Numerous Strategies Research Articles

Graph and convolutional neural network coupling with a high-performance large-eddy simulation solver

Computational Fluid Dynamics (CFD) traditionally relies on long-standing numerical simulation strategies for the Navier–Stokes equations. Recently, interest in data-driven hybrid CFD solvers has spiked, leveraging pre-computed datasets to enhance various weak links inside existing solvers, such as closure models, under-resolved physics, or even to guide numerical resolution strategies. Running these hybrid solvers, notably in High Performance Computing (HPC) environments, presents specific challenges. In particular, context-aware deep learning (e.g. Convolutional (CNN) and Graph (GNN) Neural Networks) is promising for this task, but requires passing data representations between the physics solver and the neural network. In relevant industrial configurations, CFD meshes can be Cartesian but highly irregular, or unstructured, both of which do not match the pixel/voxel structure needed to run CNNs. In addition, discrepancies in programming language and libraries are common between CFD and machine learning applications. This work explores the many challenges of running a parallel hybrid solver in an HPC context, through the coupling of the AVBP CFD solver with neural networks in turbulent combustion and wall friction modeling applications. The knowledge gained is showcased in this article, as well as assembled in an actionable open-source library.

A novel hybrid SBM-MFS methodology for acoustic wave propagation problems

In this paper, a novel hybrid meshless approach that combines the singular boundary method (SBM) and the method of fundamental solutions (MFS) to deal with two-dimensional (2D) exterior acoustic wave propagation problems is proposed and studied. The methodology is particularly devised to solve problems with complex boundary geometries containing geometric singularities such as corners and sharp edges. It employs the SBM to model intricate segments of these geometries and the MFS for the smooth ones. The proposed hybrid SBM-MFS method is studied in a 2D context in the framework of three benchmark examples involving acoustic radiation problems of circular-, square- and L-shaped objects in a full-space acoustic medium. In addition, the applicability of the proposed hybrid SBM-MFS methodology to predict the acoustic performance of a T-shaped thin barrier is also investigated. These examples are specifically designed to assess the feasibility, validity and accuracy of the hybrid SBM-MFS approach in comparison with the available analytical solutions and alternative numerical strategies such as the MFS, the SBM and the boundary element method (BEM). Numerical simulations demonstrate that the proposed method can match the level of accuracy of the MFS while keeps the robustness of the SBM when dealing with complex geometries, overcoming one the most important drawbacks of the traditional MFS. Moreover, the proposed hybrid SBM-MFS naturally avoids the non-uniqueness problem arising at the fictitious eigenfrequencies associated with the corresponding interior problems, a feature that neither the SBM nor the BEM inherently possesses.

You can count on your fingers: Finger-based intervention improves first-graders’ arithmetic learning

The question of whether finger use should be encouraged or discouraged in early mathematics instruction remains a topic of debate. Scientific evidence on this matter is scarce due to the limited number of systematic intervention studies. Accordingly, we conducted an intervention study in which first-graders (Mage = 6.48 years, SD = 0.35) completed a finger-based training (18 sessions of ∼ 30 min each) over the course of the first school year. The training was integrated into standard mathematics instruction in schools and compared with business-as-usual curriculum teaching. At the end of first grade and in a follow-up test 9 months later in second grade, children who received the finger training (n = 119) outperformed the control group (n = 123) in written addition and subtraction. No group differences were observed for number line estimation tasks. These results suggest that finger-based numerical strategies can enhance arithmetic learning, supporting the idea of an embodied representation of numbers, and challenge the prevailing skepticism about finger use in primary mathematics education.

