Elastic Framework Research Articles

Particle Shape Resilient Framework for Long-Lasting Li-Ion Batteries

The lattice structural dynamics of active materials within Li-ion batteries, contingent upon their state of charge (SoC), introduce challenges associated with volume expansion or contraction in a both particle and electrode levels1-2. This phenomenon induces mechanical fractures and alters electrode thickness, particularly problematic for high-Ni cathodes characterized by pronounced c-lattice changes during cycling, leading to substantial mechanochemical degradation and hindrance to long-term cycle retention3-4. This study proposes a resilient framework employing multi-wall carbon nanotubes to decorate the surface of active particles, imparting recoverable characteristics to high-Ni cathode materials. This surface framework, endowed with the capacity to absorb mechanical energy, effectively preserves the original secondary particle shape throughout multiple charge cycles, mitigating the serve propagation of cracking. Additionally, the control of particle shape alterations results in a noteworthy reduction of over 50% in electrode thickness changes. The consequential impact of this elastic framework particle engineering is exemplified by outstanding performance, even in a carbon-additive-free electrode with a density of 20 mg/cm². The battery exhibits superior cycle life, retaining 85% of its initial capacity after 500 cycles in a pouch cell. This advancement establishes a universal paradigm for the systematic design of Li-ion batteries with markedly extended cycle life, presenting a transformative prospect for robust and enduring energy storage W. Li, E. M. Erickson, A. Manthiram, Nat. Energy 5 , 26-34 (2020). J. Langdon, A. Manthiram, Energy Stor. Mater. 37 , 143-160 (2021). W. Li, H. Y. Asl, Q. Xie, A. Manthiram, J. Am. Chem. Soc. 141 , 5097-5101 (2019). X.-H. Meng, T. Lin, H. Mao, J.-L. Shi, H. Sheng, Y.-G. Zou, M. Fan, K. Jiang, R.-J. Xiao, D. Xiao, J. Am. Chem. Soc. 144 , 11338-11347 (2022).

DLRover-RM: Resource Optimization for Deep Recommendation Models Training in the Cloud

Deep learning recommendation models (DLRM) rely on large embedding tables to manage categorical sparse features. Expanding such embedding tables can significantly enhance model performance, but at the cost of increased GPU/CPU/memory usage. Meanwhile, tech companies have built extensive cloud-based services to accelerate training DLRM models at scale. In this paper, we conduct a deep investigation of the DLRM training platforms at AntGroup and reveal two critical challenges: low resource utilization due to suboptimal configurations by users and the tendency to encounter abnormalities due to an unstable cloud environment. To overcome them, we introduce DLRover, an elastic training framework for DLRMs designed to increase resource utilization and handle the instability of a cloud environment. DLRover develops a resource-performance model by considering the unique characteristics of DLRMs and a three-stage heuristic strategy to automatically allocate and dynamically adjust resources for DLRM training jobs for higher resource utilization. Further, DLRover develops multiple mechanisms to ensure efficient and reliable execution of DLRM training jobs. Our extensive evaluation shows that DLRover reduces job completion times by 31%, increases the job completion rate by 6%, enhances CPU usage by 15%, and improves memory utilization by 20%, compared to state-of-the-art resource scheduling frameworks. DLRover has been widely deployed at AntGroup and processes thousands of DLRM training jobs on a daily basis. DLRover is open-sourced and has been adopted by 10+ companies.

Highly elastic 2D covalent organic framework films of interwoven single crystals.

Highly elastic 2D covalent organic framework films of interwoven single crystals.

