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At least 1,063 records · Page 59

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DOE OSTI2014

New Insights for Improving the Designs of Flexible Duct Junction Boxes (Fact Sheet)

IBACOS explored the relationships between pressure and physical configurations of flexible duct junction boxes by using computational fluid dynamics (CFD) simulations to predict individual box parameters and total system pressure, thereby ensuring improved HVAC performance. Current Air Conditioning Contractors of America (ACCA) guidance (Group 11, Appendix 3, ACCA Manual D, Rutkowski 2009) allows for unconstrained variation in the number of takeoffs, box sizes, and takeoff locations. The only variables currently used in selecting an equivalent length (EL) are velocity of air in the duct and friction rate, given the first takeoff is located at least twice its diameter away from the inlet. This condition does not account for other factors impacting pressure loss across these types of fittings. For each simulation, the IBACOS team converted pressure loss within a box to an EL to compare variation in ACCA Manual D guidance to the simulated variation. IBACOS chose cases to represent flows reasonably correlating to flows typically encountered in the field and analyzed differences in total pressure due to increases in number and location of takeoffs, box dimensions, and velocity of air, and whether an entrance fitting is included. The team also calculated additional balancing losses for all cases due to discrepancies between intended outlet flows and natural flow splits created by the fitting. In certain asymmetrical cases, the balancing losses were significantly higher than symmetrical cases where the natural splits were close to the targets. Thus, IBACOS has shown additional design constraints that can ensure better system performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2013

Computational Fluid Dynamics Analysis of Flexible Duct Junction Box Design

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2016

Utilizing a Novel Acoustic Backscatter Array to Characterize Waste Consolidation and Settling in a Horizontal Flow Clarifier - 16051

As part of a collaboration between the University of Leeds, Sellafield Ltd and MMI Engineering Ltd, bespoke acoustic instrumentation and numerical simulation methods have been established to optimize nuclear legacy waste transfers. Specifically, a new ultrasonic measurement system has been developed, allowing suspended particle concentration and consolidation to be measured remotely. This technology has been combined with computational fluid dynamics (CFD) modelling of settling particulate systems, permitting full characterization, simulation and prediction of sludge separation processes. Presented here are experimental and numerical results on the transport and settling behavior of non-active nuclear-analogue suspensions in two flow geometries: gravitational settling of glass dispersions in a 250-liter column; and transport and sedimentation of flocculated calcite in a close-to-full-scale clarifier (9 m{sup 3} total volume and 4.8 m{sup 2} length). In addition, details of the design, construction and calibration of a custom-built Ultrasonic Array Research Platform (UARP) are described. Settling column results suggested that particle concentration changes could be simulated with good accuracy by a CFD model (developed using the open source OpenFOAM framework) when compared to measured samples. Large-scale clarifier trials were used to assess the effectiveness of the UARP controller with an attached in situ acoustic backscatter array (ABA). The ABA consisted of 8 transducers angled down at 20 deg. to the vertical, pulsing at 2-2.5 MHz in cycles (covering 1.8 m total depth). In comparison to a range of sampled concentrations across the length of the clarifier, results confirmed that the UARP-ABA system was highly successful at characterizing these complex flows over several hours of operation, allowing visualization of depth-wise bulk density changes and bed build-up over time. Collectively, this work highlights the capability of these tools to monitor and optimize a number of current waste transfer operations at Sellafield, with further potential applications for solid-liquid separations across waste, water treatment and minerals engineering industries. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

Elastic Properties of Plasma-Exposed Tungsten Predicted by Molecular-Dynamics Simulations