LES investigations of turbulent heat transfer structures with exponential power escalation

The BORAX-type accident is a key scenario in the safety design of pool-type experimental nuclear reactors. It considers a reactivity insertion large enough to initiate an exponential power escalation with a period as small as tens of milliseconds, necessitating effective heat removal by the coolant system. This study presents a computational analysis of single-phase transient heat transfer under thermal-hydraulic conditions relevant to such reactors, focusing on turbulent channel flow between planar fuel plates with highly subcooled water. Our investigation uses Large Eddy Simulation (LES) performed with TrioCFD, the open-source CFD code developed by the French Atomic Energy Commission (CEA). Several modeling options are tested: incompressible flow with temperature as a passive scalar, incompressible flow with varying viscosity, and quasicompressible flow with temperature-dependent viscosity and mass density. To evaluate the accuracy of our physical models, all simulations were performed under uniform conditions, using the same experimental parameters, mesh, and numerical strategies. The specific examined scenario involves a highly subcooled turbulent channel flow at moderate pressure, experiencing an exponential power increase. We ensure detailed turbulence resolution at the Batchelor scale in the wall normal direction close to the heating wall. This communication presents a comparative study of LES simulations of the single-phase flow under exponential power excursion at the wall. Subsequent analyses focused on the impact of different velocity-temperature couplings on the prediction of wall heat transfer, incorporating comparisons with experimental infrared thermometry measurements. Early in the transient phase, temperature peaks are confined within turbulent streaks, as reported in the existing literature. However, as the exponential power transient unfolds, the viscous layer and streak structures destabilize, leading to a more dispersed distribution of hot spots. Preliminary findings suggest a transition in heat transfer modes from turbulence-driven to conduction during the final stages of the single-phase transient.

Phytotherapeutics in Cancer: From Potential Drug Candidates to Clinical Translation.

Annually, a significant number of individuals succumb to cancer, an anomalous cellular condition characterized by uncontrolled cellular proliferation and the emergence of highly perilous tumors. Identifying underlying molecular mechanism(s) driving disease progression has led to various inventive therapeutic approaches, many of which are presently under pre-clinical and/or clinical trials. Over the recent years, numerous alternative strategies for addressing cancer have also been proposed and put into practice. This article delineates the modern therapeutic drugs employed in cancer treatment and their associated toxicity. Due to inherent drug toxicity associated with most modern treatments, demand rises for alternative therapies and phytochemicals with minimal side effects and proven efficacy against cancer. Analogs of taxol, Vinca alkaloids like vincristine and vinblastine, and podophyllotoxin represent a few illustrative examples in this context. The phytochemicals often work by modifying the activity of molecular pathways that are thought to be involved in the onset and progression of cancer. The principal objective of this study is to provide an overview of our current understanding regarding the pharmacologic effects and molecular targets of the active compounds found in natural products for cancer treatment and collate information about the recent advancements in this realm. The authors' interest in advancing the field of phytochemical research stems from both the potential of these compounds for use as drugs as well as their scientific validity. Accordingly, the significance of herbal formulations is underscored, shedding light on anticancer phytochemicals that are sought after at both pre-clinical and clinical levels, with discussion on the opportunities and challenges in pre-clinical and clinical cancer studies.

ITZ volume fraction and thermal conductivity of concrete: A unified random packing model for gravels and crushed rocks

This work proposes a mesostructure-guided computational framework to evaluate the effective thermal conductivity (ETC) of concrete affected by aggregate and interfacial transition zone (ITZ) configurations at a mesoscopic scale. An unified aggregate shape descriptor for gravels and crushed rocks is characterized by superellipsoid covering from convexity to non-convexity, as well as random static and dynamic packings of superellipsoidal aggregates (SAs) are carried out to generate tri-phase concrete mesostructures consisting of homogeneous mortar, densely packed aggregates and their surrounding ITZs. A closed-form expression for the ITZ volume fraction is theoretically developed in the present concrete mesostructure, which is also validated using the one-point probability function simulation. Moreover, an optimized Browanian motion numerical strategy is developed to estimate the ETC of tri-phase concrete. This strategy is confirmed to be an accurate numerical technique by comparing against the Mori-Tanaka model. The present framework reveals the quantitative responses of various configurations on the ETC, which manifest rigorous “constituent-mesostructure-ETC” relations. The results are available for tailoring components to optimize ITZ structures for the improvement of thermal efficiency and durability of buildings.