Gas prediction in tight sandstones based on the rock-physics-derived seismic amplitude variation versus offset method

Estimating gas enrichments is a key objective in exploring sweet spots within tight sandstone gas reservoirs. However, the low sensitivity of elastic parameters to gas saturations in such formations makes it a significant challenge to reliably estimate gas enrichments using seismic methods. Through rock physical modeling and reservoir parameter analyses conducted in this study, a more suitable indicator for estimating gas enrichment, termed the gas content indicator, has been proposed. This indicator is formulated based on effective fluid bulk modulus and shear modulus and demonstrates a clear positive correlation with gas content in tight sandstones. Moreover, a new seismic amplitude variation versus offset (AVO) equation is derived to directly extract reservoir properties, such as the gas content indicator and porosity, from prestack seismic data. The accuracy of this proposed AVO equation is validated through comparison with the exact solutions provided by the Zoeppritz equation. To ensure reliable estimations of reservoir properties from partial angle-stacked seismic data, the proposed AVO equation is reformulated within the elastic impedance inversion framework. The estimated gas content indicator and porosity exhibit favorable agreement with logging data, suggesting that the obtained results are suitable for reliable predictions of tight sandstones with high gas enrichments. Furthermore, the proposed methods have the potential to stimulate the advancement of other suitable inversion techniques for directly estimating reservoir properties from seismic data across various petroleum resources.

Social Elastic Band with Prediction and Anticipation: Enhancing Real-Time Path Trajectory Optimization for Socially Aware Robot Navigation

AbstractAs social robots are projected to become an integral part of human life in the coming decades, their ability to adapt movement and trajectory when in proximity to people is essential for ensuring social acceptance during human-robot interaction. A key aspect of this adaptability involves predicting and anticipating human intents during robot navigation. Despite significant strides in the social navigation of autonomous robots within human environments, opportunities for advancements in related algorithms persist. This paper presents a novel real-time path trajectory optimization algorithm for socially aware robot navigation, grounded in the social elastic band concept, incorporating prediction and anticipation of human movements to adapt its forward velocity. Building upon the elastic band framework introduced in the 1990s for adapting robot trajectories in dynamic environments, our proposal of social elastic band differentiates between objects and human presence. This distinction allows for the definition of social interaction spaces and their relationship to the elastic band, facilitating the generation of socially accepted paths that rapidly adapt to environmental changes without causing a disturbance. Integrated into the SNAPE social navigation framework, the algorithm has been tested and validated through simulations and real-world experiments in various environments.

Faster Sorption of Propylene Compared to Propane Using an Elastic Layer-Structured Metal-Organic Framework (ELM-11).

The separation of propane and propylene is the most energy-consuming and difficult separation process in the petrochemical industry because of their extremely similar physical properties. Separating propylene from propane using sorption can considerably reduce the energy consumed by current cryogenic distillation techniques. However, sorption involves several major challenges. An elastic layer-structured metal-organic framework (ELM-11) exhibited a highly efficient propane/propylene sorption separation, owing to its kinetic properties. Under equilibrium conditions, propane and propylene exhibited similar sorption capacities, gate opening pressures, and heats of sorption. Thus, their separation under equilibrium conditions is impractical. However, the sorption rates of the two gases were considerably different, showing different diffusion coefficients, resulting in a high kinetic selectivity (214 at 298 K) of propylene over propane on ELM-11. This kinetic selectivity is considerably higher than those obtained in previous studies. Thus, ELM-11 is a promising sorbent for separation technologies.

Transversely heterogeneous nonlocal Timoshenko beam theory: A reduced-order modeling via distributed-order fractional operators

