Multi-scale performance simulation and effect analysis for hydraulic concrete submitted to leaching and frost

Taking into account the constantly growing requirements for the quality, reliability and durability of concrete structures for hydraulic structures, there is a need to develop hydraulic concrete compositions with improved performance properties. The aim of the study is to improve the physico-mechanical and deformative properties of hydraulic concrete using finely dispersed aluminosilicate rocks – perlites and colloidal additives in the form of silicic acid sol. The object of the study is modified hydraulic engineering concrete based on a composite binder using finely dispersed vitreous and crystallized perlite, silicic acid sol and a polycarboxylate-based superplasticizer «Polyplast». Results of the study: The choice of silica-containing additives is substantiated and it is shown that their use has a positive effect on the properties of hydraulic concrete. The effect of complex modification on the properties of hydraulic concrete was established by reducing the content of Portland cement and replacing it with finely dispersed glassy perlite, pre-crushed to a specific surface of 300-600 m2/kg, introducing silicic acid sol and the superplasticizer "Polyplast", which make it possible to improve the physical, mechanical, deformation and hydrophysical properties: compressive strength - 53.4 MPa; ultimate tensile strength in bending - 10.9 MPa; crack resistance coefficient - 0.20; prismatic strength - 46.3 MPa; modulus of elasticity 37.345 MPa × 103; Poisson's ratio - 0.199, water absorption - by weight - 2.43%; water resistance grade - W16. It has been proven that by adding finely dispersed perlite, silica sol and superplasticizer Polyplast to Portland cement, it is possible to obtain hydraulic concrete characterized by strength indicators not inferior in strength to the control composition, and with increased indicators of water resistance and crack resistance compared to traditional compositions.

This study delves into the mechanical and durability properties of rubber fiber hydraulic concrete (RFHC) using an orthogonal testing method. The optimal mix proportion for RFHC is determined through orthogonal range and variance analysis results. The influence of polypropylene fiber content, rubber particle content, and rubber particle size on the compressive strength, flexural strength, split tensile strength, brittleness, and crack resistance of rubber fiber concrete is thoroughly examined. Durability tests are conducted based on the optimal mix ratio, and a comparative analysis of the durability between RFHC and conventional concrete is carried out. The findings indicate that the mechanical properties of the concrete exhibit a significant decline with increasing rubber content and decreasing rubber particle size. The variation in rubber content notably affects the mechanical properties of hydraulic concrete. The frost resistance and impermeability of RFHC are considerably higher than those of conventional concrete, while the erosion resistance of RFHC surpasses that of the conventional benchmark concrete. This research offers valuable benchmarks for the theoretical design and practical engineering application of RFHC, contributing to the advancement of sustainable construction materials.

Elastic modulus of self-compacting fibre reinforced concrete: Experimental approach and multi-scale simulation

Multiple mechanical properties prediction of hydraulic concrete in the form of combined damming by experimental data mining

This study aims to determine the optimal quantity of fillers to add to hydraulic concrete and to assess the influence of these fillers on its rheological characteristics and mechanical properties. The characterization of the aggregates shows that they meet the specifications for the formulation of hydraulic concrete according to the Dreux-Gorisse method. Normalizing the formula to the cubic meter enables to define the standard concrete. The cement content is 350 kg/m3. The mineral materials added to the concrete to increase its characteristics and properties are limestone, basalt, and sandstone fillers with a weight percent of 4%, 5%, and 3% respectively. Changes in concrete properties with the addition of fillers were determined through geotechnical tests. The results obtained show a decrease in the workability measured by slump test which returned 7.8 cm for the standard concrete sample, 7.2 cm with 5% of basalt, 7.3 cm with 4% of limestone, and 6.1 cm with 3% of sandstone. Regarding the bleeding, the results show that it decreases leading to a substantial improvement in stabilization reaching 26% with 5% of basalt fillers, 29% with 4% of limestone fillers, and 31% with 3% of sandstone fillers. The compressive strengths noted Rc28 at 28 days increases compared to that of the standard concrete, which is 31.5 MPa. They increase to 34.3 MPa with 5% of basalt fillers being 8.9%, 36.2 MPa with 4% of limestone fillers being 14.9%, and 36.8 MPa with 3% of sandstone fillers being 16.8%. Finally, the addition of fillers increases the degree of compaction values to 83.62% with 5% of basalt fillers, 84.2% with 4% of limestone fillers, and 84.34% with 3% of sandstone fillers.

Generating random aggregate models (RAMs) plays a key role in the mesoscopic modelling of concrete-like composite materials. The arbitrary geometry, wide gradation, and high volume ratio of aggregates pose a great challenge for fast and efficient numerical construction of concrete meso-structures. This paper presents a simple strategy for generating RAMs of concrete based on Laguerre tessellation, which mainly consists of three steps: tessellation, geometric smoothing, and scaling. The computer-assisted design (CAD) file of RAMs obtained by the proposed approach can be directly adopted for the construction of random numerical concrete samples. Combined with the image-based octree meshing algorithm, the scaled boundary finite element method (SBFEM) was adopted for an automatic stress analysis of mass concrete samples, and a parametric study was conducted to investigate the meso-structural effects on concrete elasticity properties. The modelling results successfully reproduced the increasing trend of concrete elastic modulus with the grading of coarse aggregates in literature test data and demonstrate the effectiveness of the proposed strategy.