Computer simulations of the glass transition and glassy materials

We provide an overview of the different types of computational techniques developed over the years to study supercooled liquids, glassy materials and the physics of the glass transition. We organise these numerical strategies into four broad families. For each of them, we describe the general ideas without discussing any technical details. We summarise the type of questions which can be addressed by any given approach and outline the main results which have been obtained. Finally we describe two important directions for future computational studies of glassy systems.

Towards Hybrid-Optimization Video Coding

Video coding that pursues the highest compression efficiency is the art of computing for rate-distortion optimization. The optimization has been approached in different ways, exemplified by two typical frameworks: block-based hybrid video coding and end-to-end learned video coding. The block-based hybrid framework encompasses more and more coding modes that are available at the decoder side; an encoder tries to search for the optimal coding mode for each block to be coded. This is an online, discrete, search-based optimization strategy. The end-to-end learned framework embraces more and more sophisticated neural networks; the network parameters are learned from a collection of videos, typically using gradient descent-based methods. This is an offline, continuous, numerical optimization strategy. Having analyzed these two strategies, both conceptually and with concrete schemes, this paper suggests investigating hybrid -optimization video coding, that is to combine online and offline, discrete and continuous, search-based and numerical optimization. For instance, we propose a hybrid-optimization video coding scheme, where the decoder consists of trained neural networks and supports several coding modes, and the encoder adopts both numerical and search-based algorithms for the online optimization. Our scheme achieves promising compression efficiency on par with H.265/HM for the random-access configuration.

Numerical Investigation of Some Reductions for the Gatenby–Gawlinski Model

Two (consecutive) reductions of the complete Gatenby–Gawlinski model for cancer invasion are proposed in order to investigate the mathematical framework, mainly from a computational perspective. After a brief overview of the full model, we proceed by examining the case of a two-equations-based and one-equation-based reduction, both obtained by means of a quasi-steady-state assumption. We focus on invasion fronts, exploiting a numerical strategy based on a finite volume approximation, and perform corresponding computational simulations to study the sharpness/smoothness of the traveling waves. Then, we employ a space-averaged wave speed estimate—referred to as the LeVeque–Yee formula—to quantitatively approach the propagation phenomenon. Concerning the one-equation-based model, we propose a scalar degenerate reaction-diffusion equation, which proves to be effective in order to qualitatively recover the typical trends arising from the Gatenby–Gawlinski model. Finally, we carry out some numerical tests in a specific case where the analytical solution is available.

A modeling strategy for the simulation of box sections with layered shell elements

This paper presents a numerical strategy to model box sections from bridge girders using horizontal layered shell finite elements. The basic idea is to avoid the construction of more computational expensive meshes based on the use of folded shell and brick finite elements. The numerical strategy imposes the deactivation of those layers related to empty spaces within the bridge cross-section. The performance of this technique is explored by means of two demanding practical applications related to bridge structures with constant and variable deck thicknesses. The first application deals with the static truck load test of the Caynarachi Bridge, located in Peru, and for which measured field data exists, while the second application is related to the construction stage analysis of a box bridge structure including time effects due to concrete creep, shrinkage and steel relaxation. The results demonstrate that the studied technique acceptably correlates with the measured field data,expressed in terms of vertical displacements,with correlation coefficients exceeding 0.94.Additionally, the outcomes align well with the results of other numerical techniques, allowing applicants to use this simplified modeling approach in daily design office.

