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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Measurement of branching fractions of $J/\psi$ and $\psi$(3686) decays to $\Sigma^+$< and $\overline\Sigma^-$

Using 1310.6 x 10 6 J/ψ and 448.1 x 10 6 ψ(3686) events collected with the BESIII detector, the branching fractions of J/ψ decays to $Σ^+\overline{Σ}^-$ is measured to be (10.61 ± 0.04 ± 0.36) x 10 -4 , which is significantly more precise than the current world average. The branching fractions of ψ(3686) decays to $Σ^+\overline{Σ}^-$ is measured to be (2.52 ± 0.04 ± 0.09) x 10 -4 , which is consistent with the previous measurements. In addition, the ratio of $\mathcal{B}$(ψ(3686) → $Σ^+\overline{Σ}^-$)/$\mathcal{B}$(J/ψ → $Σ^+\overline{Σ}^-$) is determined to be (23.8 ± 1.1)% which violates the “12% rule”.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Measurement of the branching fractions of the singly Cabibbo-suppressed decays $$ {\Lambda}_{\textrm{c}}^{+}\to \textrm{p}\eta $$ and $$ {\Lambda}_{\textrm{c}}^{+}\to \textrm{p}\omega $$

Abstract Based on 4.5 fb −1 e + e − collision data collected with BESIII detector at seven energy points between 4.600 and 4.699 GeV, the branching fractions for$$ {\Lambda}_c^{+}\to p\eta $$ Λ c + → pη and$$ {\Lambda}_c^{+}\to p\omega $$ Λ c + → pω were measured by means of single-tag method. The branching fractions of$$ {\Lambda}_c^{+}\to p\eta $$ Λ c + → pη and$$ {\Lambda}_c^{+}\to p\omega $$ Λ c + → pω are determined to be (1.57±0.11 stat ±0.04 syst )×10 −3 and (1.11±0.20 stat ±0.07 syst )×10 −3 , with a statistical significance of greater than 10σand 5.7σ, respectively. These results are consistent with the previous measurements by BESIII, LHCb and Belle, and the result of$$ {\Lambda}_c^{+}\to p\eta $$ Λ c + → pη is the most precise to date.

Physics↗
DOE OSTI2023

Measurement of the cross section of $$ {e}^{+}{e}^{-}\to {\Xi}^{-}{\overline{\Xi}}^{+} $$ at center-of-mass energies between 3.510 and 4.843 GeV

Abstract Usinge + e − collision data corresponding to a total integrated luminosity of 12.9 fb −1 collected with the BESIII detector at the BEPCII collider, the exclusive Born cross sections and the effective form factors of the reaction$$ {e}^{+}{e}^{-}\to {\Xi}^{-}{\overline{\Xi}}^{+} $$ e + e − → Ξ − Ξ ¯ + are measured via the single baryon-tag method at 23 center-of-mass energies between 3.510 and 4.843 GeV. Evidence for the decay$$ \psi (3770)\to {\Xi}^{-}{\overline{\Xi}}^{+} $$ ψ 3770 → Ξ − Ξ ¯ + is observed with a significance of 4.5σby analyzing the measured cross sections together with earlier BESIII results. For the other charmonium(-like) statesψ(4040),ψ(4160),Y(4230),Y(4360),ψ(4415), andY(4660), no significant signal of their decay to$$ {\Xi}^{-}{\overline{\Xi}}^{+} $$ Ξ − Ξ ¯ + is found. For these states, upper limits of the products of the branching fraction and the electronic partial width at the 90% confidence level are provided.

Physics↗
DOE OSTI2025

Measurement of the phase between strong and electromagnetic amplitudes in the decay J/ψ → ϕη

The first direct measurement of the relative phase between the strong and electromagnetic amplitudes for a J/ψ decaying into a vector-pseudoscalar final state is performed using 26 energy points of e + e − annihilation data between 3.00 GeV and 3.12 GeV. The data sets were collected by the BESIII detector with a total integrated luminosity of 452 pb −1 . By investigating the interference pattern in the cross section lineshape of e + e − → ϕη, the relative phase between the strong and electromagnetic amplitudes of J/ψ decay is determined to be within [133°, 228°] at 68% confidence level.

