SEARCH · Search NASA
Results for “ADELS”
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.
Numerical Simulations of High Prandtl Number Liquid Jets Impinging on a Flat Plate
In this work, 3D simulations of oil jets impinging on a flat, heated wall are presented. The numerical setup uses the Volume of Fluid (VoF) method to model the two-phase flow. A careful grid definition across the liquid film, along with the use of the Conjugate Heat Transfer (CHT) approach allowed local heat transfer to be solved with fine resolution at the wall. Variations of liquid flow rate, liquid temperature and surface temperature allow to cover a wide range of local Reynolds and Prandtl numbers (226 < Re < 2850, 77 < Pr < 161). Resulting surface-averaged heat transfer compares very well with experimental measurements conducted in a previous study. In-depth analysis of the flow has identified expected features from the literature. In particular, the impact of jet axial velocity profiles on the heat transfer distribution in the stagnation zone was clearly stated. The increase in heat transfer when warming the liquid film was also reproduced and explained by a decrease in oil viscosity and an increase in film velocity. All those effects were taken into account in correlations for stagnation and local values of Nusselt number. A grid sensitivity study was also conducted, showing that if the grid solving the thermal boundary layer in the stagnation zone can be coarsened without impacting local and surface-averaged predictions of heat transfer, a minimum resolution (2 to 3 cells) within the thermal boundary layer is however required for an accurate prediction of heat transfer.
A performance comparison between GaInP-on-Si and GaAs-on-Si 3-terminal tandem solar cells
The pursuit of ever-higher solar cell efficiencies has focused heavily on multijunction technologies. In tandem cells, subcells are typically either contacted via two terminals (2T) or four terminals (4T). Simulations show that the less-common three-terminal (3T) design may be comparable to 4T tandem cells in its compatibility with a range of materials, operating conditions, and methods for subcell integration, yet the 3T design circumvents shading losses of the 4T intermediate conductive layers. This study analyzes the performance of two superstrate 3T III-V-on-Si (III-V//Si) tandem cells: One has slightly greater current contribution from the Si bottom cell (GaInP//Si) and the other has substantially greater current contribution from the GaAs top cell (GaAs//Si). Our results show that both tandem cells exhibit the same efficiency (21.3%), thereby demonstrating that the third terminal allows for flexibility in the selection of the top cell material, similar to the 4T design.
Mesoscale informed parameter estimation through machine learning: A case-study in fracture modeling
Scale bridging is a critical need in computational sciences, where the modeling community has developed accurate physics models from first principles, of processes at lower length and time scales that influence the behavior at the higher scales of interest. However, it is not computationally feasible to incorporate all of the lower length scale physics directly into upscaled models. This is an area where machine learning has shown promise in building emulators of the lower length scale models, which incur a mere fraction of the computational cost of the original higher fidelity models. We demonstrate the use of machine learning using an example in materials science estimating continuum scale parameters by emulating, with uncertainties, complicated mesoscale physics. Additionally, we describe a new framework to emulate the fine scale physics, especially in the presence of microstructures, using machine learning, and showcase its usefulness by providing an example from modeling fracture propagation. Our approach can be thought of as a data-driven dimension reduction technique that yields probabilistic emulators. Our results show well-calibrated predictions for the quantities of interests in a low-strain simulation of fracture propagation at the mesoscale level. Furthermore, on average, we achieve ~10% relative errors on time-varying quantities like total damage and maximum stresses. Successfully replicating mesoscale scale physics within the continuum models is a crucial step towards predictive capability in multi-scale problems.
Growth of GaAs on single-crystal layered-2D Bi 2 Se 3
This article demonstrates the successful growth of cubic GaAs (111) on single-crystal 2D layered Bi 2 Se 3 (0001) substrates achieved using a cubic ZnSe buffer layer. This growth sequence was chosen based upon observed reactions between Bi 2 Se 3 (0001) substrates and both Ga and Zn. For the conditions used in our MOCVD reactor, triethylgallium (TEGa) interacts strongly with Bi 2 Se 3 to form Ga 2 Se 3 , which can disrupt the nucleation and growth of GaAs. Therefore, a buffer layer is needed which prevents Ga-Bi 2 Se 3 interactions while simultaneously providing a suitable growth surface for GaAs. ZnSe was chosen because it is lattice-matched to GaAs, and can be created by annealing the Bi2Se3 under a diethylzinc (DEZn) flux. A sample utilizing this growth sequence has been grown, characterized and exfoliated as a possible pathway toward reducing the substrate cost for III-V devices such as solar cells.
