Simulation Of Models Research Articles

Glucose hydrochar consists of linked phenol, furan, arene, alkyl, and ketone structures revealed by advanced solid-state nuclear magnetic resonance

The molecular structure of hydrochars produced from 13C-enriched glucose under various conditions has been elucidated based on advanced one- and two-dimensional (2D) 1H-13C and 13C–13C solid-state nuclear magnetic resonance (NMR) with spectral editing. Regardless of synthesis conditions, hydrochars consist mostly of oxygen-substituted arene rings (including diphenols) and furans connected by alkyl linkers rich in ketones. Cross-linking nonprotonated and methyne (C-H) alkyl carbons have been identified through spectrally edited 2D NMR. Alkenes and ‘quaternary’ C-O are observed only at low synthesis temperature, while some clusters of fused arene rings are generated at high temperature. Hydrochar composition is nearly independent of reaction time in the range from 1 to 5 h. Equilibration of 13C magnetization within 1 s shows that the materials are homogeneous on the 5-nm scale, refuting core–shell models of hydrochar microspheres. While furan C-O carbons bonded to alkyl groups or ketones show distinctive cross peaks in 2D NMR, phenolic C-OH is observed unambiguously by hydroxyl-proton selection. While methylene-linked furan rings are fairly common, the signal previously assigned to furan Cα-Cα linkages is shown to arise from abundant, stable catecholic ortho-diphenols, whose HO-C=C-OH structure is proved by 2D13C–13C NMR after hydroxyl-proton selection. Quantitative 13C NMR spectra of low- and high-temperature hydrochars have been matched by chemical-shift simulations for representative structural models. Mixed phenol and furan rings connected by ketones and alkyl linkers provide good fits of the experimental spectra, while literature models dominated by large clusters of fused rings and with few phenols or alkyl-linked ketones do not.

Model development and heat transfer characteristics in renewable energy systems conveying hybrid nanofluids subject to nonlinear thermal radiation

PurposeIn today’s world, the demand for energy to power industrial and domestic activities is increasing. To meet this need and enhance thermal transport, solar energy conservation can be tapped into via solar collector coating for thermal productivity. Hybrid nanofluids (HNFs), which combine nanoparticles with conventional heat transfer fluids, offer promising opportunities for improving the efficiency and sustainability of renewable energy systems. Thus, this paper explores fluid modeling application techniques to analyze and optimize heat transfer enhancement using HNFs. A model comprising solar energy radiation with nanoparticles of copper (Cu) and alumina oxide (Al2O3) suspended in water (H2O) over an extending material device is developed.Design/methodology/approachThe model is formulated using conservation laws to build relevant equations, which are then solved using the Galerkin numerical technique simulated via Maple software. The computational results are displayed in various graphs and tables to showcase the heat transfer mechanism in the system.FindingsThe results reveal the thermal-radiation-boost heat transfer phenomenon in the system. The simulations of the theoretical fluid models can help researchers understand how HNFs facilitate heat transfer in renewable energy systems.Originality/valueThe originality of this study is in exploring the heat transfer properties within renewable energy systems using HNFs under the influence of nonlinear thermal radiation.

ITER full model in MCNP for radiation safety demonstration

The development of nuclear fusion as a safe and virtually limitless power source is receiving growing attention in the context of looming energy crisis and climate change. ITER project stands as the flagship international initiative and is advancing steadily. The construction of the Tokamak Complex is nearly finished, and the assembly of core components has begun on site. Simultaneously, the design is being finalized, and the safety case is becoming more concrete. Current approaches to radiation safety demonstration using 3D nuclear analysis with the Monte Carlo code MCNP require sophisticated artifacts to sew together simulations in separate models for the Tokamak and the rest of the facility. This results in cumbersome studies and, consequently, challengeable conclusions. To address this issue, we have built the an integral MCNP model of the ITER facility: the ITER full model. Along with improvements to the D1SUNED code, we illustrate its computational practicality and pertinence in two meaningful simulations for ITER safety case. This work represents the culmination of a two-decade-long effort of ITER modelling aiming to demonstrate adequate radiation safety. Beyond supporting the remaining design tasks, this model simplifies the corresponding 3D nuclear analysis and improves the robustness of the ITER safety case.

