Computational Point Of View Research Articles

Effective numerical computation of p(x)–Laplace equations in 2D

In this article we implement a method for the computation of a nonlinear elliptic problem with nonstandard growth driven by the Laplacian operator. Our implementation is based in the decomposition–coordination method that allows us, via an iterative process, to solve in each step a linear differential equation and a nonlinear algebraic equation. Our code is implemented in MatLab in two dimensions and turns out to be extremely efficient from the computational point of view.

CASL: A Novel Collusion Attack Against Distributed Energy Management Systems

Recently, the security issues of smart grids have received wide attention. In particular, false data injection attacks against distributed energy management systems (DEMSs) are considered of high importance as they are able to cause economic losses or even damages to system stability in subtle ways. Existing studies have identified many such attacks and proposed corresponding countermeasures. However, in this paper, we show that DEMS is still insecure and in risk of economic loss by proposing and analyzing a novel attack called Collusion Attack between a Storage and a Load (CASL). In CASL, a distributed energy storage device colludes with a load by injecting an abnormal extra power supply to the load in a secret way through using two secret functions. In this way, the colluding pair behaves as normal ones in both computational and communicational viewpoints. We theoretically prove the convergence of DEMS in presence of the attack, and derive an upper bound of the social welfare losses of DEMS caused by CASL. We demonstrate the effectiveness of CASL through simulations.

Endoscopic versus laparoscopic bariatric procedures: A computational biomechanical study through a patient-specific approach

Background and ObjectivesWithin the framework of computational biomechanics, finite element models of the gastric district could be seen as a potential clinical tool not only to study the effects apported by bariatric surgery, but also to compare different surgical techniques such as the new emerging Endoscopic Sleeve Gastroplasty (ESG) with respect to well-established ones (such as the Laparoscopic Sleeve Gastrectomy, LSG). MethodsThis work realized a fully computational comparison between the outcomes obtained from 10 patient-specific stomach models, which were used to simulate ESG, and the complementary results obtained from models representing the post-LSG of the same subjects. Specifically, once the ESG was simulated, a mechanical stimulus was applied by increasing an intragastric pressure up to a maximum of 5 kPa, in order to replicate the process of food intake, as well as for post-LSG models. Results: Results revealed non negligible differences between the techniques also within the same subject. In particular, not only LSG could lead to a greater reduction in the stomach volume (about 77 % at baseline, which is strictly linked to weight loss), but also influence the gastric distension (12 % less than pre-operative models). On the contrary, if ESG would be performed, a more similar pre-operative mechanical stimulation of the gastric walls may be seen (difference of about 1 %), thus preserving the mechanosensation, but the detriment of the volume reduction (about 56 % at baseline, and even decreases with increasing pressure). Moreover, since results suggested ESG may be more influenced by the pre-operative gastric cavity than LSG, a predictive model was proposed to support the surgical planning and the estimation of the volume reduction after ESG. Conclusions: ESG and LSG have substantial differences in their protocols and post-surgical effects. This work pointed out that variations between the two procedures may be observed also from a computational point of view, especially when including patient-specific geometries. These insights support gastric modelling as a valuable tool to evaluate, design and critically compare emerging bariatric surgical procedures, not only from empirical aspects and clinical outcomes, but also from a mechanical point of view.

Electrical conductivity of crack-template-based transparent conductive films: A computational point of view.

Crack-template-based transparent conductive films (TCFs) are promising kinds of junction-free, metallic network electrodes that can be used, e.g., for transparent electromagnetic interference shielding. Using image processing of published photos of TCFs, we have analyzed the topological and geometrical properties of such crack templates. Additionally, we analyzed the topological and geometrical properties of some computer-generated networks. We computed the electrical conductance of such networks against the number density of their cracks. Comparison of these computations with predictions of the two analytical approaches revealed the proportionality of the electrical conductance to the square root of the number density of the cracks was found, this being consistent with the theoretical predictions.

Use of Gaussian-Type Functions for Describing Fast Ion-Matter Irradiation with Time-Dependent Density Functional Theory.

