5,942 research outputs found
Experimental Study of Pressure Loss in a 5 × 5–Rod Bundle With the Mixing Vane Spacer Grid
Axial and lateral pressure loss in a 5 × 5 rod–bundle with a split-type mixing vane spacer grid was experimentally measured using differential pressure transmitters at different sub-channel Reynolds numbers (Re) and orienting angles. The geometrical parameters of the 5 × 5–rod bundle are as follows: they have the same diameter (D = 9.5 mm) and pitch (p = 12.6 mm) as those of real fuel rods of a typical pressurized water reactor (PWR), with a sub-channel hydraulic diameter (D) of 11.78 mm. The characteristics and resistance models of pressure loss are discussed. The main axial pressure loss is caused by the spacer grid, and the spacer grid generates additional wall friction pressure loss downstream of the spacer grid. The lateral pressure loss shows strong correlations with orienting angles and distance from the spacer grid. The lateral pressure loss shows a sudden burst in the mixing vanes region and a slight augmentation at z = 3D. After 3D, the lateral pressure loss decays in an exponential way with distance from the spacer grid, and it becomes constant quickly at z = 20D
Same titanium glycolate precursor but different products: Successful synthesis of twinned anatase TiO2 nanocrystals with excellent solar photocatalytic hydrogen evolution capability
Exploiting a synthesis protocol to tailor TiO 2 with a unique morphology and crystal phase has received considerable interest in the energy and environmental fields. We here describe the use of a titanium glycolate precursor in a hydrothermal hydrolysis reaction to engineer TiO 2 nanocrystals with different crystal phases and structures. Anatase TiO 2 nanocrystals with twinned structures were obtained by using a lower amount of NaOH in the hydrolysis system, while brookite TiO 2 nanocrystals were formed when higher amounts of NaOH were employed. The as-synthesized different TiO 2 nanocrystals have a suitable bandgap to harvest photons and a more negative bottom level of the conduction band than the redox potential of H + /H 2 indicating their potential as hydrogen-evolution semiconductor photocatalysts. However, the TiO 2 nanotwins show promoted charge separation efficiency, and thus result in superior photocatalytic H 2 generation activity compared to the anatase and brookite TiO 2 nanocrystals. Our findings provide an effective and versatile solution for the fabrication of TiO 2 -based nanostructures with different phases and morphologies through chemical conversion of powder precursor nanoparticles, which could pave the way to the design of other functional nano-oxides with unique structures
SPEAL: Skeletal Prior Embedded Attention Learning for Cross-Source Point Cloud Registration
Point cloud registration, a fundamental task in 3D computer vision, has
remained largely unexplored in cross-source point clouds and unstructured
scenes. The primary challenges arise from noise, outliers, and variations in
scale and density. However, neglected geometric natures of point clouds
restricts the performance of current methods. In this paper, we propose a novel
method termed SPEAL to leverage skeletal representations for effective learning
of intrinsic topologies of point clouds, facilitating robust capture of
geometric intricacy. Specifically, we design the Skeleton Extraction Module to
extract skeleton points and skeletal features in an unsupervised manner, which
is inherently robust to noise and density variances. Then, we propose the
Skeleton-Aware GeoTransformer to encode high-level skeleton-aware features. It
explicitly captures the topological natures and inter-point-cloud skeletal
correlations with the noise-robust and density-invariant skeletal
representations. Next, we introduce the Correspondence Dual-Sampler to
facilitate correspondences by augmenting the correspondence set with skeletal
correspondences. Furthermore, we construct a challenging novel large-scale
cross-source point cloud dataset named KITTI CrossSource for benchmarking
cross-source point cloud registration methods. Extensive quantitative and
qualitative experiments are conducted to demonstrate our approach's superiority
and robustness on both cross-source and same-source datasets. To the best of
our knowledge, our approach is the first to facilitate point cloud registration
with skeletal geometric priors.Comment: Accepted by AAAI202
Unified nonequilibrium dynamical theory for exchange bias and training effects
We investigate the exchange bias and training effects in the FM/AF
heterostructures using a unified Monte Carlo dynamical approach. This real
dynamical method has been proved reliable and effective in simulating dynamical
magnetization of nanoscale magnetic systems. The magnetization of the
uncompensated AF layer is still open after the first field cycling is finished.
Our simulated results show obvious shift of hysteresis loops (exchange bias)
and cycling dependence of exchange bias (training effect) when the temperature
is below 45 K. The exchange bias fields decrease with decreasing the cooling
rate or increasing the temperature and the number of the field cycling. With
the simulations, we show the exchange bias can be manipulated by controlling
the cooling rate, the distributive width of the anisotropy energy, or the
magnetic coupling constants. Essentially, these two effects can be explained on
the basis of the microscopical coexistence of both reversible and irreversible
moment reversals of the AF domains. Our simulated results are useful to really
understand the magnetization dynamics of such magnetic heterostructures. This
unified nonequilibrium dynamical method should be applicable to other exchange
bias systems.Comment: Chin. Phys. B, in pres
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