QCD↗
DOE OSTI2022

Observation of the decay ψ (3686) → $ {\Sigma}^{-}{\overline{\Sigma}}^{+} $ and measurement of its angular distribution

Using (448.1 ± 2.9) × 10 6 ψ(3686) events collected with the BESIII detector at the BEPCII collider, the decay ψ(3686) → Σ - $\overline{Σ}$ + is observed for the first time with a branching fraction of (2.82 ± 0.04 stat. ± 0.08 syst. ) × 10 -4 , and the angular parameter α Σ- is measured to be 0.96 ± 0.09 stat. ± 0.03 syst. .

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Partial wave analysis of the charmed baryon hadronic decay $ {\Lambda}_c^{+} $ → Λ π +π 0

Based on e + e - collision samples corresponding to an integrated luminosity of 4.4 fb -1 collected with the BESIII detector at center-of-mass energies between 4.6 GeV and 4.7 GeV, a partial wave analysis of the charmed baryon hadronic decay ${\Lambda}_c^{+}$ → Λπ + π 0 is performed, and the decays ${\Lambda}_c^{+}$ → Λρ(770) + and ${\Lambda}_c^{+}$ → Σ(1385)π are studied for the first time. Making use of the world-average branching fraction $\mathcal{B}$ (${\Lambda}_c^{+}$ → Λπ + π 0 ), their branching fractions are determined to be $\mathcal{B}$(${\Lambda}_c^{+}$ → Λρ(770) + ) = (4.06 ± 0.30 ± 0.35 ± 0.23) x 10 -2 , $\mathcal{B}$(${\Lambda}_c^{+}$ → Σ(1385) + )π 0 ) = (5.86 ± 0.49 ± 0.52 ± 0.35) x 10 -3 , $\mathcal{B}$(${\Lambda}_c^{+}$ → Σ(1385) 0 )π + ) = (6.47 ± 0.59 ± 0.66 ± 0.38) x 10 -3 , where the first uncertainties are statistical, the second are systematic, and the third are from the uncertainties of the branching fractions $\mathcal{B}$ ${\Lambda}_c^{+}$ → Λπ + π 0 and $\mathcal{B}$(Σ(1385) → Λπ). In addition, the decay asymmetry parameters are measured to be αΛρ(770)+ = - 0.763 ± 0.053 ± 0.045, $\alpha$ Σ(1385)+π 0 = -0.917 ± 0.069 ± 0.056, and $\alpha$ Σ(1385) 0 π + = -0.789 ± 0.098 ± 0.056.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Measurement of the cross section of e + e - → ηπ + π - at center-of-mass energies from 3.872 GeV to 4.700 GeV

Using data samples with an integrated luminosity of 19 fb -1 at twenty-eight center-of-mass energies from 3.872 GeV to 4.700 GeV collected with the BESIII detector at the BEPCII electron-positron collider, the process e + e - → ηπ + π - and the intermediate process e + e - → ηρ 0 are studied for the first time. The Born cross sections are measured. No significant resonance structure is observed in the cross section lineshape.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Measurement of $$ {e}^{+}{e}^{-}\to p{K}^{-}\overline{\Lambda}+c.c. $$ cross sections between 4.009 GeV and 4.951 GeV

Abstract Usinge + e − collision datasets corresponding to a total integrated luminosity of 21.7 fb −1 collected with the BESIII detector at the BEPCII collider at center-of-mass energies ranging from 4.009 GeV to 4.951 GeV, the energy-dependent cross sections of$$ {e}^{+}{e}^{-}\to p{K}^{-}\overline{\Lambda}+c.c. $$ e + e − → p K − Λ ¯ + c . c . are measured for the first time. By fitting these energy-dependent cross sections, we search for the excitedψstatesψ(4160) andψ(4415), and the vector charmonium-like statesψ(4230),ψ(4360), andψ(4660). No evidence for these is observed and the upper limits on the branching fractions of these states decaying into$$ p{K}^{-}\overline{\Lambda}+c.c. $$ p K − Λ ¯ + c . c . are set at the 90% confidence level.

Physics↗
DOE OSTI2023

Studies of the decay $$ {\textrm{D}}_{\textrm{s}}^{+}\to {\textrm{K}}^{+}{\textrm{K}}^{-}{\mu}^{+}{\nu}_{\mu } $$