The electronic stopping power is an observable property that quantifies the ability of swift ions to penetrate matter to transfer energy to the electron cloud. The recent literature has proven the value of Real-Time Time-Dependent Density Functional Theory to accurately evaluate this property from first-principles, but questions remain regarding the capability of computer codes relying on atom-centered basis functions to capture the physics at play. In this Perspective, we draw attention to the fact that irradiation by swift ions triggers electron emission into the continuum, especially at the Bragg peak. We investigate the ability of Gaussian atomic orbitals (AOC), which were fitted to mimic continuum wave functions, to improve electronic stopping power predictions. AOC are added to standard correlation-consistent basis sets or STO minimal basis sets. Our benchmarks for water irradiation by fast protons clearly advocate for the use of AOC, especially near the Bragg peak. We show that AOC only need to be placed on the molecules struck by the ion. The number of AOC that are added to the usual basis set is relatively small compared to the total number of atomic orbitals, making the use of such a basis set an excellent choice from a computational cost point of view. The optimum basis set combination is applied for the calculation of the stopping power of a proton in water with encouraging agreement with experimental data.

On the frequency domain composite likelihood methods for estimating space-time covariance functions for large datasets

Covariance function plays very important roles in modeling and in prediction of spatial data. For instance, many statistical inferences such as maximum likelihood estimation and best linear unbiased prediction involve covariance matrices which must be inverted and this evaluation is slow when the number of observations is large, and the complexity increases even further in a space-time context. In this paper, we propose approximation methods based on three types of frequency domain composite likelihoods for the estimation of spatial-temporal covariance functions: paired, differenced, and conditional composite likelihoods to overcome this problem. The proposed likelihoods involve computationally efficient and matrix-free Gaussian likelihood approximations that have been successful in fitting parametric space-time covariance functions for massive datasets. We conduct a simulation study to evaluate the performance of the proposed estimation methods from statistical and computational viewpoints. The frequency domain spectral composite likelihood estimates demonstrate better performance compared to the observation domain composite likelihoods. Furthermore, our numerical examples show that paired and conditional frequency domain composite likelihoods work better than differenced frequency domain composite likelihood from statistical point of view.

Capturing the interactions in the BaSnF4 ionic conductor: Comparison between a machine-learning potential and a polarizable force field.

BaSnF4 is a prospective solid state electrolyte for fluoride ion batteries. However, the diffusion mechanism of the fluoride ions remains difficult to study, both in experiments and in simulations. In principle, abinitio molecular dynamics could allow to fill this gap, but this method remains very costly from the computational point of view. Using machine learning potentials is a promising method that can potentially address the accuracy issues of classical empirical potentials while maintaining high efficiency. In this work, we fitted a dipole polarizable ion model and trained machine learning potential for BaSnF4 and made comprehensive comparisons on the ease of training, accuracy and efficiency. We also compared the results with the case of a simpler ionic system (NaF). We show that contrarily to the latter, for BaSnF4 the machine learning potential offers much higher versatility. The current work lays foundations for the investigation of fluoride ion mobility in BaSnF4 and provides insight on the choice of methods for atomistic simulations.

Optimization Problems for PDEs in Weak Space-Time Form

Optimization problems constrained by time-dependent Partial Differential Equations (PDEs) are challenging from a computational point of view: even in the simplest case, one needs to solve a system of PDEs coupled globally in time and space for the unknown solutions (the state, the costate and the control of the system). Typical and practically relevant examples are the control of nonlinear heat equations as they appear in laser hardening or the thermic control of flow problems (Boussinesq equations). Specifically for PDEs with a long time horizon, conventional time-stepping methods require an enormous amount of computer memory allocations for the respective other variables. In contrast, adaptive-in-time-and-space methods aim at distributing the available degrees of freedom in an a-posteriori fashion to capture singularities and are, therefore, most promising. Recently, well-posed weak variational formulations have been introduced for time-dependent PDEs such as the heat equation, linear transport and the wave equation. Those formulations also allow for a sharp relation between the approximation error and the residual, which is particularly relevant for model reduction. Moreover, for those tensor-basis formulations, advanced algebraic solvers designed to take into account these multiarray (tensorial) formulations appear to be particularly competitive with respect to time-marching schemes, especially in higher dimensions. We plan to discuss whether these techniques can be extended to nonlinear PDEs like Hamilton-Jacobi-Bellman equations, or stochastic PDEs and variational inequalities. Another topic will be adaptive schemes which, when properly designed, inherit the stability of the continuous formulation. The central goals of the workshop are the analysis, fast solvers and model reduction for PDE-constrained control and optimization problems based on weak formulations of the underlying PDE(s).