Comparison of Single-Phase Mathematical Models for Solid-State Packed Beds for Thermal Energy Storage

This article presents an analytical solution for the evaluation of the thermal performance of packed bed sensible heat storage. The numerical model developed was tested for four different solid storage mediums. The thermal energy equation is solved numerically by deploying the finite difference method. The presented analytical solution is based on a novel mathematical approach. The numerical model was validated using the computer simulation package Comsol Multiphysics v5.3. Our numerical model results are in good agreement with the published experimental data, with an overall difference of ~10%. Hence, the numerical model is an efficient way of evaluating the thermal performance of packed bed thermal energy storage systems compared to other numerical strategies or computer simulation techniques. This proves that the novel analytical model has shown to be a reliable and broadly accurate approach to acquire the thermal performance of sensible heat storage.

Fractional cyclic cohesive zone model for time-dependent fatigue behavior of soft adhesives under mode-II loading

Soft adhesives exhibit nonlinear viscoelastic behavior during cyclic loading, suggesting that their cycle-dependent fatigue behavior can be significantly affected by the time-dependent deformation behavior. Soft adhesives are frequently subject to various cyclic conditions in practical applications, presenting potential safety risks. This paper has proposed a fractional cyclic cohesive zone model (CZM) to characterize the time-dependent fatigue behavior of soft adhesives. To capture the nonlinear evolution of fatigue damage under asymmetric stress-controlled cyclic loading, a fatigue damage model that considers the contribution of in-situ stress/strain was incorporated into fractional-order viscoelastic model. The proposed CZM was validated through single-lap shear creep tests at various stress levels and fatigue tests at various stress amplitudes and loading periods. Furthermore, the impacts of stress ratio and peak stress level on fatigue failure under different loading periods were investigated using the proposed CZM. The results indicate that under a short loading period, the failure of the soft adhesives is primarily influenced by cycle-dependent fatigue damage, while under a long loading period, it is primarily affected by time-dependent creep deformation. Overall, this work offers a numerical strategy to describe the fatigue failure of nonlinear viscoelastic soft adhesives and to understand their time-dependent fatigue failure mechanism, with implications for product design and optimization.

Kosambi-Cartan-Chern perspective on chaos: Unveiling hidden attractors in nonlinear autonomous systems.

This article confronts the formidable task of exploring chaos within hidden attractors in nonlinear three-dimensional autonomous systems, highlighting the lack of established analytical and numerical methodologies for such investigations. As the basin of attraction does not touch the unstable manifold, there are no straightforward numerical processes to detect those attractors and one has to implement special numerical and analytical strategies. In this article we present an alternative approach that allows us to predict the basin of attraction associated with hidden attractors, overcoming the existing limitations. The method discussed here is based on the Kosambi-Cartan-Chern theory which enables us to conduct a comprehensive theoretical analysis by means of evaluating geometric invariants and instability exponents, thereby delineating the regions encompassing chaotic and periodic zones. Our analytical predictions are thoroughly validated by numerical results.

New insights into estimating the cementation exponent of the tight and deep carbonate pore systems via rigorous numerical strategies

One of the main constituents of any reservoir characterization is an accurate forecast of water saturation, which is highly dependent upon the cementation exponent. Even though there have been a lot of studies, the most common correlations depend on total porosity. This means that they do not work as well in heterogeneous carbonate reservoirs, especially tight formations with total porosities less than 10%. This study aims to develop accurate and universal models for estimating the cementation exponent in deep and tight carbonate pore systems located in West Asia. Two heuristic algorithms, including the radial basis function neural network optimized by ant colony optimization (RBFNN-ACO) and gene expression programming (GEP), were employed to calculate the cementation exponent. To do this, we prepared a databank incorporating cementation exponents, total porosity, and various pore types. Then, the databank is classified into the test subset (for model prediction checking) and the train subset (for model construction). The reliability of the new recommended models is inspected by applying several statistical quality measures associated with graphical analyses. So, the consequences of the modeling disclose the large precision of the above-mentioned RBFNN-ACO, GEP Model-I, and GEP Model-II by average absolute percentage relative deviations (AAPRD%) of 6.28%, 6.39%, and 7.45%, respectively. Based on the outliers analysis, nearly 95% of the databank and model estimations are, respectively, valid and reliable. Additionally, the three input variables, including moldic porosity (with a + 70% impact value), non-fabric-selective dissolution (connected) porosity (with a -30% impact value), and interparticle porosity (with a -23% impact value), exhibit the main affecting parameters on the cementation exponent. Comparing current results with traditional literature correlations demonstrates the supremacy of the RBFNN-ACO model (AAPRD = 6.28% and root mean squared error (RMSE) = 0.17) over the examined literature correlations such as Borai’s equation (AAPRD = 12.30% and RMSE = 0.41). In addition, RBFNN-ACO can give better results than Borai’s Eqn. for tight (porosity less than 10%) and deep carbonate samples.