Estimating carbon leakage from aviation by combining sectoral and general equilibrium models

This article describes a procedure for estimating carbon leakage from policies targeting aviation based on alternative scenarios. The key innovation to ensure greater robustness is that all scenarios are simulated by two different types of models: a sectoral model for aviation and a computable general equilibrium model.•The implementation of scenario simulation in both models is explained•The calculation of carbon leakage is explained step by step

The atomic-level structure and stability of interfaces of Pt nanoparticles in alumina: An experimental and computational evaluation

The atomic-level structure of interfaces between Pt and a transition form of Al2O3 were studied using a combination of electron microscopy and first principles calculations. A model system of Pt nanoprecipitates in Al2O3 were formed in sapphire wafers via high-energy ion implantation of Pt followed by thermal annealing at 1000 °C in air. The Pt nanoparticles took the form of tetrahedra and truncated tetrahedra primarily bound by {111}Pt facets. The high prevalence of these facets motivated the development of density functional theory (DFT) based models of (111)Pt interfaces with six different chemical terminations of (2¯01) θ-alumina. The atomic-level structure of the Pt/Al2O3 interfaces was characterized with aberration-corrected scanning transmission electron microscopy (STEM) and the experimental images were compared to STEM image simulations of the DFT models. The model interface with Pt bonded to oxygen-terminated θ-Al2O3, with the Pt located on top of the O and with an underlying layer of octahedral Al, provided the best match to the experimental images. This interfacial termination is also the most stable for the thermal annealing conditions used based on thermodynamic calculations of the interfacial energy as a function of temperature and oxygen partial pressure. This experimentally verified model provides a basis for improving models of Pt/γ-alumina interfaces.

Development of a Multi‐Scale Meteorological Large‐Eddy Simulation Model for Urban Thermal Environmental Studies: The “City‐LES” Model Version 2.0

AbstractTo bridge the gaps between meteorological large‐eddy simulation (LES) models and computational fluid dynamics (CFD) models for microscale urban climate simulations, the present study has developed a meteorological LES model for urban areas. This model simulates urban climates across both mesoscale (city scale) and microscale (city‐block scale). The paper offers an overview of this LES model, which distinguishes itself from standard numerical weather prediction models by resolving buildings and trees at the microscale simulations. It also differs from standard CFD models by accounting for atmospheric stratification and physical processes. Noteworthy features of this model include: (a) the calculation of long‐ and short‐wave radiations in three dimensions, incorporating multiple reflections within urban canopy layers using the radiosity method, and accounting for building and tree shadows in the simulations; (b) the provision of various heat stress indices (Universal Thermal Climate Index, Wet Bulb Globe Temperature, MRT, THI); (c) the assessment of the efficacy of heat stress mitigation measures such as dry‐mist spraying, roadside trees, cool pavements, and green/cool roofs strategies; (d) the capability to run on supercomputers, with the code parallelized in a three‐dimensional manner, and the model can also run on a graphics processing unit cluster. Following the introduction of this model, the study confirms its basic performance through various numerical experiments, including simulations of thermals in the convective boundary layer, coherent structure of turbulence over urban canopy, and thermal environment and heat stress indices in urban districts. The model developed in this study is intended to serve as a community tool for addressing both fundamental and applied studies in urban climatology.