"STUDY OF THE MECHANICAL CHARACTERISTICS OF FDM (3D PRINTED) PARTS: EMPIRICAL AND COMPUTATIONAL METHODS"

"Fused deposition modelling, alternatively referred to as Fused Filament Fabrication, is an additive manufacturing technique that has garnered significant attention across various industries due to its diverse applications. This research investigates the ultimate tensile strength and elastic modulus of 3D-printed Polylactic acid samples, following the ISO-527-2-2012 standard. The mechanical performance of the created parts is considered from both experimental and computational points of view. The finite element method within the ANSYS environment was employed for computer-based load and strength calculations. Tensile specimens are fabricated using the Fused deposition modelling approach. The experimental outcomes were utilised to derive all the essential engineering constants required for evaluating the mechanical behaviour. The validity of the theoretical model has been confirmed through a comprehensive comparison with a substantial volume of experimental data, exhibiting mean errors of approximately 1 %. The objective of this research is to assess the effectiveness and efficacy of analytical models in predicting the structural and mechanical behaviour of components produced through fused deposition modelling. Keywords: additive technology, fused deposition modelling (FDM), PLA, mechanical properties, analytical model."

Multi-output time series forecasting with randomized multivariate Fuzzy Cognitive Maps

Fuzzy Cognitive Maps (FCMs) have become a relevant technique for modeling and forecasting time series due to their advantages in dealing with uncertainty and simulating the dynamics of complex systems. Although numerous univariate and multivariate FCM-based forecasting models have been presented in the literature, one of the still open questions is how to enable FCMs to forecast multivariate time series for multiple-input, multiple-output (MIMO) systems with an efficient learning mechanism. from a computational point of view. This paper suggests a randomized MIMO FCM-based forecasting approach called MO-RHFCM to predict low-dimensional multivariate time series. More specifically, MO-RHFCM is a hybrid model merging the concepts of multivariate fuzzy time series, high order FCM (HFCM), and Echo State Networks (ESN). The structure of MO-RHFCM consists of three layers: input layer, reservoir (internal) layer, and output layer. Only the output layer is trainable using the Least Squares minimization algorithm; hence training the proposed MO-RHFCM method is fast and simple. The weights inside each sub-reservoir are selected randomly and remain fixed during the training process. The obtained results indicate the efficacy and validity of the proposed MO-RHFCM technique compared with some machine learning and deep learning baseline models.

Brownian dynamics simulations and Ornstein-Zernike equation for charged fluids using the Wolf potential

From a computational point of view, a promising description for charged-stabilized fluids is the so-called Wolf method, which is faster than the standard Ewald summation technique. Using Monte Carlo computer simulations, it was recently shown that the static correlation functions of strong electrolytes obtained with the Wolf potential satisfy the Stillinger–Lovett sum rules, which are a consequence of perfect screening, and are directly related to the local electroneutrality condition and the electrostatic screening within the Debye–Hückel regime. In this contribution, the Wolf potential is adapted to Brownian dynamics simulations to study both the structural and dynamical properties of charged fluids. In addition, we have solved the Ornstein-Zernike equation with the Wolf potential using the HNC closure relation. The resulting radial distributions agree well with the simulation data and previous results obtained with the Ewald technique. Simulation results using Brownian dynamics for weakly charged colloidal dispersions and asymmetric electrolytes are presented. The results include mean square displacement, self-intermediate scattering, and shear relaxation function, with no consideration of hydrodynamic interactions. Including the Wolf potential in Brownian dynamics simulations opens up the possibility of studying interesting transport phenomena in the low Reynolds and Peclet regime, even in those cases where hydrodynamic interactions become relevant.