This special section of Multiscale Modeling and Simulation: A SIAM Interdisciplinary Journal, on multiscale modeling in life and materials sciences, is based on a summer program with the same theme, held August 4--30, 2003, at the Universita della Svizzera Italiana in Lugano, Switzerland, and organized by the ETH Zurich Computational Laboratory (CoLab). This workshop emphasized that multiscale modeling and simulation can serve as a commonscientific language enabling cross-fertilization across disciplines and the definition of new scientific frontiers. Materials science was one of the first disciplines that emphasized the need for novel multiscaling approaches bridging continuum and atomistic models. In life sciences, the rapid growth of the role played by computational methods has led to the early realization that a multiscaling approach is necessary to tackle inherently complex phenomena involving cells, organisms, and ecosystems. This summer program provided the opportunity to scientists from these different scientific fields to exchange ideas and to pursue research in an interdisciplinary fashion using multiscale computational methods as a common language. These interactions demonstrated that research in multiscaling is a fertile breeding ground for the development of novel powerful computational tools for domains where computing is established as a tool for scientific inquiry and for the dissemination of this know-how to disciplines for which computation is an emerging form of scientific investigation. This special section of MMS is a representative sample of theoretical and computational approaches to multiscale modeling and simulation in materials and life sciences. The papers discuss multiscale coupling of physical models and time/space multiresolution techniques. The problem of coupling models across space and time scales is a fundamental issue in multiscale modeling and simulation. Multiscale approaches often require the rigorous integration of diverse physical models, such as the case of blood flow in the circulatory system (Fernandez, Milisic}, and Quarteroni). The multiscale integration of diverse physical models in nanoscale magnetic systems involves the proper coupling of quantum effects in classical descriptions (Wessel, Trebst, and Troyer). Multiscale simulations of materials with heterogeneous properties are tackled using asymptotic expansions for the case of conducting inclusions embedded in a homogeneous matrix phase (Ben Hassen and Bonnetier). A broad class of multiscale problems involves the description of macroscale systems using only the information available by suitable microscopic models. This technique is demonstrated in simulating the evolution of parabolic homogenization problems with nonlinear reactions, using as a microscopic model a partial differential equation with rapidly oscillating coefficients (Samaey, Roose, and Kevrekidis). A related work extends this concept to problems with a wide separation in time scales. Using as an application a model of bacterial chemotaxis it is shown that projective time integration of coarse variables can be carried out in time scales that are long compared to those of the microscopic dynamics (Setayeshgar, Gear, Othmer, and Kevrekidis). In problems with time-dependent, large space scale variations a new class of multiresolution particle methods enhances their inherent adaptivity and presents an alternative to grid-based methods (Bergdorf, Cottet, and Koumoutsakos). Future research in multiscale modeling would continue to rely on synergies across disciplines for the development of novel computational methods, the integration of physical models, and the formulation of new challenges. This principle will continue to guide future workshops in multiscale modeling and simulations at the CoLab and, we believe, an ever-increasing number of scientific institutions. The editors in charge would like to credit, for their hard work and dedication, those editors who helped handle the peer-review process for papers submitted to this special section. Thanks to Emily Carter, Wolfgang Hackbusch, Robert Kohn, Hans Christian Ottinger, Olivier Pironneau, and John Willis. We also wish to thank SIAM Publications Manager Mitch Chernoff for his invaluable advice and help in preparing this section.

Toawrd rational design of polymer electrolyte fuel cell (PEFC), understandings of both atomistic scale and systems characteristics are important. Multi-scale simulation can contribute to bridging microscopic and macroscopic understandings effectively. In this manuscript, we described a multi-scale simulation approach based on an original porous structure simulator and computational chemistry methods. Proton conductivity was estimated by molecular dynamics method and structures of porous catalyst layer were modeled by the porous structure simulator. Multi-scale simulations for macroscopic current-voltage characteristics of PEFC were performed considering both atomistic-scale properties and porous microstructure. Influences of atomistic properties and microstructure of porous catalyst layer on macroscopic current-voltage characteristics were successfully studied. Effectiveness of the developed multi-scale simulation approach was confirmed from the simulation results.

Edge-localized modes (ELMs) produce an intense transient heat flux on castellated divertor mono-blocks (MBs). The parallel transport of electrons and ions from the ELM burst varies due to their different velocities, which significantly influences the power load on the MBs. This study shows that two heat load phases are caused by an ELM burst. In the first phase, the horizontal surface withstands the intense heat flux because the faster electrons generate a high sheath potential drop. The leading edge of the MBs suffers a great power load from energetic ions in the second phase. This study applies a multiscale hybrid numerical approach to investigate the temporal evolution of heat flux on the poloidal gap from the ELM source in the scrape-off layer to the castellated divertor surface. Our results indicate that the power load on both the MB surface and the leading edge should be considered in the future design of castellated divertors.

