to change the behavior of drivers or travelers by reducing trips, shifting travel to more efficient modes, or improving the efficiency of existing modes. This report summarizes findings documented in existing literature to identify strategies with the greatest potential impact. The estimated effects of implementing the most significant and aggressive individual driver behavior modification strategies range from less than 1% to a few percent reduction in transportation energy use and GHG emissions. Combined strategies result in reductions of 7% to 15% by 2030. Pricing, ridesharing, eco-driving, and speed limit reduction/enforcement strategies are widely judged to have the greatest estimated potential effect, but lack the widespread public acceptance needed to accomplish maximum results. This is one of a series of reports produced as a result of the Transportation Energy Futures (TEF) project, a Department of Energy-sponsored multi-agency project initiated to pinpoint underexplored strategies for abating GHGs and reducing petroleum dependence related to transportation.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRSDate not supplied

Development of Slat-gap and Slat-cove Filler Treatments for Noise Reduction Assessment on the High Lift Common Research Model in the NASA LaRC 14x22

Airframe noise is an important issue during the approach and landing phases of flight because engines are throttled back, and airframe sources become an equal or greater contributor to environmental noise. The leading-edge slat component of the high-lift system for transport aircraft has been identified as a prominent source of airframe noise. Noise production mechanisms and potential treatments to mitigate airframe noise have been areas of active research for nearly three decades. Two promising technologies for slat noise reduction are the slat-gap filler (SGF) and slat-cove filler (SCF). Previous work was done using a 2D section of a transport class aircraft wing to demonstrate their feasibility via benchtop and computational models. A wind tunnel test campaign was recently undertaken to test the SGF and SCF technologies in a flight-like environment by installing dynamically scaled treatments on the 10%-scale High-Lift Common Research Model (CRM-HL), which is representative of a commercial twin-aisle transport aircraft. Advancement of the SGF and SCF technologies to overcome challenges of integration with 3D airframe structures, computational simulations of the CRM-HL predicting the aerodynamic and aeroacoustic performance and structural loads, and design of wind tunnel model hardware with features accommodating noise treatments were conducted in parallel. Details of innovations in the SGF and SCF technologies for 3D airframe feasibility will be described. The loads the treatments must sustain in flight and the influence of those loads on the design is also discussed. Finally, the design of wing leading-edge assemblies to treat the 10% CRM-HL with dynamically scaled SGF and SCF components is presented. The treatments were successfully tested in 2020/2021 providing a demonstration of leading-edge noise reduction using components that would allow slat articulation.

airframe noise reduction↗
DOE OSTI2021

Power System Network Reduction for Power Hardware-in-the-Loop Simulation: Preprint

This paper proposes a single-port equivalent and a two-port equivalent to respectively reduce single-port and two-port areas in a large power network. Parameters of the reduced systems are rigorously derived, which guarantees that the electrical quantities at the port remain unchanged over the reduction, including voltage magnitude and phase, active and reactive power injections into the area to be reduced. The proposed techniques are applied to reduce a practical Maui grid, where the total numbers of buses, lines and transformers are respectively reduced from 212, 106 and 108 to 45, 30 and 13. Dynamic behaviors between the full model and the reduced model are compared in detail to illustrate the efficacy and accuracy of the proposed network reduction.

41 EE - Solar Energy Technologies Office (EE-4S)↗