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Results for “Coefficient of performance”

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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Measurements of azimuthal anisotropies at forward and backward rapidity with muons in high-multiplicity p–Pb collisions at $\sqrt{s_{NN}} = 8.16$ TeV

The study of the azimuthal anisotropy of inclusive muons produced in p–Pb collisions at $\sqrt{s_{NN}}$ = 8.16 TeV, using the ALICE detector at the LHC is reported. The measurement of the second-order Fourier coefficient of the particle azimuthal distribution, v 2 , is performed as a function of transverse momentum p T in the 0–20% high-multiplicity interval at both forward (2.03 < y CMS < 3.53) and backward (-4.46 < y CMS < -2.96) rapidities over a wide pT range, 0.5 < p T < 10 GeV/c, in which a dominant contribution of muons from heavy-flavour hadron decays is expected at p T > 2 GeV/c. The v 2 coefficient of inclusive muons is extracted using two different techniques, namely two-particle cumulants, used for the first time for heavy-flavour measurements, and forward–central two-particle correlations. Both techniques give compatible results. A positive v 2 is measured at both forward and backward rapidities with a significance larger than 4.7σ and 7.6σ, respectively, in the interval 2 < p T < 6 GeV/c. Comparisons with previous measurements in p–Pb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV, and with AMPT and CGC-based theoretical calculations are discussed. The findings impose new constraints on the theoretical interpretations of the origin of the collective behaviour in small collision systems

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

System Design of a 2.0 MWth Sodium/Molten Salt Pilot System

Nitrate molten salt concentrating solar power (CSP) systems are currently deployed globally and are considered state-of the art heat transfer fluids (HTFs) for present day high-temperature operation. Although slightly higher limits may be possible with molten salt, to fully realize SunShot efficiency goals of $15/kWhth HTFs and an LCOE of 6¢/kWh, HTF technologies working at higher temperatures (e.g., 650 °C to 750 °C) will require an alternative to molten salts, such as with alkali metal systems. This investigation explores the development of a 2.0 MWth sodium receiver system that employs a sodium receiver as the HTF, as well as with a ternary chloride (20%NaCl/40%MgCl/40%KCl by mol wt.%) salt as a thermal energy storage (TES) medium to facilitate a 6-hr. storage duration. A sodium-to-salt heat exchanger model as well as a salt-to-sCO2 primary heat exchanger model are employed and evaluated in this investigation. A thermodynamic system design model was developed using Engineering Equation Solver (EES) where state properties were calculated at inlets and outlets along both hot and cold legs of the pilot-scale plant. This investigation assesses receiver performance as well as system efficiency studies for the pump and system operational ranges. Results found that high efficiency sodium receivers were found to have higher heat transfer coefficients and required far less spreading of incident flux. The system performance model results suggest that for a pump speed of 2400 RPM, respective hot and cold pump TDH values were determined to be 260.1–307 ft. and 260.1–307 ft for pump flow rates of 90–120 GPM.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2026

Modeling Flow and Mass Transfer within Hollow Fiber Packaging for Gas Separation

Hollow fiber membrane modules are used for gas purification by their selective permeation properties. Intensification of the process to minimize the retentate loss and gas pressure involves optimization at various scales. In this work, we outline a numerical investigation of the gas separation performance at the scale of fiber bundles and its impact on module performance. Flow channeling and anisotropy govern the mass-transfer coefficient in axial and cross-flow configurations. These effects are quantified in terms of a permeability tensor or an anisotropy ratio and the effective mass-transfer coefficient or the Sherwood number. The results show a trade-off between purification and recovery. While smaller fibers offer a large specific surface area to enable high purification, it comes at a huge penalty on the separation performance due to reduced penetration within bundles. Optimum performance indicators are emphasized.

Fibers↗
DOE OSTI2022

Calculation of the Heat Transfer Coefficient in the Outer Body for a Rotational Detonation

Unsteady heat transfer characterization on the combustion surfaces of Rotational Detonation Engines (RDE) is not well understood. It is generally thought that the complex nature of the unsteady, reacting, compressible fluid flow inside the combustion anulus of the RDE causes the convective heat transfer coefficient to be significantly higher than it is in other applications. Empirical models that have been used to analyze this strictly apply to steady flows where dimensionless groups can be employed. The reacting flow fields that are characteristic of RDEs are inherently compressible, three dimensional, unsteady, and turbulent, having properties that change by orders of magnitude throughout the flow field. They will therefore contain multiple length scales and time scales operating everywhere in the flow during all times. Because of this it is not likely that the RDE flow fields will lend themselves to explanation using simple dimensionless parameters. The dimensionless groups have meaning only in situations where length scales and time scales are singular and well defined. In spite of this it may be possible to get a relatively good idea of what the convection heat transfer coefficient is. In this work a numerical study is performed where the inside wall surface temperature distribution in the RDE outer body is systematically changed over a given range that would be characteristic of the start-up flows inside an RDE. For each case, temperature distributions inside the outer containment wall of the RDE was calculated and compared with experimental data. The closet match can then be used to directly calculate the convection heat transfer coefficient on the inside surface of the RDE.