Coupled fluid flow, solute transport and dissolution processes in discrete fracture networks: An advanced Discontinuous Galerkin model

Modeling dissolution processes in discrete fracture networks (DFNs) is a challenging task. Challenges are related to the highly nonlinear coupling between flow, mass transport, and reactive processes associated with fracture aperture evolution by dissolution. Further, advection-dominated transport due to fast fluid flow in fractures renders the problem more complex from a computational point of view, as traditional numerical methods may introduce unphysical oscillations or excessive numerical diffusion. The Discontinuous Galerkin (DG) method is known to be suitable for the simulation of advection-dominated transport. In this work, an advanced DG model is developed to model transport with dissolution in DFNs. We propose an upwind formulation to deal with the upstream concentration at the intersection of several fractures. The upstream concentration at an intersection node is calculated based on the average nodal concentrations of all the fractures having an inflow at that node, weighted by the volumetric fluxes of these fractures. The dispersion term is discretized with the Mixed Finite Element (MFE) method, which ensures the continuity of the dispersive flux at the intersection of fractures with different apertures. The obtained nonlinear coupled flow-transport-dissolution equations are discretized in time with a high-order scheme via the method of lines (MOL). Numerical examples and comparisons with standard finite element (FE) and finite volume (FV) solutions are performed to investigate the correctness and efficiency of the developed model. Results show that the new DG-DFN model avoids unphysical oscillations encountered with the standard FE method and strongly reduces the numerical diffusion observed with the upwind FV scheme. The DG-DFN model is then used to investigate the effect of the dissolution rate on the flow, transport, and aperture evolution processes for a single fracture and for a DFN. A quasi-linear evolution of the fracture aperture is observed for low dissolution rates. For high dissolution rates, a funnel-shaped enlargement is observed with a significant widening for the fractures near the inlet and minor effects for those away from the injection location.

Quantitative Characterization of Marble Natural Aging through Pore Structure Image Analysis

The goal of this study is the quantitative characterization of the degree of natural alteration of marble samples by using image analysis for the automatic characterization and comparison of the pore structure of rock samples before and after weathering. The proposed methodology is based on a pore exploration path-finding algorithm for the identification of paths developing within the porous domain of marble samples in both natural conditions and after weathering. Along each identified path, the pore radius is measured, providing a thorough description of the pore space statistical distribution. The A* path-finding approach was developed and applied to binarized images obtained from two-dimensional (2D) thin sections of marble samples in both natural conditions and after 10 years of natural decay. The results are expressed in terms of 2D porosity and statistical distributions of the pore radius of the samples preweathering and postweathering. A comparison with the information obtained from standardized laboratory tests used for the physical and mechanical characterization of stone material is also provided. From a computational point of view, the presented approach is highly parallelizable. The presented approach works wells in complex porous structures characterized by high path tortuosity, pore-size heterogeneity, and pore surface roughness. Moreover, the methodology is less affected by small-scale pore features and noise produced during image binarization, compared with other algorithms for pore structure morphological analysis such as skeleton-based and maximal ball approaches.

DNA droplets for intelligent and dynamical artificial cells: from the viewpoint of computation and non-equilibrium systems.

Living systems are molecular assemblies whose dynamics are maintained by non-equilibrium chemical reactions. To date, artificial cells have been studied from such physical and chemical viewpoints. This review briefly gives a perspective on using DNA droplets in constructing artificial cells. A DNA droplet is a coacervate composed of DNA nanostructures, a novel category of synthetic DNA self-assembled systems. The DNA droplets have programmability in physical properties based on DNA base sequence design. The aspect of DNA as an information molecule allows physical and chemical control of nanostructure formation, molecular assembly and molecular reactions through the design of DNA base pairing. As a result, the construction of artificial cells equipped with non-equilibrium behaviours such as dynamical motions, phase separations, molecular sensing and computation using chemical energy is becoming possible. This review mainly focuses on such dynamical DNA droplets for artificial cell research in terms of computation and non-equilibrium chemical reactions.

