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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 1,315 records · Page 73
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Multidifferential study of identified charged hadron distributions in Z -tagged jets in proton-proton collisions at $\sqrt{s} =13$ TeV

Jet fragmentation functions are measured for the first time in proton-proton collisions for charged pions, kaons, and protons within jets recoiling against a Z boson. The charged-hadron distributions are studied longitudinally and transversely to the jet direction for jets with transverse momentum 20 < p T < 100 GeV and in the pseudorapidity range 2.5 < η <4. The data sample was collected with the LHCb experiment at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 1.64 fb –1 . Triple differential distributions as a function of the hadron longitudinal momentum fraction, hadron transverse momentum, and jet transverse momentum are also measured for the first time. This helps constrain transverse-momentum-dependent fragmentation functions. Differences in the shapes and magnitudes of the measured distributions for the different hadron species provide insights into the hadronization process for jets predominantly initiated by light quarks.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Group-equivariant autoencoder for identifying spontaneously broken symmetries

We introduce the group-equivariant autoencoder (GE autoencoder), a deep neural network (DNN) method that locates phase boundaries by determining which symmetries of the Hamiltonian have spontaneously broken at each temperature. We use group theory to deduce which symmetries of the system remain intact in all phases, and then use this information to constrain the parameters of the GE autoencoder such that the encoder learns an order parameter invariant to these “never-broken” symmetries. This procedure produces a dramatic reduction in the number of free parameters such that the GE-autoencoder size is independent of the system size. We include symmetry regularization terms in the loss function of the GE autoencoder so that the learned order parameter is also equivariant to the remaining symmetries of the system. By examining the group representation by which the learned order parameter transforms, we are then able to extract information about the associated spontaneous symmetry breaking. We test the GE autoencoder on the 2D classical ferromagnetic and antiferromagnetic Ising models, finding that the GE autoencoder (1) accurately determines which symmetries have spontaneously broken at each temperature; (2) estimates the critical temperature in the thermodynamic limit with greater accuracy, robustness, and time efficiency than a symmetry-agnostic baseline autoencoder; and (3) detects the presence of an external symmetry-breaking magnetic field with greater sensitivity than the baseline method. Lastly, we describe various key implementation details, including a quadratic-programming-based method for extracting the critical temperature estimate from trained autoencoders and calculations of the DNN initialization and learning rate settings required for fair model comparisons.

42 ENGINEERING↗
DOE OSTI2021

Identifying the fingerprints of topological states by tuning magnetoresistance in a semimetal: The case of topological half-Heusler Pt 1 - x Au x LuSb

We report that topological materials often exhibit remarkably linear nonsaturating magnetoresistance (LMR), which is both of scientific and technological importance. However, the role of topologically nontrivial states in the emergence of such a behavior has eluded clear demonstration in experiments. Here, by reducing the coupling between the topological surface states (TSS) and the bulk carriers, we controllably tune the LMR behavior in Pt 1-x Au x LuSb into distinct plateaus in Hall resistance, which we show arise from a quantum Hall phase. This allowed us to reveal how smearing of the Landau levels, which otherwise gives rise to a quantum Hall phase, results in an LMR behavior due to strong interaction between the TSS with a positive g factor and the bulk carriers. We establish that controlling the coupling strength between the surface and the bulk carriers in topological materials can bring about dramatic changes in their magnetotransport behavior. In addition, our work outlines a strategy to reveal macroscopic physical observables of TSS in compounds with a semimetallic bulk band structure, as is the case in multifunctional Heusler compounds, thereby opening up opportunities for their utilization in hybrid quantum structures.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Identifying topological superconductivity in two-dimensional transition-metal dichalcogenides

Here we study the superconducting pairing instabilities and gap functions for prototypical two-dimensional (2D) transition-metal dichalcogenides (TMDCs) WS 2 , MoTe 2 , and MoS 2 in the 2H phase under both hole and electron doping at 10 K. Our first-principles quantum many-body Green's function approach allows us to treat the full d and p manifold of orbitals with strong spin-orbit coupling, yielding pairing predictions with material-specific detail. The resulting gap functions exhibit a variety of mixed-parity superconducting states, including s, p, d, f, d ± id, and p ± ip pairing modes. In particular, we predict 3% and 4% hole-doped WS 2 to be a chiral p ± ip topological superconductor. For 1% hole-doped MoS 2 , we find a competition between three doubly degenerate chiral and nonchiral instabilities. Overall, the relative pairing strengths are found to follow the Fermi-surface topology, due to nesting between the Fermi-surface sheets. Finally, we discuss our predictions in relation to available experimental data and classify the topology of the predicted superconducting pairing symmetries.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Identifying neutron sources using recoil and time-of-flight spectroscopy

Identification of neutron sources is central to nuclear physics and its applications, from planetary science to nuclear security, yet direct source discrimination from measured neutron spectra remains fundamentally elusive. Here, we introduce a Bayesian protocol that directly infers source ensembles from measured neutron spectra by combining full-spectrum template matching with probabilistic evidence evaluation. Applying this protocol to recoil and time-of-flight spectroscopy, we recover single- and two-source configurations with strong statistical significance (beyond 4⁢𝜎) at event counts as low as ∼10 3 . These results demonstrate that neutron spectral signatures can be leveraged for robust source identification, opening a new observational window for both fundamental research and operationally driven applications.