On the accuracy of the segmentation process and transcatheter heart valve dimensions in TAVI patients

Accurate segmentation of medical images is critical for generating patient-specific models suitable for computational analyses, particularly in the context of transcatheter aortic valve implantation (TAVI). This study aimed to quantify the accuracy of the segmentation process from medical images of TAVI patients to understand the uncertainty in patient-specific geometries. We also quantified discrepancies between actual and CT-related diameter measurements due to artifacts and intra-observer variability. Segmentation accuracy was assessed using both synthetic phantom models and patient-specific data. The impact of voxelization and CT scanner resolution on segmentation accuracy was evaluated, while the intersection over union (IoU) metric was used to compare the consistency of different segmentation methodologies. The voxelization process introduced a marginal error (<1%) in phantom models relative to CAD models. CT scanner resolution impacted segmented model accuracy only after a 7.5-fold increase in voxel size compared to the baseline medical image. IoU analysis revealed higher segmentation accuracy for calcification (93.4 ± 3.1 %) compared to the aortic wall (85.4 ± 8.4 %) and native valve leaflets (75.5 ± 6.3 %). Discrepancies in THV diameter measurements highlighted a ∼5 % error due to metallic artifacts, with variability among observers and at different THV heights. Errors due to voxel size, segmentation methodologies and CT-related artifacts can impact the reliability of patient-specific geometries and ultimately computational predictions used to asses clinical outcomes and enhance decision-making. This study underscores the importance of accurate segmentation and its standardization for patient-specific modeling of TAVI simulations.

Rapid and precise calibration of soil microparameters for high-fidelity discrete element models in vehicle mobility simulation

In the realm of numerical simulations concerning vehicle mobility, the establishment of a high-fidelity soil discrete element model often necessitates substantial parameter adjustments to align with the mechanical responses of actual soil. In pursuit of a rapid and precise calibration of the microparameters of the soil model, this paper describes an approach rooted in machine learning surrogate models. This method calibrates the corresponding discrete element microparameters based on the macroscopic Mohr–Coulomb parameters derived from actual soil direct shear tests. The distinct contribution lies in the creation of a dataset that bridges the soil microparameters and macroparameters through simulated direct shear tests, which serves as training data for machine learning algorithms. Additionally, an adaptive particle swarm optimization neural network algorithm is proposed to represent the nonlinear relationships among parameters within the dataset, thus achieving intelligent calibration. To verify the reliability of the proposed soil calibration model in the context of vehicle mobility simulations, a co-simulation is conducted using a vehicle multibody dynamics simulation model and the calibrated soil model, with validation conducted across multiple criteria.

Fast interactive simulations of cardiac electrical activity in anatomically accurate heart structures by compressing sparse uniform cartesian grids

Background and Objective:Numerical simulations are valuable tools for studying cardiac arrhythmias. Not only do they complement experimental studies, but there is also an increasing expectation for their use in clinical applications to guide patient-specific procedures. However, numerical studies that solve the reaction–diffusion equations describing cardiac electrical activity remain challenging to set up, are time-consuming, and in many cases, are prohibitively computationally expensive for long studies. The computational cost of cardiac simulations of complex models on anatomically accurate structures necessitates parallel computing. Graphics processing units (GPUs), which have thousands of cores, have been introduced as a viable technology for carrying out fast cardiac simulations, sometimes including real-time interactivity. Our main objective is to increase the performance and accuracy of such GPU implementations while conserving computational resources. Methods:In this work, we present a compression algorithm that can be used to conserve GPU memory and improve efficiency by managing the sparsity that is inherent in using Cartesian grids to represent cardiac structures directly obtained from high-resolution MRI and mCT scans. Furthermore, we present a discretization scheme that includes the cross-diagonal terms in the computational cell to increase numerical accuracy, which is especially important for simulating thin tissue sections without the need for costly mesh refinement. Results:Interactive WebGL simulations of atrial/ventricular structures (on PCs, laptops, tablets, and phones) demonstrate the algorithm’s ability to reduce memory demand by an order of magnitude and achieve calculations up to 20x faster. We further showcase its superiority in slender tissues and validate results against experiments performed in live explanted human hearts. Conclusions:In this work, we present a compression algorithm that accelerates electrical activity simulations on realistic anatomies by an order of magnitude (up to 20x), thereby allowing the use of finer grid resolutions while conserving GPU memory. Additionally, improved accuracy is achieved through cross-diagonal terms, which are essential for thin tissues, often found in heart structures such as pectinate muscles and trabeculae, as well as Purkinje fibers. Our method enables interactive simulations with even interactive domain boundary manipulation (unlike finite element/volume methods). Finally, agreement with experiments and ease of mesh import into WebGL paves the way for virtual cohorts and digital twins, aiding arrhythmia analysis and personalized therapies.

