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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 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Modeling CMB lensing cross correlations with CLEFT

A new generation of surveys will soon map large fractions of sky to ever greater depths and their science goals can be enhanced by exploiting cross correlations between them. In this paper we study cross correlations between the lensing of the CMB and biased tracers of large-scale structure at high z. We motivate the need for more sophisticated bias models for modeling increasingly biased tracers at these redshifts and propose the use of perturbation theories, specifically Convolution Lagrangian Effective Field Theory (CLEFT). Since such signals reside at large scales and redshifts, they can be well described by perturbative approaches. We compare our model with the current approach of using scale independent bias coupled with fitting functions for non-linear matter power spectra, showing that the latter will not be sufficient for upcoming surveys. Furthermore, we illustrate our ideas by estimating σ 8 from the auto- and cross-spectra of mock surveys, finding that CLEFT returns accurate and unbiased results at high z. We report on uncertainties due to the redshift distribution of the tracers, and several avenues for future development.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Quenching measurements and modeling of a boron-loaded organic liquid scintillator

Organic liquid scintillators are used in a wide variety of applications in experimental nuclear and particle physics. Boron-loaded scintillators are particularly useful for detecting neutron captures, due to the high thermal neutron capture cross section of 10 B. These scintillators are commonly used in neutron detectors, including the DarkSide-50 neutron veto, where the neutron may produce a signal when it scatters o protons in the scintillator or when it captures on 10 B. Reconstructing the energy of these recoils is complicated by scintillation quenching. Understanding how nuclear recoils are quenched in these scintillators is an important and dicult problem. In this article, we present a set of measurements of neutron-induced proton recoils in a boron-loaded organic liquid scintillator at recoil energies ranging from 57-467 keV, and we compare these measurements to predictions from different quenching models. We and that a modified Birks' model whose denominator is quadratic in dE=dx best describes the measurements, with χ 2 /NDF = 1:6. This result will help model nuclear recoil scintillation in similar detectors and can be used to improve their neutron tagging efficiency.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Improving streamflow predictions across CONUS by integrating advanced machine learning models and diverse data

Accurate streamflow prediction is crucial to understand climate impacts on water resources and develop effective adaption strategies. A global long short-term memory (LSTM) model, using data from multiple basins, can enhance streamflow prediction, yet acquiring detailed basin attributes remains a challenge. To overcome this, we introduce the Geo-vision transformer (ViT)-LSTM model, a novel approach that enriches LSTM predictions by integrating basin attributes derived from remote sensing with a ViT architecture. Applied to 531 basins across the Contiguous United States, our method demonstrated superior prediction accuracy in both temporal and spatiotemporal extrapolation scenarios. Geo-ViT-LSTM marks a significant advancement in land surface modeling, providing a more comprehensive and effective tool for better understanding the environment responses to climate change.

Tayal, Kshitij↗
DOE OSTI2018

Accelerating flux balance calculations in genome-scale metabolic models by localizing the application of loopless constraints

Abstract Background Genome-scale metabolic network models and constraint-based modeling techniques have become important tools for analyzing cellular metabolism. Thermodynamically infeasible cycles (TICs) causing unbounded metabolic flux ranges are often encountered. TICs satisfy the mass balance and directionality constraints but violate the second law of thermodynamics. Current practices involve implementing additional constraints to ensure not only optimal but also loopless flux distributions. However, the mixed integer linear programming problems required to solve become computationally intractable for genome-scale metabolic models. Results We aimed to identify the fewest needed constraints sufficient for optimality under the loopless requirement. We found that loopless constraints are required only for the reactions that share elementary flux modes representing TICs with reactions that are part of the objective function. We put forth the concept of localized loopless constraints (LLCs) to enforce this minimal required set of loopless constraints. By combining with a novel procedure for minimal null-space calculation, the computational time for loopless flux variability analysis (ll-FVA) is reduced by a factor of 10–150 compared to the original loopless constraints and by 4–20 times compared to the current fastest method Fast-SNP with the percent improvement increasing with model size. Importantly, LLCs offer a scalable strategy for loopless flux calculations for multi-compartment/multi-organism models of large sizes, for example, shortening the CPU time for ll-FVA from 35 h to less than 2 h for a model with more than104 reactions. Availability and implementation Matlab functions are available in the Supplementary Material or at https://github.com/maranasgroup/lll-FVA Supplementary information Supplementary data are available at Bioinformatics online.

