Search arXivSearch

arXiv · 2409.11474

A generalized non-hourglass updated Lagrangian formulation for SPH solid dynamics

Abstract

Hourglass modes, characterized by zigzag particle and stress distributions, are a common numerical instability encountered when simulating solid materials with updated Lagrangian smoother particle hydrodynamics (ULSPH). While recent solutions have effectively addressed this issue in elastic materials using an essentially non-hourglass formulation, extending these solutions to plastic materials with more complex constitutive equations has proven challenging due to the need to express shear forces in the form of a velocity Laplacian. To address this, a generalized non-hourglass formulation is proposed within the ULSPH framework, suitable for both elastic and plastic materials. Specifically, a penalty force is introduced into the momentum equation to resolve the disparity between the linearly predicted and actual velocities of neighboring particle pairs, thereby mitigating the hourglass issue. The stability, convergence, and accuracy of the proposed method are validated through a series of classical elastic and plastic cases, with a dual-criterion time-stepping scheme to improve computational efficiency. The results show that the present method not only matches or even surpasses the performance of the recent essentially non-hourglass formulation in elastic cases but also performs well in plastic scenarios.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Shuaihao Zhang, Dong Wu, Sérgio D. N. Lourenço, Xiangyu Hu. 2024-09-17. A generalized non-hourglass updated Lagrangian formulation for SPH solid dynamics. https://arxiv.org/abs/2409.11474

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Synthetic Human Mobility Data Generation: A Structured Review of Representations, Methods, and Practical Capabilities

Human mobility data has become an increasingly important component of urban analytics. Although the range of available mobility data sources has expanded substantially, access remains highly constrained by commercial restrictions, privacy concerns, and institutional barriers. Data protection procedures also often reduce the analytical value of released datasets. Synthetic mobility data has emerged as a promising solution, but existing methods differ substantially in their underlying mechanisms, the information they preserve, the outputs they generate, and the analytical questions they can support. Their comparative strengths and trade-offs remain insufficiently understood for urban analytics. This paper presents a structured review of synthetic human mobility data generation from an urban analytics perspective. We review the literature by methodological family and index it by the mobility outputs each family generates natively and the analytical capabilities those outputs enable. We first provide a taxonomy of synthetic data products, including population and persona representations, activity schedules, trip and tour records, trajectories, and aggregate mobility patterns. We then review the major methodological families, spanning mechanistic models, survey-driven population synthesis, activity- and agent-based simulation, deep generative models, transformer-based mobility language models, and LLM-agentic systems. Building on this synthesis, we introduce a Meaning-Population-Autonomy framework that characterises these methods along three dimensions: behavioural meaning, population grounding and scale, and generation autonomy. We consider these dimensions the principal requirements for downstream urban analytics. Few methods deliver behavioural meaning, population grounding and autonomous generation at once, and fewer still with generation constrained to feasible trajectories.

cs.CE

TERRA-NG v1.0: Extreme-Scale, GPU-accelerated Mantle Convection

We present TERRA-NG, a portable, GPU-accelerated, matrix-free mantle-convection code. A single Kokkos C++ implementation runs at scale on NVIDIA, AMD, and Intel GPU supercomputers. TERRA-NG has a deliberately narrow design: built on a radially extruded mesh of spherical wedges, tailored to the spherical shell geometry, which enables domain-specific optimizations like single quadrature-point integral-evaluations, radial coordinate storage compression and radial shared-memory tiling. The corresponding low-order $W_1$-iso-$W_2/W_1$ wedge-based Stokes--energy discretisation is verified against the Zhong et al.(2008) spherical-shell convection benchmark suite. We showcase TERRA-NG through strong- and weak-scaling on the JUWELS Booster (NVIDIA A100), MareNostrum 5 (NVIDIA H100), LUMI-G (AMD MI250X), Hunter (AMD MI300A APU), and SuperMUC-NG Phase 2 (Intel PVC) supercomputers. Coupled mantle convection simulations at $\sim\!11$ km and $\sim\!5.6$ km radial spacing ($\sim 2.8$ B and $\sim 22$ B DoFs) can be run routinely on standard node partitions of all considered systems. Global $\sim\!1$ km-per-gridpoint mantle convection ($\sim 1.4$ T DoFs) is feasible on an extreme-scale allocation, and a sub-km hero-run at $\sim\!0.7$ km grid spacing scaling up to $\sim 11,000$ GPUs of LUMI-G ($\sim 11$ T DoFs) shows the potential of the code on future, larger machines.

cs.CE

FireDataForge: A Unified Framework for Multi-Source Wildfire Data Retrieval and Integration

Wildfire research, modeling, and education require geospatial data from multiple sources that vary in formats, coordinate systems, spatial resolutions, and temporal cadences. This preprocessing burden limits reproducible reuse. We present FireDataForge, an open-source Python framework that automates retrieval and harmonization of 11 wildfire-related sources spanning fire behavior, weather, land cover, vegetation, elevation, built environment, wildland-urban interface, fire history, and satellite imagery. Given an MTBS Event ID, FireDataForge retrieves relevant datasets, aligns them to a common grid, and outputs analysis-ready NumPy arrays with embedded metadata. Batch processing of historical fires demonstrates support for fire behavior simulation, educational visualization, machine learning, and AI-assisted wildfire analysis.

cs.CE