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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Net-proton number fluctuations in the presence of the QCD critical point

Event-by-event fluctuations of the net-proton number studied in heavy-ion collisions provide an important means in the search for the conjectured critical end point (CP) in the QCD phase diagram. We propose a phenomenological model in which the fluctuations of the chiral critical mode couple to protons and anti-protons. This allows us to study the behavior of the net-proton number fluctuations in the presence of the CP. Calculating the net-proton number cumulants, Cn with n=1,2,3,4, along the phenomenological freeze-out line we show that the ratio of variance and mean C2/C1, as well as kurtosis C4/C2 resemble qualitative properties observed in data in heavy-ion collisions as a function of beam energy obtained by the STAR Collaboration at RHIC. In particular, the non-monotonic structure of the kurtosis and smooth change of the C2/C1 ratio with beam energy could be due to the CP located near the freeze-out line. The skewness, however, exhibits properties that are in contrast to the criticality expected due to the CP. The dependence of our results on the model parameters and the proximity of the chemical freeze-out to the critical point are also discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Non-parametric Lagrangian biasing from the insights of neural nets

We present a Lagrangian model of galaxy clustering bias in which we train a neural net using the local properties of the smoothed initial density field to predict the late-time mass-weighted halo field.By fitting the mass-weighted halo field in the AbacusSummit simulations at z = 0.5, we find that including three coarsely spaced smoothing scales gives the best recovery of the halo power spectrum. Adding more smoothing scales may lead to 2–5% underestimation of the large-scale power and can cause the neural net to overfit.We find that the fitted halo-to-mass ratio can be well described by two directions in the original high-dimension feature space.Projecting the original features into these two principal components and re-training the neural net either reproduces the original training result, or outperforms it with a better match of the halo power spectrum. The elements of the principal components are unlikely to be assigned physical meanings, partly owing to the features being highly correlated between different smoothing scales.Our work illustrates a potential need to include multiple smoothing scales when studying galaxy bias, and this can be done easily with machine-learning methods that can take in high dimensional input feature space.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

21st century tundra shrubification could enhance net carbon uptake of North America Arctic tundra under an RCP8.5 climate trajectory

Recent observed shifts in Arctic tundra shrub cover have uncertain impacts on 21st century net ecosystem carbon exchanges. Here we applied a well-tested ecosystem model, ecosys, to examine the effects of North America Arctic tundra plant dynamics on ecosystem carbon balances from 1980-2100 under the RCP8.5 scenario. Tundra productivity was modeled to increase from enhanced carbon fixation and N mineralization under recent and future climates. Between 1982 and 2100 and averaged across the region, predicted increases in relative dominance of woody versus non-woody plants increased ecosystem annual net primary productivity by 244 g C m -2 that offset concurrent increases in annual heterotrophic respiration (139 g C m -2 ), resulting in an increasing net carbon sink over the 21st century. However, smaller increases in seasonal carbon uptake during winter (1 g C m -2 ) and autumn (22 g C m -2 ) and greater increases in ecosystem respiration (winter (23 g C m -2 ) and autumn (47 g C m -2 )) by 2100 versus 1982 resulted in larger carbon losses during these seasons that completely offset the gains in spring (13 g C m -2 ) and 25% of the gains in summer (140 g C m -2 ). Modeled soil temperatures were predicted to increase more slowly than air temperatures (~0.6 °C for every 1 °C increase in air temperature over the 21st century). This slower soil versus air warming, and thus greater increases in CO 2 fixation versus soil respiration rates, also contributed to the tundra remaining a carbon sink through 2100. However, these higher gains versus losses of carbon may be a transient response and not sustainable under further soil warming beyond 2100. Our modeling analysis allows us to extend beyond results from short-term warming experiments, which cannot characterize effects associated with decadal-scale changes in plant communities.

