Search NASA⌕ Search

SEARCH · Search NASA

Results for “braiding”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Measurement of Reactor Antineutrino Oscillation at SNO+

SNO+ Collaboration reports its second spectral analysis of reactor antineutrino oscillation using 286 ton-yr of new data. The measured energies of reactor antineutrino candidates were fitted to obtain the second-most precise determination of the neutrino mass-squared difference Δ⁢𝑚$^{2}_{21}$=⁢(7.96$^{+0.48}_{ −0.42}$) × 10 −5 eV2. Constraining Δ⁢𝑚$^{2}_{21}$ and sin 2 ⁡𝜃 12 with measurements from long-baseline reactor antineutrino and solar neutrino experiments yields Δ⁢𝑚$^{2}_{21}$=(7.58$^{+0.18}_{−0.17}$) × 10 −5 eV 2 and sin 2 ⁡𝜃 12 = 0.308 ± 0.013. This fit also yields a first measurement of the flux of geoneutrinos in the Western Hemisphere, with 73$^{+47}_{−43}$ TNU at SNO+.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Experimental study of the nature of the 1 – and 2 – excited states in 10 Be using the 11 Be (p,d) reaction in inverse kinematics

In this paper, we investigated the structure of two excited states in the nucleus 10 Be, which were suspected to have spatially extended wave functions (or "haloes"). This was done by performing a transfer reaction that removed a neutron from the core of the 11 Be nucleus, a well-known one-neutron halo nucleus. By doing so, we specifically produced configurations, whereas the halo neutron in 11 Be remained when populating excited states in 10 Be. If the two excited states in 10 Be had a strong overlap with those configurations, one would have expected them to be strongly populated in the reaction. We find that this was not quite the case and we postulated that the reason why was because those states had a mixed configuration that not only displayed halo-like, but also cluster-like, properties. The overlap was therefore not as strong as expected (and suggested by previous experiments).

6 ≤ A ≤ 19↗
DOE OSTI2020

Generalized and Mechanistic PV Module Performance Prediction From Computer Vision and Machine Learning on Electroluminescence Images

Electroluminescence (EL) imaging of photovoltiac (PV) modules offers high-speed, high-resolution information about device performance, affording opportunities for greater insight and efficiency in module characterization across manufacturing, research and development, and power plant operations and management. Predicting module electrical properties from EL image features is a critical step toward these applications. In this article, we demonstrate quantification of both generalized and performance mechanism-specific EL image features, using pixel intensity-based and machine learning classification algorithms. From EL image features, we build predictive models for PV module power and series resistance, using time-series current-voltage (I-V) and EL data obtained stepwise on five brands of modules spanning three Si cell types through two accelerated exposures: damp heat (DH) (85°C/85% RH) and thermal cycling (TC) (IEC 61215). Overall, 195 pairs of EL images and I-V characteristics were analyzed, yielding 11700 individual PV cell images. A convolutional neural network was built to classify cells by the severity of busbar corrosion with high accuracy (95%). Generalized power predictive models estimated the maximum power of PV modules from EL images with high confidence and an adjusted-R 2 of 0.88, across all module brands and cell types in extended DH and TC exposures. Mechanistic degradation prediction was demonstrated by quantification of busbar corrosion in EL images of three module brands in DH, and subsequent modeling of series resistance using these mechanism-specific EL image features. For modules exhibiting busbar corrosion, we demonstrated series resistance predictive models with adjusted-R 2 of up to 0.73.

14 SOLAR ENERGY↗
DOE OSTI2021

Solar Transposition Modeling via Deep Neural Networks With Sky Images

This article presents a notable advance toward the development of a new method of increasing the single-axis tracking photovoltaic (PV) system power output by improving the determination and near-term prediction of the optimum module tilt angle. The tilt angle of the plane receiving the greatest total irradiance changes with Sun position and atmospheric conditions including cloud formation and movement, aerosols, and particulate loading, as well as varying albedo within a module's field of view. In this article, we present a multi-input convolutional neural network that can create a profile of plane-of-array irradiance versus surface tilt angle over a full 180° arc from horizon to horizon. As input, the neural network uses the calculated solar position and clear-sky irradiance values, along with sky images. The target irradiance values are provided by the multiplanar irradiance sensor (MPIS). In order to account for varying irradiance conditions, the MPIS signal is normalized by the theoretical clear-sky global horizontal irradiance. Using this information, the neural network outputs an N -dimensional vector, where N is the number of points to approximate the MPIS curve via Fourier resampling. The output vector of the model is smoothed with a Gaussian kernel to account for error in the downsamping and subsequent upsampling steps, as well as to smooth the unconstrained output of the model. Furthermore, these profiles may be used to perform near-term prediction of angular irradiance, which can then inform the movement of a PV tracker.