We report results of systematic molecular-dynamics computations of the elastic properties of single-crystalline tungsten containing structural defects, voids and overpressurized He nanobubbles, related to plasma exposure of tungsten serving as a plasma- facing component (PFC) in nuclear fusion devices. Our computations reveal that the empty voids are centers of dilatation resulting in development of tensile stress in the tungsten matrix, whereas He-filled voids (nanobubbles) introduce compressive stress in the plasma-exposed tungsten. We find that the dependence of the elastic moduli of plasma-exposed tungsten, namely, the bulk, Young, and shear modulus, on its void fraction follows a universal exponential scaling relation. We also find that the elastic moduli of plasma-exposed tungsten soften substantially as a function of He content in the tungsten matrix, following an exponential scaling relation; this He-induced exponential softening is in addition to the softening caused in the matrix with increasing temperature. A systematic characterization of the dependence of the elastic moduli on the He bubble size reveals that He bubble growth affects significantly both the bulk modulus and the Poisson ratio of plasma-exposed tungsten, while its effect on the Young and shear moduli of the plasma-exposed material is weak. Our findings contribute directly to the development of a structure-properties database that is required for the predictive modeling of the dynamical response of PFCs in nuclear fusion devices.

36 MATERIALS SCIENCE↗

DOE OSTI2015

SURVEY SIMULATIONS OF A NEW NEAR-EARTH ASTEROID DETECTION SYSTEM

We have carried out simulations to predict the performance of a new space-based telescopic survey operating at thermal infrared wavelengths that seeks to discover and characterize a large fraction of the potentially hazardous near-Earth asteroid (NEA) population. Two potential architectures for the survey were considered: one located at the Earth–Sun L1 Lagrange point, and one in a Venus-trailing orbit. A sample cadence was formulated and tested, allowing for the self-follow-up necessary for objects discovered in the daytime sky on Earth. Synthetic populations of NEAs with sizes as small as 140 m in effective spherical diameter were simulated using recent determinations of their physical and orbital properties. Estimates of the instrumental sensitivity, integration times, and slew speeds were included for both architectures assuming the properties of newly developed large-format 10 μm HgCdTe detector arrays capable of operating at ∼35 K. Our simulation included the creation of a preliminary version of a moving object processing pipeline suitable for operating on the trial cadence. We tested this pipeline on a simulated sky populated with astrophysical sources such as stars and galaxies extrapolated from Spitzer Space Telescope and Wide-field Infrared Explorer data, the catalog of known minor planets (including Main Belt asteroids, comets, Jovian Trojans, planets, etc.), and the synthetic NEA model. Trial orbits were computed for simulated position-time pairs extracted from the synthetic surveys to verify that the tested cadence would result in orbits suitable for recovering objects at a later time. Our results indicate that the Earth–Sun L1 and Venus-trailing surveys achieve similar levels of integral completeness for potentially hazardous asteroids larger than 140 m; placing the telescope in an interior orbit does not yield an improvement in discovery rates. This work serves as a necessary first step for the detailed planning of a next-generation NEA survey.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Statistical methods for design and testing of 3D-printed polymers

Different statistical methods are used in various fields to qualify processes and products, especially in emerging technologies like Additive Manufacturing (AM) or 3D printing. Since several statistical methods are being employed to ensure quality production of the 3D-printed parts, an overview of these methods used in 3D printing for different purposes is presented in this paper. The advantages and challenges, to understanding the importance it brings for design and testing optimization of 3D-printed parts are also discussed. The application of different metrology methods is also summarized to guide future researchers in producing dimensionally-accurate and good-quality 3D-printed parts. Here, this review paper shows that the Taguchi Methodology is the commonly-used statistical tool in optimizing mechanical properties of the 3D-printed parts, followed by Weibull Analysis and Factorial Design. In addition, key areas such as Artificial Intelligence (AI), Machine Learning (ML), Finite Element Analysis (FEA), and Simulation require more research for improved 3D-printed part qualities for specific purposes. Future perspectives are also discussed, including other methods that can help further improve the overall quality of the 3D printing process from designing to manufacturing.