Nonlocal effects are widely identified in micro/nano systems and have been studied extensively in recent decades. Nevertheless, existing studies mostly focus on the modeling of homogeneous nonlocal systems and as a result, cannot model micro/nanostructures with heterogeneously distributed nonlocal effects. This study introduces a reduced-order nonlocal Timoshenko beam model that facilitates precise modeling of micro-elastic beams with layer-wise heterogeneous nonlocal effects. First, a fully-resolved two-dimensional (2D) plane-strain fractional-order nonlocal elastic framework is developed to provide reference solutions for 2D nonlocal beam problems. Building upon this general nonlocal elasticity theory, an equivalent one-dimensional (1D) nonlocal Timoshenko model is then developed. The layer-wise heterogeneous nonlocal information is captured by using distributed-order (DO) operators, and the impacts arising from the nonlocal heterogeneity are further characterized by introducing two auxiliary parameters. Both 1D and 2D approaches are applied to simulate the mechanical responses of nonlocal beams. Direct comparisons of numerical simulations produced by either the 1D or the fully-resolved 2D model confirm that the DO Timoshenko beam formulation (together with the two auxiliary parameters) can capture not only the overall beam deflection but also the additional shear effect induced by the heterogeneous nonlocality. Computational cost assessment also indicates the proposed approach’s superior performance. The proposed DO Timoshenko model enables model-order reduction without compromising the heterogeneous nonlocal description of the material hence leading to an efficient and accurate reduced-order nonlocal modeling approach that addresses the limitations of prior nonlocal microstructural theories and extends applicability to a broader range of heterogeneous nano/microstructures.

Octadecyl acrylate-based self-supporting elastic phase change framework materials for the enhancement of photovoltaic conversion efficiency

Self-supporting phase change aerogel was developed, capable of transforming into phase change gels by absorbing SLPCMs or more efficiently through a simplified "one-step" process.

An effective free-meshing and linear Step-Wise procedure to predict crack initiation and propagation

An automated procedure is proposed to calculate fracture initiation and propagation in two-dimensional structures. The application uses and enriches the Hybrid Semi-Analytical Method to calculate mixed-mode stress intensity factors in linear elastic fracture mechanics framework. The strategy does not necessitate prior knowledge of the starting crack tip location within the structure, resulting in suitable for analyzing structures without initial defects. When neither a priori invitation nor pre-existing notch is provided, the fracture initiation is assumed to occur when the structure reaches its elastic limit, and a non-linear analysis is conducted under the prescribed load to identify the first occurrence of permanent strain and to locate where the crack opening occurs. As the crack propagates, solely linear elastic analyses are performed to calculate the stress intensity factors. Relying upon these parameters, the procedure implements an evolutionary algorithm based on a step-by-step evaluation of the crack increment and direction. A global–local energy-based criterion is employed to drive fracture propagation. Neither local refinement nor mapped meshes around the crack tip are required, resulting in a streamlined and efficient free-meshing strategy.The calculation has been implemented in a standard finite element environment, and several numerical examples have been investigated, both from a qualitative and quantitative point of view. Qualitative crack paths have been shown for a square hollow plate under pure tensile and shear loads, and a modified Single Edge Notched specimen in bending, as a function of the mutual void positions, distance from the plate edges, and different notch lengths. Finally, an effective comparison in terms of forces-displacement curves has been presented between the proposed procedure, and a lab test for a modified Compact Tension specimen under tensile loading, demonstrating the overall accuracy and reliability of the presented strategy.It is felt that the proposed procedure could pave the way for designing hollowed structures and investigating the corresponding evolutionary laws that relate structural geometries to the insurgence and spread of fractures, and envisaging crack initiation and propagation within inhomogeneous media.

An elastic framework construction method based on task migration in edge computing

AbstractEdge computing (EC) serves as an effective technology, empowering end‐users to attain high bandwidth and low latency by offloading tasks with high computational demands from mobile devices to edge servers. However, a major challenge arises when the processing load fluctuates continuously, leading to a performance bottleneck due to the inability to rescale edge node (EN) resources. To address this problem, the approach of task migration is introduced, and EN load prediction model, the resource constrained model, optimal communication overhead model, optimal task migration model, and energy consumption model are built to form a theoretical foundation from which to propose a task migration based resilient framework construction method in EC. With the aid of the domino effect and the combined effect of task migration, a dynamic node‐growing algorithm (DNGA) and a dynamic node‐shrinking algorithm (DNSA), both based on the task migration strategy, are proposed. Specifically, the DNGA smoothly expands the EN scale when the processing load increases, while the DNSA shrinks the EN scale when the processing load decreases. The experimental results show that for standard benchmarks deployed on an elastic framework, the proposed method realizes a smooth scaling mechanism in the EC, which reduces the latency and improves the reliability of data processing.