A review of the ICA model of stakeholders in a spatial data infrastructure (SDI)

Abstract. The Commission on SDI & Standards (and its predecessors) of the International Cartographic Association (ICA) has developed formal models of a spatial data infrastructure (SDI), using the viewpoints of the Reference Model for Open Distributed Processing (RM-ODP) and the Unified Modeling Language (UML). The Commission described an SDI from the Enterprise Viewpoint (purpose, scope and policies for an SDI), Information Viewpoint (semantics of information and information processing in an SDI) and Computational Viewpoint (functional decomposition of the SDI into a set of services that interact through interfaces). The Enterprise Viewpoint model included six types of stakeholders in an SDI: Policy Maker, Producer, Provider, Broker, Value-added Reseller and End User. Subsequently, the Commission identified 39 sub-types of these stakeholders, though a better description of them might be as specializations, special cases, attributes, activities or roles.We have identified and reviewed all the literature that has referenced the Commission’s papers on the SDI models. Most use the Commission’s papers for the definitions of an SDI and of the parts, processes and stakeholders in an SDI. In this paper, we discuss all those that have applied the ICA SDI stakeholder model, proposed improvements to the model and/or highlighted parts of the model that are not well understood. We also discuss some issues we identified when reviewing the models. Subsequently, we plan on proposing some revisions and expansions on this ICA model of the stakeholders in a spatial data infrastructure.

Modeling a solar pressurized volumetric receiver integrated in a parabolic dish: Off-design heat transfers, temperatures, and efficiencies

Concentrated solar power plants are commonly recognized as one of the most attractive options within carbon-free power generation technologies because of their high efficiency and feasible hybridization and/or storage implementation. In this work, a complete heat transfer analysis for an air volumetric receiver coupled to a parabolic dish focused on distributed generation (in the range of kWe) is carried out. It includes most relevant heat losses. Dish collector optical efficiency is computed by means of a ray-tracing software while the thermal performance of the solar receiver is modeled under steady-state conditions using a comprehensive set of equations with a clear physical origin and meaning. Detailed information on the temperatures and heat transfers along the different inner and outer receiver zones are computed with a built from scratch in-house code programmed in Mathematica®. The model considers the main losses from convection, conduction and radiation and through the surrounding insulator. The resulting thermal efficiency mainly depends on the incoming solar irradiance at the glass window, the receiver geometry and the type of materials considered, as well as on the ambient temperature. Explicit numerical results are given at two locations under different meteorological conditions. Optical efficiencies reach values of about 84%. For irradiance values around 800–900 W/m2, at the receiver outlet, air can reach temperatures of about 1200K and receiver thermal efficiency is over 80%. It is expected that this model (precise but not too expensive from the computational viewpoint) could help to identify the main efficiency bottlenecks, paving the way for optimization when designing this type of concentrated solar plants through further coupling with a power block, as Brayton or other cycles.

Increasing the Sodium Metal Electrode Compatibility with the Na3 PS4 Solid-State Electrolyte through Heteroatom Substitution.

Rechargeable batteries are essential to the global shift towards renewable energy sources and their storage. At present, improvements in their safety and sustainability are of great importance as part of global sustainable development goals. A major contender in this shift are rechargeable solid-state sodium batteries, as a low-cost, safe, and sustainable alternative to conventional lithium-ion batteries. Recently, solid-state electrolytes with a high ionic conductivity and low flammability have been developed. However, these still face challenges with the highly reactive sodium metal electrode. The study of these electrolyte-electrode interfaces is challenging from a computational and experimental point of view, but recent advances in molecular dynamics neural-network potentials are finally enabling access to these environments compared to more computationally expensive conventional ab-initio techniques. In this study, heteroatom-substituted Na3 PS3 X1 analogues, where X is sulfur, oxygen, selenium, tellurium, nitrogen, chlorine, and fluorine, are investigated using total-trajectory analysis and neural-network molecular dynamics. It was found that inductive electron-withdrawing and electron-donating effects, alongside differences in heteroatom atomic radius, electronegativity, and valency, influenced the electrolyte reactivity. The Na3 PS3 O1 oxygen analogue was found to have superior chemical stability against the sodium metal electrode, paving the way towards high-performance, long lifetime and reliable rechargeable solid-state sodium batteries.