Chan, Siu H. J. (ORCID:000000027707656X)↗
DOE OSTI2023

Systematics in asteroseismic modelling: application of a correlated noise model for oscillation frequencies

ABSTRACT The detailed modelling of stellar oscillations is a powerful approach to characterizing stars. However, poor treatment of systematics in theoretical models leads to misinterpretations of stars. Here, we propose a more principled statistical treatment for the systematics to be applied to fitting individual mode frequencies with a typical stellar model grid. We introduce a correlated noise model based on a Gaussian process (GP) kernel to describe the systematics given that mode frequency systematics are expected to be highly correlated. We show that tuning the GP kernel can reproduce general features of frequency variations for changing model input physics and fundamental parameters. Fits with the correlated noise model better recover stellar parameters than traditional methods that either ignore the systematics or treat them as uncorrelated noise.

Li, Tanda (ORCID:0000000163962563)↗
DOE OSTI2021

Universality class for loopless invasion percolation models and a percolation avalanche burst model for hydraulic fracturing

Invasion percolation is a model that was originally proposed to describe growing networks of fractures. In this work, we describe a loopless algorithm on random lattices, coupled with an avalanche-based model for bursts. The model reproduces the characteristic b-value seismicity and spatial distribution of bursts consistent with earthquakes resulting from hydraulic fracturing (“fracking”). We test models for both site invasion percolation and bond invasion percolation. These have differences on the scale of site and bond lengths l. But since the networks are characterized by their large-scale behavior, l << L, we find small differences between scaling exponents. Though data may not differentiate between models, our results suggest that both models belong to different universality classes.

58 GEOSCIENCES↗
DOE OSTI2017

Modelling and validation of particle size distributions of supported nanoparticles using the pair distribution function technique

The particle size of supported catalysts is a key characteristic for determining structure–property relationships. It is a challenge to obtain this information accurately and in situ using crystallographic methods owing to the small size of such particles (<5 nm) and the fact that they are supported. In this work, the pair distribution function (PDF) technique was used to obtain the particle size distribution of supported Pt catalysts as they grow under typical synthesis conditions. The PDF of Pt nanoparticles grown on zeolite X was isolated and refined using two models: a monodisperse spherical model (single particle size) and a lognormal size distribution. The results were compared and validated using scanning transmission electron microscopy (STEM) results. Both models describe the same trends in average particle size with temperature, but the results of the number-weighted lognormal size distributions can also accurately describe the mean size and the width of the size distributions obtained from STEM. Since the PDF yields crystallite sizes, these results suggest that the grown Pt nanoparticles are monocrystalline. This work shows that refinement of the PDF of small supported monocrystalline nanoparticles can yield accurate mean particle sizes and distributions.

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

Modeling and Control of Cascaded Bridgeless Multilevel Rectifier Under Unbalanced Load Conditions

The goal of this project is to model and control a novel unidirectional cascaded multilevel bridgeless rectifier as an active front end in medium and high voltage applications. This topology has many advantages over a conventional cascaded H-bridge rectifier, such as lower implementation cost, higher reliability, and greater flexibility with similar power quality.The steady-state mathematical model is used to develop a method for the voltage balancing of dc cells. Power factor analysis is discussed to achieve unity power factor using fully controlled hbridge cells. Power loss, efficiency, and cost comparison studies between the traditional cascaded H-Bridge converter and the proposed bridgeless converter demonstrate the advantages. A novel control strategy is proposed to achieve dc voltage balancing, fast and robust grid synchronization and power factor correction under unbalanced load conditions. Simulation and experimental results validate the models and control method.