Surface Conversion of Single-Crystal Bi 2 Se 3 to β-In 2 Se 3
In this work, we demonstrate that the surface layers of single-crystal layered-2D Bi 2 Se 3 can be converted to layered-2D rhombohedral ß-In 2 Se 3 by annealing under a trimethylindium (TMIn) flux. Samples were prepared in a metalorganic chemical vapor deposition (MOCVD) chamber, then transferred under vacuum to a surface analysis chamber for analysis with low-energy electron diffraction (LEED) and Auger electron spectroscopy. Additional ex situ characterization included x-ray diffraction, transmission-electron microscopy (TEM), energy dispersive x-ray spectroscopy (EDS) elemental mapping, and Raman spectroscopy. The resulting single-crystal ß-In 2 Se 3 adopts the rhombohedral crystal structure (space group R-3m) and orientation of the underlying Bi 2 Se 3 , and the excess Bi atoms generated by this process create an underlying region of Bi-rich Bi x Se y . Due to the difference in bandgap between Bi 2 Se 3 and In 2 Se 3 , this conversion reaction presents a pathway to lateral heterojunctions if only selected regions are converted by masking the surface to spatially define the TMIn exposure. The conversion may also have implications for heteroepitaxy, because the in-plane lattice constants of Bi 2 Se 3 and In 2 Se 3 (0001) surfaces match those of InP and GaAs (111), respectively, and the natural cleavage planes of a layered-2D crystal facilitate substrate removal and reuse.
A multiscale model for solute transport in stream corridors with unsteady flow
Not Available
Effects of sample bias on adhesion of magnetron sputtered Cr coatings on SiC
Swelling of SiC at 300 C due to in-service neutron irradiation causes tensile residual stresses in coatings which are expected to adversely affect the performance of coated SiC composite fuel cladding for light water reactors. Matching the coating swelling with the substrate, a solution common for thermal expansion, is not practical in the case of neutron irradiation. Biasing samples during magnetron sputtering deposition induces compressive residual stress which may counteract this. In this study, chromium coatings were deposited on SiC by DC magnetron sputtering with no external heating at bias voltages of –50V, –75V, and –100V. The effects of the bias voltage on morphology, residual stress, microstrain, texture, and adhesion are shown. The low deposition temperature resulted in the coating microstructure evolution following an energetic particle bombardment dominated trend. Additionally, at the two lower bias voltages knock-on implantation dominated increasing the residual stress and microstrain while at the highest bias voltage, thermal spike migration allowed for defect relaxation. When the knock-on induced compressive residual stress exceeded 0.8 GPa microcrack formation in the SiC substrate decreased coating adhesion. While no microcracks formed at the lowest bias voltage, insufficient atomic mobility during coating growth lead to voids forming in the coating. A balance is needed to form void-free coatings that have high compressive residual stress.
Structural and magnetic properties of churchite-type REPO[subscript 4]?2H[subscript 2]O materials
Explore the source record for details and available documents.
High-Efficiency Silicon Heterojunction Solar Cells: Materials, Devices and Applications
Photovoltaic (PV) technology offers an economic and sustainable solution to the challenge of increasing energy demand in times of global warming. The world PV market is currently dominated by the homo-junction crystalline silicon (c-Si) PV technology based on high temperature diffused p-n junctions, featuring a low power conversion efficiency (PCE). Recent years have seen the successful development of Si heterojunction technologies, boosting the PCE of c-Si solar cells over 26%. This article reviews the development status of high-efficiency c-Si heterojunction solar cells, from the materials to devices, mainly including hydrogenated amorphous silicon (a-Si:H) based silicon heterojunction technology, polycrystalline silicon (poly-Si) based carrier selective passivating contact technology, metal compounds and organic materials based dopant-free passivating contact technology. The application of silicon heterojunction solar cells for ultra-high efficiency perovskite/c-Si and III-V/c-Si tandem devices is also reviewed. In the last, the perspective, challenge and potential solutions of silicon heterojunction solar cells, as well as the tandem solar cells are discussed.