A powerful heuristic method for generating efficient database systems

Heuristic functions are an integral part of MapReduce software, both in Apache Hadoop and Spark. If the heuristic function performs badly, the load in the reduce part will not be balanced and access times spike. To investigate this problem closer, we run an optimal database program with numerous different heuristic functions on database. We will leverage the Amazon elastic MapReduce framework. The paper investigates on general purpose, implementation, and evaluation of heuristic algorithm for generating optimal database system, checksum, and special heuristic functions. With the analysis, we present the corresponding runtime results. For the coding part, the records counting part is hasty and can only work for local Hadoop part, it can be debugged and optimized for general purpose implement on Hadoop and Spark and turn into an effective performance monitor tool. As mentioned before, there are strange issue, also the performance of BLAKE2s is unexpectedly slow in that it’s widely accepted the performance of BLAKE2s is much better than MD5 and SHA256, we would like to figure out why the common-sense performance of heuristics is deferent from what we got in distributed frameworks.

An Advanced Gel Polymer Electrolyte for Solid-State Lithium Metal Batteries.

All-solid-state lithium metal batteries (LMBs) are regarded as one of the most viable energy storage devices and their comprehensive properties are mainly controlled by solid electrolytes and interface compatibility. This work proposes an advanced poly(vinylidene fluoride-hexafluoropropylene) based gel polymer electrolyte (AP-GPEs) via functional superposition strategy, which involves incorporating butyl acrylate and polyethylene glycol diacrylate as elastic optimization framework, triethyl phosphate and fluoroethylene carbonate as flameproof liquid plasticizers, and Li7La3Zr2O12 nanowires (LLZO-w) as ion-conductive fillers, endowing the designed AP-GPEs/LLZO-w membrane with high mechanical strength, excellent flexibility, low flammability, low activation energy (0.137eV), and improved ionic conductivity (0.42 × 10-3 S cm-1 at 20 °C) due to continuous ionic transport pathways. Additionally, the AP-GPEs/LLZO-w membrane shows good safety and chemical/electrochemical compatibility with the lithium anode, owing to the synergistic effect of LLZO-w filler, flexible frameworks, and flame retardants. Consequently, the LiFePO4/Li batteries assembled with AP-GPEs/LLZO-w electrolyte exhibit enhanced cycling performance (87.3% capacity retention after 600 cycles at 1 C) and notable high-rate capacity (93.3 mAh g-1 at 5 C). This work proposes a novel functional superposition strategy for the synthesis of high-performance comprehensive GPEs for LMBs.

Guest Molecules Play Tug of War in a Breathing MOF: The Stepwise Monitoring of an Elastic Framework Deformation via SC-SC Transformations

Guest Molecules Play Tug of War in a Breathing MOF: The Stepwise Monitoring of an Elastic Framework Deformation via SC-SC Transformations

Theoretical and computational aspects of configurational forces in three-dimensional crack problems

Mechanics in material space yields configurational forces (CFs) as an appropriate means to describe driving forces acting at defects or inhomogeneities in magnitude and direction. A thermodynamically based approach to obtain a weak formulation of CFs with virtual defect displacements as test functions is introduced first, starting from a global variational energy balance instead of from the currently employed local configurational balance equation. A theoretical basis and issues of three-dimensional implementation are presented to calculate vectors of CFs along a crack front, providing information on local loading and deflection angles. Spurious nodal CFs are separated from physical ones, inter alia requiring surface integrals along the crack faces. Within a linear elastic framework, these are efficiently replaced by an algebraic expression, providing CFs at crack front nodes in a very good approximation, which is eventually demonstrated by means of analytical and numerical examples.