Cascaded Bridgeless Rectifier, Power factor analys↗
DOE OSTI2023

Real-Time Model-Adaptive Relaying Applied to Microgrid Protection

In microgrids, the short-circuit current magnitude is significantly limited by more than an order of magnitude due to the relatively small inverter-based resources. Commercially available protective devices for distribution cannot reliably protect a microgrid due to their dependence on the magnitude of the fault current. Moreover, overcurrent relays typically cannot function properly for a microgrid because they are incapable of detecting faults and/or performing the coordination between the relays in inverter-based microgrids operated in the islanded mode. This paper proposes a model-adaptive relay designed to adjust the relay curves based on the available generation and the network topology. The proposed method runs a real-time model of the microgrid, which gathers information from the network to calculate the available short-circuit current in the specified node. The fault current from the model is then used for the adaptive algorithm to calculate the relay settings, considering coordination with the downstream fuses and upstream reclosers. This work presents the validation of the proposed method in Hardware-in-the-Loop, in a hardware testbed as well as field deployed in a real microgrid in East-Tennessee.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Linear Approximation Line Pack Model for Integrated Electricity and Natural Gas Systems OPF

With the increasing installation of renewable energy electricity generation capacity, the penetration of natural gas fired generation is also substantailly increasing in power systems due to their fast response capability to mitigate system variability and uncertainty. The dependencies between the electricity system and the natural gas system are becoming more important to maintain the reliability of both systems. In integrated electricity and natural gas systems (IEGSs) operations, the nonlinearity of the natural gas system brings challenges to the system operators. In this paper, the IEGSs optimal power flow (OPF) model is proposed that includes the line pack of the gas system via linear approximation. The simulation results demonstrate that the proposed model can obtain optimal solutions efficiently that are comparable in accuracy as the nonlinear line pack model.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Outage Forecast-Based Preventative Scheduling Model for Distribution System Resilience Enhancement

Distribution system resilience enhancement is an important topic to ensure customers have access to power supply during extreme events. In fact, certain weather-related extreme events can be predicted ahead of time. Therefore, it is important to investigate how to predict grid outages using extreme weather forecasts, and how outage predictions can be incorporated into distribution system resilience enhancement. In this paper, a preventative scheduling model for distribution systems is proposed. The model targets at allocating resources, especially mobile responsive resources such as mobile backup generators and mobile energy storage systems, to prepare for an extreme event in the day-ahead context. To achieve efficient resource allocation and scheduling, a machine learning-based outage prediction module is developed to predict vulnerable or risky segments of the distribution system based on historical operating records and extreme weather event forecast. By integrating the outage prediction results into the scheduling model, optimal resource allocation can be derived to help distribution systems prepare for an upcoming event and improve resilience performance. A real distribution feeder in North Carolina, U.S. is used in the case study to validate the proposed approach.

distributed energy resources↗
DOE OSTI2021

Roles of Dynamic State Estimation in Power System Modeling, Monitoring and Operation

—Power system dynamic state estimation (DSE) remains an active research area. This is driven by the absence of accurate models, the increasing availability of fast-sampled, time synchronized measurements, and the advances in the capability, scalability, and affordability of computing and communications. This paper discusses the advantages of DSE as compared to static state estimation, and the implementation differences between the two, including the measurement configuration, modeling framework and support software features. The important roles of DSE are discussed from modeling, monitoring and operation aspects for today’s synchronous machine dominated systems and the future power electronics-interfaced generation systems. Several examples are presented to demonstrate the benefits of DSE on enhancing the operational robustness and resilience of 21st century power system through time critical applications. Future research directions are identified and discussed, paving the way for developing the next generation of energy management systems.

Zhao, Junbo↗
DOE OSTI2020

Lab-Scale, Closed-Loop Experimental Characterization, Model Refinement, and Validation of a Hydrokinetic Energy-Harvesting Ocean Kite

This paper presents a study wherein we experimentally characterize the dynamics and control system of a lab-scale ocean kite, and then refine, validate, and extrapolate this model for use in a full-scale system. Ocean kite systems, which harvest tidal and ocean current resources through high-efficiency cross-current motion, enable energy extraction with an order of magnitude less material (and cost) than stationary systems with the same rated power output. However, an ocean kite represents a nascent technology that is characterized by relatively complex dynamics and requires sophisticated control algorithms. In order to characterize the dynamics and control of ocean kite systems rapidly, at a relatively low cost, the authors have developed a lab-scale, closed-loop prototyping environment for characterizing tethered systems, whereby 3D printed systems are tethered and flown in a water channel environment. While this system has been shown to be capable of yielding similar dynamic characteristics to some full-scale systems, there are also fundamental limitations to the geometric scales and flow speeds within the water channel environment, making many other real-world scenarios impossible to replicate from the standpoint of dynamic similarity. Here, to address these scenarios, we show how the lab-scale framework is used to refine and validate a scalable dynamic model of a tethered system, which can then be extrapolated to full-scale operation. In this work, we present an extensive case study of this model refinement, validation, and extrapolation on an ocean kite system intended for operation in the Gulf Stream or similar current environments.