Efficient light-trapping in ultrathin GaAs solar cells using quasi-random photonic crystals
Ultrathin solar cells reduce material usage and allow the use of lower-quality materials thanks to their one order of magnitude smaller thickness than their conventional counterparts. However, efficient photonic light-trapping is required to harvest the incident light efficiently for an otherwise insufficient absorber thickness. Quasi-random photonic crystals are predicted to have high efficient light-trapping while being more robust under angle and thickness variations than simple photonic crystals. In this study, we experimentally demonstrate a light-trapping solution based on quasi-random photonic crystals fabricated by polymer blend lithography. We control the average lattice parameter by modifying the spin-coating speed. We demonstrate an ultrathin GaAs cell of 260 nm with a rear quasi-random pattern with submicron features, and a J sc = 26.4 mA/cm 2 and an efficiency of 22.35% under the global solar spectrum.
Student nurses, increasing placement capacity and patient safety. A retrospective cohort study
Not Available
Beyond interpolation: Physics-inspired gating transformers for extrapolating irradiation conditions to novel nuclear fuels
The qualification of advanced nuclear fuels relies on irradiation experiments in test reactors that emulate commercial conditions. Designing these tests requires accurate prediction of key irradiation quantities, particularly heat generation rate and burnup, yet obtaining them typically involves computationally expensive multi-step simulation workflows. We propose a physics-inspired gating transformer (PIGT) that integrates an inverse-square, distance-based attenuation into the encoder representation to bias attention toward physically relevant spatial relationships while retaining data-driven flexibility. Using MiniFuel irradiation data from the High Flux Isotope Reactor at Oak Ridge National Laboratory, we benchmark against ensemble methods, feedforward and recurrent networks, convolutional models, and standard transformers. While baseline models perform well under interpolation, they exhibit a pronounced generalization gap when evaluated on fuels not included in the training set. The proposed model consistently improves extrapolative accuracy and stability, yielding the strongest performance on unseen fuel configurations. These results indicate that a lightweight physics structure embedded within attention mechanisms can substantially improve robustness, enabling more reliable surrogate predictions to accelerate the design of nuclear fuel irradiation experiments.
Deep spectral CNN for laser induced breakdown spectroscopy
This work proposes a spectral convolutional neural network (CNN) operating on laser induced breakdown spectroscopy (LIBS) signals to learn to (1) disentangle spectral signals from the sources of sensor uncertainty (i.e., pre-process) and (2) get qualitative and quantitative measures of chemical content of a sample given a spectral signal (i.e., calibrate). Once the spectral CNN is trained, it can accomplish either task through a single feed-forward pass, with real-time benefits and without any additional side information requirements including dark current, system response, temperature and detector-to-target range. Our experiments demonstrate that the proposed method outperforms the existing approaches used by the Mars Science Lab for pre-processing and calibration for remote sensing observations from the Mars rover, ‘Curiosity’.
Ensemble methods for quantification of potassium oxide in ChemCam Mars and laboratory spectra
In this paper we test new approaches for predicting the amount of element oxides in rock samples from the ChemCam instrument suite onboard the NASA Curiosity rover by focusing on K 2 O. Using the expanded dataset compiled by Gasda et al. (2021) with and without the Earth to Mars (E2M and NoE2M) transformation discussed in Clegg et al. (2017) we trained blended submodels using the “double blending” technique and compared these to ensemble methods (Random Forest, ExtraTrees, and Gradient Boosting Regression). We found that ensemble methods performed similar to blended submodels when looking at RMSE-P on the laboratory spectra and provided significant advantages when looking at spectra coming from Mars. For the full model, blended submodels achieved an RMSE-P of 0.62 and 0.60 (E2M and NoE2M respectively) while Gradient Boosting Regression resulted in a slightly improved RMSE-P of 0.59 and 0.60. More importantly, by employing a local RMSE-P estimation technique where model performance is evaluated based on nearby test samples we found that using ensemble methods can lower the quantification limit for K 2 O from the current value of ≈0.6 wt% to ≈0.08 wt% using Extra Trees and Random Forest. This would allow for a much larger range of K 2 O values to be quantified on Mars with greater certainty given that most targets seen on Mars tend to have <1 wt% K2O. Finally, we used both Mean Decrease in Impurity (MDI) and permutation importance techniques to investigate the wavelengths used by the ensemble methods and found that they correspond to known potassium emission lines. This suggests that ensemble methods can provide an easier to train and improved alternative to blended submodels for predicting potassium compositions from Laser Induced Breakdown Spectroscopy (LIBS) data.