VanOsdol, John↗
DOE OSTI2023

Development of an accelerator-based neutron source to prototype Mo-99 production, Part II: A liquid LBE loop under a high vacuum

To provide US domestic supply of Mo-99 without using high-enriched uranium (HEU), a subcritical uranium target assembly (UTA) is irradiated by an accelerator-based neutron source to create Mo-99 through fission. Part I of this work discusses the design of a liquid lead–bismuth eutectic (LBE) windowless target for an accelerator-based neutron source development. Part II discusses how to couple this windowless target to an accelerator operating at an ultra-high vacuum and the subcritical UTA cooled by water at room temperature. Due to the windowless design of the target, the liquid LBE flow shares an ultra-high vacuum (<1.3 × 10 -7 Pa or 10 -9 Torr) space with the accelerator. As a result of this shared vacuum space, the LBE system must operate at a high vacuum (10 -3 ~10 -6 Pa or 10 -5 ~10 -8 Torr). A magnetic rotary motion feedthrough unit utilizes magnetic fluid to allow rotation of the pump while maintaining a high vacuum environment. Prior to testing the LBE system under vacuum, a pump curve measurement is performed to estimate flowrate in the system. This measurement also generates data on orifice loss coefficients, which are compared to correlations in literature. The second experiment investigates vacuum level in the LBE system during operation. High vacuum is maintained (10 -3 ~10 -5 Pa or 10 -5 ~10 -7 Torr) during system operation, and a residual gas analyzer (RGA) scan shows that partial pressures of residual gases in the LBE system lower over the duration of LBE system operations. The third experiment investigates the gravity driven liquid LBE flowing out of the target chamber in the return line, which is partially full. If the liquid LBE is not drained quickly enough, flooding in the target chamber could occur. The coefficient n in the Manning equation is found to be around 0.008 s/m 1/3 . The last experiment performed is a demonstration that a vacuum jacket could provide sufficient thermal insulation to allow coupling between 300 °C LBE loop and a water tank at room temperature. In conclusion, the results from these experiments have influenced the development of the neutron source for the future commercial scale Mo-99 production system.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Predictive analytics of selections of russet potatoes

We explore the application of machine learning algorithms specifically to enhance the selection process of Russet potato (Solanum tuberosum L.) clones in breeding trials by predicting their suitability for advancement. This study addresses the challenge of efficiently identifying high-yield, disease-resistant, and climate-resilient potato varieties that meet processing industry standards. Leveraging manually collected data from trials in the state of Oregon, we investigate the potential of a wide variety of state-of-the-art binary classification models. The dataset includes 1086 clones, with data on 38 attributes recorded for each clone, focusing on yield, size, appearance, and frying characteristics, with several control varieties planted consistently across four Oregon regions from 2013 to 2021. We conduct a comprehensive analysis of the dataset that includes preprocessing, feature engineering, and imputation to address missing values. We focus on several key metrics such as accuracy, F1-score, and Matthews correlation coefficient (MCC) for model evaluation. The top-performing models, namely a feedforward neural network classifier (Neural Net), a histogram-based gradient boosting classifier (HGBC), and a support vector machine classifier (SVM), demonstrate consistent and significant results. To further validate our findings, we conducted a simulation study using the aims, data-generating mechanisms, estimands, methods, and performance measures (ADEMP) framework, simulating different data-generating scenarios to assess model robustness and performance through true positive, true negative, false positive, and false negative distributions, area under the receiver operating characteristic curve (AUC-ROC) and MCC. The simulation results highlight that non-linear models like SVM and HGBC consistently show higher AUC-ROC and MCC than logistic regression, thus outperforming the traditional linear model across various distributions, and emphasizing the importance of model selection and tuning in agricultural trials. Variable selection further enhances model performance and identifies influential features in predicting trial outcomes. The findings emphasize the potential of machine learning in streamlining the selection process for potato varieties, offering benefits such as increased efficiency, substantial cost savings, and judicious resource utilization. Our study contributes insights into precision agriculture and showcases the relevance of advanced technologies for informed decision-making in breeding programs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Impact of gel concentration on filter fluxes in microfiltration of Hanford tank wastes and simulants