Abstract The$$ {D}_s^{+}\to {K}^{+}{K}^{-}{\mu}^{+}{\nu}_{\mu } $$ D s + → K + K − μ + ν μ decay is studied based on 7.33 fb −1 ofe + e − collision data collected with the BESIII detector at center-of-mass energies in the range from 4.128 to 4.226 GeV. The absolute branching fraction is measured as$$ \mathcal{B}\left({D}_s^{+}\to \phi {\mu}^{+}{\nu}_{\mu}\right)=\left(2.25\pm 0.09\pm 0.07\right)\times {10}^{-2} $$ B D s + → ϕ μ + ν μ = 2.25 ± 0.09 ± 0.07 × 10 − 2 , the most precise measurement to date. Combining with the world average of$$ \mathcal{B}\left({D}_s^{+}\to \phi {e}^{+}{\nu}_e\right) $$ B D s + → ϕ e + ν e , the ratio of the branching fractions obtained is$$ \frac{\mathcal{B}\left({D}_s^{+}\to \phi {\mu}^{+}{\nu}_{\mu}\right)}{\mathcal{B}\left({D}_s^{+}\to \phi {e}^{+}{\nu}_e\right)}=0.94\pm 0.08 $$ B D s + → ϕ μ + ν μ B D s + → ϕ e + ν e = 0.94 ± 0.08 , in agreement with lepton universality. By performing a partial wave analysis, the hadronic form factor ratios atq 2 = 0 are extracted, finding$$ {r}_V=\frac{V(0)}{A_1(0)}=1.58\pm 0.17\pm 0.02 $$ r V = V 0 A 1 0 = 1.58 ± 0.17 ± 0.02 and$$ {r}_2=\frac{A_2(0)}{A_1(0)}=0.71\pm 0.14\pm 0.02 $$ r 2 = A 2 0 A 1 0 = 0.71 ± 0.14 ± 0.02 , where the first uncertainties are statistical and the second are systematic. No significantS-wave contribution fromf 0 (980)→ K + K − is found. The upper limit$$ \mathcal{B}\left({D}_s^{+}\to {f}_0(980){\mu}^{+}{\nu}_{\mu}\right)\cdot \mathcal{B}\left({f}_0(980)\to {K}^{+}{K}^{-}\right)<5.45\times {10}^{-4} $$ B D s + → f 0 980 μ + ν μ ⋅ B f 0 980 → K + K − < 5.45 × 10 − 4 is set at 90% credibility level.

Physics↗
DOE OSTI2024

The first ensemble of kilometer-scale simulations of a hydrological year over the third pole

An accurate understanding of the current and future water cycle over the Third Pole is of great societal importance, given the role this region plays as a water tower for densely populated areas downstream. An emerging and promising approach for skillful climate assessments over regions of complex terrain is kilometer-scale climate modeling. As a foundational step towards such simulations over the Third Pole, we present a multi-model and multi-physics ensemble of kilometer-scale regional simulations for the hydrological year of October 2019 to September 2020. The ensemble consists of 13 simulations performed by an international consortium of 10 research groups, configured with a horizontal grid spacing ranging from 2.2 to 4 km covering all of the Third Pole region. These simulations are driven by ERA5 and are part of a Coordinated Regional Climate Downscaling EXperiment Flagship Pilot Study on Convection-Permitting Third Pole. The simulations are compared against available gridded and in-situ observations and remote-sensing data, to assess the performance and spread of the model ensemble compared to the driving reanalysis during the cold and warm seasons. Although ensemble evaluation is hindered by large differences between the gridded precipitation datasets used as a reference over this region, we show that the ensemble improves on many warm-season precipitation metrics compared with ERA5, including most wet-day and hour statistics, and also adds value in the representation of wet spells in both seasons. As such, the ensemble will provide an invaluable resource for future improvements in the process understanding of the hydroclimate of this remote but important region.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Charge carrier transport in thin conjugated polymer films: influence of morphology and polymer/substrate interactions

The performance of conjugated polymer (CP)-based electronic devices relies on optimal charge carrier mobilities, which are determined by monomeric architecture, degree of polymerization, chain conformation, and the nano- and mesoscale morphologies. With regard to the latter, we discuss two effects that have received limited attention in the literature, yet important for device performance optimization: (1) the role of morphological disorder and of CP/substrate interactions on the in-plane and out-of-plane carrier transport in CPs; (2) the impact of morphological disorder on charge transfer at the CP/substrate interface. The emergence of film thickness-dependent carrier mobilities, varying over two orders of magnitude within a length scale of 200 nm, and band-bending phenomena, extending tens of nanometers within the CP, are associated with these effects. These findings suggest areas for further research in order to enable widespread applications of next-generation CP-based devices.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Crack fault diagnosis of rotating machine in nuclear power plant based on ensemble learning