21st century carbon cycle↗
DOE OSTI2023

Much of zero emissions commitment occurs before reaching net zero emissions

Abstract We explore the response of the Earth’s coupled climate and carbon system to an idealized sequential addition and removal of CO 2 to the atmosphere, following a symmetric and continuous emissions pathway, in contrast to the discontinuous emissions pathways that have largely informed our understanding of the climate response to net zero and net negative emissions to date. We find, using both an Earth system model and an ensemble of simple climate model realizations, that warming during the emissions reduction and negative emissions phases is defined by a combination of a proportionality of warming to cumulative emissions characterized by the transient climate response to emissions (TCRE), and a deviation from that proportionality that is governed by the zero emissions commitment (ZEC). About half of the ZEC is realized before reaching zero emissions, and the ZEC thus also controls the timing between peak cumulative CO 2 emissions and peak temperature, such that peak temperature may occur before peak cumulative emissions if ZEC is negative, underscoring the importance of ZEC in climate policies aimed to limit peak warming. Thus we argue that ZEC is better defined as the committed warming relative to the expected TCRE proportionality, rather than as the additional committed warming that will occur after reaching net zero CO 2 emissions. Once established, the combined TCRE and ZEC relationship holds almost to complete removal of prior cumulative CO 2 emissions. As cumulative CO 2 emissions approach zero through negative CO 2 emissions, CO 2 concentrations drop below preindustrial values, while residual long-term climate change continues, governed by multicentennial dynamical processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Implications of an emission trading scheme for India’s net-zero strategy: a modelling-based assessment

To help meet its near-term NDC goals and long-term net-zero 2070 target, the Government of India has planned to establish a Carbon Credit Trading Scheme (CCTS), i.e. a domestic emission trading scheme (ETS). An ETS is an inherently cost-effective policy instrument for emission reduction, providing the greatest flexibility to reduce emissions from within and across sectors. An effective ETS requires design features that consider country-specific challenges and reflect its role within the larger policy package to achieve long-term emission reduction. Within the Indian context and in this study we therefore investigate—(i) what might be the role of the ETS in achieving India’s long-term mitigation targets? (ii) How might the various sectors interact under an emissions cap? (iii) How might the ETS interact with existing energy and climate policies? We do this analysis by running four main scenarios using the integrated assessment model GCAM (v6.0), adapted to India-specific assumptions and expectations. These scenarios are—(i) NZ (net-zero), (ii) NZ + ETS, (iii) NZ + CC (command and control), and (iv) NZ + RPO (renewables purchase obligations) + ETS. The NZ scenario assumes India’s near-term and long-term climate commitments of net zero by 2070. Scenarios with ETS (ii) and (iv) apply an emissions cap on four sectors—electricity, iron and steel, cement, and fertilizer. The scenario with CC applies a homogenous emission cap on each of the chosen sectors but does not allow cross-sectoral trading. The last scenario includes renewables purchase obligations (RPOs along with an ETS. We show that under a specific ETS emissions cap: (i) the electricity sector emerges as the largest source of cost-effective greenhouse gas (GHG) reduction options; (ii) ETS with trading across sectors is around 24% more cost-effective than ETS with trading only within sectors, (iii) RPOs can be complementary to an ETS although the impact of RPOs on GHG reductions in the electricity sector would need to be considered when setting the level of the ETS cap (or emissions intensity targets) or the RPO targets to avoid low carbon prices, and (iv) the direction and volume of financial transfers across sectors depends on allocation targets set by the government. Based on these results we provide design recommendations for India’s ETS.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

InClass nets: independent classifier networks for nonparametric estimation of conditional independence mixture models and unsupervised classification

Abstract Conditional independence mixture models (CIMMs) are an important class of statistical models used in many fields of science. We introduce a novel unsupervised machine learning technique called the independent classifier networks (InClass nets) technique for the nonparameteric estimation of CIMMs. InClass nets consist of multiple independent classifier neural networks (NNs), which are trained simultaneously using suitable cost functions. Leveraging the ability of NNs to handle high-dimensional data, the conditionally independent variates of the model are allowed to be individually high-dimensional, which is the main advantage of the proposed technique over existing non-machine-learning-based approaches. Two new theorems on the nonparametric identifiability of bivariate CIMMs are derived in the form of a necessary and a (different) sufficient condition for a bivariate CIMM to be identifiable. We use the InClass nets technique to perform CIMM estimation successfully for several examples. We provide a public implementation as a Python package called RainDancesVI.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Efficacy of long-lasting insecticide-incorporated nets on 2 scolytinae pests, the coffee berry borer Hypothenemus hampei and tropical nut borer Hypothenemus obscurus under laboratory conditions