Abstract Treatment processes have been proposed that will utilize crossflow filtration to concentrate sludge waste streams at the Department of Energy's Hanford Site. Challenges associated with solid–liquid separation of the waste streams drive a necessary evaluation of available Hanford high level waste (HLW) filtration data. Limiting flux conditions during crossflow filtration are elucidated with the formation of a cake layer on the membrane surface. A mass transfer coefficient between the gel and bulk concentrations plays a critical role in determining filter flux. A correlation between the gel concentration and mass transfer coefficient is made to assist in determining filter performance of select HLW streams. As a process alternative to crossflow filtration, gravity settling of waste streams may be deployed as a solid–liquid separation technique. However, this results in a contrasting performance with the centrifuged solids concentration. A method was developed to estimate expected filtration and settling performance based on physical characterization data for Hanford tank waste samples. By assessing the estimated processing performance of HLW, technical support can be provided during flowsheet planning.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Bright NIR‐II Photoluminescence in Rod‐Shaped Icosahedral Gold Nanoclusters

Abstract Fluorophores with high quantum yields, extended maximum emission wavelengths, and long photoluminescence (PL) lifetimes are still lacking for sensing and imaging applications in the second near‐infrared window (NIR‐II). In this work, a series of rod‐shaped icosahedral nanoclusters with bright NIR‐II PL, quantum yields up to ≈ 8%, and a peak emission wavelength of 1520 nm are reported. It is found that the bright NIR‐II emission arises from a previously ignored state with near‐zero oscillator strength in the ground‐state geometry and the central Au atom in the nanoclusters suppresses the non‐radiative transitions and enhances the overall PL efficiency. In addition, a framework is developed to analyze and relate the underlying transitions for the absorptions and the NIR‐II emissions in the Au nanoclusters based on the experimentally defined absorption coefficient. Overall, this work not only shows good performance of the rod‐shaped icosahedral series of Au nanoclusters as NIR‐II fluorophores, but also unravels the fundamental electronic transitions and atomic‐level structure‐property relations for the enhancement of the NIR‐II PL in gold nanoclusters. The framework developed here also provides a simple method to analyze the underlying electronic transitions in metal nanoclusters.

Li, Qi↗
DOE OSTI2024

A formalism for extracting track functions from jet measurements

The continued success of the jet substructure program will require widespread use of tracking information to enable increasingly precise measurements of a broader class of observables. The recent reformulation of jet substructure in terms of energy correlators has simplified the incorporation of universal non-perturbative matrix elements, so called “track functions”, in jet substructure calculations. These advances make it timely to understand how these universal non-perturbative functions can be extracted from hadron collider data, which is complicated by the use jet algorithms. In this paper we introduce a new class of jet functions, which we call (semi-inclusive) track jet functions, which describe measurements of the track energy fraction in identified jets. These track jet functions can be matched onto the universal track functions, with perturbatively calculable matching coefficients that incorporate the jet algorithm dependence. We perform this matching, and present phenomenological results for the charged energy fraction in jets at the LHC and EIC/HERA at collinear next-to-leading logarithmic accuracy. We show that higher moments of the charged energy fraction directly exhibit non-linear Lorentzian renormalization group flows, allowing the study of these flows with collider data. Our factorization theorem enables the extraction of universal track functions from jet measurements, opening the door to their use for a precision jet substructure program.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

A priori analysis on deep learning of subgrid-scale parameterizations for Kraichnan turbulence

In the present study, we investigate different data-driven parameterizations for large eddy simulation of two-dimensional turbulence in the a priori settings. These models utilize resolved flow field variables on the coarser grid to estimate the subgrid-scale stresses. We use data-driven closure models based on localized learning that employs a multilayer feedforward artificial neural network with point-to-point mapping and neighboring stencil data mapping, and convolutional neural network fed by data snapshots of the whole domain. The performance of these data-driven closure models is measured through a probability density function and is compared with the dynamic Smagorinksy model (DSM). The quantitative performance is evaluated using the cross-correlation coefficient between the true and predicted stresses. We analyze different frameworks in terms of the amount of training data, selection of input and output features, their characteristics in modeling with accuracy, and training and deployment computational time. We also demonstrate computational gain that can be achieved using the intelligent eddy viscosity model that learns eddy viscosity computed by the DSM instead of subgrid-scale stresses. Here, we detail the hyperparameters optimization of these models using the grid search algorithm.

