Mass-interaction model of emergent collective phenomena

International audienceThis paper is a position paper on the concepts of emergence and individual / collective paradox, from both philosophical and experimental point of view. It presents successively: (1) some sociological and philosophical issues related to collective emergent behaviors; (2) a non-conventional point of view of physical mass-interaction modeling as a cellular automata system; (3) a proposition of a generic physical mass-interaction model for emergent collective phenomena able to render the main expected figures of non-deliberative emergent collective phenomena as those that define crowd behaviors

A consistent debris flow model with intergranular friction and dynamic pore-fluid pressure

This work presents the thermodynamically consistent development of a scaled, depth-integrated model for granular-fluid flows. Considering a general topography, the model is used for the numerical simulation of debris flows in different scenarios. With regard to important physical mechanisms in such flows and the underlying dynamics, additional fields are included, an extra pore-fluid pressure and hypoplastic, intergranular friction. The combined recourse to these two fields takes place in the context of a derivation with the entropy principle, beginning with general laws of thermodynamics, and ends with the application to real, large-scale debris flow events. As a starting point, within the framework of mixture theory and the entropy principle, a continuum model for a general granular-fluid mixture is derived. Amending the basic fields of mass, momentum and energy, as well as a balance equation for the volume fraction, an additional field for the intergranular contact forces is considered, together with, newly introduced in the context of thermodynamic consistent modeling, a dynamic partial pressure. Assuming a shallow, saturated flow, the derived model is then non-dimensionalized and depth-integrated. The resulting model is further transferred into general coordinates. This allows for the easy representation of debris flows on real mountainous topography. Implemented with a shock-capturing NOC scheme, several numerical simulations are performed, ranging from parameter studies on a laboratory scale and the comparison with a dam break experiment to a large-scale event. The numerical parameter studies confirm the expected behavior of the additional physical fields. Since the extra pore-fluid pressure arises from the interaction of the granular skeleton and the pore-fluid, it interferes with the hydrostatic pressure and is able to push the granular particles apart, thus reducing their apparent friction and prolongating the movement of the bulk mass. It accelerates the whole mixture and prevents the mass from settling, while the intergranular friction helps the granular structure to maintain its form, hindering it from dissolving like a fluid and accounting for the non-linear, anelastic behavior of granular material. It should be emphasized that the presented modeling establishes a transfer from investigations on granular materials in the context of the entropy principle to the more practically orientated class of depth-integrated models. With this, the additional fields can be seen as the incorporation of information on the granular skeleton, i.e. the microstructure, in its interdependency with the fluid phase - something that is usually not depicted similarly in the framework of mixture theory. A central aim here is therefore to provide a consistent debris flow model, developed with regard to these additional fields, which is applicable for numerical studies

Coupling granular size segregation and cyclic-loading-induced crushing effects in a continuum model

The kinetic sieving mechanism is a well-known continuum method for modelling granular size segregation phenomena, typically solved for steady-state chute flows. In this thesis, we develop a model to include normal confining pressure dependence, time-dependence and evolving velocity profiles through the grain body. This enabled more sophisticated granular behaviours and feed-backs to be explored - in particular, those relevant to cyclic loading of soils through e.g. wind turbine foundations. An iterative expansion approach is proposed to self-expand the classic bi-disperse or tri-disperse segregation problem towards arbitrarily poly-disperse systems, with minimal change of input parameters and data structures. Under pair-wise stress partition and segregation relationships, behaviour can be directly linked to size ratios. Simulations show the dependence on particle size distribution alongside the controlling non-dimensional parameters: inter-particle drag, diffusion rate and confining pressure. The final goal of this project was to couple particle crushing with the validated poly-disperse segregation model. After exploring possible ways of incorporating breakage, solid volume fraction re-distribution was tested in a 10-population problem with clear feed-backs from the prescribed crushing

Coupling granular size segregation and cyclic-loading-induced crushing effects in a continuum model