neutron physics↗
DOE OSTI2023

Frequency Response Analysis to Monitor and Identify Changes in the Impedance of a Photovoltaic Panel Measured Online using a Power Optimizer

Photovoltaic (PV) cells are generally modeled as a current source due to photocurrent, p-n junction diodes with parasitic resistance, capacitance, and inductance. This paper proposes online frequency response analysis (FRA) to measure the impedance of a PV panel using an existing panel-level power optimizer in a PV system. The algorithm will actively perturb a small signal into a 300 W rooftop PV panel and compute its small signal impedance. The technology discussed is easy to incorporate, requires no additional hardware, doesn't alter the stability of the system, and is implemented at a steady-state point. The power optimizer initially stabilizes at an operating point and then perturbs the PV current via FRA and computes PV panel impedance. The relative standard deviation test conducted indoors under 300 W/m 2 illumination on a PV panel shows a less than 5% error rate in PV panel impedance magnitude and phase is measured using a power optimizer.

14 SOLAR ENERGY↗
DOE OSTI2022

Identifying Hydrometeorological Factors Influencing Reservoir Releases Using Machine Learning Methods

Simulation of reservoir releases plays a critical role in social-economic functioning and our nation's security. How-ever, it is challenging to predict the reservoir release accurately because of many influential factors from natural environments and engineering controls such as the reservoir inflow and storage. Moreover, climate change and hydrological intensification causing the extreme precipitation and temperature make the accurate prediction of reservoir releases even more challenging. Machine learning (ML) methods have shown some successful applications in simulating reservoir releases. However, previous studies mainly used inflow and storage data as inputs and only considered their short-term influences (e.g, previous one or two days). In this work, we use long short-term memory (LSTM) networks for reservoir release prediction based on four input variables including inflow, storage, precipitation, and temperature and consider their long-term influences. We apply the LSTM model to 30 reservoirs in Upper Colorado River Basin, United States. We analyze the prediction performance using six statistical metrics. More importantly, we investigate the influence of the input hydrometeorological factors, as well as their temporal effects on reservoir release decisions. Results indicate that inflow and storage are the most influential factors but the inclusion of precipitation and temperature can further improve the prediction of release especially in low flows. Additionally, the inflow and storage have a relatively long-term effect on the release. These findings can help optimize the water resources management in the reservoirs.

Fan, Ming↗
DOE OSTI2020

Dark Lock-in Thermography Identifies Solder Bond Failure as the Root Cause of Series Resistance Increase in Fielded Solar Modules

Correlating the electrical performance of photovoltaic modules to the spatially resolved photoluminescence, electroluminescence, and dark lock-in thermography (DLIT) images is an important long-term goal for developing solar cell technology. These images offer highly sophisticated and detailed information about the spatial distribution and (if imaged at successive time intervals) temporal degradation of local series and shunt resistances. There have been extensive studies to correlate these imaging techniques to local characteristics at the cell level. However, it has been difficult to extract and quantify module-level information from the techniques. In this study, we interpret module current-voltage (I-V) measurements along with corresponding DLIT images within a module-scale simulation framework, demonstrating that series resistance degradation of the module I-V characteristics, in this case, can be attributed to solder bond failures. Our simulations highlight how current crowding associated with a failed solder joint (or a section of the solder pad) translates to the characteristic point-like (asymmetric doublet) heating pattern in neighboring solder joints (or the neighboring regions of the solder pad). Overall, the correlation between series resistance and solder joint degradation could inform expedited diagnosis of field degradation of solar modules.

42 ENGINEERING↗
DOE OSTI2025

Sequential Stress Identifies Processing Defects in Bifacial Photovoltaic Modules That Limit Durability

Here, we use sequential stress to investigate hurdles to bifacial photovoltaic (PV) module durability from lamination defects. We test mini-modules with glass/glass (G/G) and glass/transparent-backsheet (G/TB) constructions using either ethylene vinyl acetate or polyolefin elastomer (POE) based encapsulants under a modified IEC 63209-2 sequential stress. This sequence includes multiple iterations of damp heat (DH200), full spectrum light exposure (A3), thermal cycling (TC50), and humidity/freeze (HF10). We compare indoor stress with outdoor exposure. Results show similar relative trends in degradation after a year outdoors compared to our first stress cycle. Subsequent stress cycles impart more severe damage than outdoor exposure for the short outdoor duration used here. Edge-pinch lamination defects in G/G mini-modules limit durability causing delamination and cell cracks. Conversely, we observe greater degradation in G/TB mini-modules compared to G/G in the later stages of the stress sequence when the backsheets are directly exposed to UV-containing light. Our results highlight: 1) the utility of sequential stress testing to uncover degradation modes in bifacial PV, 2) implications of using mini-modules for testing PV quality, and 3) the importance of lamination defects that must be avoided to ensure durability as the industry adopts G/G or G/TB packaging.

14 SOLAR ENERGY↗