42 ENGINEERING↗
DOE OSTI2022

Electronic Structure Calculations of the Fundamental Interactions in Solvent Extraction Desalination

Solvent extraction desalination (SED) is a promising membrane-free desalination technology, but new solvents are still needed that can improve the molecular-level water selectivity, while minimizing solvent contamination in the aqueous phase. Improvements in solvent design for SED require a fundamental understanding of the molecular interactions governing solvation behavior. Herein, quantum chemical calculations are used to delineate different fundamental interactions in water-solvent systems involving 38 different solvents, with a particular focus on the role of the electrostatic potential characteristics. Furthermore, these interactions are rigorously analyzed using the independent gradient model based on Hirshfeld partition (IGMH), Symmetry-Adapted Perturbation Theory (SAPT), as well as the energy decomposition analysis based on force field (EDA-FF) to compare against a standard force field model. The solvation free energies and partition coefficients are also calculated to estimate water extraction performance in a bulk solvent system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Development of machine learning framework for interface force closures based on bubble tracking data

Interfacial force closures in the two-fluid model play a critical role for the predictive capabilities of void fraction distribution. However, the practices of interfacial force modeling have long been challenged by the inherent physical complexity of the two-phase flows. The rapidly expanding computational capabilities in the recent years have made high-fidelity data from the interface-captured direct numerical simulation become more available, and hence potential for data-driven interfacial force modeling has prevailed. In this work, we established a data-driven modeling framework integrated to the HZDR multiphase Eulerian-Eulerian framework for computational fluid dynamics simulations. The data-driven framework is verified in a benchmark problem, where a feedforward neural network managed to capture the non-linear mapping between bubble Reynolds number and drag coefficient and reproduce the void distribution resulting from the baseline model in the test case. The second focus is on utilizing the bubble tracking data set to form a closure for the bubble drag in the turbulent bubbly flow, in which the drag coefficient is set to be correlated with the bubble Reynolds number and the Eötvös number. Pseudo-steady state filtering in the Frenet Frame was carried out to obtain the drag coefficient from the turbulent bubbly flow data. The performance of the data-driven drag model is also examined through a case study, where improvement of model’s prediction near-wall is regarded necessary. In conclusion, discussion and further plans of investigation are provided.

42 ENGINEERING↗
DOE OSTI2021

A Ranked-Orbital Approach to Select Active Spaces for High-Throughput Multireference Computation

The past decade has seen a great increase in the application of high-throughput computation to a variety of important problems in chemistry. However, one area which has been resistant to the high-throughput approach is multireference wave function methods, in large part due to the technicalities of setting up these calculations and in particular the not always intuitive challenge of active space selection. As we look toward a future of applying high-throughput computation to all areas of chemistry, it is important to prepare these methods for large-scale automation. Here, we propose a ranked-orbital approach to select active spaces with the goal of standardizing multireference methods for high-throughput computation. This method allows for the meaningful comparison of different active space selection schemes and orbital localizations, and we demonstrate the utility of this approach across 1120 multireference calculations for the excitation energies of small molecules. Our results reveal that it is helpful to distinguish the method used to generate orbitals from the method of ranking orbitals in terms of importance for the active space. Additionally, we propose our own orbital ranking scheme that estimates the importance of an orbital for the active space through a pair-interaction framework from orbital energies and features of the Hartree–Fock exchange matrix. Here, we call this new scheme the “approximate pair coefficient” (APC) method and we show that it performs quite well for the test systems presented.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Warpage-Resistant, Under-Extrusion-Free, High-Surface-Quality Additive Manufacturing Process for Polyethylene-Based Composite Radiation Shielding Material

Polyethylene (PE) is one of the best shielding materials for primary space radiation due to its high hydrogen content. For effective secondary neutron shielding, boron-rich fillers are incorporated to enhance performance. The semicrystalline nature and high thermal expansion coefficient of PE impede its adoption for in situ additive manufacture in space via the fused deposition modeling (FDM) 3D printing. Here, we developed an optimized PE blend to mitigate the effects of under-extrusion and warpage. Guided by studies on extrusion and warpage, we developed an optimal set of printing parameters for the proposed PE blend. The optimum PE blend─both in its pure form and when doped with fillers─has been tested on different FDM printers. The printed structures exhibit high and uniform density, smooth surfaces, no warpage, and competitive mechanical properties. The FDM-printed plates demonstrate efficient shielding from thermal neutrons, predicted via modeling and confirmed experimentally using extended Q-range small-angle neutron scattering.

additive manufacturing↗
DOE OSTI2021

Tuning Epoxy Thermomechanics via Thermal Isomerization: A Route to Negative Coefficient of Thermal Expansion Materials

