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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A Simplified Approach Based on Cellular Automata for Describing Direct Reduced Iron Production in Different Reducing Conditions

A quick computation approach based on cellular automata is developed and implemented to describe the reduction of iron ore pellets by a mixture of reducing agents featured by different H 2 /CO ratios. The evolution of oxygen concentration inside the pellet is followed from the beginning to the end of contact between reducing agent and pellet. The variation of thermal state of pellets and gas mixture is computed based on their initial temperature, considering the heat involved and the convective heat exchange between pellet and gas mixture. The use of cellular automata and finite‐difference method to solve the diffusion equation point out the absence of any diffusion coefficient value, allowing to make the model fit the experimental trial, because the problem is that it is not ruled just by diffusion but also by the concentration variation of reducing agent inside the pellet due to porosity increasing during reduction. The updating of the reducing agents concentration implies a sharp decrease of oxygen concentration that the cellular automata model considers. The developed model is able to provide the in‐line control of reduction process and could be used to adjust the chemical concentration and temperature of injected reducing agents.

Metallurgy & Metallurgical Engineering↗
DOE OSTI2018

A Study of the Trade-off Between Reducing Precision and Reducing Resolution for Data Analysis and Visualization

There presently exist two dominant strategies to reduce data sizes in analysis and visualization: reducing the precision of the data, e.g., through quantization, or reducing its resolution, e.g., by subsampling. Both have advantages and disadvantages and both face fundamental limits at which the reduced information ceases to be useful. The paper explores the additional gains that could be achieved by combining both strategies. In particular, we present a common framework that allows us to study the trade-off in reducing precision and/or resolution in a principled manner. We represent data reduction schemes as progressive streams of bits and study how various bit orderings such as by resolution, by precision, etc., impact the resulting approximation error across a variety of data sets as well as analysis tasks. Additionally, we compute streams that are optimized for different tasks to serve as lower bounds on the achievable error. Scientific data management systems can use the results proposed in this paper as guidance on how to store and stream data to make efficient use of the limited storage and bandwidth in practice.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

ON THE PROPER USE OF THE REDUCED SPEED OF LIGHT APPROXIMATION

I show that the reduced speed of light (RSL) approximation, when used properly (i.e., as originally designed—only for local sources but not for the cosmic background), remains a highly accurate numerical method for modeling cosmic reionization. Simulated ionization and star formation histories from the “Cosmic Reionization on Computers” project are insensitive to the adopted value of the RSL for as long as that value does not fall below about 10% of the true speed of light. A recent claim of the failure of the RSL approximation in the Illustris reionization model appears to be due to the effective speed of light being reduced in the equation for the cosmic background too and hence illustrates the importance of maintaining the correct speed of light in modeling the cosmic background.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Dynamic Succession of Groundwater Sulfate-Reducing Communities during Prolonged Reduction of Uranium in a Contaminated Aquifer

To further understand the diversity and dynamics of SRB in response to substrate amendment, we sequenced genes coding for the dissimilatory sulfite reductase (dsrA) in groundwater samples collected after an emulsified vegetable oil (EVO) amendment, which sustained U(VI)-reducing conditions for one year in a fast-flowing aquifer. EVO amendment significantly altered the composition of groundwater SRB communities. Sequences having no closely related-described species dominated (80%) the indigenous SRB communities in nonamended wells. After EVO amendment, Desulfococcus , Desulfobacterium , and Desulfovibrio , known for long-chain-fatty-acid, short-chain-fatty-acid and H 2 oxidation and U(VI) reduction, became dominant accounting for 7 ± 2%, 21 ± 8%, and 55 ± 8% of the SRB communities, respectively. Succession of these SRB at different bioactivity stages based on redox substrates/products (acetate, SO 4 –2 , U(VI), NO 3 – , Fe(II), and Mn(II)) was observed. Desulfovibrio and Desulfococcus dominated SRB communities at 4–31 days, whereas Desulfobacterium became dominant at 80–140 days. By the end of the experiment (day 269), the abundance of these SRB decreased but the overall diversity of groundwater SRB was still higher than non-EVO controls. Up to 62% of the SRB community changes could be explained by groundwater geochemical variables, including those redox substrates/products. A significant ( P < 0.001) correlation was observed between groundwater U(VI) concentrations and Desulfovibrio abundance. Lastly, our results showed that the members of SRB and their dynamics were correlated significantly with slow EVO biodegradation, electron donor production and maintenance of U(VI)-reducing conditions in the aquifer.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Temperature dependence of nitrate-reducing Fe(II) oxidation by Acidovorax strain BoFeN1 – evaluating the role of enzymatic vs. abiotic Fe(II) oxidation by nitrite