36 MATERIALS SCIENCE↗

DOE OSTI2017

First-Principles-Based Computational Study on Nucleation and Growth Mechanisms of U on Mo(110) Surface Solvated in an Eutectic LiCl-KCl Molten Salt

In nuclear engineering and industry electrochemical methods have been intensively employed in pyro-processing technology to recycle used nuclear fuels for substantial alleviation of the concern with the safe treatment of high-level radioactive nuclear wastes. Pyro-processing is based on sequential electrochemical reactions at high temperature (typically above 700 K at ambient pressure) to enhance the efficiency for decoupling nuclear fuels from unfavorable fission fragments. Using a molten salt in electrorefining process, at which actinides can be selectively deposited in solid cathode from ionic states, gives several advantages over an aqueous solution: higher thermal conductivity (it is liquid phase at high temperature, molten salt plays a role as medium at high temperature over 773 K in electrochemical reaction, lower kinetic barrier in ionic diffusion mobility and better stability against undesired side reactions with radioactive materials. Therefore, the volume of high-level nuclear wastes to be, otherwise, disposed into secured sites is dramatically reduced. Most of all it conforms to nuclear non-proliferation treaty. Electrochemical reactions in a molten salt medium are, however, much more complicated than in aqueous solution rendering the accurate understanding of thermodynamic and kinetic properties formidably challenging. It is represented by extensive researches in experiments and computations to identify underlying mechanisms on how U and other fission fragments behave in the electrorefining system. Regardless of the researches there is a general agreement that the initial stage of the nucleation and growth process of the U deposit is critical for determining the properties of final product and the electrorefining efficiency. In spite of the focused studies it still remains unclear. Theoretical and computational researches have been often carried out with various empirical parameter for interatomic potential energy, which are unknown variables until numerical fits to experimental measurements or just assumption is made. Thus, empirical simulations sometimes predicted outcomes are inconsistent depending on materials of interest with experimental measurements. This may be largely because the accuracy of an empirical interatomic potential energy models critically depends on the input parameters and thus, can be limited in transferability of the results. Furthermore, it may be devoid of fundamental physics or chemistry. This situation requires special caution when an electrorefining system is studied to recycle spent nuclear fuels because of the extreme difficulty in reliable acquisition of thermodynamic and kinetic data from direct experimental measurements. In this presentation, we extensively utilized first principles density functional theory (DFT) calculations and ab-initio molecular dynamic (AIMD) simulations to figure out underlying mechanisms of the initial stage of nucleation and growth behaviors of U on Mo(110) surface in a eutectic LiCl-KCl molten salt at T = 773 K. To rigorously investigate the effect of the molten salt electrolyte we compared the results with those calculated under vacuum condition.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2018

Soil moisture drives microbial controls on carbon decomposition in two subtropical forests

Knowledge of microbial mechanisms is critical to understand Earth's biogeochemical cycle under climate and environmental changes. However, large uncertainties remain in model simulations and predictions due to the lack of explicit parameterization of microbial data and few applications beyond the laboratory. In addition, most experimental and modeling studies of warming-induced changes in soil carbon (C) focus on temperature sensitivity, neglecting concomitant effects of changes in soil moisture. Soil microbes are sensitive to moisture, and their responses can dramatically impact soil biogeochemical cycles. Here we represent microbial and enzymatic functions in response to changes in moisture in the Microbial-ENzyme Decomposition (MEND) model. Through modeling with long-term field observations from subtropical forests, we demonstrate that parameterization with microbial data in addition to respiration fluxes greatly increases confidence in model simulations. We further employ the calibrated model to simulate the responses of soil organic C (SOC) under multiple environmental change scenarios. The model shows significant increases in SOC in response to decreasing soil moisture and only minor changes in SOC in response to increasing soil temperature. Increasing litter inputs also cause a significant increase in SOC in the pine forest, whereas an insignificant negative effect is simulated in the broadleaf forest. We also demonstrate the co-metabolism mechanism for the priming effects, i.e., more labile inputs to soil could stimulate microbial and enzymatic growth and activity. Furthermore our study provides strong evidence of microbial control over soil C decomposition and suggests the future trajectory of soil C may be more responsive to changes in soil moisture than temperature, particularly in tropical and subtropical environments.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Density pedestal prediction model for tokamak plasmas