A New Robust Weak Supervision Deep Learning Approach for Reservoir Properties Prediction in Malaysian Basin Field

The conventional seismic inversion approach is practical for operational work, as it only uses simple linearized algorithms and assumptions, but may be less applicable when dealing with a complex geological setting, especially in the Malay basin fields, as it may introduce non-linear noises and non-unique solutions. In the Malay basin, we also frequently struggle with a scarcity of reliable well data when performing seismic inversions. This makes finding an accurate prior model for inversion challenging and contributes to high uncertainty in properties’ estimation. Implementation of deep learning for seismic inversion has become routine and has shown increasing capability in addressing nonlinearity in inverse problems. In this work, we develop a robust approach to deep learning-based seismic inversion to predict elastic properties from seismic data. The approach incorporates synthetic well and seismic data generation from a set of rock physics knowledge called the rock physics library, which plays a significant role in dataset input for network training, validation, and testing to improve elastic properties in this field. The deep learning network architecture comprising UNET and RESNET-18 with weak supervision networks has proven to be useful to enhance computational work efficiency and prediction accuracy while handling the non-linearity of the data and the non-uniqueness of the solutions. We successfully validated the proposed method on actual field data from a clastic fluvial-dominated field in the Malay basin. Upon comparative analysis with the conventional method, both inversion results are comparable and capable of identifying the reservoir occurrence and distribution. The conventional method exposed the presence of scattered amplitude noises and prominent seismic imprints masking the reservoir. Meanwhile, the proposed method showed more stable, clearer definition and fewer noise inversion results but with a faster turn-around time and a more efficient workflow. There are substantial improvements of up to 31% in correlation accuracy achieved upon implementing the proposed method for elastic properties prediction compared to the conventional. The result implies that the proposed method can provide a good elastic properties prediction framework while addressing data limitations and sparsity issues in typical deep learning-based inversions.

ETER: Elastic Tessellation for Real-Time Pixel-Accurate Rendering of Large-Scale NURBS Models

We present ETER, an elastic tessellation framework for rendering large-scale NURBS models with pixel-accurate and crack-free quality at real-time frame rates. We propose a highly parallel adaptive tessellation algorithm to achieve pixel accuracy, measured by the screen space error between the exact surface and its triangulation. To resolve a bottleneck in NURBS rendering, we present a novel evaluation method based on uniform sampling grids and accelerated by GPU Tensor Cores. Compared to evaluation based on hardware tessellation, our method has achieved a significant speedup of 2.9 to 16.2 times depending on the degrees of the patches. We develop an efficient crack-filling algorithm based on conservative rasterization and visibility buffer to fill the tessellation-induced cracks while greatly reducing the jagged effect introduced by conservative rasterization. We integrate all our novel algorithms, implemented in CUDA, into a GPU NURBS rendering pipeline based on Mesh Shaders and hybrid software/hardware rasterization. Our performance data on a commodity GPU show that the rendering pipeline based on ETER is capable of rendering up to 3.7 million patches (0.25 billion tessellated triangles) in real-time (30FPS). With its advantages in performance, scalability, and visual quality in rendering large-scale NURBS models, a real-time tessellation solution based on ETER can be a powerful alternative or even a potential replacement for the existing pre-tessellation solution in CAD systems.