Modeling of chloride diffusion in concrete containing low-calcium fly ash

This research presents the development of a data-driven machine learning-assisted approach to simulate and optimize the design of composite materials subjected to low-velocity impacts. It focuses on a specific type of hybrid composite comprised of fiberglass and Kevlar fabrics stitched with Kevlar threads. The study begins by creating a multiscale finite element simulation for the composite under a low-velocity impact, which operates across three scales: microscale, mesoscale, and macroscale. This simulation accepts various inputs, such as different layer configurations and orientations, and provides impact outputs including maximum load and energy absorption capacity and displacement at failure. Python and MATLAB scripts manage the simulation, automating data generation to create a large dataset for analyzing the composite under low-velocity impacts at both the mesoscale and macroscale levels. This data then trains artificial neural networks (ANNs) at each scale, which are coupled to form a multiscale machine learning-assisted simulation. The mesoscale ANN uses homogenized mechanical properties (HMPs) from the microscale model of the composite layers to predict mesoscale HMPs where a combination of layers and stitching threads are considered in a mesoscale representative volume element (RVE). The macroscale ANN then uses the mesoscale HMPs to produce the composite's impact response, including maximum force and energy absorption capacity and displacement at failure. Finally, the multiscale ANN system is integrated with a particle swarm optimization (PSO) algorithm to explore the design space and find the optimal composite configuration for specific impact conditions and constraints. All simulation results and predictions from the multiscale ANN are validated against experimental data on composites subjected to low-velocity impacts. The multiscale ANN approach introduced in this study is flexible and can be applied to other complex materials and structures where several variables across different scales influence the results. This study shows the efficiency of the introduced approach specifically for the optimization process where several possible combinations of the design space need to be evaluated to find the best combination for a given set of constraints.

Development of porous structure simulator for multi-scale simulation of irregular porous catalysts

Developing efficient and robust computational models is essential to improve our understanding of protein solution behavior. This becomes particularly important to tackle the high-concentration regime. In this context, the main challenge is to put forward coarse-grained descriptions able to reduce the level of detail, while retaining key features and relevant information. In this work, we develop an efficient strategy that can be used to investigate and gain insight into monoclonal antibody solutions under different conditions. We use a multi-scale numerical approach, which connects information obtained at all-atom and amino-acid levels to bead models. The latter has the advantage of reproducing the properties of interest while being computationally much faster. Indeed, these models allow us to perform many-protein simulations with a large number of molecules. We can, thus, explore conditions not easily accessible with more detailed descriptions, perform effective comparisons with experimental data up to very high protein concentrations, and efficiently investigate protein–protein interactions and their role in phase behavior and protein self-assembly. Here, a particular emphasis is given to the effects of charges at different ionic strengths.

Multiscale simulation of gas transport in mixed-matrix membranes with interfacial polymer rigidification

We propose a multiscale simulation strategy capable of investigating and predicting the interfacial structure and dynamics of polymer/solid interfaces over a wide range of time and length scales. The lowest level of description involves atomistic molecular dynamics simulations providing useful information for the parametrization of the higher level of description, a mesoscopic hybrid particle/field slip-spring model [Macromolecules 2017, 50, 3004, 4524]. The mesoscopic model is extended so as to capture the interfacial properties of polymer/solid interfaces observed in atomistic simulations and to make predictions for long polymer chains exhibiting relaxation phenomena over times inaccessible by conventional atomistic simulations. As a case study, our multiscale approach is applied to polyethylene/graphite interfaces; our approach is nevertheless generic and can be implemented for various other combinations of polymers and solid surfaces. The conformational properties of the mesoscopic chains in the vicinity of the interface are resolved through the incorporation of springs with variable, wall-distance-dependent stiffness. The interfacial kinetics is described in terms of adsorption and desorption processes of the mesoscopic beads, which are classified as adsorbed and free in relation to solid substrates; these processes are tracked by the kinetic Monte Carlo in the course of the mesoscopic simulation, with the relevant rate constants derived by hazard plot analysis of the atomistic molecular dynamics. For the classification of the polymer segments we develop a new mesoscopic definition for the adsorbed state, taking into account the position, energy, and previous history of the polymer beads, which yields results that are insensitive to the time-step of the simulation. The residence time of the macromolecules in the adsorbed state is quantified through various approaches (rate constants from cumulative hazard slopes and monitoring of detachment times); overall, the characteristic detachment time of long polymer chains adsorbed from the melt is found to increase with molar mass, M, as ∼M2.5 while the relaxation time from the autocorrelation of the end-to-end vector increases as ∼M3.5; the latter scaling has been observed for bulk samples also. The impact of the time-step and of diffusion-driven recrossings between adsorbed and free states of beads is assessed through an analytical solution to a simple model for “adsorption” near repulsive walls.