An inverse problem for nonlocal reaction-diffusion equations with time-delay

The reaction-diffusion equations have been studied in various aspects of nature, such as heat transfer and biological dynamic modelling. In this paper, we study a nonlocal reaction-diffusion model with time delay associated with the density and diffusion behaviour of biological populations. Based on our previous conclusions and numerical strategy of the direct problem, we continue to study the inverse problem about the diffusion coefficient as well as the parameters in the birth function, and also perform numerical simulations with error analysis. In addition, we further generalize the constant diffusion coefficient to the position-dependent diffusion rate, which is intended to improve the generalizability to multiple organisms diffusion in nature.

An efficient method for electro-thermo-mechanical coupling effect in electrical contact on rough surfaces

In this study, a numerical strategy based on the boundary element method (BEM) is developed to simulate the electrical contact behavior on rough surfaces. By using the Fourier transform, the solutions of the electric potential, temperature rise, and displacement fields in the Fourier space are derived with finite boundaries, in which the electro-thermo-mechanical coupling effect is considered. With the consideration of the contact condition, an iterative algorithm is proposed to seek the solutions of rough surface electrical contact under a given contact force and total electric potential drop. The contact pressure, real contact area, electrical contact resistance, electric current, and temperature rise distribution are predicted. For the validation, the electrical contact results of a spherical punch-half space contact and a rough surface-elastic substrate contact are calculated based on the finite element method. The good agreement between the results from the BEM-based method and the finite element method indicates the accuracy of the presented method. Then, the effect of the total electric potential and the thermal expansion on the electrical contact behavior, the relationship between the contact conductance and contact stiffness, and the probability density function of the contact pressure and temperature rise distributions are evaluated using the proposed method.

Numerical Simulation of MHD Radiative Casson Nanofluid Flow over a Nonlinearly Heated Stretching Sheet Embedded in Porous Medium

This study intends to assess the heat transmission of a steady two-dimensional MHD Casson nanofluid flow across a non-linearly stretching sheet concerning viscous dissipation, heat generation, thermal radiation, and chemical radiate parameters. Mathematically, the flow is outlined as PDE systems, converted to ODE systems by a well-posed transformation, which are afterwards solved numerically in Matlab employing the fourth-order precision program (Bvp4c). The comparison indicates adequate agreement, demonstrating the correctness of the numerical strategy. It is observed that in the existence of a chemical reaction, the temperature is elevated, while the concentration profile is markedly reduced, in contrast to a scenario where no chemical reaction is taking place. In consideration of viscosity, heat radiation, thermophoresis impact, and suction or injection parameter, the Nusselt number reveals a growing tendency. In contrast, it exhibits opposing behavior with regard to the other addressed factors. The findings of this study have applications in the engineering, biomedical and industrial sectors, including food processing, polymer making, glass and fiber production, material processing, and enhanced oil recovery.

Multi-Step Mechanical and Thermal Homogenization for the Warpage Estimation of Silicon Wafers.