The kinetic sieving mechanism is a well-known continuum method for modelling granular size segregation phenomena, typically solved for steady-state chute flows. In this thesis, we develop a model to include normal confining pressure dependence, time-dependence and evolving velocity profiles through the grain body. This enabled more sophisticated granular behaviours and feed-backs to be explored - in particular, those relevant to cyclic loading of soils through e.g. wind turbine foundations. An iterative expansion approach is proposed to self-expand the classic bi-disperse or tri-disperse segregation problem towards arbitrarily poly-disperse systems, with minimal change of input parameters and data structures. Under pair-wise stress partition and segregation relationships, behaviour can be directly linked to size ratios. Simulations show the dependence on particle size distribution alongside the controlling non-dimensional parameters: inter-particle drag, diffusion rate and confining pressure. The final goal of this project was to couple particle crushing with the validated poly-disperse segregation model. After exploring possible ways of incorporating breakage, solid volume fraction re-distribution was tested in a 10-population problem with clear feed-backs from the prescribed crushing

Measurements of granular flow dynamics with high speed digital images

The flow of granular materials is common to many industrial processes. This dissertation suggests and validates image processing algorithms applied to high speed digital images to measure the dynamics (velocity, temperature and volume fraction) of dry granular solids flowing down an inclined chute under the action of gravity. Glass and acrylic particles have been used as granular solids in the experiment. One technique utilizes block matching for spatially averaged velocity measurements of the glass particles. This technique is compared with the velocity measurement using an optic probe which is a conventional granular flow velocity measurement device. The other technique for measuring the velocities of individual acrylic particles is developed with correspondence using a Hopfield network. This technique first locates the positions of particles with pattern recognition techniques, followed by a clustering technique, which produces point patterns. Also, several techniques are compared for particle recognition: synthetic discriminant function (SDF), minimum average correlation energy (MACE) filter, modified minimum average correlation energy (MMACE) filter and variance normalized correlation. The author proposes an MMACE filter which improves generalization of the MACE filter by adjusting the amount of averaged spectrum of training images in the spectrum whitening stages of the MACE filter. Variance normalized correlation is applied to measure the velocity and temperature of flowing glass particles down the inclined chute. The measurements are taken for the steady and wavy flow and qualitatively compared with a theoretical model of granular flow

Measurements of granular flow dynamics with high speed digital images

The flow of granular materials is common to many industrial processes. This dissertation suggests and validates image processing algorithms applied to high speed digital images to measure the dynamics (velocity, temperature and volume fraction) of dry granular solids flowing down an inclined chute under the action of gravity. Glass and acrylic particles have been used as granular solids in the experiment. One technique utilizes block matching for spatially averaged velocity measurements of the glass particles. This technique is compared with the velocity measurement using an optic probe which is a conventional granular flow velocity measurement device. The other technique for measuring the velocities of individual acrylic particles is developed with correspondence using a Hopfield network. This technique first locates the positions of particles with pattern recognition techniques, followed by a clustering technique, which produces point patterns. Also, several techniques are compared for particle recognition: synthetic discriminant function (SDF), minimum average correlation energy (MACE) filter, modified minimum average correlation energy (MMACE) filter and variance normalized correlation. The author proposes an MMACE filter which improves generalization of the MACE filter by adjusting the amount of averaged spectrum of training images in the spectrum whitening stages of the MACE filter. Variance normalized correlation is applied to measure the velocity and temperature of flowing glass particles down the inclined chute. The measurements are taken for the steady and wavy flow and qualitatively compared with a theoretical model of granular flow

Building Social Media Observatories for Monitoring Online Opinion Dynamics

Social media house a trove of relevant information for the study of online opinion dynamics. However, harvesting and analyzing the sheer overload of data that is produced by these media poses immense challenges to journalists, researchers, activists, policy makers, and concerned citizens. To mitigate this situation, this article discusses the creation of (social) media observatories: platforms that enable users to capture the complexities of social behavior, in particular the alignment and misalignment of opinions, through computational analyses of digital media data. The article positions the concept of "observatories" for social media monitoring among ongoing methodological developments in the computational social sciences and humanities and proceeds to discuss the technological innovations and design choices behind social media observatories currently under development for the study of opinions related to cultural and societal issues in European spaces. Notable attention is devoted to the construction of Penelope: an open, web-services-based infrastructure that allows different user groups to consult and contribute digital tools and observatories that suit their analytical needs. The potential and the limitations of this approach are discussed on the basis of a climate change opinion observatory that implements text analysis tools to study opinion dynamics concerning themes such as global warming. Throughout, the article explicitly acknowledges and addresses potential risks of the machine-guided and human-incentivized study of opinion dynamics. Concluding remarks are devoted to a synthesis of the ethical and epistemological implications of the exercise of positioning observatories in contemporary information spaces and to an examination of future pathways for the development of social media observatories