A constitutive model for amorphous thermoplastics from low to high strain rates: Formulation and computational aspects

In this paper, a recently proposed finite strain visco-elastic visco-plastic (three-dimensional) constitutive model is extended to predict the nonlinear response of amorphous polymers from low to high strain rates. The model accounts for the influence of distinct molecular mechanisms, which become active at different deformation rates. Therefore, the constitutive equations include two relaxation phenomena to describe the strain rate sensitivity of amorphous polymers. Well-established rheological elements are adopted to define visco-elasticity (generalized Maxwell elements) and visco-plasticity (Eyring dashpots). In addition, strain hardening is modeled with a plasticity-induced (nonlinear) hardening element which is extended to distinguish between the contribution of the two transitions. From a computational viewpoint, a fully implicit integration algorithm is derived, and a highly efficient implementation is obtained. It is shown that it is possible to reduce the return mapping system of equations to only two independent (scalar) nonlinear equations. A four-stage optimization-based calibration procedure is proposed to identify the model’s material parameters in a completely unsupervised way. The predictive capability of the constitutive model is validated against literature results for polycarbonate and poly(methyl methacrylate), accounting for temperature and strain rate dependencies under different loading conditions. The results show that the model can capture the transition in the yield behavior and predict the post-yield large strain behavior over a wide range of strain rates. The efficiency of the calibration procedure and the overall numerical strategy is also demonstrated. Despite the adiabatic conditions observed under high strain rates, the model replicates the associated effect of temperature through strain rate dependency.

Traveling waves and free boundaries arising in tumor angiogenesis

In a paper appeared in 2021, a mathematical model was developed for tumor invasion with vessel cooption, including a fine analysis of angiogenesis driven by chemotaxis. Numerical solutions in spherical symmetry revealed that a traveling wave sets in. Results were in agreement with experimental data. In the present paper, we introduce some nontrivial changes in the model and we further analyze the structure of the solutions as well as their dependence on some critical biological parameters, emphasizing for instance which are the most active zones where angiogenesis takes place. Moreover, we propose an alternative model characterized by the presence of a free boundary (playing the role of the invasion front), showing that the new formulation (which is advantageous from the computational point of view) matches the results of the previous model for some biologically significant range of the parameters.

Advances in Methodology for Fatigue Assessment of Composite Steel–Concrete Highway Bridges Based on the Vehicle–Bridge Dynamic Interaction and Pavement Deterioration Model

Fatigue cracking is one of the most prominent causes of mechanical failure limiting the service life of existing steel and composite steel–concrete bridges and is among the central concerns of structural and bridge engineers. In this context, the current work presents some recent advancements in an existing methodology for fatigue analysis developed by the authors throughout the years. The methodology is specifically devoted to the fatigue assessment of composite steel–concrete bridges employing the local hot-spot S-N approach and a coupled vehicle–pavement–bridge system considering progressive pavement deterioration with stochastically generated roughness profiles. Two different methodologies were used to solve the dynamic equilibrium equations: the modal superposition method to solve the bridge dynamic equations and a direct integration method to solve the vehicle dynamic equations. From a computational point of view, the present approach is more efficient and detailed than previous versions, as it allows a significant reduction in the analysis time and the use of complex bridge and vehicle finite element models. In this regard, a case study of a highway composite steel–concrete bridge spanning 40 m was selected in order to demonstrate the usefulness of the presented improved methodology by carrying out a fatigue analysis. The results of this investigation (displacements and stresses) are presented, aiming to verify the factors that directly influence the structural response and, consequently, the service life of steel–concrete composite highway bridges.