Abstract A model for the pedestal density prediction based on neutral penetration combined with pedestal transport is presented. The model is tested against a pedestal database of JET-ILW Type I ELMy H-modes showing good agreement over a wide range of parameters both in standalone modelling (using the experimental temperature profile) and in full Europed modelling that predicts both density and temperature pedestals simultaneously. The model is further tested for ASDEX Upgrade and MAST-U Type I ELMy H-modes and both are found to agree with the same model parameters as for JET-ILW. The JET-ILW experiment where the isotope of the main ion is varied in a D/T scan at constant gas rate and constant β N is successfully modelled as long as the separatrix density ( n e,sep ) and pedestal transport coefficient ratio ( D / χ ) are varied in accordance with the experimentally observed variation of n e,sep and the isotope dependence of D / χ found in gyrokinetic simulations. The predictions are found to be sensitive to n e,sep which is why the model is combined with an n e,sep model to predict the pedestal for the STEP fusion reactor.

Physics↗

DOE OSTI2019

A rigorous process modeling methodology for biomass fast pyrolysis with an entrained-flow reactor

A biomass fast pyrolysis model was developed for implementation in equation-oriented modeling software. Based on a previous framework of coupled 1-dimensional mass, momentum, and heat balance equations, this model includes updated reaction kinetics to provide a more detailed representation of biomass components, intermediates, and products. A recently published derivation of thermodynamic properties for the species in this model has allowed the energy balance around the pyrolysis reactor to be rigorously redefined. With these improvements, the optimum pyrolysis temperature for bio-oil production predicted by the model is increased by up to 50°C, bringing it in line with experimental data and increasing the overall agreement. More importantly, the reactor energy balance is strictly enforced. The resulting model can be used for the design and optimization of biomass fast pyrolysis processes, and comparisons with other options for biomass utilization.

09 BIOMASS FUELS↗

DOE OSTI2016

Electronic doping of transition metal oxide perovskites

CaFeO{sub 3} is a prototypical negative charge transfer oxide that undergoes electronic metal-insulator transition concomitant with a dilation and contraction of nearly rigid octahedra. Altering the charge neutrality of the bulk system destroys the electronic transition, while the structure is significantly modified at high charge content. Using density functional theory simulations, we predict an alternative avenue to modulate the structure and the electronic transition in CaFeO{sub 3}. Charge distribution can be modulated using strain-rotation coupling and thin film engineering strategies, proposing themselves as a promising avenue for fine tuning electronic features in transition metal-oxide perovskites.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

Neural network emulation of flow in heavy-ion collisions at intermediate energies

Applications of new techniques in machine learning are speeding up progress in research in various fields. In this work, we construct and evaluate a deep neural network (DNN) to be used within a Bayesian statistical framework as a faster and more reliable alternative to the Gaussian process (GP) emulator of an isospin-dependent Boltzmann-Uehling-Uhlenbeck (IBUU) transport model simulator of heavy-ion reactions at intermediate beam energies. We found strong evidence of the DNN being able to emulate the IBUU simulator's prediction on the strengths of protons' directed and elliptical flow very efficiently even with small training datasets and with accuracy about ten times higher than the GP. Here, limitations of our present work and future improvements are also discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2017

Charge Exchange of Highly Charged Ne and Mg Ions with H and He

Cross sections for single electron capture (SEC), or charge exchange (CX), in collisions of Ne{sup (8–10)+} and Mg{sup (8–12)+} with H and He, are computed using an approximate multichannel Landau–Zener (MCLZ) formalism. Final-state-resolved cross sections for the principal (n), orbital angular momentum (ℓ), and where appropriate, total spin angular momentum (S) quantum numbers are explicitly computed, except for the incident bare ions Ne{sup 10+} and Mg{sup 12+}. In the latter two cases, nℓ-resolution is obtained from analytical ℓ-distribution functions applied to n-resolved MCLZ cross sections. In all cases, the cross sections are computed over the collision energy range 1 meV/u to 50 keV/u with LZ parameters estimated from atomic energies obtained from experiment, theory, or, in the case of high-lying Rydberg levels, estimated with a quantum defect approach. Errors in the energy differences in the adiabatic potentials at the avoided crossing distances give the largest contribution to the uncertainties in the cross sections, which are expected to increase with decreasing cross section magnitude. The energy differences are deduced here with the Olson–Salop–Tauljberg radial coupling model. Proper selection of an ℓ-distribution function for bare ion collisions introduces another level of uncertainty into the results. Comparison is made to existing experimental or theoretical results when available, but such data are absent for most considered collision systems. The nℓS-resolved SEC cross sections are used in an optically thin cascade simulation to predict X-ray spectra and line ratios that will aid in modeling the X-ray emission in environments where CX is an important mechanism. Details on a MCLZ computational package, Stueckelberg, are also provided.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2020

