Characterization of subglacial landscapes by a two-parameter roughness index

Peer reviewedPublisher PD

Dome Argus: Ideal site for deep ice drilling

Located on the centre of ice drainage range, the highest Dome Argus (Dome A) of East Antarctic Ice Sheet (EAIS), could be represented as an ideal site for deep ice cores drilling containing oldest paleo-climate records. To select a suitable drilling site for deep ice core, it needs gather all information pertaining to the local meteorology, ice sheet landforms, ice thickness, subglacial topography of bed rocks, ice velocity, internal structures of ice sheet, etc. Based on the International Partnerships in Ice Core Sciences (IPICS), we present recent achievement of glaciological research and its perspective at Dome A in this paper. We systematically discussed the merits and possible ventures of potential drilling sites around Dome A. Among all the candidates, we find that the Chinese Antarctic Kunlun Station is the best site for carrying out the first deep ice core drilling campaign. We emphasize and assess further the possibility to obtain a replicate core for studying dynamics and evolution of climate change

Investigating the critical characteristics of thermal runaway process for LiFePO4/graphite batteries by a ceased segmented method

Lithium-ion batteries (LIBs) are widely used as the energy carrier in our daily life. However, the higher energy density of LIBs results in poor safety performance. Thermal runaway (TR) is the critical problem which hinders the further application of LIBs. Clarifying the mechanism of TR evolution is beneficial to safer cell design and safety management. In this paper, liquid nitrogen spray is proved to be an effective way to stop the violent reaction of LIBs during the TR process. Based on extended-volume accelerating rate calorimetry, the liquid nitrogen ceasing combined with non-atmospheric exposure analysis is used to investigate the TR evolution about LiFePO4/graphite batteries at critical temperature. Specifically, the geometrical shape, voltage, and impedance change are monitored during the TR process on the cell level. The morphologies/constitution of electrodes and separators are presented on the component level. Utilizing the gas analysis, the failure mechanism of the prismatic LiFePO4/graphite battery is studied comprehensively

NQE: N-ary Query Embedding for Complex Query Answering over Hyper-relational Knowledge Graphs

Complex query answering (CQA) is an essential task for multi-hop and logical reasoning on knowledge graphs (KGs). Currently, most approaches are limited to queries among binary relational facts and pay less attention to n-ary facts (n>=2) containing more than two entities, which are more prevalent in the real world. Moreover, previous CQA methods can only make predictions for a few given types of queries and cannot be flexibly extended to more complex logical queries, which significantly limits their applications. To overcome these challenges, in this work, we propose a novel N-ary Query Embedding (NQE) model for CQA over hyper-relational knowledge graphs (HKGs), which include massive n-ary facts. The NQE utilizes a dual-heterogeneous Transformer encoder and fuzzy logic theory to satisfy all n-ary FOL queries, including existential quantifiers, conjunction, disjunction, and negation. We also propose a parallel processing algorithm that can train or predict arbitrary n-ary FOL queries in a single batch, regardless of the kind of each query, with good flexibility and extensibility. In addition, we generate a new CQA dataset WD50K-NFOL, including diverse n-ary FOL queries over WD50K. Experimental results on WD50K-NFOL and other standard CQA datasets show that NQE is the state-of-the-art CQA method over HKGs with good generalization capability. Our code and dataset are publicly available.Comment: Accepted by the 37th AAAI Conference on Artificial Intelligence (AAAI-2023

Chinese radioglaciological studies on the Antarctic ice sheet: progress and prospects

Chinese radioglaciological studies on the Antarctic ice sheet (AIS) began in 2004/05 when the 21st Chinese National Antarctic Research Expedition (CHINARE 21) team arrived at Dome A for the first time and radio echo sounding (RES) was conducted along the inland traverse and in the Dome A region. Subsequently, more field surveys were conducted along the traverse and in the Dome A region using different radar systems targeting different scientific purposes, such as revealing the landscape of the Gamburtsev Subglacial Mountains by detailed grid RES, or locating a deep ice core drilling site by mapping and studying internal structures, bedrock topography and subglacial conditions in the Dome A region. Furthermore, the evolution of the AIS was inferred from the typical mountain glaciation topography beneath Dome A, and the age of the deep ice core at Kunlun Station was estimated through numerical modeling. Recently, the Snow Eagle 601 airplane was acquired and an airborne geophysical system was constructed to survey the AIS in Princess Elizabeth Land during CHINARE 32 (2015/16) and CHINARE 33 (2016/17) in order to fill the large data gap there. In this paper, we review both the recent progress of Chinese radioglaciological science in Antarctica and future proposed work

Uncertainty Quantification in Small-Timescale Model-Based Fatigue Crack Growth Analysis Using a Stochastic Collocation Method

Due to the uncertainties originating from the underlying physical model, material properties and the measurement data in fatigue crack growth (FCG) processing, the prediction of fatigue crack growth lifetime is still challenging. The objective of this paper was to investigate a methodology for uncertainty quantification in FCG analysis and probabilistic remaining useful life prediction. A small-timescale growth model for the fracture mechanics-based analysis and predicting crack-growth lifetime is studied. A stochastic collocation method is used to alleviate the computational difficulties in the uncertainty quantification in the small-timescale model-based FCG analysis, which is derived from tensor products based on the solution of deterministic FCG problems on sparse grids of collocation point sets in random space. The proposed method is applied to the prediction of fatigue crack growth lifetime of Al7075-T6 alloy plates and verified by fatigue crack-growth experiments. The results show that the proposed method has the advantage of computational efficiency in uncertainty quantification of remaining life prediction of FCG

Experimental Validation of Optimal Parameter and Uncertainty Estimation for Structural Systems Using a Shuffled Complex Evolution Metropolis Algorithm

The uncertainty in parameter estimation arises from structural systems’ input and output measured errors and from structural model errors. An experimental verification of the shuffled complex evolution metropolis algorithm (SCEM-UA) for identifying the optimal parameters of structural systems and estimating their uncertainty is presented. First, the estimation framework is theoretically developed. The SCEM-UA algorithm is employed to search through feasible parameters’ space and to infer the posterior distribution of the parameters automatically. The resulting posterior parameter distribution then provides the most likely estimation of parameter sets that produces the best model performance. The algorithm is subsequently validated through both numerical simulation and shaking table experiment for estimating the parameters of structural systems considering the uncertainty of available information. Finally, the proposed algorithm is extended to identify the uncertain physical parameters of a nonlinear structural system with a particle mass tuned damper (PTMD). The results demonstrate that the proposed algorithm can effectively estimate parameters with uncertainty for nonlinear structural systems, and it has a stronger anti-noise capability. Notably, the SCEM-UA method not only shows better global optimization capability compared with other heuristic optimization methods, but it also has the ability to simultaneously estimate the uncertainties associated with the posterior distributions of the structural parameters within a single optimization run