42 ENGINEERING↗
DOE OSTI2024

Measurement of Photovoltaic Module Deformation Dynamics During Hail Impact Using Digital Image Correlation

Stereo high-speed video of photovoltaic modules undergoing laboratory hail tests was processed using digital image correlation to determine module surface deformation during and immediately following impact. The purpose of this work was to demonstrate a methodology for characterizing module impact response differences as a function of construction and incident hail parameters. Video capture and digital image analysis were able to capture out-of-plane module deformation to a resolution of ±0.1 mm at 11 kHz on an in-plane grid of 10 × 10 mm over the area of a 1 × 2 m commercial photovoltaic module. With lighting and optical adjustments, the technique was adaptable to arbitrary module designs, including size, backsheet color, and cell interconnection. Furthermore, impacts were observed to produce an initially localized dimple in the glass surface, with peak deflection proportional to the square root of incident energy. Subsequent deformation propagation and dissipation were also captured, along with behavior for instances when the module glass fractured. Natural frequencies of the module were identifiable by analyzing module oscillations postimpact. Limitations of the measurement technique were that the impacting ice ball obscured the data field immediately surrounding the point of contact, and both ice and glass fracture events occurred within 100 μs, which was not resolvable at the chosen frame rate. Increasing the frame rate and visualizing the back surface of the impact could be applied to avoid these issues. Applications for these data include validating computational models for hail impacts, identifying the natural frequencies of a module, and identifying damage initiation mechanisms.

14 SOLAR ENERGY↗
DOE OSTI2025

Advanced Photovoltaic Module Characterization: Using Image Transformers for Current–Voltage Curve Prediction From Electroluminescence Images

Individual photovoltaic (PV) module health monitoring can be a daunting task for operation and maintenance of solar farms. Modules can be inspected through luminescence, thermal imaging, and current–voltage (I–V) curve analyzes for identification of damage and power loss. I–V curves provide easily interpretable data to determine module health as they directly provide electrical performance metrics. However, in order to obtain these curves, modules must be disconnected from the array and either removed to a solar simulator or characterized in situ with corrections for module temperature, the incident solar spectrum, and intensity. Luminescence or thermal images of a module are relatively easy to acquire in situ. Electroluminescence (EL) images highlight physical defects in the modules but do not provide easily interpretable features to correlate with electrical performance. This work presents a SWin transformer network to predict I–V curves for PV modules from their corresponding EL images. The predicted I–V curves allow the accurate prediction of the maximum power point (MPP), short-circuit current I sc , and open-circuit voltage V oc with a mean error less of than 1%. Comparing single diode model (SDM) parameters extracted from the predicted curves to those extracted from the true curves, the series resistance R s demonstrates a mean error of 5.19%, and the photocurrent I a mean error of 0.197%. The shunt resistance R sh and dark current Io parameters are predicted with larger errors because of their sensitivity to small changes in the I–V curve.

Byford, Brandon K. [New Mexico State Univ., Las Cr↗
DOE OSTI2021

Mechanistic Insights to Degradation of PERC Minimodules with Differentiated Packaging Materials & Module Architectures

In this paper, we study the degradation behavior of glass-backsheet (GB) and double glass (DG) multicrystalline sil- icon monofacial PERC minimodules, fabricated using polyolefin elastomer (POE) and ethylene vinyl acetate (EVA) encapsulants, undergoing exposures of modified damp-heat (80 °C / 85% RH), with and without full spectrum light. The completed exposure time, at this time, is 1512 hours with each step of 504 hours. Step- wise measurements are conducted throughout exposures including current-voltage (I-V ) curves, Suns-V oc , electroluminescence (EL) images, and four-point proof loading with EL for tracking changes in mechanical properties in encapsulants by cell fracture probability. The results show that GB minimodules of both encapsulants, compared to DG minimodules, experience more changes in features of I-V and Suns-V oc measurements. There is no development of PV cell cracking during proof loading as seen from EL images and the load versus load line displacement plots currently.

14 SOLAR ENERGY↗
DOE OSTI2022

Evaluation of PV Module Packaging Strategies of Monofacial and Bifacial PERC Using Degradation Pathway Network Modeling

As the PV industry is rapidly expanding, it is important to thoroughly investigate the long-term impact of packaging strategies on the performance of PV modules. In this study, the variants in sets differ on the basis of manufacturer (A/B), encapsulant (EVA/POE), rear encapsulant (UV-cutoff/opaque/transparent), module architecture (GB/DG) and cell type (monofacial/bifacial). The minimodules were exposed for 2520 hours in modified damp heat, with or without full spectrum light. Every 504 hours, stepwise electrical characterization techniques were employed to track changes in minimodules. Degradation pathway modeling using network structural equation modeling was employed to study pairwise relationships between variables and service lifetime prediction in minimodules. Through this study, differences in quality control are identified in minimodules made by different manufacturers. Minimodules with UV-cutoff rear encapsulant show relatively better stability, whereas the ones with opaque rear encapsulant show greater power loss. In addition, GB having UV-cutoff rear encapsulation and GB with POE having opaque rear encapsulation were identified to be stable as they lack a best model fit. Here, the primary power loss mechanism in degrading variants is interconnect corrosion.

14 SOLAR ENERGY↗