Abstract Several pests affect coffee (Coffea spp., Rubiaceae) and macadamia, Macadamia integrifolia Maiden & Betche (Proteaceae) in Hawaii. The coffee berry borer, Hypothenemus hampei (Ferrari) (Coleoptera: Curculionidae: Scolytinae), is the most damaging to coffee, while the tropical nut borer, Hypothenemus obscurus (Fabricius) (Coleoptera: Curculionidae: Scolytinae), is one of the worst pests of macadamia. This paper investigates the potential efficacy of a long-lasting insecticide-incorporated net (LLIN) under laboratory conditions to manage these pests. The LLIN (40 denier with mesh size 625 knots/in²), incorporated with α-cypermethrin (0.34%), was excised into 100 mm circles and inserted in 100 mm Petri dishes. Nets with the same quality but without insecticides were used as control treatments. Twenty beetles (H. obscurus or H. hampei) each were placed on the treated and non-treated netting at 4 treatment or exposure hours—1, 6, 12, and 24—with 5 replicates. Subsequently, the beetles were ranked alive, affected, or dead. The results showed that the LLIN with α-cypermethrin had significant lethal and sub-lethal effects on both Hypothenemus species, causing over 90% mortality after 24 h of exposure and paralysis after 1, 6, and 12 h of exposure. The highest lethality value was recorded after 24 h of exposure for both H. obscurus and H. hampei. The LT50 of H. obscurus and H. hampei was 18.78 min and 2.15 h, respectively, while the LT90 values were 32.11 and 20.67 h. These results imply the potential effectiveness of LLINs with α-cypermethrin for management of H. obscurus and H. hampei, but field studies are warranted for optimization.

Entomology↗
DOE OSTI2023

Renormalization of Ising cage-net model and generalized foliation

A large class of type-I fracton models, including the X-cube model, have been found to be fixed points of the foliated renormalization group (RG). The system size of such foliated models can be changed by adding or removing decoupled layers of 2D topological states and continuous deformation of the Hamiltonian. In this paper, we study a closely related model—the Ising cage-net model—and find that this model is not foliated in the same sense. In fact, we point out certain unnatural restrictions in the foliated RG, and find that removing these restrictions leads to a generalized foliated RG under which the Ising cage-net model is a fixed point, and which includes the original foliated RG as a special case. The Ising cage-net model thus gives a prototypical example of the generalized foliated RG, and its system size can be changed either by condensing/uncondensing bosonic planon excitations near a 2D plane or through a linear-depth quantum circuit in the same plane. Additionally, we show that these two apparently different RG procedures are closely related, as they lead to the same gapped boundary when implemented in part of a plane. Finally, we briefly discuss the implications for foliated fracton phases, whose universal properties will need to be reexamined in light of the generalized foliated RG.

2-dimensional systems↗
DOE OSTI2018

Origin of the net magnetic moment in LaCoO 3

We use polarized neutron scattering to characterize the Bragg scattering intensity below T C =89.5 K at the (1,0,0) pseudocubic nuclear Bragg point of LaCoO 3 . Upon cooling in a field (FC), a net magnetic moment is apparent in Bragg scattering intensity, just as it was in previous magnetization measurements. Critical behavior associated with the net moment near T C upon cooling in small applied fields rapidly rounds with increasing field strength. We show, using a mean-field calculation, that this net moment can develop in a metastable state that forms upon FC, even when all the interactions in the system are antiferromagnetic.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Net-baryon diffusion in fluid-dynamic simulations of relativistic heavy-ion collisions

A hybrid (hydrodynamics + hadronic transport) theoretical framework is assembled to model the bulk dynamics of relativistic heavy-ion collisions at energies accessible in the Beam Energy Scan (BES) program at the Relativistic Heavy-Ion Collider (RHIC) and the NA61/SHINE experiment at CERN. The system's energy-momentum tensor and net baryon current are evolved according to relativistic hydrodynamics with finite shear viscosity and non-zero net baryon diffusion. Our hydrodynamic description is matched to a hadronic transport model in the dilute region. With this fully integrated theoretical framework, we present a pilot study of the hadronic chemistry, particle spectra, and anisotropic flow. Phenomenological effects of a non-zero net-baryon current and its diffusion on hadronic observables are presented for the first time. Furthermore, the importance of the hadronic transport phase is also investigated.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