Crack faults in rotating machines can cause machine shutdown or scrapping, endangering the normal operation and safety of nuclear power plants. Intelligent diagnostic techniques based on machine learning have the potential to diagnose crack faults. However, problems such as scarcity of field fault data and high noise of plant measurements pose challenges to the application of machine learning. Here this study proposes an ensemble learning approach to mitigate the negative impacts of the problems. Ensemble learning is a strategy for combining multiple machine learning models into a composite model. The basic idea of ensemble learning is that even if one model makes a mistake, other models can correct it. Case studies based on bearing and gear system fault experiments show that the proposed ensemble learning models have better diagnostic results than the single model in the presence of noise and small data.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Pre-trained network-based transfer learning: A small-sample machine learning approach to nuclear power plant classification problem

Some research topics belonging to classification problems in the nuclear industry, such as fault diagnosis and accident identification, can be solved by feature extraction and subsequent application of statistical machine learning classifiers. Recently, deep neural network-based methods with automatic feature extraction and high accuracy have gained wide attention. They usually require large-scale training data, however, plant fault or accident data are scarce or difficult to obtain. Here this paper proposes a convolutional network (CNN)-based transfer learning method to solve this problem. The network's shallow layer is derived from a pre-trained CNN based on the ImageNet database to automatically extract features, and the deep layer is customized to match the classification problem. Data in non-image formats are converted to image formats and subsequently used to train the network. Case studies of rotating machines fault diagnosis show that the proposed method requires only limited training data to achieve high accuracy.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Deep reinforcement learning for class imbalance fault diagnosis of equipment in nuclear power plants

In equipment fault diagnosis in nuclear power plants, there may be far more samples in one class (e.g., a health state) than in another class (e.g., a fault state). The distribution of data in each class is highly skewed. Most machine learning algorithms are suitable for balanced training datasets. When faced with imbalanced samples, these algorithms tend to provide good identification for the majority classes and bias for the minority classes. However, the misclassification of minority classes can lead to high costs. To address the above problem, this paper develops a deep reinforcement learning-based diagnosis method that models fault diagnosis as a sequential decision-making process. At each time step, the agent receives the state of the environment represented by the training samples and then takes a diagnosis action guided by a policy. If the action is correct/incorrect, the agent receives a positive/negative reward. The reward for minority classes is higher than that for majority classes. The agent’s goal is to obtain as many cumulative rewards as possible in the process, i.e., to identify the sample as correctly as possible. Six demonstration scenarios are constructed, depending on the selected fault datasets and the designed model structures. Experiments show that the proposed method achieves a higher weighted-averaged F1 score than the classical supervised learning method in most cases of class imbalance. Finally, the proposed method has potential applications in the field of class imbalance fault diagnosis of equipment in nuclear power plants.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Self-cleaning and de-pollution efficacies of photocatalytic architectural membranes

Photocatalytic self-cleaning “cool” roofs and walls can maintain high albedos, saving building cooling energy, reducing peak power demand, and mitigating the urban heat island effect. Other environmental benefits result from their de-polluting properties. Specimens from two different photocatalytic architectural membranes and a non-photocatalytic control were exposed alongside vertically, facing west, for two years at three California sites, and retrieved quarterly for testing. Photocatalytic materials showed excellent self-cleaning performance, retaining albedos of 0.74 – 0.75. By contrast, the control material exhibited an albedo loss of up to 0.10, with appreciable soiling observed by scanning electron microscopy. De-pollution capacity was assessed by quantifying NO removal and NOx deposition rates at 60 °C. Furthermore, efficacy varied with exposure location, weather conditions, and the nature of the photocatalytic material. Seasonal effects were observed, with partial inhibition during the dry season and reactivation during the rainy season.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Integrated multimodel analysis reveals achievable pathways toward reliable, 100% renewable electricity for Los Angeles

Climate change has prompted many communities to set targets for carbon-free power supplies, but they often lack data-driven strategies to achieve them. We present a comprehensive analysis of an entirely renewable electric power system that can maintain operating reliability and resource adequacy using detailed models of the city of Los Angeles power grid. In consultation with the operating utility, the Los Angeles Department of Water and Power (LADWP), and the local community, we develop four supply scenarios across three demand projections to analyze which types of infrastructure and operational changes would achieve reliable electricity at least cost. We find that a reliable, 100%-renewable power system yielding more than $1 billion annually in health and climate co-benefits is achievable. Solar can supply most future energy needs, while combustion turbines that use renewable, storable carbon-neutral fuels are key to maintaining reliability. This study provides a replicable methodology that other jurisdictions globally can follow.

14 SOLAR ENERGY↗