Uncertainty Assessment of CFD Investigation of the Nonlinear Difference-Frequency Wave Loads on a Semisubmersible FOWT Platform

Current mid-fidelity modeling approaches for floating offshore wind turbines (FOWTs) have been found to underpredict the nonlinear, low-frequency wave excitation and the response of semisubmersible FOWTs. To examine the cause of this underprediction, the OC6 project is using computational fluid dynamics (CFD) tools to investigate the wave loads on the OC5-DeepCwind semisubmersible, with a focus on the nonlinear difference-frequency excitation. This paper focuses on assessing the uncertainty of the CFD predictions from simulations of the semisubmersible in a fixed condition under bichromatic wave loading and on establishing confidence in the results for use in improving mid-fidelity models. The uncertainty for the nonlinear wave excitation is found to be acceptable but larger than that for the wave-frequency excitation, with the spatial discretization error being the dominant contributor. Further, unwanted free waves at the difference frequency have been identified in the CFD solution. A wave-splitting and wave load-correction procedure are presented to remove the contamination from the free waves in the results. A preliminary comparison to second-order potential-flow theory shows that the CFD model predicted significantly higher difference-frequency wave excitations, especially in surge, suggesting that the CFD results can be used to better calibrate the mid-fidelity tools.

17 WIND ENERGY↗

DOE OSTI2024

Predicted Fracture Tendency of Naturally Occurring Aluminum Surface Coatings under Tensile Loading

Naturally occurring coatings on aluminum metal, such as its oxide or hydroxide, serve to protect the material from corrosion. Understanding the conditions under which these coatings mechanically fail is therefore expected to be an important aspect of predictive models for aluminum component lifetimes. To this end, we develop and apply a molecular dynamics (MD) modeling framework for conducting tension tests that is capable of isolating factors governing the mechanical strength as a function of coating chemistry, defect morphology, and variables associated with the loading path. We consider two representative materials, including γ-Al 2 O 3 and γ-Al(OH) 3 (i.e., oxide and hydroxide), both of which form readily as aluminum surface coatings. Our results indicate that defects have a significant bearing on the strength of aluminum oxide, with grain boundaries serving to reduce the strain at failure from ε zz = 0.300 to 0.219, relative to perfect single crystal. Our simulations also predict that porosity lowers the elastic stiffness and yield strength of the oxide. Relative to perfect crystal, we find porosity factors of 5%, 10% and 20% decrease the yield stress by 26%, 36% and 53%, respectively. MD predicts that perfect hydroxide and oxide single crystal have respective strains at failure of 0.08 and 0.31 under tensile uniaxial strain loading, and that the corresponding yield stresses are respectively 1.6 and 11.1 GPa. These data indicate that the hydroxide is substantially more susceptible to mechanical failure than the oxide. Our results, coupled with literature findings that indicate hot and humid conditions favor formation of hydroxide and defective oxide coatings, indicate the potential for a complicated dependence of aluminum corrosion susceptibility and stress corrosion cracking on aging history.