Net-baryon multiplicity distribution consistent with lattice QCD

We determine a net-baryon multiplicity distribution which reproduces all cumulants measured so far by lattice QCD. We present the dependence on the volume and temperature of this distribution. We find that for temperatures and volumes encountered in heavy-ion reactions, the multiplicity distribution is very close to the Skellam distribution, making the experimental determination of it rather challenging. We further provide estimates for the statistics required to measure cumulants of the net-baryon and net-proton distributions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

Skewness and kurtosis of net baryon-number distributions at small values of the baryon chemical potential

In this paper, we present results for the ratios of mean (M B ), variance (σ$2\atop{B}$), skewness (S B ) and kurtosis (κ B ) of net baryon-number fluctuations obtained in lattice QCD calculations with physical values of light and strange quark masses. Using next-to-leading order Taylor expansions in baryon chemical potential we find that qualitative features of these ratios closely resemble the corresponding experimentally measured cumulants ratios of net proton-number fluctuations for beam energies down to √sNN ≥ 19.6 GeV. We show that the difference in cumulant ratios for the mean net baryon-number, M B /σ$2\atop{B}$ = χ$B\atop{1}$ (T, µ B )/χ$B\atop{2}$ (T, µ B ) and the normalized skewness, S B σ B = χ$B\atop{3}$ (T, µB)/χ2 (T, µB ), nat-urally arises in QCD thermodynamics. Moreover, we establish a close relation between skewness and kurtosis ratios, S B σ$B\atop{3}$/M B = χ$B\atop{3}$ (T, µ B )/χ$B\atop{1}$ (T,µ B ) and κ B σ$2\atop{B}$ = χ$B\atop{4}$ (T,μ B )/χ$B\atop{2}$ (T,μ B ), valid at small values of the baryon chemical potential.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Nonmonotonic Energy Dependence of Net-Proton Number Fluctuations

Nonmonotonic variation with collision energy (√ s NN) of the moments of the net-baryon number distribution in heavy-ion collisions, related to the correlation length and the susceptibilities of the system, is suggested as a signature for the quantum chromodynamics critical point. Here, we report the first evidence of a nonmonotonic variation in the kurtosis times variance of the net-proton number (proxy for net-baryon number) distribution as a function of √ s NN with 3.1 σ significance for head-on (central) gold-on-gold (Au+Au) collisions measured solenoidal tracker at Relativistic Heavy Ion Collider. Data in noncentral Au+Au collisions and models of heavy-ion collisions without a critical point show a monotonic variation as a function of √ s NN.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

NO-LESS: Near optimal curtailment strategy selection for net load balancing in micro grids

The integration of renewable energy sources such as solar PVs into micro grids has increased in recent years. However, the intermittent and unpredictable nature of renewable energy generation makes supply uncertain, in turn requiring continuous net load balancing to ensure grid stability. One technique to achieve net load balancing is curtailment, however, selection of the optimal set of curtailment strategies to achieve a targeted curtailment is NP-hard. State-of-the-art curtailment selection techniques approach this problem by either developing computationally expensive optimal curtailment strategies or reduce accuracy to generate sub-optimal solutions quickly. In this work, we develop NO-LESS: a Near OptimaL CurtailmEnt Strategy Selection algorithm. NO-LESS is a novel Fully Polynomial Time Approximation Scheme (FPTAS) which determines bounded (near optimal) curtailment strategies in a small amount of time while simultaneously satisfying practical constraints on strategy switching overhead and curtailment fairness. We perform both theoretical analysis and practical evaluation to show that NO-LESS is a scalable solution for performing net load balancing through curtailment strategy selection in Micro Grids.

