arXiv subjects

Raul Fuentes

Publications and source records attributed to Raul Fuentes.

7 recordsLinked to original sources

arXiv2026

Scaling laws for rockfall impact fragmentation emerging from diverse lithologies

Impact-induced fragmentation is a fundamental dissipative process in geosciences, yet its stochastic nature makes predicting debris evolution a persistent challenge. Here, we introduce a discrete element framework to resolve fragmentation mechanics across a diverse lithological spectrum, from high-strength siliciclastic units to massive carbonates, validated against high-resolution field data from documented rockfall events. Our results reveal that, despite the inherent randomness of impact dynamics, fragment size distributions consistently follow a universal Weibull scaling law, independent of lithology or initial kinetic energy. By applying a relative breakage index, we demonstrate a remarkable collapse of fragmentation data onto a single statistical signature, bridging the gap between grain-scale fracture and macroscopic debris evolution. We find that this Weibullian signature acts as a proxy for lithological sensitivity, reflecting distinct efficiencies in converting kinetic energy into new fracture surfaces. This framework explicitly resolves the energy partitioning between surviving blocks and comminuted debris, providing a robust predictive link between impact mechanics and structural resilience. From an engineering perspective, our findings enable a shift from idealised single-block impact assumptions toward a realistic assessment of distributed energy in fragmented particle clouds, offering a physical basis for optimising protective galleries and hazard mitigation strategies in complex mountainous terrains.

physics.geo-ph↗

arXiv2025

Investigation of Air Fluidization during Intruder Penetration in Sand

Self-burrowing robots navigating through granular media benefit from airflow-assisted burrowing, which reduces penetration resistance. However, the mechanisms underlying airflow-granular interactions remain poorly understood. To address this knowledge gap, we employ a coupled computational fluid dynamics and discrete element method (CFD-DEM) approach, supplemented by experimental cone penetration tests (CPT) under varying airflow conditions, to investigate the effects of aeration on penetration resistance. Experimental results reveal a nonlinear relationship between penetration resistance reduction and depth, wherein resistance approaches near-zero values up to a critical depth, beyond which the effectiveness of fluidization diminishes. Simulations demonstrate that higher airflow rates enhance the mobilization of overlying grains, increasing the critical depth. A detailed meso- and micro-scale analysis of particle motion, contact forces, and fluid pressure fields reveals four distinct penetration stages: particle ejection and channel formation, channel sealing, channel refill, and final compaction. These findings contribute to a deeper understanding of granular aeration mechanisms and their implications for geotechnical engineering, excavation technologies, and the development of self-burrowing robotic systems.

cond-mat.soft↗

arXiv2025

Data-driven ANN model for estimating unfrozen water content in the thermo-hydraulic simulation of frozen soils

This study integrates a data-driven model for estimating the unfrozen water content into the thermo-hydraulic coupling simulation of frozen soils. An artificial neural network (ANN) was employed to develop this data-driven model using a dataset from the literature. Thereafter, a numerical algorithm was developed to implement the data-driven model into the thermo-hydraulic simulation. In the numerical algorithm, the frozen and unfrozen zones are distinguished first according to the freezing temperature, where the unfrozen water at frozen nodes is updated using the ANN model. Subsequently, discretized hydraulic and thermal equations are solved sequentially and iteratively using Newton-Raphson method until the temperature and unfrozen water content satisfy the tolerance simultaneously. Horizontal and vertical freezing experiments are used to verify the reliability of the proposed algorithm. The computed variations in temperature, total water, unfrozen water, and ice content achieve good agreements with measured data. Some key features of frozen soils, such as water migration and ice formation, and the increase in total water content, are reproduced by the developed algorithm. Additionally, the comparison between the ANN model and existing empirical equations for determining unfrozen water content demonstrates that the ANN model offers a better performance.

cond-mat.soft↗

arXiv2024

Gravity effects on a bio-inspired self-burrowing probe in granular soils

In recent years, self-burrowing probes have been studied since they can be suitable for soil monitoring in locations with limited access such as outer space bodies and underneath existing structures. We study the performance of a self-burrowing probe under different gravity conditions, from low gravity (i.e., 1/6g, 1/3g and 1g) to high gravity (i.e., 5g, 10g and 15g), specifically in terms of penetration distance and energy consumption. Results show that the probe reaches efficient penetration in all gravity conditions and that it achieves larger penetration distances in high gravity conditions. However, the penetration efficiency, shown as unit energy per meter, is higher in low gravity. Additionally, we prove that a simple dimensional analysis provides reasonable scaling factors for first order effects in forces, velocities and energy. The findings in this study give confidence to the potential use of self-burrowing probes in campaigns of soil testing and sensor deployment in outer space or centrifuges in which the gravity conditions can differ from Earth.

cond-mat.soft↗

arXiv2020

Spatial and temporal evolution of an experimental debris flow, exhibiting coupled fluid and particulate phases

The internal behaviour of debris flows provides fundamental insight into the mechanics responsible for their motion. We provide velocity data within a small-scale experimental debris flow, consisting of the instantaneous release of a water-granular mixture along a rectangular flume, inclined at 31 degrees. The results show a transition from a collisional, turbulent front to a viscous-type, steady flow body, exhibiting strong fluid-particulate coupling. This is the first time that both the spatial and temporal evolution of the internal mechanics of a small-scale debris flow have been considered. Our results serve as invaluable data for testing two-phase fluid-particulate numerical models.

physics.flu-dyn↗

arXiv2018

O2A: One-shot Observational learning with Action vectors

We present O2A, a novel method for learning to perform robotic manipulation tasks from a single (one-shot) third-person demonstration video. To our knowledge, it is the first time this has been done for a single demonstration. The key novelty lies in pre-training a feature extractor for creating a perceptual representation for actions that we call 'action vectors'. The action vectors are extracted using a 3D-CNN model pre-trained as an action classifier on a generic action dataset. The distance between the action vectors from the observed third-person demonstration and trial robot executions is used as a reward for reinforcement learning of the demonstrated task. We report on experiments in simulation and on a real robot, with changes in viewpoint of observation, properties of the objects involved, scene background and morphology of the manipulator between the demonstration and the learning domains. O2A outperforms baseline approaches under different domain shifts and has comparable performance with an oracle (that uses an ideal reward function).

cs.RO↗

arXiv2018

ViTac: Feature Sharing between Vision and Tactile Sensing for Cloth Texture Recognition

Vision and touch are two of the important sensing modalities for humans and they offer complementary information for sensing the environment. Robots could also benefit from such multi-modal sensing ability. In this paper, addressing for the first time (to the best of our knowledge) texture recognition from tactile images and vision, we propose a new fusion method named Deep Maximum Covariance Analysis (DMCA) to learn a joint latent space for sharing features through vision and tactile sensing. The features of camera images and tactile data acquired from a GelSight sensor are learned by deep neural networks. But the learned features are of a high dimensionality and are redundant due to the differences between the two sensing modalities, which deteriorates the perception performance. To address this, the learned features are paired using maximum covariance analysis. Results of the algorithm on a newly collected dataset of paired visual and tactile data relating to cloth textures show that a good recognition performance of greater than 90\% can be achieved by using the proposed DMCA framework. In addition, we find that the perception performance of either vision or tactile sensing can be improved by employing the shared representation space, compared to learning from unimodal data.

cs.RO↗