An electrochemical technique for sensing uranium adsorption and desorption
Not Available
SEARCH · Engineering Papers
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.
Not Available
Natural convection in porous media is a highly nonlinear multiphysical problem relevant to many engineering applications (e.g., the process of CO 2 sequestration). Here, we extend and present a non-intrusive reduced order model of natural convection in porous media employing deep convolutional autoencoders for the compression and reconstruction and either radial basis function (RBF) interpolation or artificial neural networks (ANNs) for mapping parameters of partial differential equations (PDEs) on the corresponding nonlinear manifolds. To benchmark our approach, we also describe linear compression and reconstruction processes relying on proper orthogonal decomposition (POD) and ANNs. Further, we present comprehensive comparisons among different models through three benchmark problems. The reduced order models, linear and nonlinear approaches, are much faster than the finite element model, obtaining a maximum speed-up of 7 × 10 6 because our framework is not bound by the Courant–Friedrichs–Lewy condition; hence, it could deliver quantities of interest at any given time contrary to the finite element model. Our model’s accuracy still lies within a relative error of 7% in the worst-case scenario. We illustrate that, in specific settings, the nonlinear approach outperforms its linear counterpart and vice versa. We hypothesize that a visual comparison between principal component analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) could indicate which method will perform better prior to employing any specific compression strategy.
Not Available
In-situ strain measurements of fuel cladding can enable high-throughput data collection and validation to support accelerated qualification of cladding materials. Here, in this work, 3D digital image correlation was used to map strain for both Zircaloy-4 (Zry-4) and Cr-coated Zry-4 during two types of cladding rupture experiments: isobaric temperature ramp tests at 5 °C/s and isothermal pressure jump tests at 600 °C. Zry-4 strain data initially showed a temperature dependence expected for creep deformation, yet a shift to a new plastic deformation mechanism not reported in literature dominated during the finals seconds prior to rupture. Cr-coated Zry-4 did not show the change in deformation mechanism at the end of life and showed a delay in measurable creep deformation. Stress dependences were similar for Zry-4 and Cr/Zry-4 during pressure jump tests. Cr-coatings were found to decrease the strain rate during both testing scenarios. Creep parameters were calculated to support modelling efforts regarding design basis accidents.
Not Available
Flow boiling heat transfer tests were conducted to evaluate the Critical Heat Flux (CHF) and Heat Transfer Coefficient (HTC) of conventional and accident tolerant fuel (ATF) cladding materials, i.e., bare Zircaloy-4 (Zr4), and Zircaloy-4 coated with Chromium using physical vapor deposition (PVD) (Zr4-Cr-PVD) and cold spray process (Zr4-Cr-CS), respectively. The tests were performed on a single heater rod with a uniform heat flux profile, at the atmospheric pressure, inlet temperature (24 °C) and mass flow rate (750 kg/m 2 s). HTC’s were increased by 5.2% and 3.3% on Zr4-Cr-CS compared to bare Zr4 and Zr4-Cr-PVD. Improved HTCwas attributed to the increased void fraction and high roughness (Ra = 532 nm) of the Zr4-Cr-CS. In detail, the micro-cavities, which act as bubble seeds, were entirely distributed on the Zr4-Cr-CS, and these micro-cavities generated smaller and faster bubbles, thus the void fraction increased by 10.9% compared to bare Zr4. HTC is improved by these smaller and faster bubbles which can remove heat from the heater rod surface more efficiently. To identify the exact location of CHF, the surface temperature profile was measured using advanced fiber-optic sensors which have high temporal/spatial resolution (distance between point to point: ~2.5 mm, frequency ~100 Hz). CHF occurred at 80–95% along the heated length and showed an 11.6% reduction on Zr4-Cr-CS compared to bare Zr4. We hypothesize the CHF reduction was caused by the lower wettability of the Zr4-Cr-CS which reduces the liquid supply to the surface, and vigorous bubble accumulation owing to high void fraction near the outlet, which results in early formation of the vapor film. During the post-CHF quenching phase, the rougher Zr4-Cr-CS showed larger cooling rates compared to the bare Zr4 and Zr4-Cr-PVD, preventing of oxidation by chromium layer. In conclusion, we demonstrate that the high roughness on the Zr4-Cr-CS increased the number of micro-cavities on the surface, resulting in 5.2% increase in HTC and improvement in the quenching heat transfer performance whereas CHF was 11.6% reduced compared to bare Zr4. Overall statistically there is little effect in the coating with regards to CHF and slight improvement in HTC.
Explore the source record for details and available documents.
Not Available
Not Available
Here, additive manufacturing of dense SiC parts was achieved via an extrusion-based process followed by electrical-field assisted pressure-less sintering. The aim of this research was to study the effect of the rheological behavior of SiC slurry on the printing process and quality, as well as the influence of 3D printing parameters on the dimensions of the extruded filament, which are directly related to the printing precision and quality. Different solid contents and dispersant- Darvan 821A concentrations were studied to optimize the viscosity, thixotropy and sedimentation rate of the slurry. The optimal slurry was composed of 77.5 wt% SiC, Y 2 O 3 and Al 2 O 3 powders, 0.25 wt% dispersant and 0.01 wt% defoamer. The printing parameters studied included extrusion pressure, nozzle size, layer height and printing speed; the one that had the most prominent effect on filament width and height was indicated as layer height. The nozzle inner diameter of 1.04 mm, speed of 350 mm/min, layer height of 0.7 mm and extrusion air pressure of 0.31 MPa were the optimal printing parameters. Furthermore, the relationship between the printing parameters and the filament dimensions was successfully predicted by using machine learning and grey system theory. Finally, the relative density of the printed SiC parts sintered at 1900 oC reached 94.7±1.5%.
Not Available
Not Available
Not Available
Accurate identification of material parameters is crucial for predictive modeling in computational mechanics. Here, the two primary approaches in the experimental mechanics community for calibration from full-field digital image correlation data are known as finite element model updating (FEMU) and the virtual fields method (VFM). In VFM, the objective function is a squared mismatch between internal and external virtual work or power. In FEMU, the objective function quantifies the weighted mismatch between model predictions and corresponding experimentally measured quantities of interest. It is minimized by iteratively updating the parameters of an FE model. While FEMU is seen as more flexible, VFM is commonly used instead of FEMU due to its considerably greater computational expense. However, comparisons between the two methods usually involve approximations of gradients or sensitivities with finite difference schemes, thereby making direct assessments difficult. Hence, in this study, we compare VFM and FEMU in the context of numerically-exact sensitivities obtained through local sensitivity analyses and the application of automatic differentiation software. To this end, we conduct a series of test cases to assess both methods under practical challenges using a finite strain elastoplasticity model.