ABSTRACT Fe(II) oxidation coupled to nitrate reduction is a widely observed metabolism. However, to what extent the observed Fe(II) oxidation is driven enzymatically or abiotically by metabolically produced nitrite remains puzzling. To distinguish between biotic and abiotic reactions, we cultivated the mixotrophic nitrate-reducing Fe(II)-oxidizing Acidovorax strain BoFeN1 over a wide range of temperatures and compared it to abiotic Fe(II) oxidation by nitrite at temperatures up to 60°C. The collected experimental data were subsequently analyzed through biogeochemical modeling. At 5°C, BoFeN1 cultures consumed acetate and reduced nitrate but did not significantly oxidize Fe(II). Abiotic Fe(II) oxidation by nitrite at different temperatures showed an Arrhenius-type behavior with an activation energy of 80±7 kJ/mol. Above 40°C, the kinetics of Fe(II) oxidation were abiotically driven, whereas at 30°C, where BoFeN1 can actively metabolize, the model-based interpretation strongly suggested that an enzymatic pathway was responsible for a large fraction (ca. 62%) of the oxidation. This result was reproduced even when no additional carbon source was present. Our results show that at below 30°C, i.e. at temperatures representing most natural environments, biological Fe(II) oxidation was largely responsible for overall Fe(II) oxidation, while abiotic Fe(II) oxidation by nitrite played a less important role.

Microbiology↗
DOE OSTI2023

Deep Reinforcement Learning Based HVAC Control for Reducing Carbon Footprint of Buildings

In this paper, we present our work on deep reinforcement learning (DRL) based intelligent control of Heating, Ventilation, and Air Conditioning (HVAC) with the goal of reducing carbon emission. We performed this task using 1) Marginal Operating Emission Rates (MOER), where the objective was to shift the demand to the low emission period of the day and 2) Time-Of-Use (TOU) demand-response price where the objective was to shift the demand to low price period of the day. This was achieved by learning an optimal pre-cooing strategy. We found the carbon emission reduction in the range of 6%-16% depending on the opportunity presented by the MOER signal. Similarly, we observed the carbon emission reduction in the range of 23%-29% during the peak price period when TOU price was used. The results clearly demonstrated the applicability of our approach in reducing the carbon footprint of the building.

carbon emission↗
DOE OSTI2019

Can I/O Variability Be Reduced on QoS-Less HPC Storage Systems?

For a production high-performance computing (HPC) system, where storage devices are shared between multiple applications and managed in a best effort manner, I/O contention is often a major problem. In this paper, we propose a balanced messaging-based re-routing in conjunction with throttling at the middleware level. This work tackles two key challenges that have not been fully resolved in the past: whether I/O variability can be reduced on a QoS-less HPC storage system, and how to design a runtime scheduling system that can scale up to a large amount of cores. The proposed scheme uses a two-level messaging system to re-route I/O requests to a less congested storage location so that write performance is improved, while limiting the impact on read by throttling re-routing. An analytical model is derived to guide the setup of optimal throttling factor. We thoroughly analyze the virtual messaging layer overhead and explore whether the in-transit buffering is effective in managing I/O variability. Contrary to the intuition, in-transit buffer cannot completely solve the problem. It can reduce the absolute variability but not the relative variability. The proposed scheme is verified against a synthetic benchmark as well as being used by production applications.