Forward and inverse modeling of granular flows using differentiable graph neural network simulator

Granular flows such as landslides can cause extensive damage to infrastructure and pose significant hazards. Accurate forward and inverse modeling granular flows are critical for developing effective risk mitigation strategies and designs. However, conventional high-fidelity forward simulators, like the material point method (MPM), or discrete element method (DEM), are computationally intensive, which limits their ability to efficiently solve inverse problems like parameter optimization or optimal design. Additionally, their non-differentiable nature makes gradient-based optimization methods, known for their efficiency in high-dimensional problems, inapplicable. While machine learning-based surrogate models offer computational efficiency and differentiability, they often struggle to generalize beyond their training data due to their reliance on low-dimensional input-output mappings that fail to capture the complete physics of granular flows. This study introduces a differentiable graph neural network simulator (GNS) as a generalizable surrogate model for high-fidelity simulators to accelerate forward simulation for granular flows. Graphs represent the state of dynamically changing granular flows and their interactions. By learning the interaction law that governs the granular flow behavior, GNS is generalizable to predict granular flow dynamics not seen during training. It also shows great computation efficiency compared to the high-fidelity model (CB-Geo MPM) by showing up to 2000x speed up. We then propose a novel approach for solving inverse problems by combining differentiable GNS with gradient-based optimization leveraging reverse mode automatic differentiation (AD) of GNNs. The AD-GNS solves various inverse problems in granular flows. Besides the forward and inverse modeling of granular flows, this study addresses a machine learning approach to predicting pore water pressure response in liquefiable sands under cyclic loading. The pore pressure response in liquefiable sands is largely affected by the history of the cyclic shear stress. When the amplitude of cyclic shear stress is lower than the previous peak amplitude, excess pore pressure does not increase—an effect known as shielding. Many advanced constitutive models do not accurately capture this shielding effect observed in cyclic simple shear tests. We develop a data-driven machine learning model based on the long short-term memory (LSTM) neural network to capture the liquefaction response of soils under cyclic loading. We train the LSTM model on the data from 12 laboratory cyclic simple shear tests performed on Nevada sand samples with varying relative densities and subjected to different cyclic simple shear loading conditions. The model inputs include the soil's relative density and previous stress history to predict the pore water pressure response. The LSTM model successfully replicated the pore pressure response for three cyclic simple shear test cases, accounting for the shielding and density effects.Civil, Architectural, and Environmental Engineerin

From model conception to verification and validation, a global approach to multiphase Navier-Stoke models with an emphasis on volcanic explosive phenomenology

Large-scale volcanic eruptions are hazardous events that cannot be described by detailed and accurate in situ measurement: hence, little to no real-time data exists to rigorously validate current computer models of these events. In addition, such phenomenology involves highly complex, nonlinear, and unsteady physical behaviors upon many spatial and time scales. As a result, volcanic explosive phenomenology is poorly understood in terms of its physics, and inadequately constrained in terms of initial, boundary, and inflow conditions. Nevertheless, code verification and validation become even more critical because more and more volcanologists use numerical data for assessment and mitigation of volcanic hazards. In this report, we evaluate the process of model and code development in the context of geophysical multiphase flows. We describe: (1) the conception of a theoretical, multiphase, Navier-Stokes model, (2) its implementation into a numerical code, (3) the verification of the code, and (4) the validation of such a model within the context of turbulent and underexpanded jet physics. Within the validation framework, we suggest focusing on the key physics that control the volcanic clouds—namely, momentum-driven supersonic jet and buoyancy-driven turbulent plume. For instance, we propose to compare numerical results against a set of simple and well-constrained analog experiments, which uniquely and unambiguously represent each of the key-phenomenology. Ke