Atomistic simulation of chemical short-range order on the irradiation resistance of HfNbTaTiZr high entropy alloy

High entropy alloys (HEAs) have been considered as one of the potential structural material candidates for fourth-generation nuclear reactors and fusion reactors due to their excellent irradiation resistance. Current studies have shown that the chemical short-range order (CSRO) usually exists in HEAs, which has a significant effect on the mechanical properties and irradiation resistance of HEAs. Refractory high entropy alloys (RHEAs), as a new class of HEAs have better mechanical properties at high temperatures than face-centered cubic (FCC) HEAs, and therefore have better prospects of application in the nuclear field. In this study, CSRO and its effect on the irradiation resistance of HfNbTaTiZr are analyzed via molecular dynamics (MD) and Monte Carlo (MC). The primary cascade simulations, multi-cascade simulations and surface bombardment simulations are carried out to simulate the generation and accumulation of irradiation damage. The results of the primary cascade simulations and surface bombardment simulations of CSRO models show that the presence of CSRO induces cascade splitting into subcascades. The presence of subcascades reduces the thermal peak enhancement effect and thus lowers the recombination rate of Frenkel pairs (FPs) in the damage zone when FPs concentrations are low. However, the creation of subcascades increases the size of the damage zone caused by the cascade. Thus, when the concentrations of FPs are high, the larger area of damage zone allows more of the already existing FPs to be included, thus promoting their recombination, i.e., impedes their accumulation when concentrations are high. These subcascades lower the recombination of FPs at low FPs concentrations but inhibit their accumulation at high FPs concentrations. The presence of CSRO is also beneficial in inhibiting the growth of point defect clusters, which further improves the resistance of HfNbTaTiZr to dislocation generation. Furthermore, the presence of CSRO facilitates the irradiation-induced phase transition. But it is found that HfNbTaTiZr shows suppression of HCP cluster growth. And the tendency to break down large HCP clusters into smaller ones is demonstrated in the CSRO model. From our calculations we also find that the irradiation-induced HCP atoms have a higher potential energy relative to the matrix. The potential energy difference between those energetic HCP atoms and the matrix can lead to generating a great number of insurmountable barriers pervading the matrix and largely suppressing the long-term mobility of FPs, thus limiting their aggregation and growth into clusters.

Evaluation of Extended Decision Outcomes

ABSTRACTSome decision outcomes consist of sequences of single experiences. The aim is to propose a conceptualization of how such sequences are evaluated if affective evaluations of single experiences evoke transient emotional responses with lasting changes in current mood. The conceptualization implies three modes in which the sequences of single experiences are evaluated: (i) Aggregation of affective evaluations of the single experiences retrieved from memory; (ii) Aggregation of current moods associated with emotional responses to the single experiences retrieved or reconstructed from memory; and (iii) Updating of current mood. Simulations of parametrized models are used to compare the different evaluation modes to each other and to show to which extent the simulation results are consistent with some common findings in previous research. The previous research has primarily investigated different rules for aggregation of affective evaluations of single experiences. The simulation results motivate research comparing this mode to the other proposed modes.

A multi-scenario ensemble approach incorporating stepwise cluster analysis to reduce uncertainty in large-scale watershed precipitation projections: a case study of Pearl River Basin, South China.