42 ENGINEERING↗
DOE OSTI2021

Discrete-Direct Model Calibration and Uncertainty Propagation Method Confirmed on Multi-Parameter Plasticity Model Calibrated to Sparse Random Field Data

A discrete direct (DD) model calibration and uncertainty propagation approach is explained and demonstrated on a 4-parameter Johnson-Cook (J-C) strain-rate dependent material strength model for an aluminum alloy. The methodology's performance is characterized in many trials involving four random realizations of strain-rate dependent material-test data curves per trial, drawn from a large synthetic population. The J-C model is calibrated to particular combinations of the data curves to obtain calibration parameter sets which are then propagated to “Can Crush” structural model predictions to produce samples of predicted response variability. These are processed with appropriate sparse-sample uncertainty quantification (UQ) methods to estimate various statistics of response with an appropriate level of conservatism. This is tested on 16 output quantities (von Mises stresses and equivalent plastic strains) and it is shown that important statistics of the true variabilities of the 16 quantities are bounded with a high success rate that is reasonably predictable and controllable. The DD approach has several advantages over other calibration-UQ approaches like Bayesian inference for capturing and utilizing the information obtained from typically small numbers of replicate experiments in model calibration situations—especially when sparse replicate functional data are involved like force–displacement curves from material tests. The DD methodology is straightforward and efficient for calibration and propagation problems involving aleatory and epistemic uncertainties in calibration experiments, models, and procedures.

42 ENGINEERING↗
DOE OSTI2018

A Review of Degradation Behavior and Modeling of Capacitors

This paper firstly reviews the failure causes, modes and mechanisms of two major types of capacitors used in power electronic systems - metallized film capacitors and electrolytic capacitors. The degradation modeling related to these capacitors is then presented. Both physics-of-failure and data-driven degradation models for reliability and lifetime estimation are discussed. Based on the exhaustive literature review on degradation modeling of capacitors, we provide a critical assessment and future research directions.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

A two-phase model that unifies and extends the classical models of membrane transport

Two models describe solvent transport through swollen, nonporous membranes. The pore-flow model, based on fluid mechanics, works for porous membranes, whereas the solution-diffusion model invokes molecular diffusion to treat nonporous membranes. Both approaches make valid arguments for swollen polymer membranes, but they disagree in their predictions of intramembrane pressure and concentration profiles. Using a fluid-solid model that treats the solvent and membrane matrix as separate phases, we show both classical models to be valid, to represent complementary approaches to the same phenomenon, and to make identical predictions. The fluid-solid model clarifies recent reverse osmosis measurements; provides a predictive and mechanistic basis for empirical high-pressure limiting flux phenomena, in quantitative agreement with classic measurements; and gives a framework to treat nonporous but mechanically heterogeneous membrane materials.

Science & Technology - Other Topics↗
DOE OSTI2021

Integrated Framework of Vehicle Dynamics, Instabilities, Energy Models, and Sparse Flow Smoothing Controllers

This work presents an integrated framework of: vehicle dynamics models, with a particular attention to instabilities and traffic waves; vehicle energy models, with particular attention to accurate energy values for strongly unsteady driving profiles; and sparse Lagrangian controls via automated vehicles, with a focus on controls that can be executed via existing technology such as adaptive cruise control systems. This framework serves as a key building block in developing control strategies for human-in-the-loop traffic flow smoothing on real highways. In this contribution, we outline the fundamental merits of integrating vehicle dynamics and energy modeling into a single framework, and we demonstrate the energy impact of sparse flow smoothing controllers via simulation results.

Lee, Jonathan W.↗