Huang, Dan↗
DOE OSTI2024

Using Median Point in Keeling Plot to Reduce the Uncertainty of the Isotopic Composition of Evapotranspiration

Abstract The isotopic composition of evapotranspirationδ ET is a crucial parameter in isotope-based evapotranspiration (ET) partitioning and moisture recycling studies. The Keeling plot method is the most prevalent method to calculateδ ET , though it contains large extrapolated uncertainties from the least squares regression. Traditional Keeling regression uses the mean point of individual measurements. Here, a modified Keeling plot framework was proposed using the median point of individual measurements. We tested theδ ET uncertainty using the mean point [σ ET (mean)] and median point [σ ET (median)]. Multiple resolutions of input and output data from six independent sites were used to test the performance of the two methods. Theσ ET (mean) would be greater thanσ ET (median) when the mean value of inverse vapor concentration () is greater than the median value of inverse vapor concentration []. When applying the filter ofr 2 > 0.8, around 70% ofσ ET (mean) was greater thanσ ET (median). This phenomenon might be due to the normality of the vapor concentrationC υ producing the asymmetric distribution of 1/C υ . The median method could perform significantly better than the mean method when inputting high-resolution measurements (e.g., 1 Hz) and when the water vapor concentrationC υ is relatively low. Compared to the mean method, applying the median method could on average reduce 6.88% of ET partitioning uncertainties and could on average reduce 9.00% of moisture recycling uncertainties. This study provided a new insight of the Keeling plot method and emphasized handling model output uncertainty from multiple perspectives instead of only from input parameters.

Meteorology & Atmospheric Sciences↗
DOE OSTI2020

Reducing communication in algebraic multigrid with multi-step node aware communication

Algebraic multigrid (AMG) is often viewed as a scalable [Formula: see text] solver for sparse linear systems. Yet, AMG lacks parallel scalability due to increasingly large costs associated with communication, both in the initial construction of a multigrid hierarchy and in the iterative solve phase. This work introduces a parallel implementation of AMG that reduces the cost of communication, yielding improved parallel scalability. It is common in Message Passing Interface (MPI), particularly in the MPI-everywhere approach, to arrange inter-process communication, so that communication is transported regardless of the location of the send and receive processes. Performance tests show notable differences in the cost of intra- and internode communication, motivating a restructuring of communication. In this case, the communication schedule takes advantage of the less costly intra-node communication, reducing both the number and the size of internode messages. Node-centric communication extends to the range of components in both the setup and solve phase of AMG, yielding an increase in the weak and strong scaling of the entire method.

Computer Science↗
DOE OSTI2021

Reducing Ground-based Astrometric Errors with Gaia and Gaussian Processes

Stochastic field distortions caused by atmospheric turbulence are a fundamental limitation to the astrometric accuracy of ground-based imaging. This distortion field is measurable at the locations of stars with accurate positions provided by the Gaia DR2 catalog; we develop the use of Gaussian process regression (GPR) to interpolate the distortion field to arbitrary locations in each exposure. We introduce an extension to standard GPR techniques that exploits the knowledge that the 2D distortion field is curl-free. Applied to several hundred 90 s exposures from the Dark Energy Survey as a test bed, we find that the GPR correction reduces the variance of the turbulent astrometric distortions ≈12× , on average, with better performance in denser regions of the Gaia catalog. The rms per-coordinate distortion in the riz bands is typically ≈7 mas before any correction and ≈2 mas after application of the GPR model. The GPR astrometric corrections are validated by the observation that their use reduces, from 10 to 5 mas rms, the residuals to an orbit fit to riz-band observations over 5 yr of the r = 18.5 trans-Neptunian object Eris. We also propose a GPR method, not yet implemented, for simultaneously estimating the turbulence fields and the 5D stellar solutions in a stack of overlapping exposures, which should yield further turbulence reductions in future deep surveys.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

The Reduced Effectiveness of EGR to Mitigate Knock at High Loads in Boosted SI Engines