Assessing and selecting climate models with lower uncertainty is necessary to predict future climate and hydrological risks at the watershed scale. In this study, we integrated stepwise cluster analysis (SCA) to propose a multi-model ensemble downscaling framework aimed at reducing the uncertainty of GCM-based precipitation projections in large-scale watersheds. The Pearl River Basin (PRB) in southern China was selected as the study area to validate the reliability of this framework. Spatially, we investigated the features of terrain-related spatial heterogeneity in precipitation simulation of different climate models using a stepwise cluster zoning approach. The spatial performance of most CMIP6 models was effective in capturing the annual mean precipitation from the source region to the downstream of the PRB. To further evaluate the model's skill in simulating precipitation patterns, we conducted a seasonal analysis for different periods throughout the year. However, the seasonal precipitation cycle exhibited a wet bias during cold seasons, and the most significant deviation of precipitation percentage intervals occurred during winter. The TSS ranking of CMIP6 models was used to select the top-performing models to construct an improved multi-model ensemble mean (MEM5), resulting in a more accurate precipitation simulation for PRB. Results showed consistent precipitation increases (p < 0.05) for all scenarios in the PRB, with the middle and lower reaches being the most sensitive to changes in precipitation. The improved MEM5 can serve as a valuable reference for accurately simulating hydrological regimes and extreme weather events in the PRB. The proposed multi-model ensemble downscaling framework, which incorporates SCA, offers a new approach for high-resolution and low-uncertainty climate simulations in other large-scale watersheds.

Formability prediction of perforated sheet metal by representative volume element and homogenized sheet models

Perforated sheet metal (PSM) can be formed with plastic deformation. Modeling a perforated model in finite element analysis requires domain discretization with many small elements, which can lead to significant computation time and cost. This study introduces a novel approach using representative volume elements (RVE) and homogenized sheet (HS) models as an efficient alternative for PSM analysis. The forming limit of the PSM was established by analyzing localized necking in RVE. The HS model in simulations of the limit dome height and Swift cup tests results were in good agreement with those from the computational intensive perforated sheet models.

Analysis of Surface Drag Reduction Characteristics of Non-Smooth Jet Coupled Structures

To enhance the service life of shipping equipment and minimize surface wear, this study employs biomimetic principles, integrating fitted structures with jet dynamics to model three configurations: non-smooth structures, single jet structures, and non-smooth jet-coupled structures. We utilized the SST k-ω turbulence model for numerical simulations to investigate the drag reduction characteristics of these structural models. By varying the jet angle and speed, we analyzed the changes in viscous resistance, pressure differential resistance, and drag reduction rates at the wall surface. Furthermore, the mechanisms of compressive stress, velocity fields, vortex structures, and shear stress on drag-reducing surfaces were elucidated, revealing how these factors contribute to drag reduction in non-smooth jet-coupled structures. The results indicate that the non-smooth jet-coupled structure exhibits superior drag reduction performance at a main flow field velocity of 20 m/s. As the jet velocity increases, the viscous drag on the surface of the non-smooth jet-coupled structure decreases, while the pressure differential drag increases. Conversely, variations in the jet angle have a minimal effect on viscous drag but lead to a reduction in pressure differential drag. Specifically, when the jet velocity is set at 1 m/s, and the jet angle is 60°, the drag reduction achieved by the non-smooth jet-coupled structure peaks at 7.48%. Additionally, the non-smooth jet-coupled structure features a larger area characterized by low shear stress, along with an increased boundary layer thickness at the bottom; this configuration effectively reduces surface velocity and consequent viscous drag.