Fine control over the thermal expansion and contraction behavior of polymer materials is challenging. Most polymers have large coefficients of thermal expansion (CTEs), which preclude long performance lifetimes of composite materials. Herein, we report the design and synthesis of epoxy thermosets with low CTE values below their T g and large contraction behavior above T g by incorporating thermally contractile dibenzocyclooctane (DBCO) motifs within the thermoset network. This atypical thermomechanical behavior was rationalized in terms of a twist-boat to chair conformational equilibrium of the DBCO linkages. We anticipate these findings to be generally useful in the preparation of materials with designed CTE values.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Identification of mobile genetic elements with geNomad

Identifying and characterizing mobile genetic elements in sequencing data is essential for understanding their diversity, ecology, biotechnological applications and impact on public health. Here we introduce geNomad, a classification and annotation framework that combines information from gene content and a deep neural network to identify sequences of plasmids and viruses. geNomad uses a dataset of more than 200,000 marker protein profiles to provide functional gene annotation and taxonomic assignment of viral genomes. Using a conditional random field model, geNomad also detects proviruses integrated into host genomes with high precision. In benchmarks, geNomad achieved high classification performance for diverse plasmids and viruses (Matthews correlation coefficient of 77.8% and 95.3%, respectively), substantially outperforming other tools. Leveraging geNomad’s speed and scalability, we processed over 2.7 trillion base pairs of sequencing data, leading to the discovery of millions of viruses and plasmids that are available through the IMG/VR and IMG/PR databases. geNomad is available at https://portal.nersc.gov/genomad.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Bayesian inference in band excitation scanning probe microscopy for optimal dynamic model selection in imaging

The universal tendency in scanning probe microscopy (SPM) over the last two decades is to transition from simple 2D imaging to complex detection and spectroscopic imaging modes. The emergence of complex SPM engines brings forth the challenge of reliable data interpretation, i.e., conversion from detected signals to descriptors specific to tip–surface interactions and subsequently to material’s properties. In this work, we implemented a Bayesian inference approach for the analysis of the image formation mechanisms in band excitation SPM. Compared to the point estimates in classical functional fit approaches, Bayesian inference allows for the incorporation of extant knowledge of materials and probe behavior in the form of corresponding prior distribution and return the information on the material functionality in the form of readily interpretable posterior distributions. We explore the nonlinear mechanical behaviors spatially in a classical ferroelectric material, PbTiO 3 . We observe the non-trivial evolution of the Duffing stiffness term and the nonlinearity of the sample surface, determine spatial clustering of the nonlinear response, and perform a Landau analysis on predicting the nonlinear coefficient, which indicates that ferroelectric behavior can be a cause of the observed results. These observations suggest that the spectrum of anomalous behaviors at the ferroelectric domain walls may be broader than previously believed and can extend to non-conventional mechanical properties in addition to static and microwave conductance.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Improved quality metrics for association and reproducibility in chromatin accessibility data using mutual information

Correlation metrics are widely utilized in genomics analysis and often implemented with little regard to assumptions of normality, homoscedasticity, and independence of values. This is especially true when comparing values between replicated sequencing experiments that probe chromatin accessibility, such as assays for transposase-accessible chromatin via sequencing (ATAC-seq). Such data can possess several regions across the human genome with little to no sequencing depth and are thus non-normal with a large portion of zero values. Despite distributed use in the epigenomics field, few studies have evaluated and benchmarked how correlation and association statistics behave across ATAC-seq experiments with known differences or the effects of removing specific outliers from the data. Here, we developed a computational simulation of ATAC-seq data to elucidate the behavior of correlation statistics and to compare their accuracy under set conditions of reproducibility. Using these simulations, we monitored the behavior of several correlation statistics, including the Pearson’s R and Spearman’s ρ coefficients as well as Kendall’s τ and Top–Down correlation. We also test the behavior of association measures, including the coefficient of determination R 2 , Kendall’s W, and normalized mutual information. Our experiments reveal an insensitivity of most statistics, including Spear man’s ρ, Kendall’s τ, and Kendall’s W, to increasing differences between simulated ATAC-seq replicates. The removal of co-zeros (regions lacking mapped sequenced reads) between simulated experiments greatly improves the estimates of correlation and association. After removing co-zeros, the R 2 coefficient and normalized mutual information display the best performance, having a closer one-to-one relationship with the known portion of shared, enhanced loci between simulated replicates. When comparing values between experimental ATAC-seq data using a random forest model, mutual information best predicts ATAC-seq replicate relationships. Collectively, this study demonstrates how measures of correlation and association can behave in epigenomics experiments. We provide improved strategies for quantifying relationships in these increasingly prevalent and important chromatin accessibility assays.

59 BASIC BIOLOGICAL SCIENCES↗