A Sophisticated Method of the Mechanical Design of Cable Accessories Focusing on Interface Contact Pressure

The most critical positions of a prefabricated cable accessory, from the electrical point of view, are the interfaces between the stress cone and its surroundings. Accordingly, the contact pressure on those interfaces needs to be carefully designed to assure both good dielectric strength and smooth installation of the stress cone. Nevertheless, since stress cones made from rubber are under large deformation after installation, their internal stress distribution is neither practical to measure directly by planting sensors, nor feasible to compute accurately with the conventional theory of linear structural mechanics. This paper presents one sophisticated method for computing the mechanical stress distribution in rubber stress cones of cable accessories by employing hyperelastic models in a computation model based on the finite element method. This method offers accurate results for rubber bodies of complex geometries and large deformations. Based on the method, a case study of a composite prefabricated termination for extruded cables is presented, and the sensitivity analysis is given as well

The impact of long-term water stress on tree architecture and production is related to changes in transitions between vegetative and reproductive growth in the ‘Granny Smith’ apple cultivar

UMR AGAP - équipe AFEF - Architecture et fonctionnement des espèces fruitièresInternational audienceWater stress (WS) generates a number of physiological and morphological responses in plants that depend on the intensity and duration of stress as well as the plant species and development stage. In perennial plants, WS may affect plant development through cumulative effects that modify plant functions, architecture and production over time. Plant architecture depends on the fate of the terminal and axillary buds that can give rise, in the particular case of apple, to reproductive or vegetative growth units (GUs) of different lengths. In this study, the impact of long-term WS (7 years) on the fate of terminal and axillary buds was investigated in relation to flowering occurrence and production pattern (biennial vs regular) in the ‘Granny Smith’ cultivar. It was observed that WS decreased the total number of GUs per branch, regardless of their type. Conversely, WS did not modify the timing of the two successive developmental phases characterized by the production of long and medium GUs and an alternation of floral GUs over time, respectively. The analysis of GU successions over time using a variable-order Markov chain that included both the effects of the predecessor and water treatment revealed that WS reduced the transition towards long and medium GUs and increased the transition toward floral, short and dead GUs. WS also slightly increased the proportion of axillary floral GUs. The higher relative frequency of floral GUs compared with vegetative ones reduced the tendency to biennial bearing under WS. The accelerated ontogenetic trend observed under WS suggests lower vegetative growth that could, in turn, be beneficial to floral induction and fruit set

The Intraseasonal and Interannual Variability of Arctic Temperature and Specific Humidity Inversions

Temperature and humidity inversions are common in the Arctic's lower troposphere, and are a crucial component of the Arctic's climate system. In this study, we quantify the intraseasonal oscillation of Arctic temperature and specific humidity inversions and investigate its interannual variability using data from the Surface Heat Balance of the Arctic (SHEBA) experiment from October 1997 to September 1998 and the European Centre for Medium-Range Forecasts (ECMWF) Reanalysis (ERA)-interim for the 1979-2017 period. In January 1998, there were two noticeable elevated inversions and one surface inversion. The transitions between elevated and surface-based inversions were associated with the intraseasonal variability of the temperature and humidity differences between 850 and 950 hPa. The self-organizing map (SOM) technique is utilized to obtain the main modes of surface and elevated temperature and humidity inversions on intraseasonal time scales. Low (high) pressure and more (less) cloud cover are related to elevated (surface) temperature and humidity inversions. The frequency of strong (weak) elevated inversions over the eastern hemisphere has decreased (increased) in the past three decades. The wintertime Arctic Oscillation (AO) and Arctic Dipole (AD) during their positive phases have a significant effect on the occurrence of surface and elevated inversions for two Nodes only.National Key Research and Development Program of China [2017YFE0111700]; Opening Fund of Key Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions, CAS [LPCC2018001, LPCC2018005]; Opening fund of State Key Laboratory of Cryospheric Science [SKLCS-OP-2019-09]; U.S. National Science FoundationOpen access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Multi-node Acceleration for Large-scale GCNs

Limited by the memory capacity and compute power, singe-node graph convolutional neural network (GCN) accelerators cannot complete the execution of GCNs within a reasonable amount of time, due to the explosive size of graphs nowadays. Thus, large-scale GCNs call for a multi-node acceleration system (MultiAccSys) like TPU-Pod for large-scale neural networks. In this work, we aim to scale up single-node GCN accelerators to accelerate GCNs on large-scale graphs. We first identify the communication pattern and challenges of multi-node acceleration for GCNs on large-scale graphs. We observe that (1) coarse-grained communication patterns exist in the execution of GCNs in MultiAccSys, which introduces massive amount of redundant network transmissions and off-chip memory accesses; (2) overall, the acceleration of GCNs in MultiAccSys is bandwidth-bound and latency-tolerant. Guided by these two observations, we then propose MultiGCN, the first MultiAccSys for large-scale GCNs that trades network latency for network bandwidth. Specifically, by leveraging the network latency tolerance, we first propose a topology-aware multicast mechanism with a one put per multicast message-passing model to reduce transmissions and alleviate network bandwidth requirements. Second, we introduce a scatter-based round execution mechanism which cooperates with the multicast mechanism and reduces redundant off-chip memory accesses. Compared to the baseline MultiAccSys, MultiGCN achieves 4~12x speedup using only 28%~68% energy, while reducing 32% transmissions and 73% off-chip memory accesses on average. It not only achieves 2.5~8x speedup over the state-of-the-art multi-GPU solution, but also scales to large-scale graphs as opposed to single-node GCN accelerators.Comment: To appear in T

HiHGNN: Accelerating HGNNs through Parallelism and Data Reusability Exploitation

Heterogeneous graph neural networks (HGNNs) have emerged as powerful algorithms for processing heterogeneous graphs (HetGs), widely used in many critical fields. To capture both structural and semantic information in HetGs, HGNNs first aggregate the neighboring feature vectors for each vertex in each semantic graph and then fuse the aggregated results across all semantic graphs for each vertex. Unfortunately, existing graph neural network accelerators are ill-suited to accelerate HGNNs. This is because they fail to efficiently tackle the specific execution patterns and exploit the high-degree parallelism as well as data reusability inside and across the processing of semantic graphs in HGNNs. In this work, we first quantitatively characterize a set of representative HGNN models on GPU to disclose the execution bound of each stage, inter-semantic-graph parallelism, and inter-semantic-graph data reusability in HGNNs. Guided by our findings, we propose a high-performance HGNN accelerator, HiHGNN, to alleviate the execution bound and exploit the newfound parallelism and data reusability in HGNNs. Specifically, we first propose a bound-aware stage-fusion methodology that tailors to HGNN acceleration, to fuse and pipeline the execution stages being aware of their execution bounds. Second, we design an independency-aware parallel execution design to exploit the inter-semantic-graph parallelism. Finally, we present a similarity-aware execution scheduling to exploit the inter-semantic-graph data reusability. Compared to the state-of-the-art software framework running on NVIDIA GPU T4 and GPU A100, HiHGNN respectively achieves an average 41.5$\times$ and 8.6$\times$ speedup as well as 106$\times$ and 73$\times$ energy efficiency with quarter the memory bandwidth of GPU A100