Optimal representation of tree foliage for local urban climate modeling

Trees impact the local urban climate, notably at street level by intercepting solar radiation and providing shading. Evapotranspiration in foliage may reduce the air temperature although it may increase relative humidity and leaf drag may reduce wind speed, affecting thermal comfort. To document and quantify this impact, microclimate modeling with Computational Fluid Dynamics (CFD) simulations requires explicit information of the urban configuration, including trees. However, trees are complex individuals with a variety of shapes and a variety of foliage distribution. This study aims to investigate the sensibility to the tree modeling of the urban climate simulations. Starting with terrestrial LiDAR data from trees of different species, ages, and forms, we propose a systematic evaluation of the optimal representation of arboreal configurations in terms of local urban comfort. One way to represent the foliage of trees accurately is to apply Delaunay triangulation on the LiDAR data, which yields a convex envelope model. The resulting foliage shape is very close to the actual tree, but includes a high number of facets leading to complex objects to model numerically. Comparing four species and three maturity level of trees with this method, the paper shows that the size of the zone shadowed by a tree is the parameter with the largest impact on thermal comfort, as the ability of trees to absorb solar radiation is the main asset to improve thermal comfort. The UTCI could be up to 2.1°C lower for a mature ACPL than for a sapling, mainly because the zone covered by the tree is larger. In addition, polyhedron shape rhombicuboctahedron (RBC) produces accurate shadowed zones. Mostly, in literature, tree canopies are modeled with cubic representations while we see that they overestimate the size of the shadowed zone. To have reliable compromise between accuracy and time for conception and computational time, this paper shows that the RBC is the best alternative to common tree models. Despite requiring a good knowledge of the canopy geometry, RBC provides a strong capacity for accurately modelling complex canopy shapes of most tree species and offers large benefits in reduced complexity. We show that the RBC shape, thanks to its simple but flexible geometry, is an efficient and accurate methodological approach to model trees and allows savings in computational time (up to 15% faster than the convex envelope) and costs; and we expect that this method will improve the modeling of further parametric studies on vegetation impact on thermal urban comfort.

GlyCompute: towards the automated analysis of protein N-linked glycosylation kinetics via an open-source computational framework.

Understanding the complex biosynthetic pathways of glycosylation is crucial for the expanding field of glycosciences. Computer-aided glycosylation analysis has greatly benefited in recent years from the development of tools found in web-based portals and open-source libraries. However, the in silico analysis of cellular glycosylation kinetics is underrepresented in current glycoscience-related tools and databases. This could be partly attributed to the limited accessibility of kinetic models developed using proprietary software and the difficulty in reliably parameterising such models. This work aims to address these challenges by proposing GlyCompute, an open-source framework demonstrating a novel, streamlined approach for the assembly, simulation, and parameterisation of kinetic models of protein N-linked glycosylation. Specifically, given one or more sets of experimentally observed N-glycan structures and their relative abundances, minimum representations of a glycosylation reaction network are generated. The topology of the resulting networks is then used to automatically assemble the material balances and kinetic mechanisms underpinning the mathematical model. To match the experimentally observed relative abundances, a sequential parameter estimation strategy using Bayesian inference is proposed, with stages determined automatically based on the underlying network topology. The proposed framework was tested on a case study involving the simultaneous fitting of the kinetic model to two protein N-linked glycoprofiles produced by the same CHO cell culture, showing good agreement with experimental observations. We envision that GlyCompute could help glycoscientists gain quantitative insights into the effect of enzyme kinetics and their perturbations on experimentally observed glycoprofiles in biomanufacturing and clinical settings.

A Simple Bias Reduction for Chatterjee’s Correlation

Chatterjee’s rank correlation coefficient ξn\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\xi _n$$\\end{document} is an empirical index for detecting functional dependencies between two variables X and Y. It is an estimator for a theoretical quantity ξ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\xi $$\\end{document} that is zero for independence and one if Y is a measurable function of X. Based on an equivalent characterization of sorted numbers, we derive an upper bound for ξn\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\xi _n$$\\end{document} and suggest a simple normalization aimed at reducing its bias for small sample size n. In Monte Carlo simulations of various models, the normalization reduced the bias in all cases. The mean squared error was reduced, too, for values of ξ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\xi $$\\end{document} greater than about 0.4. Moreover, we observed that non-parametric confidence intervals for ξ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\xi $$\\end{document} based on bootstrapping ξn\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\xi _n$$\\end{document} in the usual n-out-of-n way have a coverage probability close to zero. This is remedied by an m-out-of-n bootstrap without replacement in combination with our normalization method.