СТРУКТУРА БРОНХИАЛЬНЫХ АРТЕРИЙ КРЫС ПРИ ЧЕРЕПНО-МОЗГОВОЙ ТРАВМЕ И СИСТЕМНОМ ВОСПАЛЕНИИ

Purpose of the study: to present a histochemical and morphometric assessment of the state of the blood vessels of the bronchial tree and lung parenchyma of rats in experimental traumatic brain injury (TBI). and systemic inflammation caused by the administration of pyrogenal. Materials and methods. The experiment was carried out on mature male Wistar rats (200-250 g). Potentially painful interventions in the ongoing experiments and euthanasia were performed under combined injection anesthesia. The model of the falling weight modified by us for rats was used to reproduce TBI. To initiate systemic inflammation (SI), the animals were intramuscularly injected with pyrogenal solution. The study included the following groups: intact rats (n=12); after applying TBI (n=12)-TBI; triple administration (1, 3, 4 days) of pyrogenal without TBI (n=16)-SV; triple administration of pyrogenal (1, 3, 4 days) after TBI (n=12)-TBI+SV. On days 4 and 6 from the beginning of the experiment, under general anesthesia, the animals were dissected, their lungs were removed, after which histological sections were made. Morphological and functional changes in the blood vessels of the bronchial tree and lung parenchyma were analyzed in experimental traumatic brain injury (TBI) and systemic inflammation in rats. It has been established that in injured rats, the administration of pyrogenal has a significant effect on the structure of the bronchial blood vessels of the lungs, while the application of only TBI does not initiate such changes. At the same time, a significant change in the morphometric characteristics of blood vessels is detected, a spasm of arterioles is determined with a decrease in the diameter of the lumen on days 4 and 6 of the study, and an accumulation of segmented leukocytes, macrophages and other bronchial secretion cells in the perivascular space of the vessels is noted. Thus, the results of the study show that with the comorbidity of TBI and SI in the lungs, structural changes occur in the elastic framework of the distal bronchial arteries, which manifests itself changes in the structure of the elastic membranes of these blood vessels.

Variational schemes and mixed finite elements for large strain isotropic elasticity in principal stretches: Closed‐form tangent eigensystems, convexity conditions, and stabilised elasticity

AbstractA new computational framework for large strain elasticity in principal stretches is presented. Distinct from existing literature, the proposed formulation makes direct use of principal stretches rather than their squares that is, eigenvalues of Cauchy‐Green strain tensor. The proposed framework has three key features. First, the eigen‐decomposition of the tangent elasticity and initial (geometric) stiffness operators is obtained in closed‐form from principal information alone. Crucially, these newly found eigenvalues describe the general convexity conditions of isotropic hyperelastic energies. In other words, convexity is postulated concisely through tangent eigenvalues supplementing the original work of Ball (Arch Ration Mech Anal. 1976; 63(4): 337–403). Consequently, this novel finding opens the door for designing efficient automated Newton‐style algorithms with stabilised tangents via closed‐form semipositive definite projection or spectral shifting that converge irrespective of mesh resolution, quality, loading scenario and without relying on path‐following techniques. A critical study of closed‐form tangent stabilisation in the context of isotropic hyperelasticity is therefore undertaken in this work. Second, in addition to high order displacement‐based formulation, mixed Hu‐Washizu variational principles are formulated in terms of principal stretches by introducing stretch work conjugate Lagrange multipliers that enforce principal stretch‐stress compatibility. This is similar to enhanced strain methods. However, the resulting mixed finite element scheme is cost‐efficient, specially compared to approximating the entire strain tensors since the formulation is in the scalar space of singular values. Third, the proposed framework facilitates simulating rigid and stiff systems and those that are nearly‐inextensible in principal directions, a constituent of elasticity that cannot be easily studied using standard formulations.

Elastic hydrogen-bonded ionic framework

Elastic hydrogen-bonded ionic framework

Quakes of compact stars

ABSTRACTGlitches are commonly observed for pulsars, which are explained by various mechanisms. One hypothesis attributes the glitch effect to the instantaneous moment of inertia change of the whole star caused by a starquake, which is similar to earthquakes caused by fast dislocation occurring on planar faults for the static stress, though the quake-induced dynamics responsible for glitch (superfluid vortex versus pure starquake) remains still unknown. However, a theoretical model to quantitatively explain the stress loading, types of starquakes, and co-seismic change of moment of inertia is rarely discussed. In this study, we incorporate elastic deformation theories of earthquakes into the starquake problems. We compute the field of stress loading associated with rotation deceleration and determine the optimal type of starquakes at various locations. Two types of pulsar structure models, i.e. neutron and strangeon star models, are included in the computation, and their differences are notable. Our calculation shows that the observed glitch amplitude can be explained by the starquakes in the strangeon star model, though the required scaled starquake magnitude is much larger than that occurred on Earth. We further discuss the possibility to compute the energy budget and other glitch phenomena using the starquake model in the elastic medium framework.