36 MATERIALS SCIENCE↗

DOE OSTI2025

The Water Table Model (WTM) (v2.0.1): coupled groundwater and dynamic lake modelling

Abstract. Ice-free land comprises 26 % of the Earth's surface and holds liquid water that delineates ecosystems, affects global geochemical cycling, and modulates sea levels. However, we currently lack the capacity to simulate and predict these terrestrial water changes across the full range of relevant spatial (watershed to global) and temporal (monthly to millennial) scales. To address this knowledge gap, we present the Water Table Model (WTM), which integrates coupled components to compute dynamic lake and groundwater levels. The groundwater component solves the 2D horizontal groundwater flow equation using non-linear equation solvers from the C++ PETSc (Portable, Extensible Toolkit for Scientific Computation) library. The dynamic lake component makes use of the Fill–Spill–Merge (FSM) algorithm to move surface water into lakes, where it may evaporate or affect groundwater flow. In a proof-of-concept application, we demonstrate the continental-scale capabilities of the WTM by simulating the steady-state climate-driven water table for the present day and the Last Glacial Maximum (LGM; 21 000 calendar years before present) across the North American continent. During the LGM, North America stored an additional 14.98 cm of sea-level equivalent (SLE) in lakes and groundwater compared to the climate-driven present-day scenario. We compare the present-day result to other simulations and real-world data. Open-source code for the WTM is available on GitHub and Zenodo.

Callaghan, Kerry L. (ORCID:0000000226740838)↗

DOE OSTI2013

A FIRST LOOK AT CREATING MOCK CATALOGS WITH MACHINE LEARNING TECHNIQUES

We investigate machine learning (ML) techniques for predicting the number of galaxies (N{sub gal}) that occupy a halo, given the halo's properties. These types of mappings are crucial for constructing the mock galaxy catalogs necessary for analyses of large-scale structure. The ML techniques proposed here distinguish themselves from traditional halo occupation distribution (HOD) modeling as they do not assume a prescribed relationship between halo properties and N{sub gal}. In addition, our ML approaches are only dependent on parent halo properties (like HOD methods), which are advantageous over subhalo-based approaches as identifying subhalos correctly is difficult. We test two algorithms: support vector machines (SVM) and k-nearest-neighbor (kNN) regression. We take galaxies and halos from the Millennium simulation and predict N{sub gal} by training our algorithms on the following six halo properties: number of particles, M{sub 200}, {sigma}{sub v}, v{sub max}, half-mass radius, and spin. For Millennium, our predicted N{sub gal} values have a mean-squared error (MSE) of {approx}0.16 for both SVM and kNN. Our predictions match the overall distribution of halos reasonably well and the galaxy correlation function at large scales to {approx}5%-10%. In addition, we demonstrate a feature selection algorithm to isolate the halo parameters that are most predictive, a useful technique for understanding the mapping between halo properties and N{sub gal}. Lastly, we investigate these ML-based approaches in making mock catalogs for different galaxy subpopulations (e.g., blue, red, high M{sub star}, low M{sub star}). Given its non-parametric nature as well as its powerful predictive and feature selection capabilities, ML offers an interesting alternative for creating mock catalogs.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2019

Finding high-redshift strong lenses in DES using convolutional neural networks

We search Dark Energy Survey (DES) Year 3 imaging data for galaxy-galaxy strong gravitational lenses using convolutional neural networks. We generate 250 000 simulated lenses at redshifts > 0.8 from which we create a data set for training the neural networks with realistic seeing, sky and shot noise. Using the simulations as a guide, we build a catalogue of 1.1 million DES sources with 1.8 < g - i < 5, 0.6 < g - r < 3, r_mag > 19, g_mag > 20, and i_mag > 18.2. We train two ensembles of neural networks on training sets consisting of simulated lenses, simulated non-lenses, and real sources. We use the neural networks to score images of each of the sources in our catalogue with a value from 0 to 1, and select those with scores greater than a chosen threshold for visual inspection, resulting in a candidate set of 7301 galaxies. During visual inspection, we rate 84 as 'probably' or 'definitely' lenses. Four of these are previously known lenses or lens candidates. We inspect a further 9428 candidates with a different score threshold, and identify four new candidates. We present 84 new strong lens candidates, selected after a few hours of visual inspection by astronomers. This catalogue contains a comparable number of high-redshift lenses to that predicted by simulations. Based on simulations, we estimate our sample to contain most discoverable lenses in this imaging and at this redshift range.

Jacobs, C.↗