In response to the increasing demand for high-performance capacitors, with a simultaneous emphasis on minimizing their physical size, a common practice involves etching deep vias and coating them with functional layers to enhance operational efficiency. However, these deep vias often cause warpages during the processing stage. This study focuses on the numerical modeling of wafer warpage that occurs during the deposition of three thin layers onto these vias. A multi-step mechanical and thermal homogenization approach is proposed to estimate the warpage of the silicon wafer. The efficiency and accuracy of this numerical homogenization strategy are validated by comparing detailed and homogenized models. The multi-step homogenization method yields more accurate results compared to the conventional direct homogenization method. Theoretical analysis is also conducted to predict the shape of the wafer warpage, and this study further explores the impact of via depth and substrate thickness.

Optimization of MTI structure based on material characteristics under extreme aerodynamic heating environment

Thermal insulation structure is a key factor to protect a high-speed aircraft to work safely and complete various tasks. The multilayer thermal insulation (MTI) structure has attracted much attention because of its light weight and good thermal insulation effect. The performance of an MTI structure is not only related to its physical sizes, but also closely related to the material optimization and temperature influences. A transient nonlinear thermal transfer model of the MTI structure is established, and a numerical solution strategy of the nonlinear thermal transfer model is proposed. Considering material selection, introducing the temperature influence of materials, and taking the geometric dimensions and equivalent mechanical properties of the MTI structures as constraints, an optimization frame of the MTI structures is established. Then two intelligent methods are adopted to solve the problem. Using the aerodynamic heating environment of X-43 flight trajectory, the results of a typical high-speed aircraft show that the proposed optimization framework can reduce the structural mass density from 13.95 kg‧m−2 to 9.10 kg‧m−2 by about 34.8 %. The temperature effect analysis shows that the temperature variation has an important influence on the optimization results. If the temperature effect on material properties is not considered, the optimized design scheme may not meet the temperature design constraints (The maximum temperature of the inner surface is 410 K, far exceeding the maximum allowable temperature of 320 K) which further leads to structural failures. Finally, the reliability and rationality of the optimization scheme are verified by a finite element model (FEM). The equivalent modulus of the optimized MTI structure is 146 GPa and the mass area density of the MTI structure is 7.7 kg‧m−2. The optimization design method of an MTI structure proposed in the current paper provides a technical support and lays a foundation for the overall design of a high-speed aircraft.

Revisiting Tensor Basis Neural Network for Reynolds stress modeling: Application to plane channel and square duct flows

Recent advances in computing power devoted to Computational Fluid Dynamics (CFD) have opened up a new avenue for addressing the turbulence closure problem through Machine Learning (ML). Several Tensor Basis Neural Network (TBNN) frameworks aimed at enhancing turbulence Reynolds-Averaged Navier–Stokes (RANS) modeling have recently been proposed in the literature as data-driven constitutive models for systems with known invariance properties. However, persistent ambiguities remain regarding the physical adequacy of applying the General Eddy Viscosity Model (GEVM) within these frameworks. This work aims at investigating this aspect in an a priori stage for better predictions of the Reynolds stress anisotropy tensor, while preserving the Galilean and rotational invariances. In particular, we propose a general framework providing optimal tensor basis models for two types of canonical flows with increasing complexity: Plane Channel Flow (PCF) and Square Duct Flow (SDF). Subsequently, deep neural networks based on these optimal models are trained using state-of-the-art strategies to achieve a balanced and physically sound prediction of the full anisotropy tensor. A priori results obtained by the proposed framework are in very good agreement with the reference Direct Numerical Simulations (DNS) data. Notably, our shallow network with three layers excels in providing accurate predictions of the anisotropy tensor for PCF at unobserved friction Reynolds numbers, both in interpolation and extrapolation scenarios. The learning of the SDF case is more challenging because of its physical nature but also due to a lack of training data at various regimes. We propose a numerical strategy to alleviate this problem based on Transfer Learning (TL). In order to more efficiently generalize to an unseen intermediate Reτ regime, we take advantage of our prior knowledge acquired from a training with a larger and wider dataset. Our results indicate the potential of the developed network model, and demonstrate the feasibility and efficiency of the TL process in terms of training data size and training time. Based on these results, we believe there is a promising future by integrating these neural networks into an adapted in-house RANS solver.