14 SOLAR ENERGY↗
DOE OSTI2024

Forte: An Interactive Visual Analytic Tool for Trust-Augmented Net-Load Forecasting

Accurate net-load forecasting is vital for energy planning, aiding decisions on trade and load distribution. However, assessing the performance of forecasting models across diverse input variables, like temperature and humidity, remains challenging, particularly for eliciting a high degree of trust in the model outcomes. In this context, there is a growing need for data-driven technological interventions to aid scientists in comprehending how models react to both noisy and clean input variables, thus shedding light on complex behaviors and fostering confidence in the outcomes. In this paper, we present Forte, a visual analytics-based application to explore deep probabilistic net-load forecasting models across various input variables and understand the error rates for different scenarios. With carefully designed visual interventions, this web-based interface empowers scientists to derive insights about model performance by simulating diverse scenarios, facilitating an informed decision-making process. We discuss observations made using Forte and demonstrate the effectiveness of visualization techniques to provide valuable insights into the correlation between weather inputs and net-load forecasts, ultimately advancing grid capabilities by improving trust in forecasting models.

Bhattacharjee, Kaustav↗
DOE OSTI2023

Evaluation of Distributed Power Apportioning with Net Load Management Engine in Microgrids Using Power Hardware-in-the-Loop Simulation

This article presents the performance evaluation of ratio consensus-based distributed power apportioning engine along with centralized net load management (NLM) engine that ensures viable and stable operation of an islanded microgrid. Managing net load variability in a microgrid with high penetrations of uncertain renewable generation and ever-changing load demands is a crucial need in order to ensure viable and stable operation of the microgrid. Centralized “dispatch-rule”-based and/or multi-agent-based distributed control of distributed energy resources (DERs) in microgrid are well accepted for microgrid by adapting ANSI/ISA-95-based hierarchical control architecture. In the application where microgrid network has large geographical span with multiple DERs dispersed in the network, high penetration of uncertain renewable energy resources, and ever-changing load demands, a judicious selection of techniques/solutions for managing net-load resources for maintaining viability and stability is required. With this motivation, this article proposes a novel solution to mitigate the challenges by incorporating a mixed centralized NLM engine and distributed power apportioning control of DERs and loads. A power-hardware-in-the-loop (PHIL) -based experiment is conducted with the centralized NLM engine and the distributed power apportioning engine along with two commercial inverters. The experimental results validates the efficacy of the proposed method in ensuring viability and stability of a microgrid.

hardware-in-the-loop simulation↗
DOE OSTI2021

UIR-Net: Object Detection in Infrared Imaging of Thermomechanical Processes in Automotive Manufacturing

Thermomechanical processes (TMPs) such as resistance spot welding (RSW) and hot stamping are widely used in automotive manufacturing. Recent advancement in sensing technology has led to an increasing adoption of thermographic cameras to capture the infrared (IR) radiation of a metal part (or component of a part) during its thermomechanical processing or immediately after the process when the part is still hot. Detecting the object(s) of interest from raw IR images is an essential step in analyzing these data. Deep learning (DL) has been a recent success for object detection (OD), but the application of DL-based OD for industrial IR images in manufacturing is largely lagging behind. The major contribution of this work, which is also the distinction from previous OD studies, is the capability of building the OD model with unlabeled IR images, i.e., imaging data without accurate information indicating the object position. Here, the architecture of Unsupervised IR Image Net (UIR-Net) is designed to accommodate the unique characteristics of IR images from TMPs in manufacturing. This study presents a novel method for OD in unlabeled IR images from TMPs. The proposed method, called UIR-Net, consists of two components: label generation and DL model construction. Two case studies from automotive manufacturing, RSW and hot stamping, are reported to demonstrate the feasibility and effectiveness of the proposed method.

42 ENGINEERING↗
DOE OSTI2021

Improving the Accuracy of Clustering Electric Utility Net Load Data using Dynamic Time Warping

Identifying patterns in electric utility net load data in a time-series format is very useful in preparing the operation for next day. Machine learning algorithms have been used in other domains and those concepts are applied in this paper on real-world net load measurement data. Clustering is the practice of grouping data with similar characteristics as determined by the distance measure. The K-means clustering algorithm is utilized here with actual electric utility data. The paper uses the standard distance measure, Euclidean distance (ED), and compares its performance against the dynamic time warping (DTW) measure. An actual case study with real data is presented, and DTW distance measure-based method observed to result better accuracy compared to the ED based method for substation net load measurements predominantly with residential customers.

clustering↗