Numerous studies have demonstrated that exhaust gas recirculation (EGR) can attenuate knock propensity in spark ignition (SI) engines at naturally aspirated or lightly boosted conditions. In this paper, we investigate the role of cooled EGR under higher load conditions with multiple fuel compositions, where highly retarded combustion phasing typical of modern SI engines was used. It was found that under these conditions, EGR attenuation of knock is greatly reduced, where EGR doesn’t allow significant combustion phasing advance as it does under lighter load conditions. Detailed combustion analysis shows that when EGR is added, the polytropic coefficient increases causing the compressive pressure and temperature to increase. At sufficiently highly boosted conditions, the increase in polytropic coefficient and additional trapped mass from EGR can sufficiently reduce fuel ignition delay to overcome knock attenuation effects. Kinetic modeling demonstrates that the effectiveness of EGR to mitigate knock is highly dependent on the pressure-temperature condition. Experiments at 2000 rpm have confirmed reduced fuel ignition delay under highly boosted conditions relevant to modern downsized boosted SI engines, where in-cylinder pressure is higher and the temperature is cooler. Finally, at these conditions, charge reactivity increases compared to naturally aspirated conditions, and attenuation of knock by EGR is reduced.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2017

Dimensionality reducibility for multi-physics reduced order modeling

Applications of reduced order modeling (ROM) to support analysis of complex reactor behavior using high fidelity simulations have developed rapidly in recent years. Reduction implies any computational approach aiming to reduce the cost of the simulation, especially for situations involving repeated executions such as probabilistic risk assessment and uncertainty quantification applications. This article presents a novel non-intrusive methodology to render reduction for multi-physics models by taking advantage of the combined reduction introduced by each sub-physics in the simulation. Next, a surrogate model is constructed in terms of the reduced dimensions. A key component of the proposed methodology is to upper-bound the errors resulting from the reduction to ensure its reliability for subsequent engineering applications. To implement and demonstrate the proposed ROM algorithm, the INL’s MAMMOTH environment is employed to analyze the level of reduction in the coupled radiation-thermal transport modeling of a 2D quarter fuel pin in a light water reactor spectrum. MAMMOTH couples the neutronics model of Rattlesnake module and the fuel performance model of BISON module. Here, the results show that the reduction obtained with coupled physics is more significant than that with individual sub-physics models.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

A Multifidelity Ensemble Kalman Filter with Reduced Order Control Variates

This work develops a new multi delity ensemble Kalman lter (MFEnKF) algorithm based on linear control variate framework. The approach allows for rigorous multi delity extensions of the EnKF, where the uncertainty in coarser delities in the hierarchy of models represent control variates for the uncertainty in ner delities. Small ensembles of high delity model runs are com- plemented by larger ensembles of cheaper, lower delity runs, to obtain much improved analyses at only small additional computational costs. We investigate the use of reduced order models as coarse delity control variates in the MFEnKF, and provide analyses to quantify the improvements over the traditional ensemble Kalman lters. We apply these ideas to perform data assimilation with a quasi-geostrophic test problem, using direct numerical simulation and a corresponding POD-Galerkin reduced order model. Numerical results show that the two- delity MFEnKF provides better analyses than existing EnKF algorithms at comparable or reduced computational costs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

An Adaptive Reduced Basis Collocation Method Based on PCM ANOVA Decomposition for Anisotropic Stochastic PDEs

The combination of reduced basis and collocation methods enables efficient and accurate evaluation of the solutions to parameterized PDEs. Here, we study the stochastic collocation methods that can be combined with reduced basis methods to solve high-dimensional parameterized stochastic PDEs. We also propose an adaptive algorithm using a probabilistic collocation method (PCM) and ANOVA decomposition. This procedure involves two stages. First, the method employs an ANOVA decomposition to identify the effective dimensions, i.e., subspaces of the parameter space in which the contributions to the solution are larger, and sort the reduced basis solution in a descending order of error.Then, the adaptive search refines the parametric space by increasing the order of polynomials until the algorithm is terminated by a saturation constraint. We demonstrate the effectiveness of the proposed algorithm for solving a stationary stochastic convection-diffusion equation, a benchmark problem chosen because solutions contain steep boundary layers and anisotropic features. Lastly, we show that two stages of adaptivity are critical in a benchmark problem with anisotropic stochasticity.

97 MATHEMATICS AND COMPUTING↗