How to mathematically model a drill-string: lumped or continuous models?

This work compares the efficacy of lumped and continuous models in simulating drill-string dynamics. We aim to critically explore the inadequacy of lumped models and challenge their perpetuated use in the literature. Lumped models represent the drill-string with a pre-determined number of discrete masses, sometimes as few as one or two. This inherently restricts their ability to capture the distributed nature of drill-strings, compromising their accuracy. In this paper, these limitations are made evident by comparing simulations of lumped models with simulations of continuous models that increase in complexity. First, predictions of the axial behaviour obtained with two models, one lumped and another continuous, are analysed. Second, simulations involving a 3-D fully coupled continuous model are evaluated. The simulations demonstrate the inaccuracy of lumped models. Also, they refute the frequent assumption of representing the bottom hole assembly (BHA) as a single lumped mass. Beyond these results, theoretical arguments based on fundamental wave-reflection phenomena are employed to further support the inadequacy of lumped formulations for this purpose. Third, this paper also discusses the prevalent strategy of validating lumped models through comparisons with laboratory experiments. This approach suffers from being a makeshift validation, as similitude principles are often violated in laboratory setups, resulting in a practice that may have helped perpetuate lumped models in the literature. Consequently, using lumped models for general drill-string dynamics prediction is not appropriate. Recognising their limitations and exploring alternative approaches, such as continuous models, is crucial for accurate drill-string dynamics prediction.

Parallelization of Three Dimensional Cardiac Simulation on GPU.

The simulation of electrophysiological cardiac models plays an important role in facilitating the investigation of cardiac behavior under various conditions. However, these simulations often require a lot of computational resources. To address this challenge, this study introduced a method for speeding up three-dimensional cardiac simulations using GPU parallelization. A series of optimizations was introduced, encompassing various aspects such as data storage, algorithmic enhancements, and data transfer. The experimental results reveal that the optimized GPU parallel simulations achieve an approximate 50-fold acceleration compared with their CPU serial program. This investigation substantiates the considerable potential of GPUs in advancing the field of cardiac electrophysiology simulations.

Continuum models for meso-scale simulations of HMX (1,3,5,7-tetranitro-1,3,5,7-tetrazocane) guided by molecular dynamics: Pore collapse, shear bands, and hotspot temperature

Meso-scale calculations of energy localization and initiation in energetic material microstructures must capture the deformation and collapse of pores and high-temperature shear bands, which lead to hotspots. Because chemical reaction rates depend sensitively on temperature, predictive continuum models need to get the pore-collapse dynamics and resulting hotspot temperatures right; this imposes stringent demands on the fidelity of thermophysical model forms and parameters and on the numerical methods employed to perform high-resolution meso-scale calculations. Here, continuum material models for β-HMX are examined in the context of shock-induced pore collapse, treating predictions from all-atom molecular dynamics (MD) simulations as ground truth. Using atomistics-consistent material properties, we show that the currently available strength models for HMX fail to correctly capture pore collapse and hotspot temperatures. Insights from MD are then employed to advance a Modified Johnson–Cook (M-JC) strength model, which is shown to capture key aspects of the physics of shock-induced localization in HMX. The study culminates in a MD-guided strength model for β-HMX that produces continuum pore-collapse results in better alignment on several aspects with those predicted by MD, including pore-collapse mechanism and rate, shear-band formation in the collapse zone, and temperature, strain, and stress fields in the hotspot zone and the surrounding material. The resulting MD-informed/MD-determined M-JC model should improve the fidelity of meso-scale simulations to predict the detonation initiation of HMX-based energetic materials in microstructure-aware multi-scale frameworks.